Oilseeds (Genome Mapping and Molecular Breeding in Plants)

Genome Mapping and Molecular Breeding in Plants Volume 2 Series Editor: Chittaranjan Kole Volumes of the Series Genom...

Author: Chittaranjan Kole (Editor)

84 downloads 1410 Views 21MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!

Report copyright / DMCA form

DOWNLOAD PDF

Genome Mapping and Molecular Breeding in Plants Volume 2 Series Editor: Chittaranjan Kole

Volumes of the Series Genome Mapping and Molecular Breeding in Plants

Volume 1 Cereals and Millets Volume 2 Oilseeds Volume 3 Pulses, Sugar and Tuber Crops Volume 4 Fruits and Nuts Volume 5 Vegetables Volume 6 Technical Crops Volume 7 Forest Trees

Chittaranjan Kole (Ed.)

Oilseeds With 42 Illustrations, 7 in Color

123

Chittaranjan Kole Department of Horticulture 316 Tyson Building The Pennsylvania State University University Park, PA 16802 USA e-mail: [email protected]

Library of Congress Control Number: 2006926550

ISBN-10 3-540-34387-3 Springer Berlin Heidelberg New York ISBN-13 978-3-540-34387-5 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permissions for use must always be obtained from Springer. Violations are liable for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2007 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Editor: Dr. Sabine Schreck, Heidelberg, Germany Desk Editor: Dr. Jutta Lindenborn, Heidelberg, Germany Cover design: WMXDesign GmbH, Heidelberg, Germany Typesetting and production: LE-TEX Jelonek, Schmidt & Vöckler GbR, Leipzig, Germany 39/3100/YL 5 4 3 2 1 0 – Printed on acid-free paper

Preface to the Series

Genome science has emerged unequivocally as the leading discipline of this new millennium. Progress in molecular biology during the last century has provided critical inputs for building a solid foundation for this discipline. However, it has gained fast momentum particularly in the last two decades with the advent of genetic linkage mapping with RFLP markers in humans in 1980. Since then it has been ﬂourishing at a stupendous pace with the development of newly emerging tools and techniques. All these events are due to the concerted global efforts directed at the delineation of genomes and their improvement. Genetic linkage maps based on molecular markers are now available for almost all plants of signiﬁcant academic and economic interest, and the list of plants is growing regularly. A large number of economic genes have been mapped, tagged, cloned, sequenced, or characterized for expression and are being used for genetic tailoring of plants through molecular breeding. An array of markers in the arsenal from RFLP to SNP; tools such as BAC, YAC, ESTs, and microarrays; local physical maps of target genomic regions; and the employment of bioinformatics contributing to all the “-omics” disciplines are making the journey more and more enriching. Most naturally, the plants we commonly grow on our farms, forests, orchards, plantations, and labs have attracted emphatic attention, and deservedly so. The two-way shuttling from phenotype to genotype (or gene) and genotypte (gene) to phenotype has made the canvas much vaster. One could have easily compiled the vital information on genome mapping in economic plants within some 50 pages in the 1980s or within 500 pages in the 1990s. In the middle of the ﬁrst decade of this century, even 5,000 pages would not sufﬁce! Clearly genome mapping is no longer a mere “promising” branch of the life science; it has emerged as a full-ﬂedged subject in its own right with promising branches of its own. Sequencing of the Arabidopsis genome was complete in 2000. The early 21st century witnessed the complete genome sequence of rice. Many more plant genomes are waiting in the wings of the national and international genome initiatives on individual plants or families. The huge volume of information generated on genome analysis and improvement is dispersed mainly throughout the pages of periodicals in the form of review papers or scientiﬁc articles. There is a need for a ready reference for students and scientists alike that could provide more than just a glimpse of the present status of genome analysis and its use for genetic improvement. I personally felt the gap sorely when I failed to suggest any reference works to students and colleagues interested in the subject. This is the primary reason I conceived of a series on genome mapping and molecular breeding in plants. There is not a single organism on earth that has no economic worth or concern for humanity. Information on genomes of lower organisms is abundant and highly useful from academic and applied points of view. Information on higher animals including humans is vast and useful. However, we ﬁrst thought to concentrate only on the plants relevant to our daily lives, the agronomic, horticultural and technical crops, and forest trees, in the present series. We will come up soon with commentaries on food and ﬁber animals, wildlife and companion animals, laboratory animals, ﬁshes and aquatic animals, beneﬁcial and harmful insects,

VI

Preface to the Series

plant- and animal-associated microbes, and primates including humans in our next “genome series” dedicated to animals and microbes. In this series, 82 chapters devoted to plants or their groups have been included. We tried to include most of the plants in which signiﬁcant progress has been made. We have also included preliminary works on some so-called minor and orphan crops in this series. We would be happy to include reviews on more such crops that deserve immediate national and international attention and support. The extent of coverage in terms of the number of pages, however, has nothing to do with the relative importance of a plant or plant group. Nor does the sequence of the chapters have any correlation to the importance of the plants discussed in the volumes. A simple rule of convenience has been followed. I feel myself fortunate to have received highly positive responses from nearly 300 scientists of some 30-plus countries who contributed the chapters for this series. Scientists actively involved in analyzing and improving particular genomes contributed each and every chapter. I thank them all profoundly. I made a conscientious effort to assemble the best possible team of authors for certain chapters devoted to the important plants. In general, the lead authors of most chapters organized their teams. I extend my gratitude to them all. The number of plants of economic relevance is enormous. They are classiﬁed from various angles. I have presented them using the most conventional approach. The volumes thus include cereals and millets (Volume I), oilseeds (Volume II), pulse, sugar and tuber crops (Volume III), fruits and nuts (Volume IV), vegetables (Volume V), technical crops including ﬁber and forage crops, ornamentals, plantation crops, and medicinal and aromatic plants (Volume VI), and forest trees (Volume VII). A signiﬁcant amount of information might be duplicated across the closely related species or genera, particularly where results of comparative mapping have been discussed. However, some readers would have liked to have had a chapter on a particular plant or plant group complete in itself. I ask all the readers to bear with me for such redundancy. Obviously the contents and coverage of different chapters will vary depending on the effort expended and progress achieved. Some plants have received more attention for advanced works. We have included only introductory reviews on fundamental aspects on them since reviews in these areas are available elsewhere. On other plants, including the “orphan” crop plants, a substantial amount of information has been included on the basic aspects. This approach will be reﬂected in the illustrations as well. It is mainly my research students and professional colleagues who sparked my interest in conceptualizing and pursuing this series. If this series serves its purpose, then the major credit goes to them. I would never have ventured to take up this huge task of editing without their constant support. Working and interacting with many people, particularly at the Laboratory of Molecular Biology and Biotechnology of the Orissa University of Agriculture and Technology, Bhubaneswar, India as its founder principal investigator; the Indo-Russian Center for Biotechnology, Allahabad, India as its ﬁrst project coordinator; the then-USSR Academy of Sciences in Moscow; the University of Wisconsin at Madison; and The Pennsylvania State University, among institutions, and at EMBO, EUCARPIA, and Plant and Animal Genome meetings among the scientiﬁc gatherings have also inspired me and instilled conﬁdence in my ability to accomplish this job. I feel very fortunate for the inspiration and encouragement I have received from many digniﬁed scientists from around the world, particularly Prof. Arthur

Preface to the Series

VII

Kornberg, Prof. Franklin W. Stahl, Dr. Norman E. Borlaug, Dr. David V. Goeddel, Prof. Phillip A. Sharp, Prof. Gunter Blobel, and Prof. Lee Hartwell, who kindly opined on the utility of the series for students, academicians, and industry scientists of this and later generations. I express my deep regards and gratitude to them all for providing inspiration and extending generous comments. I have been especially blessed by God with an affectionate student community and very cordial research students throughout my teaching career. I am thankful to all of them for their regards and feelings for me. I am grateful to all my teachers and colleagues for the blessings, assistance, and affection they showered on me throughout my career at various levels and places. I am equally indebted to the few critics who helped me to become professionally sounder and morally stronger. My wife Phullara and our two children Sourav and Devleena have been of great help to me, as always, while I was engaged in editing this series. Phullara has taken pains (“pleasure” she would say) all along to assume most of my domestic responsibilities and to allow me to devote maximum possible time to my professional activities, including editing this series. Sourav and Devleena have always shown maturity and patience in allowing me to remain glued to my PC or “printed papers” (“P3” as they would say). For this series, they assisted me with Internet searches, maintenance of all hard and soft copies, and various timely inputs. Some ﬁgures included by the authors in their chapters were published elsewhere previously. The authors have obtained permission from the concerned publishers or authors to use them again for their chapters and expressed due acknowledgement. However, as an editor I record my acknowledgements to all such publishers and authors for their generosity and good will. I look forward to your valuable criticisms and feedback for further improvement of the series. Publishing a book series like this requires diligence, patience, and understanding on the part of the publisher, and I am grateful to the people at Springer for having all these qualities in abundance and for their dedication to seeing this series through to completion. Their professionalism and attention to detail throughout the entire process of bringing this series to the reader made them a genuine pleasure to work with. Any enjoyment the reader may derive from this books is due in no small measure to their efforts. Pennsylvania, 10 January 2006

Chittaranjan Kole

Preface to the Volume

I believe some sort of explanation is due to readers regarding the contents of this volume. The conventional grouping of economic plants species, particularly crop plants, is conveniently based on the mode or purpose of their use. Some crop plants are unique in their use and ﬁnd place in a particular group, say, rice under cereals, peanut under oilseeds, or apple under fruits. Certain crops have two or more important agricultural purposes and may belong to different groups. Such crops posed a problem for chapter allocation under the volumes in this series. My initial intention was to have a volume for edible oil-yielding crops. This would have required grouping together oilseeds, oil palm, coconut, cottonseed, and olive, and then why not corn or rice! In that case, agricultural scientists would doubt on my minimum knowledge about plantation and cereal crops, and I had to retreat. I went for just the oilseed crops. At least four oilseed crops have multifarious agronomic purposes. Soybean could with equal justiﬁcation be categorized as an oilseed or as a pulse crop. But considering its dominating presence among oil-producing ﬁeld crops, it should be included in the oilseed volume. The second problematic entrant was Brassica rapa. It could boast of being an oilseed as well as a vegetable. Moreover, it has several distinct subspecies under these two categories. Traditional breeding must have dealt with them separately with distinct objectives for genetic improvement. However, genetics, basic or modern, needed them together. Nucleotides do not distinguish between oil and vegetable! We placed it in this volume with detailed review on molecular aspects and also in the volume on vegetables with details on the basic aspects. Black mustard, which is a traditional condiment. But it has been of immense use in studies of comparative genomics in Brassicaceae and provided many clues to the evolution of genes and genomes. We placed it in this volume as well. A horticulturist would surely prefer to treat sunﬂower as an ornamental. However, it is among the four leading seed-oil- producing species, and so it is here in volume II on oilseed crops. Some oilseed crops are grown in Asian countries, like sesame and safﬂower, which have recently attracted the attention of molecular biologists and been the object of considerable efforts at genome analysis. We had to omit them from the current volume with the hope of including chapters on them in future editions. To our delight we were able to sign on highly eminent scientists to author the chapters in this volume. In addition, some of the chapters represent the results of the multilab and multinational efforts of their authors, who took pains for coordinated and concerted endeavors. I am thankful to all the authors for the high academic quality of their ﬁnal output. I worked on oilseed Brassicas, speciﬁcally B. rapa and B. napus, in the lab of Prof. Thomas C. Osborn at UW-Madison and later continued works in my own labs in India and have many close colleagues in that fraternity. It was a real pleasure and enriching to work on this volume with some of these friends, whom I used to meet annually in January at the Town & Country Hotel in San Diego during the Plant and Animal Genome conferences. I also wish to record my thanks to my wife, Phullara, who used to work with me at UW-Madison and India, and my colleagues and research students who worked with me on oilseed Brassicas for their continued

X

Preface to the Volume

interactions and inputs that made my life easier while editing this volume. The ﬁrst volume produced by the publishers of this series has been well received by readers. The publishers have done an equally elegant job for this volume as well. I am thankful for their dedicated service to science. Suggestions from any corner on how to improve this volume for future editions are welcome. Pennsylvania, 26 February 2006

Chittaranjan Kole

Contents

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVI Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XXI 1 Soybean G.-J. Lee, X. Wu, J. G. Shannon, D. A. Sleper, H. T. Nguyen . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Construction of Soybean Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Soybean Genetic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 First Generation of Soybean Maps Constructed Based on a Single Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Integrated Soybean Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Mapping of Genes in Soybean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Resistant Genes to Soybean Diseases . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Genes for Herbicide Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Nodulation Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.4 Genes for Growth Habit, Flowering, and Morphology . . . . . . . . 1.3.5 Soybean Sterility Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.6 Genes for Mineral Toxicity or Deﬁciency . . . . . . . . . . . . . . . . . . . 1.3.7 Genes for Soybean Pigmentation . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.8 Genes for Fatty Acid Composition . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 QTL Mapping in Soybean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Pest Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Tolerance to Abiotic Stresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Growth and Development Responses . . . . . . . . . . . . . . . . . . . . . . 1.4.4 Seed Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.5 Yield-Related Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Marker-Assisted Breeding in Soybean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Marker Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Marker-Assisted Introgression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4 Gene Pyramiding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Progress in Map-Based Cloning, Transformation, and Other Candidate Gene Approaches in Soybean . . . . . . . . . . . . . . . . . 1.6.1 Map-Based Cloning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.2 Soybean Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.3 Functional Genomics for Candidate Gene Discovery . . . . . . . . . 1.7 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Oilseed Rape R. Snowdon, W. Lühs, W. Friedt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Origin and History of Oilseed Rape . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Economic Importance of Oilseed Rape . . . . . . . . . . . . . . . . . . . . . 2.1.4 Nutritional and Chemical Composition of Rapeseed Oil . . . . . .

1 1 7 7 7 9 10 10 20 20 20 22 22 23 23 24 24 36 37 38 38 40 40 40 41 42 42 42 43 43 44 45 55 55 55 56 56 57

XII

Contents

2.2

Breeding of Oilseed Rape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Breeding Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Breeding for Improved Productivity . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Improvement of Seed Components . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Hybrid Breeding and Cytoplasmic Male Sterility Systems . . . . . 2.2.5 Use of Male Sterility Systems in Oilseed Rape Breeding . . . . . . 2.2.6 Genetic Diversity for Heterosis and Hybrid Breeding . . . . . . . . 2.2.7 Expanding the Genetic Variability in Oilseed Rape by Interspeciﬁc Hybridization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Cytogenetic Studies of Brassica Crops and Interspeciﬁc Hybrids . . . . . . 2.3.1 History of Cytogenetic Studies in Brassica . . . . . . . . . . . . . . . . . . 2.4 Genetic Diversity Studies in Brassica napus . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Genetic Modiﬁcation: Status and Potential of Transgenic Brassica napus . . . . . . . . . . . . . . . . . . 2.5.1 Herbicide Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Genetic Engineering of Fatty Acid Biosynthesis . . . . . . . . . . . . . 2.6 Molecular Markers and Genetic Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Use of Isoenzymes in Oilseed Rape Breeding . . . . . . . . . . . . . . . 2.6.2 Brassica napus Genetic Maps: From RFLP to PCR Markers . . . 2.6.3 Mapping of Genes and QTLs for Morphological and Quality Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.4 Mapping of Genes and QTL for Disease Resistance . . . . . . . . . . 2.6.5 Mapping QTLs for Abiotic Stress . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.6 Towards an Integrated B. napus Genetic Map . . . . . . . . . . . . . . . 2.7 Comparative Genomic Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Physical Mapping and Genomics Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.1 Physical Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.2 Public Genome Resources: The Multinational Brassica Genome Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.3 Genome Sequencing in B. oleracea and B. rapa . . . . . . . . . . . . . . 2.9 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Peanut S. L. Dwivedi, D.J. Bertioli, J. H. Crouch, J. F. Valls, H. D. Upadhyaya, A. Fávero, M. Moretzsohn, A. H. Paterson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Botanical Types and Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Crop Production and Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.4 Improved Quality Requirements: Reduced Allergenicity and Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Genetic Resources in Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Wild Arachis Species and Interspeciﬁc Gene Introgression into Cultivated Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Cultivated Germplasm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Core Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Appropriate Germplasm and Evaluation Systems for Mapping Economically Important Traits in Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Phenotypic Screens, Resistance/Tolerance Mechanism, and Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 59 59 60 62 64 64 65 67 67 71 72 72 73 77 77 77 88 91 92 93 94 98 98 100 100 103 103

115 115 115 115 116 118 118 118 119 119 121 121

Contents

Germplasm with Beneﬁcial Traits for Mapping and Genetic Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Genomic Resources in Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 DNA Markers (RFLPs, RAPDs, AFLPs, SSRs) . . . . . . . . . . . . . . . 3.4.2 Molecular Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Mapping Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Genetic Linkage Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.5 Comparative Mapping with Model Genomes . . . . . . . . . . . . . . . . 3.4.6 BAC Libraries and New Generation Markers . . . . . . . . . . . . . . . . 3.5 Successes and Limitations of Conventional Breeding in Peanut . . . . . . . 3.6 Biotechnological Applications to Genetic Enhancement in Peanut . . . . 3.6.1 Marker/Trait Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Unlocking the Genetic Variation from Wild Genetic Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 Transgenics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusions and Future Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

XIII

3.3.2

126 128 128 131 131 132 133 133 136 137 137 137 140 142 143

4 Sunflower N. Paniego, R. Heinz, P. Fernandez, P. Talia, V. Nishinakamasu, H. Esteban Hopp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Brief History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.4 Conventional Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Molecular Markers and Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Genomics and Transcriptomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Structural Analysis by in situ Hybridization . . . . . . . . . . . . . . . . . . . . . . . 4.5 Resistance Genes in Cultivated and Wild Sunﬂowers . . . . . . . . . . . . . . . . 4.6 QTL Analysis for Developmental and Agronomic Traits . . . . . . . . . . . . . 4.7 In vitro Tissue-Culture-Aided Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Genetic Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.1 Transgenic Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.2 Transgenic Sunﬂowers: Biosafety Concerns . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153 153 153 153 154 155 156 157 163 164 168 169 170 170 170 172

5 Indian Mustard D. Edwards, P. A. Salisbury, W. A. Burton, C. J. Hopkins, J. Batley . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Brief History of Brassica juncea . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.4 Breeding Objectives and Progress . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.5 Overcoming Limitations of Classical Endeavours . . . . . . . . . . . . 5.1.6 Classical Mapping Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.7 Utility of Molecular Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Construction of Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Genetic Mapping in B. juncea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 First-Generation Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Second-Generation Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

179 179 179 179 180 181 183 185 185 186 186 186 188

XIV

Contents

5.2.4 Comparative Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gene Mapping and Marker-Assisted Selection . . . . . . . . . . . . . . . . . . . . . 5.3.1 White Rust Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Seed Coat Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Fatty Acid/Oil Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Glucosinolate Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Marker-Assisted Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Transgene-Assisted Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Advanced Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Gene Discovery and Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Future Scope of Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193 195 195 197 198 201 202 203 203 203 204 205

6 Brassica Rapa P. Quijada J. Cao, X. Wang, M. Hirai, C. Kole . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 History of the Crop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Breeding Objectives and Achievements . . . . . . . . . . . . . . . . . . . . 6.2 Construction of Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Brief History of Mapping Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Mapping Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Mapping Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.5 Comparative Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Gene Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Seed Coat Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Disease Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Vernalization Requirements and Flowering Time . . . . . . . . . . . . 6.3.4 Fatty Acid Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.5 Self-Incompatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.6 Dwarﬁsm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.7 Markers Linked to Microspore Embryogenic Ability . . . . . . . . . 6.4 QTL Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Morphological Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Heat Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Resistance to Clubroot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Resistance to White Rust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.5 Linolenic Acid Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.6 Flowering Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.7 Abiotic Stresses: Winter Survival and Freezing Tolerance . . . . . 6.4.8 Mendelization of QTLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Marker-Assisted Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Marker Conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Germplasm Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.3 Marker-Assisted Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.4 Marker-Assisted Introgression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.5 Gene Pyramiding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Map-Based Cloning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.1 Principles of Map-Based Cloning . . . . . . . . . . . . . . . . . . . . . . . . . .

211 211 211 211 213 213 215 215 216 217 218 221 224 224 227 229 231 231 232 233 233 233 236 236 236 237 239 240 242 243 243 243 246 248 250 251 251

5.3

Contents

XV

6.6.2 Genetic Resources and Mapping Populations . . . . . . . . . . . . . . . 6.6.3 BAC Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.4 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Future Scope of Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

252 252 253 253 254

7 Black Mustard S. Das, U. Lagercrantz, M. Lascoux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Genetic Relationship and Evolution of Brassica species . . . . . . . 7.1.3 Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 DNA Marker Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Genetic Linkage Mapping in Brassica nigra . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Comparative Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Mapping of Flowering-Time Trait in Brassica nigra . . . . . . . . . . . . . . . . . 7.5 Future Scope of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

265 265 265 265 266 267 267 267 269 270 271 272

8 Flax C.A. Cullis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Origin and History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Biological Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.3 Karyotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.4 Genome Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.5 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.6 Breeding Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Construction of Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Classical Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Molecular Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Germplasm Identiﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Germplasm Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Molecular Markers for Germplasm Identiﬁcation . . . . . . . . . . . . 8.3.3 Inducing New Variability or Traits . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Gene Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Quantitative Trait Loci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Future Scope of Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

275 275 275 275 277 278 278 280 282 282 285 287 287 289 290 291 291 291 292

Subject Index

297

Contributors

Jacqueline Batley Plant Biotechnology Centre Primary Industries Research Victoria Department of Primary Industries Victorian AgriBiosciences Centre 1 Park Drive, Bundoora Victoria 3083, Australia [email protected] D.J. Bertioli Universidade Catolica de Brasilia Pos Graduacao Campus II SGAN 916, DF CEP 70.790-160 Brasilia, Brazil [email protected] Wayne A. Burton Grains Innovation Park Private Bag 260 Horsham, Victoria 3401, Australia [email protected] Jiashu Cao Laboratory of Cell & Molecular Biology Institute of Vegetable Science Zhejiang University No. 268 Kaixuan Road, Hangzhou 310029, China [email protected] J.H. Crouch International Crops Research Institute for the Semi Arid Tropics (ICRISAT) ICRISAT Patancheru PO, 502324, AP India [email protected] Christopher A. Cullis Department of Biology Case Western Reserve University Cleveland, OH 44106-7080, USA [email protected]

Sandip Das Max Planck Institute for Developmental Biology Department of Molecular Biology Spemannstrasse 37–39, Tuebingen 72076, Germany and Present Address: Center for Biotechnology Hamdard University, Delhi 110 062 India [email protected] S.L. Dwivedi International Crops Research Institute for the Semi Arid Tropics (ICRISAT) ICRISAT Patancheru PO, 502324, AP India [email protected] David Edwards Plant Biotechnology Centre Primary Industries Research Victoria Department of Primary Industries Victorian AgriBiosciences Centre 1 Park Drive, Bundoora Victoria 3083, Australia [email protected] Alessandra Pereira Fávero EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN) Parque Estação Biológica-pqEB Final Av. W5 Norte, Brasília-DF CEP: 70770-900, Brazil [email protected] Paula Fernandez Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA CC 25, 1712 Castelar, Argentina

XVIII

Contributors

Wolfgang Friedt Department of Plant Breeding Research Centre for Biosystems, Land Use and Nutrition Justus Liebig University of Giessen Heinrich-Buff-Ring 26–32 35392 Giessen, Germany [email protected] Ruth Heinz Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA CC 25, 1712 Castelar, Argentina [email protected] Masashi Hirai National Research Institute of Vegetables, Ornamental Plants & Tea, Ano, Mie 5142392, Japan [email protected] Clare J. Hopkins Plant Biotechnology Centre Primary Industries Research Victoria Department of Primary Industries Victorian AgriBiosciences Centre 1 Park Drive, Bundoora Victoria 3083, Australia [email protected] H. Esteban Hopp Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina [email protected] Chittaranjan Kole Department of Horticulture The Pennsylvania State University 316 Tyson Building University Park, PA 16802, USA [email protected] Ulf Lagercrantz Department of Evolutionary Functional Genomics Evolutionary Biology Centre

Uppsala University, Norbyv. 18D SE-752 36 Uppsala, Sweden [email protected] Martin Lascoux Department of Evolutionary Functional Genomics Evolutionary Biology Centre Uppsala University, Norbyv. 18D SE-752 36 Uppsala, Sweden [email protected] Geung-Joo Lee Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri 1-31 Agriculture Building, Columbia MO 65211, USA [email protected] Wilfried Lühs Department of Plant Breeding Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University of Giessen Heinrich-Buff-Ring 26–32 35392 Giessen, Germany [email protected] Marcio de Carvalho Moretzsohn EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN) Parque Estação Biológica-pqEB Final Av. W5 Norte, Brasília-DF CEP: 70770-900, Brazil [email protected] Veronica Nishinakamasu Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina Henry T. Nguyen Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri 1-31 Agriculture Building, Columbia MO 65211, USA [email protected]

Contributors

Norma Paniego Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina [email protected] A.H. Paterson Distinguished Research Professor and Director Plant Genome Mapping Laboratory University of Georgia, Rm. 228 111 Riverbend Road, Athens GA 30602, USA [email protected] Pablo A. Quijada Laboratory of Genetics University of Wisconsin-Madison 425-G Henry Mall, Madison, WI 53706 USA [email protected]

XIX

Rod Snowdon Department of Plant Breeding Research Centre for Biosystems, Land Use and Nutrition Justus Liebig University of Giessen Heinrich-Buff-Ring 26–32 35392 Giessen, Germany [email protected] Paola Talia Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina H.D. Upadhyaya International Crops Research Institute for the Semi Arid Tropics (ICRISAT) ICRISAT Patancheru PO, 502324, AP India [email protected]

Philip A. Salisbury Faculty of Land and Food Resources The University of Melbourne Victoria 3010, Australia [email protected]

J.F. Valls EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN) Parque Estação Biológica-pqEB Final Av. W5 Norte, Brasília-DF CEP: 70770-900, Brazil [email protected]

J. Grover Shannon Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri, P.O. Box 160 147 State Highway T, Portageville MO 63873, USA [email protected]

Xiaowu Wang Institute of Vegetables and Flowers Chinese Academy of Agricultural Science No. 12, Zhongguancun Nandajie Beijing, 100081, China [email protected]

David A. Sleper Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri 271-F Life Sciences Center Columbia, MO 65211, USA [email protected]

Xiaolei Wu Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri 1-31 Agriculture Building, Columbia MO 65211, USA [email protected]

Abbreviations

Δ9DES AAFC AC ACP ACS AFLP AHAS ANOVA ARS AS-PCR ASPE AT ATP BAC BAGI BC BSA BSR CAPS cDNA CENARGEN CID CIM CLG cM CMS CO CR CSHL DAF DAG DAGAT DAP DAS DD-RT-PCR DH DH DRE DTF DUS ECSs ELISA ELS EMBRAPA EMS

Δ9-Steroyl-ACP-Desaturase Agriculture and Agri-Food Canada Albugo candida Acetyl Carrier Protein Acyl-CoA Synthase Ampliﬁed Fragment Length Polymorphism Acetohydroxyacid Syntheses Analysis of Variance Agricultural Research Service Allele-Speciﬁc Polymerase Chain Reaction Allele-Speciﬁc Primer Extension Acyltransferase Adenosine Triphosphate Bacterial Artiﬁcial Chromosome Brassica/Arabidopsis Genomic Institute Backcross Bulked Segregant Analysis Brown Stem Rot Cleaved Ampliﬁed Polymorphic Sequences Complementary DNA National Center of Genetic Resources Carbon Isotope Discrimination Composite Interval Mapping Classical Linkage Group centi-Morgan Cytoplasmic Male Sterility Constans (gene) Clubroot Resistance Cold Spring Harbor Laboratory DNA Ampliﬁcation Fingerprint DiAcylGlycerol DiAcylGlyceral AcylTransferase Days After Pollination Days After Sowing Differential Display Reverse Transcription PCR Direct Hybridization Doubled Haploid Drought Responsive Elements Days To Flowering Distinctness, Uniformity and Stability Evolutionary Conserved Sequences Enzyme-Linked ImmunoSorbent Assay Early Leaf Spot Empresa Brasileira de Pesquisa Agropecuaria Ethyl Methane Sulphonate

XXII

Abbreviations

EPSP EST FA FAD FAEI FAO FAS FISH FLC G3P G3PAT GFP GISH GMO GO GPI GRAS GRAV GRV GS GST GUS HDL HEAR HI HPLC IC ICRISAT IDC IFDB IMI InDel IP IPCV ISSR JSC KAS LDL LEAR LG LLS LOD LPA LPAAT MAS MBGP MCFA MD MIPS MMT MTA

5-EnolPyruvyl 3-Shikimate Phosphate Expressed Sequenced Tag Fatty Acid Fatty Acid Desaturase (gene) Fatty Acid Elongase (gene) Food and Agricultural Organization Fatty Acid Synthase Fluorescence In Situ Hybridization Flowering Locus C Glycerol-3-Phosphate Glycerol-3-Phosphate AcylTransferase Green Florescence Protein Genomic In Situ Hybridization Genetically Modiﬁed Organism Gene Ontology Glucose-6-Phosphate Isomerase Generally Recognized As Safe Groundnut Rosette Assistor Virus Groundnut Rosette Virus Glutamine Synthetase Gene Sequence Tag Glucuronidase High Density Lipoprotein High Erucic Acid Rapeseed Harvest Index High Performance Liquid Chromatography Intercross International Crops Research Institute for the Semi-Arid Tropics Iron Deﬁciency Chlorosis International Flax Data Base Imidazalinones Insertion Deletion Interaction Phenotype Indian Peanut Clump Virus Inter-Simple Sequence Repeat Jaccard’s Similarity Coefﬁcient KetoAcyl-ACP Synthase Low Density Lipoprotein Low Erucic Acid Rapeseed Linkage Group Late Leaf Spot Logarithm Of Odds LysoPhosphatidic Acid-phosphate LysoPhosphatidic Acid Transferase Marker-Assisted Selection Multinational Brassica Genome project Medium-Chain Fatty Acid Microspore-Derived Munich Information Center for Protein Million Metric tons Material Transfer Agreement

Abbreviations

MUFA NBS NIAB NILs NIR NMR NOR OL ORF PAP PBC PBNV PBP PC PCR PCV PD PEP PFM PGRC PH PMC PPR PPT PSND PT PTRD PUFA PW QTL RAPD RFLP RGA RGC RIL RKN RNAi RT-PCR RWC Sat RNA SBE SCAR SCH SCN SCR SDS SFA SI SLA SLG SLN

Monounsaturated Fatty Acid Nucleotide-Binding Sites National Institute of Agricultural Biotechnology Near Isogenic Lines Near Infrared Nuclear Magnetic Resonance Nuclear Organizing Region Oligonucleotide Ligation Open Reading Frame Phosphatidic Acid Phosphates Plant Biotechnology Center Peanut Bud Necrosis Virus Plant by Plant PhosphatidylCholine Polymerase Chain Reaction Peanut Clump Virus Pod Dehiscence PhosphoEnol Pyruvate Physical Functional Markers Plant Gene Resource of Canada Plant Height Peanut Mottle Potyvirus PentatricoPeptide Repeat PhosPhonoThricin Peanut Stem Necrosis Disease Petiole Thickness Peanut Transcript Responsive to Drought PolyUnsaturated Fatty Acid Petiole Width Quantitative Trait Loci Random Ampliﬁed Polymorphic DNA Restriction Fragment Length Polymorphism Resistant Gene Analogue Recipient Genome content Recombinant Inbred Line Root-Knot Nematode RNA Interference Reverse Transcription PCR Relative Water Content Satellite RiboNucleic Acid Single-Base Extension Sequence Characterized Ampliﬁed Region Seed Coat Hardiness Soybean Cyst Nematode S-locus Cysteine Rich (Protein) Sudden Death Syndrome Single Factor Analysis Self-Incompatibility Speciﬁc Leaf Area S-Locus Glycoprotein Speciﬁc Leaf Nitrogen

XXIII

XXIV

Abbreviations

SLW SMT SMV SNP SP SPD SRAP SRK SSD SSR STS SUS SWP TAG TBA TE TE TIGR TLCSF TSF TSM TSV TSWV TuMV TuYv UCB UNESP UPGMA USDA VFR WLL WUE WUE YAC

Speciﬁc Leaf Weight Selenocysteine Methyl Transferase Soybean Mosaic Virus Single Nucleotide Polymorphism S-locus Protein Single-Pod-Descendent Sequence Related Ampliﬁed Polymorphism S-locus Receptor Kinase Single-Seed-Descent Simple Sequence Repeat Sequence-Tagged Site Sulphoneluries Saskatchewan Wheat Pool TriAcylGlycerols Thio Barbiutric Acid ThioEsterases Transpiration Efﬁciency The Institute for Genomic Research Total Long Chain Saturated Fatty Acid Total Saturated Fat Thousand Seed Mess Tobacco Streak Virus Tomato Spotted Wilt Virus Turnip Mosaic Virus Turnip Yellow Virus Catolica de Brasilia Universidade Estadual de Sao Paulo Unweighted Pair Group Method with Arithmetic Mean United States Department of Agriculture Vernalization-Responsive Flowering-Time in (Brassica) Rapa Water Loss from (Excised) Leaves Water-Use Efﬁciency Water Use Efﬁciency Yeast Artiﬁcial Chromosome

CHAPTER 1

1 Soybean Geung-Joo Lee1 , Xiaolei Wu1 , J. Grover Shannon2 , David A. Sleper3 , and Henry T. Nguyen1 1

2

3

Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, 1-31 Agriculture Building, Columbia, MO 65211, USA e-mail: [email protected] Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, P.O. Box 160, 147 State Highway T, Portageville MO 63873, USA Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, 271F Life Sciences Center, Columbia, MO 65211, USA

1.1 Introduction Soybean [Glycine max (L.) Merr.], grown for its edible seed protein and oil, is often called the miracle crop because of its many uses. Seed composition averages 40% protein, 20% oil, 35% carbohydrate, and 5% ash on a dry-weight basis (Liu 1997). Each component is affected by the growing environment and varies among soybean genotypes (Fig. 1). A 50-kg bag of soybeans yields about 40 kg of protein-rich meal and 9 kg of oil. The expansion in world soybean production and the increasing importance of soybeans as a world crop are great (http://www.soystats.com). Soybean is now an essential and dominant source of protein and oil with numerous uses in feed, food, and industrial applications (Table 1). It is the world’s primary source of vegetable oil (Fig. 2) and protein feed supplement for livestock (Fig. 3). Recent nutritional studies claim that

Fig. 1. Composition of soybean seed (Liu 1997) and major factors affecting seed composition

consumption of soybean reduces cancer, blood serum cholesterol, osteoporosis, and heart disease (Birt et al. 2004). It has sparked increased demand for the many edible soybean products (Table 1). The priority for more meat in diets among the world’s population has also increased the demand for soybean protein for livestock and poultry feed. In addition to feed and food, soybean has numerous industrial applications (Table 1) such as building materials, plastics, printing inks, paints, hydraulic ﬂuids, cosmetics, pharmaceuticals, and soy diesel fuel that burns cleaner and pollutes less than petroleum-derived fuels. World production of soybeans has tripled in the last 20 years (www.soystats.com), rising from about 70 million metric tons to over 200 million metric tons (Fig. 4). The soybean plant is bushy and green and is a legume related to clover, peas, and alfalfa. Soybean has a different seed composition than other legumes in that it is high in both protein and fat with little carbohydrate content. Soybean protein is well balanced compared to other protein sources. The oil portion of the seed is composed primarily of ﬁve fatty acids. Palmitic and stearic acids are saturated fatty acids and comprise 15% of the oil. Soybean is rich in the unsaturated fatty acids, oleic, linoleic, and linolenic which make up 85% of the oil. Soybeans are a good source of minerals, B vitamins, folic acid, and isoﬂavones, which are credited with slowing cancer development, heart disease, and osteoporosis (Wilson 2004). Soybean products have long been consumed by humans in various forms for its protein in China and parts of eastern and southern Asia. The center of origin of soybean is China, and domestication probably took place about 3000 to 1500 BC. From northern and southern China,

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

2

G.-J. Lee et al.

Table 1. Uses of soybean (Source: The American Soybean Association; www.soygrowers.com) WHOLE SOYBEAN PRODUCTS

OIL PRODUCTS

Glycerol, sterols, Reﬁned soyoil fatty acids

Oleochemistry EDIBLE USES Soy diesel Seed Solvents Stock feed Soy sprouts Baked soybeans Full fat soy ﬂour Bread Candy Doughnut mix Frozen desserts Instant milk drinks Pancake ﬂour Pan grease extender Pie crust Sweet goods Roasted Soybeans Candies/confections Cookie ingredient/ Topping Crackers Dietary items Soynut butter Soy coffee Soybean derivates Miso Soymilk Tempeh Tofu

Soybean lecithin

SOYBEAN PROTEIN PRODUCTS Soy ﬂour concentrates and isolates

EDIBLE USES Alimentary pastes Baby food Bakery ingredients Candy products Cereals Diet food products Food drinks Nutritional uses Hypoallergenic milk Dietary Meat products Medical Noodles TECHNICAL USES Prepared mixes Sausage casings Antifoam agents Yeast Alcohol TECHNICAL USES Beer and ale Anticorrosion agents Yeast Antistatic agents TECHNICAL USES Caulking compounds Antispattering Adhesives Composite building agents Antibiotics Margarine material Asphalt emulsions Concrete release Dispersing agents Composite building agents Material Paint Core oils Fermentation aids/ Ink Crayons nutrients Insecticides Dust control agent Fibers Electrical insulation Magnetic tape Films for packaging Paper Epoxies Fire ﬁghting foams Rubber Fungicides Inks Hydraulic ﬂuids Leather substitutes Stabilizing agent Inks – printing Paints – water based Shortening Linoleum backing Paper coatings Wetting agents Lubricants Calf milk replacers Particle boards Metal casting/ Plastics Cosmetics working Polyesters Paint pigments Oiled fabrics Pharmaceuticals Paints Pesticides/fungicides Pesticides Textiles Plasticizers Protective coatings Putty Soap/shampoos/ Detergents Vinyl plastics Waterproof cement EDIBLE USES Coffee creamers Cooking oils Filled milks Margarine Mayonnaise Medicinals Pharmaceuticals Salad dressings Salad oils Sandwich spreads Shortenings

EDIBLE USES Emulsifying agent Bakery products Candy/chocolate Coatings Pharmaceuticals

Soybean meal

FEED USES Aquaculture Bee foods Calf milk replacer Fish food Fox and mink feed Livestock feeds Poultry feeds Protein concentrates Pet foods HULLS Dairy Feed

Chapter 1 Soybean

soybeans moved to Korea, Japan, and other parts of Southeast Asia by the ﬁrst century AD. In the 17th century soybean was introduced into Europe. It was brought into the United States in 1765, then into South America during the mid-1900s (Hymowitz 2004). It is the world’s number one oil seed, crop well ahead of rapeseed, cottonseed, peanut, and sunﬂower seed (Fig. 5). About 80% of the soybeans are produced in North and South America (www.soystats.com). The United States, Brazil, and Argentina are the major soybean-producing countries (Fig. 6). Soybean is a member of the genus Glycine willd., which is a member of the legume family Leguminoseae, subfamily Papilionoideae, and tribe Phaseoleae. The tribe Phaseoleae is the most important tribe of the Leguminoseae, with members that have great importance for food and feed, such as common bean, lima bean, mungbean, and cowpea. Soybeans are divided into two subgenera, Glycine (perennials) and Soja (Moench) F. J. Herm. (annuals). The subgenus Soja includes Glycine max, the cultivated soybean, and G. soja, the wild annual soybean (Hymowitz 2004). Wild soybean grows in China, Japan, Korea, Russia, and Taiwan in ﬁelds and hedgerows and along roadsides and riverbanks. G. soja plants are annual, procumbent with slender twining growth, and generally have purple ﬂowers and tawny pubescence. Soybean plants are diploid with 20 pairs of chromosomes on which currently 20 linkage groups (LGs) have been constructed (Hymowitz 2004; Song et al. 2004). The soybean genome consists of ∼1.1 Mbp, which is relatively larger than those of Arabidopsis (7.5 times; Mbp/C) or rice (2.5 times; Mbp/C) but smaller than corn (2.4 times; Mbp/C) or wheat (14 times; Mbp/C) (Arumuganathan and Earle 1991). Cultivated soybean [Glycine max (L.) Merr.] was domesticated by humans from wild soybean G. soja. G. max also has 20 chromosomes (2n = 40) and can be intercrossed or hybridized with G. soja. Cultivated soybean is morphologically variable because of the development of speciﬁc soybean landraces with speciﬁc traits by individual farm families throughout East Asia (Hymowitz 2004). Morphological traits are often qualitatively inherited and controlled by a few genes. Plants generally grow erect and are sparsely or densely branched depending on genotype and growing conditions such as day length, soil fertility, plant spacing, and water availability. Leaves are primarily tri-

3

foliolate alternatively arranged in two opposite rows with leaﬂets varying from oblong to ovate to lanceolate in shape. Pubescence on leaves, pods, and stems can be dense to almost absent. Pubescence color is gray, tawny, or light tawny. The root system consists of a taproot and a large number of ﬁbrous, secondary roots. Root nodules are 3- to 6-mm spherical swellings of the root cortex inhabited by Bradyrhizobium japonicum bacteria, which establish a symbiotic relationship with the soybean to ﬁx N2 from the air and supply N to the plant (Lersten and Carlson 2004). Soybean ﬂowers have bilateral symmetry and are white or purple. Seeds are spherical produced in one to four seeded pods with pod walls at maturity colored tan, brown, or black. Seeds of soybean have black, brown, green, yellow, or mottled seed coats and yellow or green cotyledons. However, commercially grown soybeans usually have yellow seed coats and yellow cotyledons. Seed size of commercially grown soybeans are generally 10 to 20 g/100 seeds but can be signiﬁcantly larger than 30 g/100 seeds or signiﬁcantly smaller than 6 g/100seeds in size (Lersten and Carlson 2004). Farmers generally plant soybeans in the spring to early summer. During the summer, soybean plants ﬂower and produce 60 to 80 pods, each holding from one to four pea-sized

Fig. 2. World vegetable oil consumption 2003 (SoyStats: www.soystats.com/2004)

Fig. 3. World protein meal consumption 2003 (SoyStats: www.soystats.com/2004)

4

G.-J. Lee et al.

Fig. 4. World soybean production in million metric tons from 1965 to 2006 (projected) (Chicago Board of Trade – www.cbot.com)

beans. In the early fall, farmers harvest their crop for the beans, which are high in protein and oil. In tropical environments, soybeans can be grown year round. Efforts to seriously improve soybeans through plant breeding did not begin until the 1940s. An effort to improve soybeans through breeding has expanded into all major soybean-growing regions of the world. Most of the improvement has been through conventional breeding practices, but molecular breeding techniques have played a more important role in recent soybean improvements. The most important traits in traditional breeding programs include soybean yield, pest resistance, and seed composition. Improved yield is the most important trait in soybean breeding because it has the most impact on growers’ proﬁts (Orf et al. 2004). Estimates indicate that soybean yields are improving at a rate of 23 kg ha−1 year−1 due to improved varieties, production practices, and higher atmospheric CO2 (Specht et al. 1999). To insure yield stability over various growing conditions, considerable effort has been made to select for resistance to soybean pathogens such as diseases and nematodes. Tolerance to abiotic stresses such as drought, ﬂooding, and nutrient deﬁciency or toxicity is also of interest to increase and stabilize yields. Recently traits that improve the value and functionality of soybeans such as modiﬁcation of protein and oil to give greater utility in food, health, and industrial uses have been emphasized by soybean breeders. Soybean breeding research has been directed toward modifying the fatty acid proﬁle in the oil to expand uses in food and industrial applications.

Modifying soybean oil to lower saturates to less than 7%, increase the oleic acid content to 50 to 60%, and lower the linolenic acid content to <3% would greatly improve soybean quality for greater functionality in food and industrial applications. Other areas of emphasis include improving protein by modifying the amino acid proﬁle, improving digestible phosphorus, and reducing antinutritional factors such as trypsin inhibitors and allergenic factors (Wilson 2004). Breeding of the self-pollinating soybean includes parental selection followed by hybridization of the selected parents. Parent selection is one of the most important decisions, affecting gene frequencies and genetic variability of populations, which eventually change population means. Germplasm resources for soybean breeding include commercial cultivars, advanced breeding lines, and plant introductions. Depending on breeding goals, parental combinations can be between superior parents for higher mean performance or between an elite line and plant introduction, which will hopefully result in transgressive segregation The genetic base, especially of US cultivars, is narrow and traces back to ca. 15 ancestors. Efforts are being made to utilize more plant introductions in breeding programs to broaden the germplasm base and to introduce new genes for yield, pest resistance, oil quality, and protein quality (Carter et al. 2004). Procedures following hybridization of the selected parents are to identify desirable individuals or families from segregating generations, which include pedigree- selection, bulk-population, and single-seed-descent methods. The pedigree-selection method is based on practical selection among F2 plants. The procedure includes making crosses

Chapter 1 Soybean

Fig. 5. World www.soystats.com)

Oilseed

Production 2003

5

(SoyStats:

Fig. 6. World Soybean Production 2003 (Soy Stats: www.soystats.com)

among the chosen parents followed by individual plant selections until the desired level of homozygosity is achieved. Usually in the F4 or F5 generation, the plant row will be bulked or composited. This seed is then used to conduct preliminary yield tests. A high percentage of the loci are homozygous in the F3 and F4 generations, and families are largely homogeneous at this time, which hopefully provides for effective selection among the composited F4 or F5 families (Poehlman and Sleper 1995). This method is desirable for selecting qualitative traits that can be identiﬁed in the early segregating generations and allows for the elimination of undesirable individuals before testing. Selection in each cycle represents expression of diverse variability from different environments, but this method is labor intensive, requires extensive record keeping of parent-progeny relationships, and is less efﬁcient for quantitative traits. The bulk-population method is a breeding procedure for advancing segregating populations in bulk until a desired level of homozygosity is achieved. Advancement of the early generations is carried out as bulk populations without regard to individual plant selections. The breeder might allow for natural selection in the early bulk generations. This could be particularly useful for pest resistance if the environment contains natural infestations of the pest. Another example where bulk-population breeding could be successful is in the selection for seed size. Segregating populations could be harvested and desirable seed size selected for advancement in the next bulk generation. The procedure in this method is to keep planting and harvesting seeds in bulk until F3 or later generations, then plant individual spaced-

plants, single-plant thresh, ﬁnally, grow the plants in progeny rows. Progeny rows would likely be F5 but could be earlier or later generations depending upon the objectives and resources available (Poehlman and Sleper 1995). Compared to pedigree selection, the bulk-population method is simpler and less labor intensive. Natural selection can be very effective in the bulk-population method, particularly in breeding for certain biotic or abiotic stresses, by eliminating undesirable genotypes in early segregating generations. The most favored selection method in soybean cultivar development is the single-seed-descent (SSD) method. In soybean, single pod descent (SPD) modiﬁed from the SSD has been used in which one or a few pods from a single plant are harvested and bulked to grow the next generation. The procedures for the SPD method include bulking seeds in the F1 and harvesting one or a few pods per plant in early segregating generations until the desired level of homozygosity is achieved. After the desired level of homozygosity is achieved, perhaps in the F4 generation, plants are single-plant threshed and seed from each plant seeded to a progeny or plant row (Poehlman and Sleper 1995). Selected progeny rows are bulked and yield-tested in the following generation. Advancement toward homozygosity can be hastened through the use of winter nurseries. One of the major disadvantages of the single-seed-descent method is the narrow exploitation of the F2 generation where only one seed or pod represents the genetic base of the F2 population. However, single-seed descent is more often the method of choice for variety development because it is often less expensive and less labor intensive than other methods.

6

G.-J. Lee et al.

The backcrossing method is used to substitute one trait for which a recurrent parent (elite cultivar) is deﬁcient by introgressing a desirable allele from a donor parent. The aim of this method is to rapidly recover the genomic constituents of the recurrent parent along with substituting the desirable allele after successive backcrosses. The procedure involves backcrossing desirable individuals to the recurrent parent until the desired level of the recurrent parent is captured. This method is desirable for improving an otherwise excellent genotype with the addition of one or a few desirable alleles from the donor parent. It works most easily for quantitative traits where only a few genes are involved. With the practical use of molecular markers, backcrossing is being increasingly used for quantitative traits and yield testing may be limited compared to other breeding methods. Many complex traits important in the development of cultivars can be difﬁcult to measure or are environmentally sensitive, and thus it is often difﬁcult to make progress in breeding programs (Lee 1995). This is especially true for traits with low heritability such as soybean yield. For these traits, extensive testing is required over multiple locations and years prior to release of the improved variety. As outlined above, with some classical breeding methods release of new soybean cultivars can take 8 to 10 years. Also, it is difﬁcult to combine numerous important traits simultaneously into cultivars in the processes of classical breeding. Technological advancements in molecular biology have allowed the opportunity for genetic dissection of complex traits affected by several genes. The association of phenotypic data with genetic markers among lines segregating for genes conditioning a complex trait provides a way to identify a set of speciﬁc quantitative trait loci (QTLs) affecting that trait. New germplasm in the classical breeding system is explored by phenotypic evaluation of the individuals collected, but identiﬁcation of superior genotypes and their genetic relationships can be achieved using DNA markers associated with desirable traits. Also, information on DNA markers closely linked to a gene of interest is useful for selecting individuals without phenotypic investigation in breeding programs, especially for agronomic traits that are difﬁcult to phenotype. Better estimation of breeding values at identiﬁed loci for traits with low heritability might be achieved using molecular markers for selection (Sills and Nienhuis 1998). There are many traits whose genetic potential is masked by epistatic interactions between genes or by

genes in the repulsion linkage phase. In this case, dissection and linkage breakdown of the unfavorable relationship at the genetic level will be obtained by using molecular markers (Lee 1995). DNA marker analysis can measure the effect of an individual locus at the allelic level. For example, the contribution of alleles from each parent at a particular locus can be determined separately from other loci affecting the trait. This provides an effective method to pyramid desired genes for complex traits or traits with low heritability into adapted varieties (Orf et al. 2004). Therefore, high-throughput molecular breeding techniques, coupled with traditional breeding approaches, will be more effective and more efﬁcient for introducing important quantitative traits into soybean. Large sets of DNA markers have been developed that cover the soybean genome. Several highdensity genetic maps of soybean are available (Cregan et al. 1999; SoyBase, http://soybase.ncgr.org/cgibin/ace/generic/search/soybase). Orf et al. (2004) give a history of molecular-marker development in soybean. Soybean restriction fragment length polymorphisms (RFLPs) were ﬁrst used in the late 1980s. Random ampliﬁed polymorphic DNA (RAPD) and DNA ampliﬁcation ﬁngerprint (DAF) markers were developed in the early 1990s. In the mid-1990s simple sequence repeat (SSR) markers were introduced. SSR markers are highly polymorphic in elite soybean populations. They are adaptable to automation, affordable, and amenable to high-throughput mapping technology. Single nucleotide polymorphism (SNP) markers are on the horizon and will have a greater impact on molecular breeding than SSR and other markers because they are even more polymorphic, cheaper, easier to use, and also very adaptable to high-throughput mapping methods (P. Cregan, pers. comm.). Molecular markers have been used to map the genomic location of both major genes and QTLs for many agronomic, physiological, pest-resistance, and seed-composition traits in soybeans. Orf et al. (2004) reported at least 319 QTLs signiﬁcantly associated with various quantitative traits, with 162 unique QTLs conditioning 10% or more of the phenotypic variation. Most DNA marker analysis has been applied to discovery of QTLs for traits of high economic importance to soybean growers. The highest priority generally has been given to mapping QTLs for resistance to pests such as soybean cyst nematode and for seed protein and oil.

Chapter 1 Soybean

Marker-assisted selection (MAS) with genetic markers among soybean breeders is of great interest but not widely used at present. Markers will be widely applied only after they have proven effective and economical. Soybean-breeding programs conduct simultaneous selection for ten or more traits over several environments and will use MAS as a tool to enhance conventional breeding methods. Potential uses of genetic markers in soybean breeding include: (1) selection of parental combinations with the highest potential for producing a superior variety, (2) use in backcrossing for introducing a trait and for recovering the recurrent parent phenotype more quickly, (3) trait selection in segregating populations to select only phenotypes for further testing with desired loci or QTLs of interest, and (4) identiﬁcation of positive alleles from exotic, unadapted germplasm (Orf et al. 2004).

1.2 Construction of Soybean Genetic Maps 1.2.1 Soybean Genetic Map The main goal of crop breeders is to develop new cultivars that have desired traits superior to the existing cultivars. A conventional breeding method is to make a cross of two parents, followed by selecting recombinants with the trait of interest among the segregation progenies, which is laborious and time consuming, requiring several crosses, generations, and trait evaluations. One technical handicap of the conventional breeding procedure is linkage dragging of tight linkage of the desired locus with undesired loci. Several types of genetic markers and molecular breeding strategies are now available to crop breeders that help them overcome those problems that arise in conventional breeding. Also, DNA markers are applicable to locations of the genes of interest and subject to dissection of the gene effect. One of the powerful applications of genetic markers is linkage analysis and map construction. Two types of genetic markers are morphological and molecular markers. Additional details on the DNA markers available in soybean are described in Sect. 1.5 in the current soybean chapter. In this section, we introduce the current state of

7

progress in soybean genetic maps that have been constructed at different institutes (Tables 2 and 3).

1.2.2 First Generation of Soybean Maps Constructed Based on a Single Population Prior to the use of DNA markers, the ﬁrst soybean linkage map was constructed using the 57 classical markers spanning ca. 420 cM (Palmer and Kilen 1987). Another classical soybean genetic map contains 49 markers and covers 530 cM (Palmer and Kiang 1990). The ﬁrst genetic map of soybean published in 1990 was based on RFLP markers (Apuya et al. 1988; Keim et al. 1990a). The complete soybean genetic map was constructed using 150 RFLPs and three classical markers in an F2 segregating population from the interspeciﬁc cross of G. max (A81-356022) and G. soja (PI 468916) by USDA/Iowa State University. Twentysix genetic LGs containing ca. 1200 recombination units were reported in the map. G. soja (Seib. and Zucc.) is a wild relative of the current domesticated soybean and is considered the progenitor of G. max. The interspeciﬁc population used in the study exhibited higher polymorphism (40%), which was a twofold increase over two G. max parents used earlier (Apuya et al. 1988). Except for 20 markers that remained unlinked, 130 linked markers were used to construct 26 LGs. This segregating population was also used to associate eight agronomic traits (R1, R8, seed-ﬁll length, stem diameter, canopy height, stem length, internode length, leaf width, and leaf length) with RFLP markers, and those traits were linked to cosegregating markers (Keim et al.1990a). Some DNA probes were found to be multiple RFLP loci, indicating that soybean had many duplicated regions. In fact, ∼90% of all RFLP probes were reported to have duplicated loci in soybean (Shoemaker et al. 1996). Based on the initial construction of the map, Diers et al. (1992a,b) expanded the map to 246 markers in 31 LGs covering 2,147 cM. The genetic map was continuously saturated with the additional 100 RFLP markers having 25 LGs encompassing 355 RFLPs, 10 RAPDs, 3 classical markers, and 3 isozymes (Shoemaker and Olson 1993). This population was used for integrating different soybean maps along with other genetic maps (Cregan et al. 1999). The ﬁrst map constructed with families from the intraspeciﬁc cross of two G. max parents, Minsoy

31 35 26 29 20 35 21

68 F2 lines

69 F3 lines 284 RILs 60 F2 lines

190 F2 lines

149 F2 lines 184 RILs

E.I. DuPont Bonus × G. soja PI 81762 Minsoy × Noir 1

1400 3596

1550 1981 1056 1486 2909

1200 2147 3371 2678 3 1 7 7

5 3

26 23 22 28 20

60 F2 lines 59 F2 lines

240 RILs 57 F2 lines 59 F2 lines

240 RILs 57 F2 lines 233 RILs 240 RILs

25

59 F2 lines

A81-356022 (G. max) × PI 468916 (G. soja) Clark × Harosoy A 81-356022 (G. max) × PI 468916 (G. soja) Minsoy × Noir 1 Clark × Harosoy A 81-356022 (G. max) × PI 468916 (G. soja) Minsoy × Noir 1 Clark × Harosoy Minsoy × Archer Archer × Noir 1 2413 2787 2534

1004 3003

2473

2 7 10

4

4

Linkage Map group length (cM) Isozyme

Population size and type

Mapping population

10 14 24

20 3

3

Classical

4 4

5 6 13 13 5

3 3 3

Classical

Table 3. Integrated soybean genetic maps (modiﬁed from Shoemaker et al. 2004)

Misuzudaizu × Moshidou Gong503 Noir 1 × BARC-2 Kefeng No. 1 × Nannong 1138-2

Clark × Harosoy

26 31 23 21

59 F2 lines

G. max A81-356022 × G. soja PI468916

Linkage Map group length (cM) Isozyme

Population size and type

Mapping population

209 95 709

110 501

358

RFLP

39 229

148 224 110 110 401

50 238 355 600+

RFLP

57 73

8 10

10

Marker type RAPD

17

8 8 1

10

Marker type RAPD

11 6

AFLP

105

AFLP

412 339 1015

486

SSR

25 219

40 96

45

SSR

Table 2. Initial and saturated soybean genetic maps constructed based on single population (modiﬁed from Shoemaker et al. 2004)

633 523 1849

138 1004

375

Total

207 452

156 276 138 178 503

153 253 371 600+

Total

Song et al. 2004

Cregan et al. 1999

Shoemaker and Specht 1995

Reference

Matthews et al. 2001 Zhang et al. 2004

Lark et al. 1993 Mansur et al. 1996 Shoemaker and Specht 1995 Akkaya et al. 1995 Yamanaka et al. 2001

Keim et al. 1990a Diers et al. 1992a,b Shoemaker and Olson 1993 Rafalski and Tingey 1993

Reference

8 G.-J. Lee et al.

Chapter 1 Soybean

(PI 27890) and Noir 1 (PI 290136) (MN), was released by the University of Utah (Lark et al. 1993). Of 156 marker loci used for segregation analysis among progeny, 132 markers were genetically linked and placed into 31 LGs with a LOD (logarithm of odds) score of 3 or greater. These markers include RFLPs, ﬁve morphological and biochemical markers [w1 (ﬂower color), textitI (seed-coat color), Pb (pubescence tip), Ep (seed-coat peroxidase), and R (hilum color)], and three isozymes [Aco4 (aconitase), ME (malic enzyme), Idh2 (isocitrate dehydrogenase)]. These progenies showed a high degree of trangressive variation, resulting in many extreme offspring that surpassed either parent in reproductive and morphological seed traits and yield. The LGs comprised 1,550 cM of the soybean genome. Based on RFLP features that were not usual in previous soybean maps, transposable elements in soybean were proposed to exist, which possibly led to the distribution of the repeated elements. Recombinant inbred lines (RILs) were developed from this cross, and the restriction fragments were found to be stable sufﬁciently through the F1 0 generation (Mansur et al. 1993). The ﬁrst G. max population was further investigated for a genetic map and an association analysis using newly developed RILs (Mansur et al. 1996). RILs are good resources for genetic studies because they were inbred to near homozygosity, which provides more opportunity for segregation and recombination and less environmental attribution to particular traits necessary for establishing small genetic differences. This population developed from a cross of Minsoy and Noir 1 consisted of 284 F7 -derived RILs by SSD and was used for locating genetic loci controlling several agronomic traits including days to ﬂower (R1), days to maturity (R8), reproductive period (R8R1), plant height, lodging score, height divided by lodging, seed protein and oil content, seed size, yield, seed number, yield divided by height, and leaf traits (width, length, area). The total linkage of the genetic map was 1,981 cM, encompassing 224 RFLPs, 45 SSRs, 1 isozyme, and 6 classical markers in 35 LGs. Sixty F2 isogenic lines from the cross of the nearisogenic parents of the cultivars Clark and Harosoy at the University of Nebraska were developed to initiate map integration (Shoemaker and Specht 1995). This population was used for the construction of a genetic map using classical genetic markers as well as molecular markers, consisting of 13 classical markers, 7 isozymes, 110 RFLP markers, and 8 RAPD markers. An initial effort was made to integrate the

9

various marker types into a common linkage map using this population, where anchoring RFLP markers were used to identify the same LGs between the maps from the above two populations (Shoemaker and Specht 1995). Akkaya et al. (1995) saturated the previous genetic map with the developed soybean SSR markers, which overcame the problems of using the RFLP markers with lower polymorphisms and higher genome duplication. This new integrated map using SSR markers resulted in higher resolution and solved ambiguous assignments of many RFLP loci across mapping populations. Linkage analysis using the SSR markers exhibited a total map length of 1,486 cM in 29 LGs, which yielded a relatively limited amount of clustering of soybean map and highly informative loci associated with ﬁve out of seven pigmentation traits, four of the six morphological traits, and three of the seven isozymes in the segregating population.

1.2.3 Integrated Soybean Genetic Maps The advent of comparative mapping and new software brought integration of the different soybean genetic maps. The classical LGs having many informative loci controlling various morphological and biochemical traits were integrated into the molecular linkage map (Shoemaker and Specht 1995). An interspeciﬁc population between G. max and G. soja and an intraspeciﬁc population derived from the cross of the near-isogenic lines (NILs) in the Clark and Harosoy were used to merge more of the LGs of the classical linkage map with the soybean RFLP map. The integration effort resulted in about half of the 19 soybean classical LGs that were found to be associated with those morphological and biochemical traits. Along with classical markers, the ﬁrst widely accepted map in soybean was developed from three previous maps from the populations of A81-356022 × G. soja PI 468916, Minsoy × Noir 1, and Clark × Harosoy, which were widely used as a frame of linkage mappings for various traits associated with molecular markers (Cregan et al. 1999). Marker loci were grouped at LOD 5.0 within each of the three mapping populations using Mapmaker 3.0b (Lincoln and Lander 1993) and then aligned with the order of the genetic loci by repetitive use of the Ripple command of Mapmaker at a window size of 6 among the populations and the classical markers. A total of 1423 unique loci were mapped in one or more of three popula-

10

G.-J. Lee et al.

tions distributed among the 20 plus LGs, presumably corresponding to the 20 pairs of soybean chromosomes. These loci encompass 606 SSRs, 689 RFLPs, 79 RAPDs, 11 ampliﬁed fragment length polymorphisms (AFLPs), 10 isozymes, and 26 classical loci. This integrated map has 544 new loci that had not been reported previously (Akkaya et al. 1995; Mansur et al. 1996). This consensus map exhibited the consistency of marker order and genetic distance among three different populations. A genetic map based on the Minsoy × Noir 1 population that was used for the construction of the consensus map was further positioned with additional 412 SSR loci and improved to 67 distinct intervals with less than 0.01 cM between adjacent SSR markers, but 36 intervals of at least 20 cM or 79 intervals of at least 10 cM distance, indicating the necessity of saturating more genetic markers into the genomic gaps. The most recent complete integration of the maps based on the ﬁve mapping populations consists of a total of 1845 markers, including 1,010 SSRs, 798 RFLPs, 73 RAPDs, 6 AFLPs, 24 classical traits, 10 isozymes, and 12 others (Fig. 7; Song et al. 2004). These ﬁve populations consist of a USDA/Iowa State University population of F2 - derived A81-356022 and PI 468916 (G. soja) families, three RIL populations from the University of Utah [284 families from the cross of Minsoy (PI 27890) and Noir 1 (PI 290136) (MN), 233 families from the cross of Minsoy and Archer (MA), and 240 families from the cross of Noir 1 and Archer (NA)], and a population derived from the cross of NILs of Clark and Harosoy from the University of Nebraska. Four hundred fourteen new SSR markers were further added to the map constructed by Cregan et al. (1999), which improved map resolution from 12 to 29 markers saturated to each LG and with 90 SSRs positioned to 30 gaps of 20 cM or more in the previous map. Information on primer sequence, polymerase chain reaction (PCR) reagents for ampliﬁcation, and thermocycling proﬁle for ampliﬁcation of all publicly accessible SSR markers is available at the SoyBase Website (http://soybase.agron.iastate.edu).

which includes disease- related genes, agronomic trait-related genes, metabolite genes, herbicide resistant genes, growth and morphology genes, and seed-quality genes. The current section of this soybean chapter deals with gene reports and location in the soybean map, which will be helpful for future directions of gene mapping of those genes that have not been located. A summary of the most useful and well-recognized genes is presented in Table 4.

1.3.1 Resistant Genes to Soybean Diseases

Bacterial blight caused by Pseudomonas syringae pv. glycinea causes small and circular lesions and yellow-to-brown spots on leaves, especially leaves on the top of plants (http://www.soydiseases.uiuc.edu/ index.cfm). Genes resistant to bacterial blight have been reported Rpg1 (resistant to P. syringae pv. glycinea race 1) (Mukherjee et al. 1966; Keen and Buzzel 1991; Ashﬁeld et al. 1998), Rpg2 (resistant to race 4) (Palmer and Kilen 1987), Rpg3 (resistant to race 4) (Palmer and Kilen 1987), and Rpg4 (resistant to race 4) (Palmer and Kilen 1987). Of these genes, Rpg1 and Rpg4 were mapped in the LG-F and N, respectively, having ﬂanking markers K644 and B212 for Rpg1, and B162_1 and Satt521 for Rpg4 (Palmer and Kilen 1987; Ashﬁeld et al. 1998). Bacterial pustule caused by Xanthomonas axonopodis pv. glycines leads to premature defoliation, which in turn leads to yield decrease by reducing seed size and number. Initially small and pale green spots form pustules in the center of lesions, but mostly later under the leaf surface. The reported recessive resistant gene, rxp, was located between SSR markers Satt372 and Satt014 in LG-D2 (Palmer and Kilen 1987; Narvel et al. 2001a). A molecular pedigree analysis conﬁrmed the locus and resistant source of the cultivar CNS, indicating that the ﬂanking markers would be useful for MAS for lines resistant to this disease. Brown stem rot is a fungal disease caused by Phialophora gregata, which causes greater yield loss when wet cool weather during the plant pod-ﬁll stage is followed by hot, dry weather. Disease symptoms 1.3 are visualized around leaf vein tissues at the R3 or Mapping of Genes in Soybean R4 stage, and a dark brown discoloration of the vascular elements and pith is evident in longitudinally Currently a total of 469 soybean genes have been split stems. Three resistant genes (Rbs1, Rbs2, Rbs3) reported in a USDA-ARS soybean genetics and were reported from resistant sources of L78- 4094, genome database (http://soybase.agron.iastate.edu), PI 437833, and PI 437970, respectively (Hanson et al.

Chapter 1 Soybean Fig. 7. Consensus genetic map of soybean representing 20 linkage groups integrated from ﬁve mapping populations (Song et al. 2004). The mapped markers consisted of 1015 SSR, 798 RFLP, 73 RAPD, 6 AFLP, 24 classical traits, 10 isozyme, and 12 others

11

12

G.-J. Lee et al.

Fig. 7. (continued)

Chapter 1 Soybean Fig. 7. (continued)

13

14

G.-J. Lee et al.

Fig. 7. (continued)

Chapter 1 Soybean

15

Table 4. Genes mapped in soybean genetic map Reaction

Trait

Genea

Linkage groupb

Flanking markers (gene) and distance

Reference

Disease

Bacterial blight

Rpg1 Rpg2 Rpg3 Rpg4 rxp Rbs1 Rbs2 Rbs3 Rcs1 Rcs2 Rcs3 Rpm1 Rpm2 Rmd Rps1 Rps2 Rps3 Rps4 Rps5 Rps6 Rps7

F – – N D2 J J J – – J – – J N J F G G G N

K644-B212 (5.6 cM)

Ashﬁeld et al. 1998

B162_1-Satt521 (5 cM) Satt372-Satt014 Satt215-Satt431 (32.9 cM) Satt244-Satt431 (33.8 cM) K375-Satt431 (14 cM)

Narvel et al. 2001a Bachman et al. 2001 Bachman et al. 2001 Lewers et al. 1999

Satt244-Satt547 (1.5 cM)

Mian et al. 1999

A233-A724 (3.8 cM) Satt159-Satt152 (0.5 cM) Satt287-Satt547 (40.6 cM) HSP176-Satt114 (2.2 cM) Satt109-Satt472 (35.5 cM) T005 Satt472-Satt191 (2.1 cM) R022-1-K395-2 (29.1 cM)

Rps8 Rdc1 Rdc2 Rdc3 Rdc4 Rfs Rpp1 Rpp2

A2 – – – – G – –

Polzin et al. 1994 Demirbas et al. 2001 Demirbas et al. 2001 Demirbas et al. 2001 Demirbas et al. 2001 Diers et al. 1992a Demirbas et al. 2001 Lohnes and Schmitthenner 1997 Burnham et al. 2003

Rpp3

–

Rpp4 Rsv1 Rsv3 Rsv4 Rpv1 rpv2 rhg1 rhg2 rhg3 Rhg4 Rhg5

– F – D1b – – G – – A2 –

Bacterial pustule Brown stem rot

Frog leaf spot

Downy mildew Powdery mildew Phytophthora root and stem rot

Stem canker

Sudden death syndrome Soybean rust

Soybean mosaic virus

Peanut mottle virus Soybean cyst nematode

a

Sat_040-Satt228 (43 cM)

Satt309-Bng122 (4.5 cM)

Satt309-O103 (2.5 cM)

Meksem et al. 1999 Mclean and Byth 1980 Hartwig and Bromﬁeld 1983 Hartwig and Bromﬁeld 1983 Hartwig 1986 Yu et al. 1994; Hayes et al. 2000 Hayes et al. 2000 Boerma and Kuhn 1976 Shipe et al. 1979 Meksem et al. 1999

A085-i locus (16.6)

Cregan et al. 1999

K644-B212 (4.1 cM) Satt542-Satt588 (12.5 cM)

Genes controlling disease resistance, nodule formation, abscission induction, normal root, late and insensitive long daylength or long juvenile trait, long or normal stem/petiole, normal height, three foliolate leaﬂets and normal leaf shape, normal density and shape of pubescence, fertility, efﬁcient nutrient responses, green leaf and yellow seed, light hilum and yellow/nonsaddle seed coat, tawny pubescence, normal palmitate/stearate/oleate/linolenate content b Linkage group based on integrated USDA consensus genetic map of soybean

16

G.-J. Lee et al.

Table 4. (continued) Reaction

Herbicide

Trait

Genea

Linkage groupb

Reniform nematode

rrn rn1 rn2 Rmi1 hs1 hs2 hs3 Als1 Hb Hm CP4 Rj1

– – – – – – – – – N – D1b

rj2 rj3 rj4 Rj5 rj6 Rj7 rj7 rfg1 Ab Rn1 Rn2 E1 E2 E3 E4 E5 e6 E7 j Dt1 dt2 F Lps1 Lps2 S(or e1) Df2 Df3 Df4 Df5 Df6 Df7 Mn Pm lf1 Lf2 Ln

J – G1∗ – – – – – – – – C2 O L – – – C2 – L CLG18∗ D1b – – – CLG6∗ – – CLG1c∗ – – – – A2 CLG16∗ I

Root-knot nematode Sulfonylurea

Bentazon Metribuzin Roundup Nodulation

Growth, ﬂower and morphology

Leaf abscission Root necrosis Flowering and maturity

Stem, petiole, plant growth

Leaf form

∗

Genes mapped on classical linkage group (CLG)

Flanking markers (gene) and distance

Reference

Williams et al. 1981 Kosslak et al. 1997 Kosslak et al. 1997 Luzzi et al. 1994

Rps1

Palmer et al. 2004

Idh1 rmd-A233 (3.9 cM)

Devine and Kuykendall 1996 Polzin et al. 1994

ACTAAA312 (25.3 cM)

Matthews et al. 2001

E7 B157_1-Mng211(20 cM) Pgd1-Dt1

Cober and Voldeng 2001

T(3.9 cM)

Cober and Voldeng 2001

A461_1-Satt006 Satt141-Satt703 (15 cM)

Kiang 1990 Karakaya et al. 2002

Satt333-Satt133 (7.5 cM)

Cregan et al. 1999 Devine 2003 Cregan et al. 1999

Satt270-Sat_268 (5.0 cM)

Chapter 1 Soybean

17

Table 4. (continued) Reaction

Trait

Pubescence type

Seed-coat structure

Fertilitysterility

Seed shape Synaptic sterility

Structural sterility Partial sterility Complete sterility

Mineral toxicity or deﬁciency

Nutrient response

Pigmentation

Chlorophyll deﬁciency

Genea

Linkage groupb

Lo Lnr lw1lw2 lb1lb2 Pa1 Pa2 p1 P2 Pb Pc pd1 pd2 ps B1 B2 B3 N Shr St2 St3 St4 St5 St6 St7 St8 Fs1Fs2 Ft Msp Ms1 Ms2 Ms3 Ms4 Ms5 Ms6 Ms7 Ms8 Ms9 Fe Np Ncl Nr V1 V2 Y3 Y4 Y5 Y6 Y7(Y8) Y9

– – – – B1 F K I E – D1a CLG16∗ H F – – – F – – – F-CLG8c∗ – –

Flanking markers (gene) and distance

Reference

B031-Satt197 (6.8 cM) Sct_188-Satt072 (1.8 cM) Sat_126-Satt588 (8.8 cM) Ln Satt411-Sat_124 (3.0 cM)

Lee et al. 1999 Lee et al. 1999 Cregan et al. 1999 Xu et al. 2000 Cregan et al. 1999

C063_1 (6 cM) Sat_158-Satt302 (7.5 cM) Sat_375-Sat_313 (3.8 cM)

Cregan et al. 1999 Devine 2003 Cregan et al. 1999 Chen and Shoemaker 1998

Satt490-Satt144 (4.1 cM)

Chen and Shoemaker 1998

ms6

Ilarslan et al. 1997

W1 Pgm1

Sneller et al. 1992; Ilarslan et al. 1997

W1

Sneller 1992, Ilarslan et al. 1997

Ln

Cregan et al. 1999

Satt411-A661 (44.6 cM)

Cregan et al. 1999

– – F-CLG8c∗ O-CLG15 – – – F-CLG8c∗ – – – – – – – I-CLG4∗ – – – – – – E

18

G.-J. Lee et al.

Table 4. (continued) Reaction

Trait

Flower color

Seed coat and hilum color

Pubescence color Fatty acid

Palmitic acid

Stearic acid

Oleic acid Linolenic acid

Genea

Linkage groupb

Flanking markers (gene) and distance

Reference

Y10 Y11 Y12 Y13 Y14 Y15 Y16 Y17 Y18 Y19 Y20 Y21 Y22 Y23 D1(D2) G1 G2 G3 W1 W3 W4 Wm Wp I K1 K2 K3 O R

N F C2-CLG1∗ A2 – – – E – – – – – F D1a D1a – – F – – F-CLG8∗ D1b A2 B1 – – A2 K

Satt022 Df2 Sp1-E1 Sat_115-A638_1 (3.3 cM)

Zou et al. 2003 Mahama et al. 2002 Cregan et al. 1999 Cregan et al. 1999

Pb

Devine 1998 Burzlaff and Palmer 1999

T Td Fap1 Fap2 Fap3 Fap4 Fap5 Fap6 Fap7 Fas St1 St2 Ol Fan1 Fan2 Fan3

C2 – – A_611-A_537 D – – – – – B2-CLG17∗ Satt070 (12.3 cM) – – – Idh2, Fas B2 – –

Burzlaff and Palmer 1999

Ms6-St5 Satt071-Sat_169 (3.9 cM) Satt071-D1 (3 cM)

Mahama et al. 2002 Lohnes et al. 1997 Luquez and Guiamet 2001

Sat_039-Sat_298 (4.4 cM) Dihydroﬂavonol 4-reductase W1 Satt660-K011_4 (11 cM) Satt315-Sat_400 (4.1 cM) Satt197-Satt298 (25.3 cM)

Karakaya et al. 2002 Fasoula et al. 1995

Sat_212-Satt424 (4.3 cM) Sat_352-Sat_293 (5.5 cM) E7 (3.9 cM)

Hegstad et al. 2000b Senda et al. 2002 Cregan et al. 1999 Chen and Palmer 1998 Weiss 1970 Karakaya et al. 2002 Cober and Voldeng 2001 Primomo et al. 2002 Nickell et al. 1994 Bravo et al. 1999 Stoltzfus et al. 2000 Stoltzfus et al. 2000 Stoltzfus et al. 2000 Spencer et al. 2003 Rahman et al. 1997 Rahman et al. 1997 Rahman et al. 1996 Brummer et al. 1995

Chapter 1 Soybean

1988; Willmot and Nickell 1989). Those three genes resistant to brown stem rot reside at the close region, linking to SSR marker Satt431 on LG-J (Bachman et al. 2001). Frogeye leafspot is a fungal disease caused by Cercospora sojina. Symptoms include small, gray spots with reddish brown borders on the upper leaves in hot summer. The fungus infects seed, and consequently the seed coat of infected seeds turns gray. Resistance was found to be in the varieties Lincoln and Wabash caused by a dominant gene Rcs1 resistant to race 1 (Athow and Probst 1952), in Kent by Rcs2 resistant to race 2 (Athow et al. 1962), and in Davis by Rcs3 resistant to race 5 and all known Brazilian isolates (Phillips and Boerma 1982; Mian et al. 1999). The Rcs3 gene was mapped on LG-J, near SSR markers Satt244 and Satt547 (Mian et al. 1999). Downy Mildew is caused by an obligate fungal disease, Peronospora manshurica. Symptoms of downy mildew appear on leaf, pod, and seed, as light-green to yellow spotted leaves, small pods, and a pale coating of spores on seeds. Genes Rpm1 and Rpm2, which are resistant to downy mildew race 1 and race 2, respectively, were reported in soybean (Bernard and Cremeens 1971; Lim 1989). However, none of these was located in the soybean genetic map. Powdery mildew is a fungal leaf disease, Microsphaera manshurica, with greater incidence in seasons with cooler than normal temperatures. The ﬁrst symptom is usually a circular area of white to light gray, superﬁcial powdery growth on the upper leaf surface. A resistant gene, Rmd, was closely mapped with other soybean genes (Rps2 and Rj2 for phytophthora root and stem rot resistance and ineffective bradyrhizobia nodulation, respectively) with ﬂanking markers of RFLP markers A233 and A724 within a 3.8-cM region on LG-J (Polzin et al. 1994). Phytophthora root and stem rot caused by Phytophthora sojae is a fungal disease that occurs in wet and poor drainage soil. To date, eight dominant loci resistant to the Phytophthora rot have been reported in soybean, including Rps1 (Bernard et al. 1957), Rps2 (Kilen et al. 1974), Rps3 (Mueller et al. 1978), Rps4 (Athow et al. 1980), Rps5 (Buzzell and Anderson 1981), Rps6 (Athow and Laviolette 1982), Rps7 (Anderson and Buzzell 1992), and Rps8 (Burnham et al. 2003). Linkage analysis indicated genomic locations of Rps1 and Rps7 on LG-N, Rps2 on LGJ, Rps3 on LG-F, Rps4, Rps5, and Rps6 on LG-G, and Rps8 on LG-A2 (Demirbas et al. 2001; Weng et al. 2001; Burnham et al. 2003).

19

Stem canker caused by the fungus Diaporthe phaseolorum leads to symptoms of reddish brown, slightly sunken lesions at the base of branches or leaf petioles and eventually brown discoloration inside the stem. Four genes resistant to the stem canker disease reported include Rdc1, Rdc2, Rdc3, and Rdc4 (Kilen and Hartwig 1987), but none of these has yet been mapped in the soybean molecular map. Sudden death syndrome caused by the fungus Fusarium solanii has symptoms of initially scattered chlorotic spots between the veins on leaves and, later, cupped and curled leaves. Resistant gene Rfs was ﬁrst reported in a cultivar Ripley and mapped on LG-G (Stephens et al. 1993; Meksem et al. 1999). Soybean rust was ﬁrst reported in Japan in 1902 and spread to other continents including other Asian countries, Africa, and, recently, Central and North America. The spread of soybean rust has rapidly picked up steam in recent years. For example, in Brazil in 2002, soybean rust was observed in about 4% of soybean-growing acres, but it was observed in 99% of those same acres in 2003. Soybean rust is caused by the fungi Phakopsora pachyrhizi and P. meibomiae. The host range is quite broad, with at least 41 and 34 natural hosts including kudzu and soybean for P. meibomiae and P. pachyrhizi, respectively. Four dominant genes have been reported to be resistant to soybean rust, which include Rpp1, Rpp2, Rpp3, and Rpp4, and none has been mapped yet (McLean and Byth 1980; Hartwig and Bromﬁeld 1983; Hartwig 1986). Soybean mosaic virus (SMV) causes three responses including resistance, susceptibility with symptoms of blistering and cupping of the leaves from the initial mosaic, and necrotic lesions on leaﬂets, petioles, and stems. Many resistant genes respond differently to various SMV strains, including Rsv1, which has seven alleles (Rsv1-t, Rsv1-y, Rsv1-m, Rsv1-k, Rsv1-s, Rsv1-r, Rsv1-sk), Rsv3, and Rsv4 (Gunduz et al. 2002). Those SMV resistant genes were assigned to different soybean genome in the genetic map (Table 4). Peanut mottle potyvirus (PMV) causes systemic dark green mottling, crinkling, blistering, and malformation of soybean leaves. There are two genes resistant to PMV (Rpv1 and rpv2) identiﬁed in the cultivars of Dorman and Peking, but neither has been located yet in the soybean map (Boerma and Kuhn 1976; Shipe et al. 1979). Soybean cyst nematode (SCN, Heterodera glycines) is the most destructive pathogen worldwide affecting soybean yield (Wrather et al. 2001). Of 130

20

G.-J. Lee et al.

SCN resistant cultivars released in the USA, most (>95%) have resistant alleles derived from a plant introduction PI 88788 due to favorable agronomic performance (Skorupska et al. 1994). Inheritance of SCN resistance was ﬁrst reported in a cultivar called Peking, and three recessive genes (rhg1, rhg2, rhg3) were assigned (Caldwell et al. 1960). In Peking, a dominant resistance gene (Rhg4) was later identiﬁed that is found to be linked to the i locus underlying seed coat color (Matson and Williams 1965). Another resistant gene, Rhg5, was reported to be in PI 88788 (Rao-Arelli 1994). It has been suggested that genes necessary for full resistance to SCN include Rhg1–5 and may extend to ten independently inherited genes. Currently, two genes, rhg1 and Rhg4 on LG-G and A2 respectively, have been independently isolated and characterized as strong candidates for resistance to SCN (Hauge et al. 2001; Lightfoot and Meksem 2002). Concibido et al. (1994) ﬁrst reported the genomic region of the Rhg4 gene in a segregation population, which was closely linked to the i locus on LG-A2 and was conﬁrmed by independent studies (Mahalingam and Shorupska 1995; Webb et al. 1995; Chang et al. 1997). Since the RFLP marker ﬂanking rhg1 (K069) on LG-G was initially found to be associated with SCN resistance in PI 209332, followup research has conﬁrmed the presence of the gene in different resistant soybean sources such as Peking, PI 88788, PI 90763, and PI 437654 (Concibido et al. 1994, 1995, 1997; Webb et al. 1995; Mudge et al. 1997). Reniform nematode, Rotylenchulus reniformis, was ﬁrst found on cowpea roots in Hawaii and ﬁrst reported as a parasite of cotton in Georgia and of tomato in Florida in the USA. Today, it is found worldwide including in tropical, subtropical, and warm temperate zones in South America, North America, the Caribbean Basin, Africa, southern Europe, the Middle East, Asia, Australia, and the Paciﬁc (Ayala and Ramirez 1964). Inheritance of the reniform nematode response indicated that the recessive rrn gene is related to resistance in soybean (Williams et al. 1981). However, the gene has not yet been located in the soybean map. Root-knot nematode by Meloidogyne spp. causes roots to produce characteristic swellings or galls that are easily distinguishable from nitrogen-ﬁxing nodules. Based on gall counts on soybean roots in F1 , F2 , and F3 generations, a single additive gene for resistance to galling, designated Rmi1, was found (Luzzi et al. 1994). Two QTLs were reported that are associated with low gall numbers on roots on LG-O and G,

but the gene location has not yet been located (Li et al. 2001).

1.3.2 Genes for Herbicide Resistance There are several reported genes that are resistant to herbicides, including genes tolerant or with enhanced tolerance to bentazon (dominant Hb), metribuzin (dominant Hm), sulfonylurea (recessive hs1, hs2, hs3), roundup (CP4), and sulfonylurea (dominant Als1) (Palmer et al. 2004). The Hm tolerant to metribuzin was located 7 cM from Rps1, which is resistant to Phytophthora root and stem rot on LG-N.

1.3.3 Nodulation Genes During interaction between Rhizobium and legume plants, formation of the peribacteroid membrane, where the bacteria are protected from direct contact with the host cell cytoplasm, is essential for rhizobia function and maintenance inside the host cells (Verma 1992). The recessive gene rj1, which is closely mapped to the f (fasciated stem) and Idh1 (isocitrate dehydrogenase) loci in LG-D1b-W (classical LG 11), controls the nonnodulation in genotypes of T181 and T201 (Devine and Kuykendall 1996). The dominant Rj2 detected in cultivars Hardee and CNS is associated with ineffective nodulation response with Bradyrhizobium japonicum and was mapped in LG-J, which is closely linked to the phytophthora resistance gene Rps2 (Devine et al. 1991; Polzin et al. 1994). Also, the dominant allele Rj4 showed ineffective nodulation in response to B. elkanii and was mapped in the soybean genetic map (Matthews et al. 2001). The other nodulation-inducing genes (rj3, Rj5, Rj6, Rj7, rfg1) and hypernodulating gene (rj7) have not yet been mapped (Vest 1970; Pracht et al. 1993; Devine and Kuykendall 1994).

1.3.4 Genes for Growth Habit, Flowering, and Morphology A gene associated with abscission, Ab, was reported, but it has not yet been mapped (Probst 1950). The shedding of plant parts, both reproductive and veg-

Chapter 1 Soybean

etative, is important for harvest, reproduction, plant defense, resistance to drought and ﬂooding, and continuation of perennial growth (Sexton and Roberts 1982). A necrotic root mutant having brown inner cortical cells adjacent to the stele was susceptible to hyphal infection by the fungus Phytophthora sojae (Kosslak et al. 1996). Necrotic root genes, rn1, rn2, and rn3, were reported to be associated with the accumulation of isoﬂavonoid phytoalexins and group 2 peroxidases under axenic conditions (Kosslak et al. 1996, 1997). The necrosis genes soon after germination also seemed to be associated with resistance to reniform nematode (Kosslak et al. 1997), but those genes were not located. Flowering time and maturity are directly related to soybean yield due to the length of vegetative period and leaf development; thus many soybean breeders are interested in ﬁnding genotypes with these characteristics. A delayed ﬂowering under short day length termed “long juvenile” is a trait controlled by the recessive jj allele in an E6 locus (Bonato and Vello 1999; Cairo et al. 2002). Six more genes (E1 to E5, E7) have been reported as being associated with ﬂowering time and maturity or photoperiod sensitivity (Cober and Voldeng 2001). Although genes for ﬂowering time, maturity, and photoperiod insensitivity were proposed to be at the same genomic region, linkage analysis using probes designed from greater gene homologs for ﬂowering time, maturity, and photoperiod insensitivity in Arabidopsis exhibited that none of the cDNA probes matched the previously positioned loci for those traits (Tasma et al. 2001; Tasma and Shoemaker 2003). Many genes control the growth of the stem (determinate vs. indeterminate vs. semideterminate; normal vs. fasciated), petiole (normal vs. short), whole plant (normal vs. dwarf; normal vs. miniature), and leaf (normal vs. dwarf and crinkled). The genes Dt1 (vs. homozygous dt1 for the determinate stem) and Dt2 (vs. dt2 for indeterminate stem) condition continuous stem elongation and node production after ﬂowering and semideterminate stem growth, respectively (Thompson et al. 1997; Lewers et al. 1998). Soybean cultivars with indeterminate stems are usually grown for their early maturity in the northern USA and Canada, whereas cultivars with determinate stems are planted in the southern USA for their later maturity group. Thus the early determinate cultivars may result in harvest difﬁculties or low yield in seasons of poor growth. Many soybean geneticists and breeders

21

have concerns regarding interactions between genes for stem elongation and other ﬂowering, maturity, or internode length. The Dt1 locus is epistatic to Dt2, which is expressed only with Dt1 and is blocked in the homozygous recessive dt1dt1 genotype (Bernard 1972). Also, one report indicated that plant height could be modiﬁed by internode length, which is controlled by the S allele affecting short internode stem type. Two alleles, Dt2 and S, resulted in early maturity, short plant height and decreased lodging, and enhanced harvest index but decreased seed weight and protein content (Lewers et al. 1998). Fasciated soybean is characterized by a broad and ﬂattened stem, which is controlled by a single recessive gene, f (Karakaya et al. 2002). A study using a fasciated mutant revealed that the mutation had a pleiotropic effect on plant development and pattern formation so that meristem enlargement and strong apical dominance in mutants generated increased leaf numbers and a branchless phenotype (Tang and Knap 1998). Two recessive genes, lps1 and lps2, were reported to control a short petiole phenotype in soybean (You et al. 1998). Short petiole and leaf movement with a pulvinus region at the base of the petiole are associated with soybean yield in terms of controlling the amount of light energy and plant density and are heritable traits. The lps2 was also found to be associated with abnormal pulvinus, and the two genes expressed at the different stages were assumed to control the development of the petiole and pulvinus (You et al. 1998). Six dwarf-related genes have been reported in soybean (df2, df3, df4, df5, df6, and df7df8) (Werner et al. 1987; Soybean Genetics Committee 1995). These genes were identiﬁed in soybean lines of T210, T243, T244, T256, T263, T286, and T261 and cause short internode, rugose leaves and relatively low seed yield (Werner et al. 1987; Soybean Genetics Committee 1995). A mutant treated with ethyl methanesulfonate (EMS) revealed that the difference in leaf size was due primarily to a difference in cell size and not the number of epidermal cells (Werner et al. 1987). Brachytic character with short internodes in a normal number of stem-noded soybeans was studied genetically and was found to be controlled by a duplicate recessive factor, sb1sb2 (Boerma and Jones 1978). Of those soybean height- or length- related traits, only df2 and df5 genes were mapped in the classical linkage map (Table 4). Some genes related to leaf forms (lf1, Lf2, Ln, Lo, Lnr, lw1 lw2, lb1lb2) are heritable, and usually necrotic

22

G.-J. Lee et al.

or abnormal leaf forms are controlled by a single gene, but duplicated genes form a wavy or bullate leaf. Genes controlling leaﬂet number (lf1 and lf2) were mapped on LG- A2 and classical LG-16, respectively (Cregan et al. 1999; Devine 2003). The gene for leaf shape (ovate vs. narrow controlled by a gene ln) resided in the middle of LG-I (Cregan et al. 1999). Trichome density and shape were recognized to be associated with negative spread of pathogens such as soybean mosaic virus and insects and positive improvement of soybean yield (Gunashinghe et al. 1988; Zhang et al. 1992). Many genes related to pubescence types (density, tip shape, growth habit, etc.) have been identiﬁed in different genotypes. Most of the genes are mapped in different LGs including LG-B1 (a recessive pa1 for semiappressed), F ( a recessive pa2 for erect or semiappressed), K (a dominant P1 for glabrous), I (a recessive p2 for puberulent), E (a recessive pb for blunt hair tip), D1a (a dominant Pd1 for dense trichomes), CLG16 linked to Lf2 locus (a dominant Pd2 for dense trichomes), and H (a dominant Ps for sparse pubescence) as shown in Table 4. Genes for seed coat bloom (dominant B1, B2, B3) and lack of abscission layer on soybean hilum (recessive n) were reported to be associated with seed coat structure (Palmer et al. 2004). Only the B1 gene was mapped on LG-F, where other seed trait genes (Gy5 for glycine subunit, Shr for shriveled seed, and Cgy1 for β-conglycinin subunit) linked together (Chen and Shoemaker 1998).

tural abnormalities is controlled genetically by duplicate recessive genes, fs1 and fs2 (Johns and Palmer 1982). Using a gamma ray-induced mutant, Singh and Jha (1978) proposed a recessive gene, ft, assigned as a transformed ﬂower, that controls multiple ﬂoral parts (phyllody, sepalody, petalody, staminody, carpellody). In these sterile plants, fertile pollen is produced, but plants are male-sterile because of poor anther dehiscence. However, none of these genes has been mapped yet (Table 4). Partially or complete sterile soybeans were also identiﬁed. Partial male sterility in soybean is controlled by a single recessive gene, msp, and nine genes associated with complete sterility of soybean have been reported (Stelly and Palmer 1980; Palmer 2000). Of those genes, three complete male and female sterile genes have been mapped in the genetic map (Cregan et al. 1999). Male sterility, while leaving female reproductive capacity unaffected, is useful in soybean breeding in terms of production of F1 hybrid seeds and insect-mediated pollination. Use of linkage information between other phenotypic or DNA markers and male sterility has been used for selecting sterile plants normally controlled by homozygous recessive genes without a segregation test in the next generation. For example, morphological marker purple hypocotyls and ﬂower color (W1) is associated with one male sterile gene, ms6 (Lewers et al. 1996), and SSR markers Satt157 and Satt412 on LG-D1b are linked to the nuclear male-sterile gene, ms (Jin et al. 1998). This genetic linkage information could be used for secetion of male sterile plants.

1.3.5 Soybean Sterility Genes Reports show diverse sterility systems in soybean including synaptic, structural, partial, and complete sterility. Most synaptic phenomena affecting chromosome pairing and disjunction leading to male and female sterilility were inherited by a single recessive gene (st1 to st8). Some microspores at the tetrad stage or male cells after microspore mitosis were found to be collapsed in the male sterile mutant (Palmer and Horner 2000). Only the st5 gene in synaptic sterility of soybean was mapped in the classical LG F-CLG8c and linked to other male sterile genes (ms1, ms6). Structural malformation of ﬂoral parts caused natural sterilility or mutant-induced sterilility and resulted in meiosis and anthesis or prevented another dehiscence (Singh and Jha 1978; Johns and Palmer 1982). A mutant exhibiting male sterility due to struc-

1.3.6 Genes for Mineral Toxicity or Deficiency Genes associated with responses to soil nutrient concentration include Fe (efﬁcient iron utilization), Np (tolerant to high phosphorous level), Ncl (chlorideexcluding type at the root surface), and Nr (presence of constitutive nitrate reductase). Fe-deﬁcient chlorosis in soybean is a typical symptom in alkaline soil where the nutrient is rarely dissolved in soil solution (Lin et al. 1997). There are some reports on genetic control of Fe-deﬁciency chlorosis either by a single recessive gene or multiple genes, but no such gene was located in the soybean map (Weiss 1943; Lin et al. 1997). Tolerance to Zn in acidic soil or deﬁciency of Mn in high-pH soil has been investigated, but none of the genes has been mapped yet (Hartwig et al. 1991;

Chapter 1 Soybean

Graham et al. 1995). In response to high phosphorus levels in growing solution, soybeans showed genetically diverse responses, and a single recessive pair of the gene npnp was assigned to plants with severe brown splotching, chlorosis, and stunting appearance (Bernard and Howell 1964). Chloride exclusion was found to be associated with the symptom of no necrosis in salt-tolerant soybean Lee cultivar and its progenies and inherited by a dominant gene, Ncl (Abel 1969). Nitrogen reductase (NR) controlled by a gene, Nr, is responsible for converting the accumulated NO−3 absorbed by plant root into a reduced form of nitrogen and minimizing NO−3 toxicity so that the NR mutant without inherent NO−3 reductase grows poorly or dies when NO−3 is the sole N source (Ryan et al. 1983). Those genes associated with efﬁciency of use of those nutrients, however, were not located in the soybean genetic map (Table 4).

1.3.7 Genes for Soybean Pigmentation Many genes conditioning expression of chlorophyll pigment in leaves and seeds have been reported (Palmer et al. 2004), including two genes for normal or variegated leaves (V1 and V2), 21 genes for normal colored green-yellow/pale or green/whitish green seedlings and plants, with the seedling becoming yellow (Y3–Y23), two genes for green seed embryo due to chlorophyll retention (D1 and D2), and three genes for green seed coat due to chlorophyll retention (G1–G3) (Table 3). Of these genes, only ten (V1, Y9–Y13, Y23, D1, G1) were located in the classical and integrated genetic map of soybean (Cregan et al. 1999). Variation in ﬂower pigment, seed coat, and pubescence serves as useful markers for hybridization in soybean breeding. Soybean has purple (controlled by W1, Wm, or Wp), white (controlled by w1), and pink (controlled by wp) ﬂowers, but some transposable elements in a soybean control chimeric ﬂowers (w4-m for purple or white chimeric) at different nodes in a plant (Hegstad et al. 2000a,b). A mutation to the pink color locus also causes changes in ﬂower color (wp-m for pink and purple) across generations (Johnson et al. 1998). Soybean has yellow, saddle, brown, and black seed coats, which are conditioned by I, K type, O, and R genes, respectively (Table 4). Soybean has two distinct pubescence colors, tawny and gray, inherited by dominant T or Td and recessive t, respectively. Temperatures below

23

15 ◦ C during seed development cause discoloration of hila and seed coat (Takahashi and Abe 1994; Takahashi and Asanuma 1996). Linkage analysis of those pigment- conditioning genes with other traits have been conducted for protein and oil content, ﬂowering and maturity, or resistance to SCN, as a type of visual marker of the soybean pigment (Todd and Vodkin 1998; Hegstad et al. 2000a; Cober and Voldeng 2001).

1.3.8 Genes for Fatty Acid Composition Soybean has two groups of fatty acid (FA): saturated FA without double bonds in the carbon chains including palmitic (16:00) and stearic acid (18:0) and unsaturated FA with double bonds including oleic (18:1), linoleic (18:2), and linolenic acid (18:3). The average FA content in soybean is 11% palmitic, 4% stearic, 24% oleic, 54% linoleic, and 7% linolenic acid (Fehr 1991). In terms of edible oil, soybean breeding targets cultivars with low saturated FA (3% for palmitic acid), high oleic acid (48%), and low linolenic acid (3.5%). Linolenic acid is the most easily oxidizable FA and is considered to be a major cause of ﬂavor problems in soybean oil. Decreased linolenic acid content will reduce or eliminate the need for hydrogenation and associated trans fatty acid production (List et al. 2000). Seven genes for controlling palmitic acid content in soybean have been reported, but only the fap2 contributed by the C1727 genotype and a major gene from N87-2122-4 were mapped on LG- D and A1, respectively (Nickell et al. 1994; Li et al. 2002). The genes controlling another saturated FA, stearic acid, are Fas, St1, and St2. Usually, most of the genotypes carrying the fas alleles exhibit dramatic seed yield, but a newly developed line FAM94-41 was found to have an agronomically robust high stearic acid line (Pantalone et al. 2002; Spencer et al. 2002). The fas gene was mapped on LG-B2, but the others have not been located yet (Spencer et al. 2003). A soybean cultivar with mid to high oleic acid content (24 to 48%) is a desirable target of soybean breeding, which has been funded recently by the United Soybean Board. Previous studies on the oleic acid content in soybean reported that the trait was quantitatively governed by multiple genes, but only the Ol gene was studied genetically (Rahman et al. 1996). The gene has not been mapped yet. A major factor controlling linolenic acid (18:3) level in plant tissues is omega-3 fatty acid de-

24

G.-J. Lee et al.

saturase, which converts linoleic (18:2) to linolenic acid (18:3) (Bilyeu et al. 2003). Of three genes (fan1, fan2, fan3) associated with linolenic acid content in soybean, only fan1 has been mapped on LG-B2, which is closely linked to other genes such as Idh2 and fas (Brummer et al. 1995).

1.4 QTL Mapping in Soybean One application of molecular markers is to locate genes for quantitative traits that are generally controlled by multiple loci with relatively low genetic contribution and more likely affected by environmental variation. Any traits with higher heritability can obviously be tagged by molecular markers in segregating populations, and the ﬂanking markers can be beneﬁcially applied to selecting progenies and/or germplasm that have favorable alleles for particular traits among diverse genetic materials as well. Likewise any DNA markers ﬂanking the traits inherited with low heritability can be used to select lines and used to introgress the positive alleles into elite germplasm without any phenotypic evaluations in descendant generations if the QTL is conﬁrmed across different genetic backgrounds. In the last decade, development of DNA markers in soybean has allowed identiﬁcation of many QTLs, dissection of genetic attributes of the QTLs, and exploration of the positive alleles for the traits. The construction of soybean genetic maps has enabled many soybean geneticists and breeders to dissect the genetic loci of interest into their genetic contribution to the trait variation, additive or dominant effects, and their interactions. Along with genetic heritability, information of the genetic contribution of each individual locus to the trait variation can be used to decide numbers of loci conditioning the particular traits, major or minor loci, and genomewide locations of the loci. Several morphological and molecular markers used in soybean were introduced in preceding sections. In this section, many DNA markers for the QTLs that are associated with pest resistance, physiological response, seed composition, and agronomic traits are reviewed and summarized (Table 5). Because information on DNA markers links qualitative traits and known genes were introduced in the previous section on soybean gene mapping, all reported tagged mark-

ers to known (cloned) genes are omitted in Table 5. A summary of the reported QTLs listed in Table 5 is modiﬁed and provides more details of the reviewed chapter by Orf et al. (2004). In this section, we provide mapping populations that were used for mapping the QTLs, LGs, ﬂanking markers and their interval (cM), phenotypic contribution (%), and software or analyzing tools used for QTL analysis. As explained by Orf et al. (2004), only putative QTLs explaining more than 10% of the phenotypic variation from a group of linked markers reported in SoyBase (a total of 963) are indicated in Table 5. Readers can see the highly signiﬁcant QTLs that have higher R2 (%) values and are detected in the same genomic locations across mapping populations, which will be highly likely to be used in MAS. Also, many quantitative traits need to be tagged with the available DNA markers, and saturation of the reported markers is necessary if those markers are to be used for MAS in soybean-breeding programs. Recent progress in the development of SNP markers and diverse genotyping platforms will allow acceleration of QTL detection and precise location of QTLs (Zhu et al. 2003).

1.4.1 Pest Resistance Mapping of QTLs for soybean resistance to insects has been demonstrated mainly against defoliating insect species including the corn earworm, which is a common soybean pest in the southern USA. Two insect resistance mechanisms were reported as antibiosis (mortality of insect growth or development) and nonpreferential antixenosis (repellence of insects from soybean plant) (Clark et al. 1972; Lambert and Kilen 1984). A limited resistance source can be traced back to soybean plant introductions PI 171451, PI 227687, and PI 229358 that were used for QTL mapping associated with corn earworm resistance in soybean (Table 5). A total of 27 QTLs have been reported, and a major QTL explaining the highest variation of antibiosis and antixenosis resistance was located on LG-M, which is being ﬁne-mapped using recombinant substitution lines identiﬁed from a population of 3,000 BC7 F2 plants (Zhu et al. 2004). QTLs that condition resistance to three root knot nematodes (RKNs), including peanut RKN (Meloidogyne arenaria), southern RKN (M. incognita), and Javanese RKN (M. javanica), have been mapped on LG-

PI 96354 × Bossier 110 F2:3

Southern RKN

CNS × PI 230977 68 F2:3

PI 96354 ×Bossier 110 F2:3, Prichard × G93-9009 BC2F2

PI 200538 × CNS 105 F2:3

Fukuyutaka × Himeshirazu 143 F2:3

Root knot nematode Peanut RKN

Common cutworm

(Antibiosis)

Minsoy × Noir 1 240 RILs Cobb × PI 229358 100 F2:3

Cobb × PI 171451 (110 F2:3), Cobb × PI 227687 (95 F2:3), Cobb × PI 229358 (103 F2:3)

Population

b

Total number of reported QTLs are shown in parentheses Only independent QTL contributing >10% to particular trait variation c SFA (single factor analysis)

a

Insect Corn earworm

Pest resistance

(Antixenosis)

Traita

Reaction

B212_1-R045_1(3.5) B212_2-A111_2(31) A882_1-G248_1(13) K493_1-Cs008_1(5) Satt492-Satt358 (9) Satt012-Satt505 (8)

G

Satt567–Satt463

M F E O G O

Satt141-Satt290 (3.7) Sat_122-Satt541(0.5) Satt463-A584_4 (5.0) Satt472-Satt191 (4.4) Satt220-Satt536 (2.5) Satt220–Satt175

A132-A670 Bng047 B212-A757 L183-L002 (13) A131-R249 A064-K401 (41) A584-A226 Satt575-Sat_112 (6)

C2 D1b F G H J M E D1b H M G M M

A343-K411 (31)

Flanking loci (cM)b

B2

LG

Table 5. QTL conditioning various soybean traits mapped in genetic map (modiﬁed from Orf et al. 2004)

18

32 16 31 14 56

16

10 15 37 14 21 28

11 12 20 19 19 19 37 17

12

3.6

8.6 2.4 6.9 2.4 16.3

6.8

2.3 3.6 9.8 3 4.6 12.7

– – 4.8 – 2–4.0 – 10 9

–

R2 (%) LOD

Mapmaker QTL

Mapmaker QTL

Mapmaker QTL

QTL Cartographer

PLABQTL Mapmaker QTL/QTL Cartographer

Mapmaker QTL

Software or analysisc

Tamulonis et al. 1997c

Tamulonis et al. 1997a Tamulonis et al. 1997b Li et al. 2001

Komatsu et al. 2005

Terry et al. 2000 Narvel et al. 2001b

Rector et al. 1999, 2000

Reference

Chapter 1 Soybean 25

Reaction

Race 5

Race 3

Race 2

SCN Race 1

Javanese RKN

Traita

Table 5. (continued)

Evans × PI 209332 Evans × Peking 110 F2:3 Peking × Essex 200 F2:3

Hamilton × PI 90763 226 F2:3

PI 468916 × A81-356022 57 F2:4

Hamilton × PI 438489B 184 F2:3

Williams 82 × Hartwig 200 F2:3 Evans × PI 90763 115 F2:3 Peking × Essex 200 F2:3 Hamilton × PI 89772 250 F2:3

Hamilton × PI 90763 226 F2:3 Magellan × PI 404198A 224 F2:3

J87-233 × Hutcheson 125 F2:3 Hamilton × PI 438489B 184 F2:3

Hamilton × PI 438489B 184 F2:3

Magellan × PI 404198A 224 F2:3 Hamilton × PI 89772 250 F2:3

Evans × PI 209332 Peking × Essex 200 F2:3

Population D1a F L B2 H G B1 D2 B1 B2 G A2 C1 G G B1 G B1 J H E G A2 D1a G E G A2 G D2 N I

LG A725_2 B212_1 A023 A593 B072 Satt309-Satt688 A006-Satt583 (25) B132-Satt372 Satt583-Sat_123 (19) Satt168-A329 (11) A096- Satt130 (11) BLT65V A463-Satt396 (40) A096- Satt130 (11) Satt163-Satt688 Satt453 Satt163-Satt309 A006 B032 B072 A135-Satt231 (19) B053-Satt309 (12) K400- T155 (13) A398-K478 (22) Satt130- Satt012 (26) Satt573-Satt598 (4) Satt288-Satt472 (24) Sat_400-Satt424 Satt163-Satt688 A064_2 A280_1 K011

Flanking loci (cM)b 13 46 24 21 13 20 17 10 13 12 16 11 10 13 15 11 13 91 19 13 16 23 19 11 14 23 27 18 28 11 14 11 – – – 10 6.8 4.6 4.2 2.8 9.1 – 2.6 7.5 7.9 5.5 7.1 – – – 3.6 13.7 7 5.5 4.5 3.1 3.8 14.5 22.1 – – –

R2 (%) LOD

ANOVA ANOVA Mapmaker QTL

QTL Cartographer

QTL Cartographer

Mapmaker QTL

Stepwise regression ANOVA Mapmaker QTL Mapmaker QTL

QTL Cartographer QTL Cartographer

Mapmaker QTL Mapmaker QTL

Mapmaker QTL

QTL Cartographer Mapmaker QTL

Mapmaker QTL

Mapmaker QTL

Software or analysisc

Concibido et al. 1996 Concibido et al. 1997 Qui et al. 1999

Guo et al. 2005

Wang et al. 2001

Yue et al. 2001b

Vierling et al. 1996 Concibido et al. 1997 Qui et al. 1999 Yue et al. 2001a

Guo et al. 2005 Guo et al. 2006

Heer et al. 1998 Yue et al. 2001b

Yue et al. 2001b

Guo et al. 2006 Yue et al. 2001a

Concibido et al. 1997 Qui et al. 1999

Reference

26 G.-J. Lee et al.

Abiotic stress

Reaction

LG

Young × PI 416937 120 F4d Minsoy × Noir 1 236 RILs

Carbon isotope discrimination

BSR101 × PI 437654 320 RILs Bell × Colfax 93 RILs & NILs Young × PI 416937 116 F4:7

Essex × Forrest 80 NILs Pyramid × Douglas 90 RILs

Essex × Forrest 100 RILs

N G N G G C2 N J J F D2 J L L

G E B1 Magellan × PI 404198A 224 F2:3 B1 N Hamilton × PI 89772 250 F2:3 B1 G Hamilton × PI 438489B 184 F2:3 B1 Hamilton × PI 438489B 184 F2:3 C1 E Evans × PI 209332 D2 Evans × Peking 110 F2:3 G N Hartwig × D2 BR92-31983 126BC3F2:3 Hamilton × PI 438489B 184 F2:3 C1 E Essex × Forrest 100 RILs C2

Hamilton × PI 90763 226 F2:3

Population

Water use efﬁciency

Bud blight

Brown stem rot

Sudden death syndrome

Race 14

Race 6

Traita

Table 5. (continued)

19 20 16 63 16 14 16 45 45 82 12 13 14 27

11 19 22

A059- A463 (23) A656- Satt452 (26) K455_1 OF04-1600 OI03-450 OC01_650 Satt309 Satt309-Satt163 (5) Satt307 Satt080-Satt387 (7) K375I-1 (5cM) 21E22.sp2 Satt114-Satt510 (14) K258_2 Cr497_1 A489_1 Dt1

13 13 11 13 10 10 10 11 11 19 11 18 14 41

– 5.1 1.8 6.2 5.2 2.5 2.7 – 6 – – – – 20

3.6 5 –

7.1 7.2 6 6.7 3 3.7 5.2 2.7 3.6 5 – – – –

R2 (%) LOD

Satt163-Satt688 Satt573-Satt204 Satt453-Satt359 Satt453 Sat_280-Satt549 A118-A006 (13) B053-Satt309 (12) Satt583- Sat_123 (19) A059-A463 (23) A656-Satt452 (26) A064-2 C006 A280 Satt082-Satt574 (7)

Flanking loci (cM)b

Yue et al. 2001a Yue et al. 2001b Yue et al. 2001b Concibido et al. 1996 Concibido et al. 1997

Mapmaker QTL Mapmaker QTL Mapmaker QTL ANOVA ANOVA

QTL Cartographer

ANOVA

QTL Cartographer ANOVA &MapQTL Mapmaker QTL

ANOVA Mapmaker QTL

ANOVA

Mapmaker QTL

Mapmaker QTL

Specht et al. 2001

Mian et al. 1996

Lewers et al. 1999 Patzoldt et al. 2005 Fasoula et al. 2003

Meksem et al. 1999 Njiti et al. 2002

Chang et al. 1997

Hnetkovsky et al. 1996

Yue et al. 2001b

Schuster et al. 2001

Guo et al. 2006

QTL Cartographer

Mapmaker QTL

Guo et al. 2005

Reference

QTL Cartographer

Software or analysisc

Chapter 1 Soybean 27

Growth and development response

Nodulation

Reaction

E L O O B1 C2 H – C2 M

PI 97100 × Coker 237 111 F2d SJ2 × Suwon 157 136 RILs SJ2 × Suwon 157 136 RILs Young × PI 416937 120 F4d

Minsoy × Noir 1 69 F2:5

Number of nodules

Nodule weight

Speciﬁc leaf weight

Leaf area

Herbicide sensitivity Chlorimuron ethyl

Salt tolerance Chilling tolerance

Iron deﬁciency

Al tolerance

G

Archer × Minsoy 122 RILs, Archer × Noir 1 86 RILs A5403 × Archer 103 RILs P9641 × Archer 67 RILs

Water logging

A1 F Young × PI 416937 120 F4d A2 B1 A81-356022 × PI 468916 60 F2:3 A2 D1a G Pride B216 × A15 120 F2:4 B2 I H Anoka × A7 92 F2:4 N I S100 × Tokyo 106 F2:5 N Toyomusume × Hayahikari C2 104 RILs L H

LG

Population

Traita

Table 5. (continued)

Blt043_1 A122_1 A381_1 Gc409_2 A397-Blt029 (7) A584-R079 (29)

Sat_274

Cr168_1 A106-B164 (11) Sat_038

Satt229 Satt635

Satt385 Satt269 M0103_1 Cr207_1 I locus C063_1 K069_1 Satt070-A593_1 A515-K644 A404-B69 Blt15-Sat_033 A515-K644 Satt237-Sat_091 T

Sat_064

Flanking loci (cM)b

18 12 12 13 20 25

19

82 14 22

10 16 10 10 17 31 11 11 19 22 73 80 45

–

– – – – 2.9 4.2

–

– – –

4.4 4.1

2.5 2 – – – – – 2.4 2.6 2.4 13.1 3.5 9–13 13.5

3

R2 (%) LOD

Mapmaker QTL

ANOVA and Multiple regression analysis ANOVA and Multiple regression analysis ANOVA

ANOVA

Mansur et al. 1993

Mian et al. 1998

Tanya et al. 2005

Tanya et al. 2005

Mian et al. 1997

Lee et al. 2004c Funatsuki et al. 2005

Lin et al. 1997

Mapmaker QTL

Map Manager QTX QTL Cartographer

Diers et al. 1992c

Bianchi-Hall et al. 2000

Cornelious et al. 2005

VanToai et al. 2001

Reference

SFA SFA ANOVA

QTL Cartographer

ANOVA

Software or analysisc

28 G.-J. Lee et al.

Reaction

Leaf ash

Stem diameter Canopy width Plant height

Early plant vigor

Leaf width

Leaf length

Young × PI 416937 120 F4d Minsoy × Archer 233 RILs Minsoy × Noir 1 240 RILs Young × PI 416937 120 F4d

LG

A2 M M G G A81-356022 × PI 468916 60 F2:3 D1a Minsoy × Noir 1 284 RILs L Minsoy × Archer 233 RILs M A81-356022 × PI 468916 60 F2:3 A2 F D1b Minsoy × Noir 1 284 RILs M Minsoy × Archer 233 RILs M S100 × Tokyo 116 F2:5 C2 F E A81-356022 × PI 468916 60 F2:3 L S100 × Tokyo 116 F2:5 C2 Minsoy × Noir 1 284 RILs L C2 PI 97100 × Coker 237 111 F2d L Young × PI 416937 120 F4d C1 M Minsoy × Noir 1 (240 RILs),¸ Archer × Minsoy (233 RILs), Noir 1 × Archer (240 RILs) A81-356022 × soja PI 468916 BC3 Kefeng No. 1 × Nannong 1138-2 184 RILs I B1 C2 C2 C2

Population

Traita

Table 5. (continued)

11 13 18 14 13 19 11 12 24 17 16 14 13 21 25 19 24 12 32 18 68 10 16

29 13 21 24 24

Satt127 GmKF082c-GmKF168b A397I-B131V Satt431-GmKF059a GmKF143-Satt319

2.8 7.3 11 16.4 12.6

– 7.2 10 – – – – – – – – – – 5.2 6.4 – – 2.8 – – 25 – 8.9

R2 (%) LOD

A085 Satt150 R079_1 A112_1 A458_1 K478_1 Satt006 Satt150 A111_1 K390_1 K411_1 R079_1 Satt150 K418-A397 (2.4) HSP176-B212 (8.4) Cr406 G173_1 A397-K365 (2.4) Satt006 Satt079 Dt1-K385_1 (24) A063_1 Satt150

Flanking loci (cM)b

Sebolt et al. 2000 Zhang et al. 2004

Lee et al. 1996b Lee et al. 1996c Orf et al. 1999

Mapmaker QTL ANOVA PLABQTL

Mapmaker QTL WINQTLCART

Keim et al. 1990a Mian et al. 1998 Mansur et al. 1996

Mansur et al. 1996 Orf et al. 1999 Mian et al. 1998

ANOVA PLABQTL Mapmaker QTL

ANOVA Mapmaker QTL ANOVA

Keim et al. 1990a Mansur et al. 1996 Orf et al. 1999 Keim et al. 1990a

Mian et al. 1998 Orf et al. 1999 Orf et al. 1999 Mian et al. 1996

Reference

ANOVA ANOVA PLABQTL ANOVA

ANOVA PLABQTL PLABQTL ANOVA

Software or analysisc

Chapter 1 Soybean 29

Traita

Protein content

Reaction

Seed composition

Table 5. (continued) LG

A81-356022 × PI 468916 60 F2:3 I E B2 L G F2d eight populations A2 C1 D1a E G I Young × PI 416937 120 F4d C1 N B2 PI 97100 × Coker 237 111 F2d H K Peking × Essex 200 F2:3 H F Noir 1 × Archer 240 RILs C1 L Minsoy × Archer 233 RILs A1 C1 A81-356022 × I soja PI 468916 BC3 Minsoy × Noir 1236 RILs M Essex × Williams 131 RILs C2 F K M N87-984-16 × TN93-99 G 101 RILs Kefeng No. 1 × B2 Nannong 1138-2 184 RILs

Population 42 24 19 16 12 11 17 28 11 16 28 13 11 10 14 11 32 17 12 11 15 12 65 27 28 18 24 13 20 12

Satt567 Satt277-Satt202 Satt335-Satt144 Satt539-Satt102 Satt540-Satt463 Satt570 A953_1H-Satt560

3.5

12.8 9.8 4.4 4.3 3 3.5

– – – – – – – – – – – – – – – – – – – – – – –

R2 (%) LOD

K011 SAC7_1 A242_1 A023_1 A245_2 A505_1 A063_1 A398_1 B174_1 A890_1 A144_1 Gc97_1 A071_2 B142_1 A566_2 A065_3 B072 B148 Satt578 Satt166 T155_1 SoyGPATR Satt127

Flanking loci (cM)b

Qui et al. 1999 Orf et al. 1999 Orf et al. 1999 Sebolt et al. 2000

Mapmaker QTL PLABQTL PLABQTL Mapmaker QTL

WINQTLCART

Zhang et al. 2004

Panthee et al. 2005

Lee et al. 1996a

ANOVA

QTL Cartographer

Lee et al. 1996a

ANOVA

Specht et al. 2001 Hyten et al. 2004

Brummer et al. 1997

ANOVA

QTL Cartographer QTL Cartographer

Diers et al. 1992b

Reference

ANOVA

Software or analysisc

30 G.-J. Lee et al.

Reaction

Linolenic acid

Oil content

Methionine

N87-984-16 × TN93-99 101 RILs F G N87-984-16 × TN93-99 101 RILs F G M A81-356022 × PI 468916 60 F2:3 I E B2 L PI 27890 × PI 290136 69 F2:5 A2 K F2d eight populations A1 B1 G H K Young × PI 416937 120 F4d D2 PI 97100 × Coker 237 111 F2d C1 G Minsoy × Noir 1 240 RILs A1 Archer × Minsoy 233 RILs A1 C1 Noir 1 × Archer 240 RILs C2 L Peking × Essex 200 F2:3 H Essex × Williams 131 RILs C2 L M N87-984-16 × TN93-99 101 RILs D1b O O A81-356022 × PI 468916 60 F2:3 E L K

Cysteine

LG

Population

Traita

Table 5. (continued)

Satt252 Satt235 Satt252 Satt564 Satt590 A407_1 SAC7_1 A242_1 A023_1 T153_1-A111_1 (18) BC1-A315_1 (26) A104_1 A109_1 A584_1 A069_1 K387_1 Cr142_1 A063_1 L154_1 T155_1 Satt174 SOYGPATR Satt432 A489_1 B072 Satt277-Satt460 Satt166-Dt1 Satt540-Satt463 Satt274 Satt420 Satt479 SAC7_1 A023 A065_3

Flanking loci (cM)b 11 13 15 19 23 28 43 39 32 36 24 19 31 19 18 16 13 13 17 13 10 11 11 19 21 32 10 12 12 15 12 31 26 20

2.2 2.8 2.8 2.6 2.4 – – – – 5.5 2.9 – – – – – – – – 3.4 4 3.3 3.3 6.1 – 12 3.3 3.6 3 3.5 3.1 – – –

R2 (%) LOD

Orf et al. 1999

Qui et al. 1999 Hyten et al. 2004

Panthee et al. 2005

PLABQTL

Mapmaker QTL QTL Cartographer

QTL Cartographer

Diers and Shoemaker 1992

Lee et al. 1996a Lee et al. 1996a

ANOVA ANOVA

ANOVA

Brummer et al. 1997

Mansur et al. 1993

Diers et al. 1992b

Panthee et al. 2006

Panthee et al. 2006

Reference

ANOVA

ANOVA

QTL Cartographer

QTL Cartographer

Software or analysisc

Chapter 1 Soybean 31

Yield-related trait

Reaction

Maturity

Glycitein

Genistein

Daidzein

Oligosaccharide Sucrose content Total isoﬂavone

Oleic acid

Palmitic acid

A81-356022 × PI 468916 60 F2:3 A1 E B1 A81-356022 × PI 468916 60 F2:3 B2 J Cook × N87-2122-4 A1 A81-356022 × PI 468916 60 F2:3 A1 E B2 Keunolkong × Iksan 10 115RILs L V71-370 × PI 407162 149 F2:3 I AC756 × RCAT Angora 207 RILs A1 M AC756 × RCAT Angora 207 RILs A1 M Essex × Forrest 100 RILs A1 N AC756 × RCAT Angora 207 RILs M Essex × Forrest 100 RILs B2 AC756 × RCAT Angora 207 RILs F Essex × Forrest 100 RILs B1 N A81-356022 × PI 468916 60 F2:3 D1a C2 PI 27890 × PI 290136 284 RILs M C2 L PI 97100 × Coker 237 111 F2d K Young × PI 416937 120 F4d B1 A81-356022 × Soja PI 468916 BC3 I Kefeng No. 1 × B1 Nannong 1138-2 184 RILs B1 B1

Linoleic acid

LG

Population

Traita

Table 5. (continued)

38 21 20 24 18 33 23 21 19 14 12 12 26 15 18 10 10 31 38 39 50 11 18 21 19 19 12 31 22 44 28 24 11

A520T-Sat_128 GmKF082c-GmKF168b

8.6 6

– – – – – 5–7 – – – – – 2 5.5 2.1 3.4 2.7 3.2 7.6 2.9 2.9 11 2.3 – – – – – 6.7 – 6 13.1

R2 (%) LOD

A082_1 A242_2 A118_1 A343_1 K375_1 Satt684 A170_1 Pb A619_2 Satt278 A144 Satt200 Satt201 Satt200 Satt201 Satt276 Satt080 Satt201 Satt063 Satt516 Satt251 Satt237 R013_2 K474_2 R079_1 Satt079 Satt006 R051-N100 (17.7) Blt043_1 Satt127 Satt597-A118T

Flanking loci (cM)b

Keim et al. 1990a Mansur et al. 1996

Lee et al. 1996b Lee et al. 1996c Sebolt et al. 2000 Zhang et al. 2004

ANOVA ANOVA

Mapmaker QTL ANOVA Mapmaker QTL WINQTLCART

Primomo et al. 2005 Kassem et al. 2004 Primomo et al. 2005 Kassem et al. 2004

Kassem et al. 2004

Mapmaker QTL QTL Cartographer Mapmaker QTL QTL Cartographer Mapmaker QTL

Primomo et al. 2005

QTL Cartographer

Kim et al. 2005 Maughan et al. 2000 Primomo et al. 2005

Li et al. 2002 Diers and Shoemaker 1992

Mapmaker QTL ANOVA

ANOVA ANOVA QTL Cartographer

Diers and Shoemaker 1992

Diers and Shoemaker 1992

Reference

ANOVA

ANOVA

Software or analysisc

32 G.-J. Lee et al.

Reaction

Flowering date

Pod dehiscence Seed coat hardness

L C2 L L C2 L C2 F D1b C2

PI 27890 × PI 290136 284 RILs

Lodging

C2 C2 Young × PI 416937 120 F4d J A81-356022 × PI 468916 60 F2:3 A2 L D1b N A81-356022 × PI 468916 60 F2:3 C2 PI 27890 × PI 290136 284 RILs C2 M Minsoy × Noir 1 240 RILs M L Archer × Minsoy 233 RILs C2 M Noir 1 × Archer 240 RILs C2 L Kefeng No. 1 × B1 Nannong 1138-2 184 RILs B1 B1 B1 C2 C2

Noir 1 × Archer 240 RILs Kefeng No. 1 × Nannong 1138-2 184 RILs

Archer × Minsoy 233 RILs

PI 97100 × Coker 237 111 F2d Young × PI 416937 120 F4d Minsoy × Noir 1 240 RILs

LG

Population

Traita

Table 5. (continued)

19 15 44 34 15 13 12 23 31 22 39 10 31 26 31 25 11 16 13 12 23 22

GmKF177-GmKF082c GmKF082c-GmKF168b GmKF168b-Gmpti_D A397I-B131V Satt431-GmKF059a

28 18 56 13 15 27 21 17 10 18

8.4 7.3 6.9 10.1 13.4

11.4 7.2 – – – – – – – – 25 5.3 19 15 19 15 5.7

– – 18 – 8.1 16 12 9.2 5 8.3

R2 (%) LOD

GmKF059a GmKF143-Satt319 B122_1 I locus G173_1 K411_1 K418_1 K474_1 A109_2 R079_1 Satt567 G173_1 Satt365 Satt150 Satt489 A489_1 GmKF104b-GmKF177

Satt006 A109_2 Dt1-K385 (24) A169 Satt489 Dt1 Satt277 Satt335 Sat_096 A397I-B131V

Flanking loci (cM)b

Keim et al. 1990a Mansur et al. 1996 Orf et al. 1999

Zhang et al. 2004

PLABQTL

WINQTLCART

Bailey et al. 1997 Keim et al. 1990b

Zhang et al. 2004

Lee et al. 1996b Lee et al. 1996c Orf et al. 1999

Mansur et al. 1996

Reference

ANOVA ANOVA

ANOVA ANOVA

WINQTLCART

Mapmaker QTL ANOVA PLABQTL

ANOVA

Software or analysisc

Chapter 1 Soybean 33

Reaction

Seed size

Seed weight

Reproductive period

Traita

Table 5. (continued) LG

C2 E PI 27890 × PI 290136 284 RILs L Minsoy × Noir 1 240 RILs M Archer × Minsoy 233 RILs C1 M Noir 1 × Archer 240 RILs L C1 PI 27890 × PI 290136 284 RILs A2 Young × PI 416937 120 F4d C1 E G PI 97100 × Coker 237 111 F2d G L M PI 27890 × PI 290136 284 RILs A2 V71-370 × PI 407162 152 F2:3 B1 G J L A81-356022 × soja PI 468916 BC3 I Ma Belle × Proto 82 F2d O I Minsoy × Noir 1 236 RILs M Pureun × Jinpum2 100 F2d B1 D2 E F Kefeng No. 1 × B1 Nannong 1138-2 184 RILs D2 Essex × Williams 131 RILs C2 D1a F

Population 20 15 13 14 12 14 19 21 13 10 14 22 10 10 11 11 14 10 11 11 29 12 12 10 11 10 10 13 10 11 14 14 10

B146H-Satt458 Satt277-Satt460 Satt179-Satt071 Satt114-Satt335

4.8 6.7 4 4.7

9.6 4.3 – 7.6 6.2 7.8 11 12 – – – – – – – – – – – – 3 4.3 – 7 – – – – 3.8

R2 (%) LOD

GmKF143-Satt319 Satt496-A374H G173_1 Satt567 G214_24 Satt150 A489_1 Sat_077 K443_2 A059_1 Blt49_2 B031_1 A235_1 Dt1 locus Cr529_1 K443_2 A118_1 A816_1 K384_1 K385_1 A144_1 Satt219 Satt562 Satt590 A089 A095 A069 Cr321 Satt509

Flanking loci (cM)b

Mansur et al. 1996 Maughan et al. 1996

Sebolt et al. 2000 Csanadi et al. 2001 Specht et al. 2001 Lee et al. 2001a

Zhang et al. 2004

ANOVA ANOVA

Mapmaker QTL Mapmaker QTL QTL Cartographer ANOVA

WINQTLCART

Hyten et al. 2004

Mian et al. 1996

ANOVA

QTL Cartographer

Mansur et al. 1996 Mian et al. 1996

Mansur et al. 1996 Orf et al. 1999

Reference

ANOVA ANOVA

ANOVA PLABQTL

Software or analysisc

34 G.-J. Lee et al.

Reaction

Sprout yield Hypocotyl length Abnormal seedlings

Seed yield

Traita

Table 5. (continued) LG

L N87-984-16 × TN93-99 101 RILs D1a D1a D2 PI 27890 × PI 290136 284 RILs M Essex × Forrest 100 RILs N Minsoy × Noir 1 240 RILs M Noir 1 × Archer 240 RILs F A81-356022 × soja PI 468916 BC3 I Minsoy × Noir 1 236 RILs M Kefeng No. 1 × B1 Nannong 1138-2 184 RILs C2 C2 C2 M Pureun × Jinpum2 100 F2d G Pureun × Jinpum2 100 F2d B1 Pureun × Jinpum2 100 F2d M

Population 28 17 11 10 13 31 19 13 26 37 10 12 12 13 10 11 11 12

Satt319-K11_3T K11_3T-Satt277 Satt557 A60V-Satt150 L154 K011n Bng222

6 5.9 6.9 3.8 – – –

14.5 4.6 3.5 2.9 – – 11 7 2.5 37 5.4

R2 (%) LOD

Satt156-Dt1 Satt147 Satt184 Satt002 R079_1 OC01_650 Satt150 Satt144 Satt127 Satt150-Satt567 (19) GmKF168b-Gmpti_D

Flanking loci (cM)b

Lee et al. 2001a Lee et al. 2001 Lee et al. 2001

Sebolt et al. 2000 Specht et al. 2001 Zhang et al. 2004

Mapmaker QTL QTL Cartographer WINQTLCART

Multiple regression Multiple regression ANOVA

Mansur et al. 1996 Hnetkovsky et al. 1996 Orf et al. 1999

Panthee et al. 2005

Reference

ANOVA Mapmaker QTL PLABQTL

QTL Cartographer

Software or analysisc

Chapter 1 Soybean 35

36

G.-J. Lee et al.

E and F, LG-O and G, and LG-D1a and F, respectively (Table 5). Of these, only QTLs associated with southern RKN resistance were conﬁrmed by using markerassisted lines having different allele combinations (Li et al. 2001). The soybean cyst nematode (SCN) is the most destructive pest in soybean, and thus a number of studies on QTL mapping for SCN resistance have been conducted. A total of 61 QTLs associated with SCN were reported, which were located on 18 soybean LGs, but most of them await conﬁrmation (Concibido et al. 2004). DNA markers ﬂanking Rhg1 on LG-G and rhg4 genes on LG-A2 conditioning resistance to SCN were not included in this section because they had been cloned and characterized earlier and listed in the previous section (Table 4). So far, about 130 SCN-resistant cultivars have been developed (57 from the public sector and 69 from the private sector), but the resistant alleles in most of the resistant cultivars were traced back to only a few sources (PI 88788, Peking, PI 90763). To avoid virulence of the different SCN races (or HG types) on those resistant cultivars, it is essential to broaden and explore genetic sources for SCN resistance. Currently, breeding efforts on SCN resistance are focusing on other sources that have broadband resistance to multiple races or wild ancestors such as G. soja that have a relatively low genetic relationship with elite lines (Wang et al. 2001; Concibido et al. 2004). Phenotypic evaluation of resistance to sudden death syndrome (SDS) in soybean is difﬁcult because expression of the disease symptom is variable depending on environmental conditions. So the use of molecular markers linked to QTLs associated with SDS resistance is a preferential tool for soybean cultivar development. Three QTL regions on LG-C2, G, and N were detected to have resistance genes to SDS (Table 5), and genomic regions on LG-G and N seem to be common in different genetic backgrounds of soybean (Chang et al. 1997; Njiti et al. 2002). Brown stem rot (BSR) caused by soilborne fungus is a disease prevalent in soybean growing in cooler regions including northern USA and Canada. Three resistance genes were identiﬁed as shown in the previous section, but other resistance sources and their genetic control have been investigated (Lewers et al. 1999). Of the ﬁve QTLs conditioning BSR resistance, only the QTL on LG-J explains 45% of the phenotypic variation (Table 5). Bud blight, considered more destructive in soybean yield in tropical and subtropical regions and in China, is one of the three most dominant diseases

of soybean (Orellana 1981). Two QTLs contributing more than 10% to the disease expression were located on LG-F and D2, and the resistant allele was inherited from the cultivar Young. In a different population crossed between the susceptible PI 416937 and Benning, Lee et al. (2003) identiﬁed those two QTLs in naturally infected ﬁeld plots with the pathogen, tobacco ringspot virus.

1.4.2 Tolerance to Abiotic Stresses In soybean, drought stress should be studied in connection with yield performance rather than only crop survival, meaning that genes for increasing transpiration of water under drought stress need to be emphasized. There are many physiological determinations for yield performance under drought stress, and some QTLs related to those speciﬁc traits have been reported (Table 5). Direct evaluation of soybean yield in irrigated and rainfed environments was conducted in a population of 160 F4 -derived lines from the cross of Hutcheson (high yielding) × PI471938 (drought tolerant) (Lee et al. 2002). Seed yield was determined in a total of 14 environments (nine irrigated and ﬁve rainfed) in the southern USA for 2 years, and information on QTL for yield under drought stress will be reported soon. In water-limited environments, Passioura (1977) formulated seed yield in the function of seasonal transpiration, water-use efﬁciency, and harvest index. Water-use efﬁciency (WUE) is measured as total dry weight of plant per liter of water consumed, while carbon isotope discrimination (CID) for measuring transpiration efﬁciency is the ratio of C13 /C12 measured in a juvenile trifoliolate leaf. Of nine reported QTLs for WUE, only two QTLs explaining the variation of the trait greater than 10% were provided in Table 5. Of ﬁve QTLs for CID, one QTL on LG-L closely linked to Dt1 (a gene for determinate stem in soybean) explains 27% of the variation (Table 5). In contrast to drought, excess water in soil due to periodic ﬂooding and inadequate drainage of soil water can cause waterlogging (submergence of root or part of shoot) or complete submergence, which results in soybean growth and production adversely (Reyna et al. 2003). A QTL study indicated that lines with Archer alleles at the locus on LG-G exhibited improved plant growth (11 to 18%) and grain yield (47 to 180%) in waterlogging conditions (Table 5).

Chapter 1 Soybean

Tolerance to Al toxicity was measured as root extension under high Al treatment and relative percentage of root extension compared to control without Al (Bianchi-Hall et al. 2000). Of the six QTLs reported to be associated with root growth under high Al stress, two QTLs on LG-A2 and B1 contributed more than 10% to the trait variation (Table 5). Based on information of the inheritance of the resistant allele from PI 416937, lines with different allele combinations at two QTLs on LG-A2 in a different genetic background (Benning × PI 416937) demonstrated that lines homozygous for the PI alleles at both loci exhibited higher root extension (Lee et al. 2001b). Iron deﬁciency chlorosis (IDC) occurs in high-pH soils such as calcareous soil where Fe solubility was decreased in the soil solution. The chlorotic and/or necrosis symptom was evaluated on soybean plants at V4 or V5 stage (four or ﬁve nodes with fully developed leaves) based on visual scorings of no yellowing to severe yellowing and necrosis. Three mapping populations were used for mapping IDC QTL, and seven QTLs were found to explain more than 10% of IDC variation of the total 36 QTLs reported. However, only one locus on LG-I was commonly detected to be associated with IDC (Table 5). Salt stress in soybean production becomes important because the ﬁeld may be salinized heavily through inefﬁcient fertilizer practices, seawater ﬂooding, and irrigation practices with poor water quality (Lee et al. 2004c). A major QTL was found to reside on LG-N explaining 30, 29, and 42% of the phenotypic variation, which account for 41, 60, and 79% of the total genetic variation for salt tolerance in the ﬁeld, greenhouse, and combined environments (Lee et al. 2004c; our Table 5). By evaluating the inherited alleles at two ﬂanking loci of the QTL in descendants of two parents, Lee et al. (2004c) found that there is a strong relationship between alleles at the SSR loci and salt tolerance. This suggests that these markers could be used for MAS in commercial breeding for salt-tolerant soybean. Chlorimuron ethyl is a herbicide for weed control in soybean, and genotypic differences in chlorimuron ethyl sensitivity exist (Lloyd and Wax 1984). Of 14 total QTLs identiﬁed in a population of PI 97100 and Coker 237, two QTLs on LG-E and L contributed more than 10% to the variation of sensitivity to chlorimuron ethyl (Table 5).

37

1.4.3 Growth and Development Responses Speciﬁc leaf weight (SLW) expressed as dry weight (mg) divided by leaf area (cm−2 ) was reported to be positively related with photosynthetic proteins per unit ground area and a canopy photosynthesis rate but negatively related with individual leaf size (Wiebold and Kenworthy 1985; Wells et al. 1986). The use of molecular markers linked to the SLW trait can improve the photosynthetic rate for higher yield in soybean without declining photosynthetic leaf area. A total of six QTLs for SLW were reported and four loci in one population were found to have more than 10% higher phenotypic contribution (Table 5). A total of 16 QTLs for leaf area were detected in four genetic populations, and three QTLs on LG-C2, M, and A2 were found to be associated with leaf area. Because none of the QTLs for SLW and leaf area are linked together, the pleiotropic effect of the QTLs for the two traits does not seem to be a concern. Leaf ash was measured as milligrams per gram of dry weight (mg g−1 ) of leaves harvested 36 d after planting. A total of 11 QTLs conditioning the amount of leaf ash were reported, but only two independent QTLs on LG-G accounted for more than 10% trait variation (Table 5). Traits of leaf length and width were studied in the three mapping populations (Table 5). A total of 15 QTLs for leaf length and leaf width were identiﬁed, but one QTL on LG-M closely tagged with Satt150 marker conditions both traits, but two other QTLs on LG-D1a and L for leaf length are independent from three QTLs on LG-A2, F, and D1b for leaf width. Stem diameter is the average length of stems between the unifoliolate and the ﬁrst trifoliolate nodes for three mature plants. Among three QTLs reported in a population, one locus linked to RFLP marker G173_1 appeared to account for more than 10% variation in stem diameter (Table 5). Vigorous growth of soybean at early growth stages affects suppression of weed invasion and growth, which leads to reduced use of herbicide and results in economic and environmental beneﬁts. Of three QTLs for plant height at V7 and V10 stages that explain more than 10% phenotypic variation, two QTLs on LG-C2 and F were found to be associated with canopy width (Mian et al. 1998). Of 32 total QTLs associated with plant height, ﬁve QTLs contributing to variation (>10%) in plant height were reported in six mapping populations and found to locate on LG-L, C2, C1, M,

38

G.-J. Lee et al.

and I. Only one QTL closely linked to a Dt1 gene of these QTLs were detected in a single population, but (determinate stem growth) on LG-L was commonly one QTL on LG-A1 was conﬁrmed in different genetic identiﬁed in southern and northern USA populations backgrounds. There were reports on seven, six, ﬁve, and six QTLs (Table 5). for linolenic, linoleic, palmitic, and oleic acid, respectively, in soybean fatty acid (FA) composition in SoyBase (http://soybase.agron.iastate.edu). All QTLs with 1.4.4 more than 10% of phenotypic contribution were sumSeed Composition marized in Table 5, including three QTLs for linolenic Soybean is the predominant source for seed protein, acid on LG-E, K, and L; three QTLs for linoleic acid on which ranges from 347 to 552 g kg−1 on a dry seed ba- LG-A1, B1, and E; three QTLs for palmitic acid on LGsis among USDA soybean germplasms (Chung et al. A1, B2, and J; and three QTLs for oleic acid on LG-A1, 2003). QTLs conditioning protein content in soybean B2, and E. Only one QTL on LG- E conditions the conwere widely investigated on 17 soybean populations tent of three FAs such as linolenic, linoleic, and oleic and found to be located on the soybean genome ex- acids. Recently six QTLs associated with increased cept for LG-B1, D1b, D2, J, and O (Table 5). A total oleic acid were mapped on LG-A1, D2, G, and L with of 61 QTLs for protein content were reported and 16 SSR markers in a population of G99-G725 (low) × putative QTLs (25%) accounting for more than 10% N00-3350 (high), and four of them were conﬁrmed phenotypic variation were identiﬁed. Among them, in a different population of G99-G3438 x N00-3350 ten QTLs were detected in more than two soybean (Monteros et al. 2004). Soybean has desirable carbohydrates (i.e., glucose, populations. The traits for protein content is highly heritable, as shown in the locus on LG-I (Satt127) fructose, and sucrose) and undesirable carbohydrates with a maximum phenotypic contribution of 65% (i.e., rafﬁnose, stachyose). Out of a total of 17 mark(Table 5). Fine-mapping studies have been used to ers signiﬁcantly associated with seed sucrose content clone the gene close to the Satt127 marker on LG-I on seven LGs, only QTL on LG-I was found to have and resulted in QTL localization within 1.1 cM and a phenotypic contribution greater than 10% (Table 5). using BAC libraries (Joseph et al. 2004; Nichols et al. Based on a comparison of the reported QTLs for seed 2004). MAS of lines with or without positive alleles at composition traits, it is likely to be a gene cluster or the QTL on LG-I was applied to compare the allelic ef- a major QTL with pleiotropic effect (Maughan et al. fect and conﬁrm the QTL, which resulted in a 56 g kg−1 2000). Among the seven genomic regions underlying increase in protein content in lines carrying homozy- QTLs for sucrose content, four regions (LG-E, F, I, L) gous alleles from the high protein parent (Yates et al. also affect protein content, and three regions (LG-A2, I, L) are associated with oil content as well. 2004). Soybean is also an important oil source for humans, as illustrated in Figs. 2 and 5. Among the accessions in the USDA soybean germplasm, seed oil con- 1.4.5 tent ranges from 65 to 287 g kg−1 (Chung et al. 2003). Yield-Related Traits QTL for oil content are distributed widely on the soybean genome except for LG-D1a, D1b, F, J, N, and O Soybean maturity is determined by the number of (Table 5) so that three LGs (D1b, J, O) do not carry days after 31 August when 50% of the individuals genes for both protein and oil content. Eleven out of in a plot had mature seed pod color (95% of the 16 (69%) and 19 QTLs (58%) that were associated with pods/plant) or the number of days after 31 July when protein and oil content, respectively, commonly con- 95% of the normal pods on the main stem had reached trol both traits. Because strong negative correlations their mature pod color (Keim et al. 1990a; Mansur exist between protein and oil content, it needs to be et al. 1993). Of a total of 48 marker loci for maturity redetermined genetically whether the negative relation- ported in SoyBase (http://soybase.agron.iastate.edu), ship is due to pleiotropism or repulsion linkages be- 7 genomic regions appeared to account for more than tween protein and oil alleles. Of 53 oil QTLs reported 10% phenotypic variation on LG-B1, C2, D1a, I, K, L, in SoyBase (http://soybase.agron.iastate.edu), 19 pu- and M in ﬁve soybean populations (Table 5). Only one tative QTLs explain more than 10% of the phenotypic QTL region on LG-C2 was identiﬁed in two different variation (Table 5). In contrast to protein QTLs, most populations.

Chapter 1 Soybean

The lodging score was determined either by the average angle the plants in the plot made with the soil surface or the visual score with ranges from all erect plants to all prostrate plants (Mansur et al. 1993; Lee et al. 1996b). A total of 36 marker loci were reported to be associated with the soybean lodging trait in six different populations, and ﬁve genomic regions on LG-C2, D1b, F, and L (two putative loci) were found to have more than 10% phenotypic contribution (Table 5). Of these ﬁve QTL regions, two loci on LG-C2 and L were detected in three independent studies. A genomic region on LG-L ﬂanking a gene for stem determinate (Dt1) was also found to be associated with stem diameter (Table 5). Pod dehiscence (PD; shattering) directly affects soybean yield potential, which is more likely a problem in cultivars derived from the original cross with the wild G. soja. Of 12 marker loci associated with the trait on ﬁve genomic regions on LG-E, J, L, and two unlinked loci, one locus on LG-J explains 44% of the PD variation, which accounts for 48% of the total genetic variation, meaning that one or two minor QTLs remain to be identiﬁed. Seed coat hardiness (SCH) is determined by the percent seed germination in the absence of scariﬁcation compared with scariﬁcated seeds as control (Keim et al. 1990b). A total of seven loci were reported to condition the SCH trait, and four putative loci on LG-A2, D1b, L, and N were found to have greater contribution to the trait variation (Table 5). Interestingly one locus on LG-A2 closely linked to the I locus underlying seed coat color is also associated with the SCH, and cultivars with black seed color are generally considered to have a hard seed coat. Flowering date is determined either by the date on which 50% of the plants in a plot have begun to ﬂower or by the ﬁrst date after 31 May when an open ﬂower is found at any node on the main stem (Keim et al. 1990a; Mansur et al. 1993). Reports indicated that there were 32 total loci controlling the trait of ﬂowering date in the ﬁve soybean populations in SoyBase (http://soybase.agron.iastate.edu). Three genomic regions on LG-C2, L, and M were found to explain the greater part of phenotypic variation of the ﬂowering date and were identiﬁed in four, two, and three independent studies, respectively (Table 5). These three QTL regions were reported to be associated with the maturity trait, as expected. The reproductive period is based on the difference in dates between the ﬁrst blooming (R1) and matu-

39

rity (R8) where the particular stages were determined by the number of days after planting when 50% of the plants in a plot have reached the R1 or R8 stage (Mansur et al. 1993). A total of 14 loci were identiﬁed as conditioning the trait, and three genomic regions on LG-C1, L, and M seemed to have major genes (Table 5). Of these three regions, two loci on LG-L and M were reported to have genes for maturity and ﬂowering date. Seed weight is determined either by the weight of 100 seeds in grams or by milligrams per seed from the weight of 200 seed samples per plot. A total of 66 QTLs for seed weight were reported in SoyBase, and loci accounting for a relatively larger (>10%) proportion of the effect on the trait variation were located on 12 LGs in the nine soybean populations (Table 5). Only one locus ﬂanking the Dt1 gene on LG-L was found to be detected in three different populations, and two loci on LG-B1 and G were detected in two populations. The locus on LG-L was also associated with yieldrelated traits of maturity, lodging, seed coat hardness, ﬂowering date, and reproductive period. Seed yield determined by kilograms per hectare on a 13% moisture basis has been the trait of the highest interest in soybean breeding, but the identiﬁed QTLs were characterized as having a relatively small effect. Only 29 marker loci were identiﬁed as being signiﬁcantly associated with seed yield, but four of those loci have a larger phenotypic effect, including genomic regions on LG-F, I, M, and N in six mapping populations investigated (Table 5). Only one locus on LG-M was identiﬁed in multiple populations. Two of the clustered intervals for reproductive or morphological traits were found to be associated with seed yield (Mansur et al. 1993). Interestingly one locus on LG-I that conditions seed protein content was found to be associated with seed yield, implying that further study is required to uncover the allelic relations (i.e., pleiotropic effect or repulsion phase of the opposite alleles for two traits). Soybean as a vegetable source was evaluated with traits of seed weight (<120 mg seed−1 , hypocotyls length, germination rate, water absorption rate, and sprout yield (Lee et al. 2001a). Mapping of QTLs conditioning hypocotyl length, abnormal seedlings, and sprout yield was conducted in the F2 -derived lines. Multiple regression analysis resulted in ﬁnding four QTL regions for sprout yield on LG- B1, G, K, and L; three regions for hypocotyl length on LG- B1, F, and L; and three regions for abnormal seedlings on LG- B1, C2, and N (Lee et al. 2001a). Of these, one

40

G.-J. Lee et al.

QTL for each trait was found to have more than 10% 1.5.2 phenotypic contribution, and these include a QTL on Marker Systems LG-G, B1, and M for sprout yield, hypocotyl length, and abnormal seedlings, respectively (Table 5). Seed composition and resistance against pathogens and pests in soybean breeding can be considerably improved by molecular markers (RFLPs, RAPDs, AFLPs, 1.5 Marker-Assisted Breeding in Soybean SSRs, STSs, SNPs) as they facilitate (1) efﬁcient genotyping and estimation of genetic diversity, (2) reliable selection on a single plant level independent of comprehensive performance in the ﬁeld, (3) acceleration 1.5.1 of backcrossing procedures, (4) pyramiding of mulAdvantages tiple genes, (5) detection of QTL- and marker-based DNA markers that are closely linked to target genes combination of positive alleles, and (6) isolation of have been used in soybean-breeding programs since targeted genes via map-based cloning. The ﬁrst large-scale effort to produce the soythey are stable and easily detect favorite alleles from a large number of breeding populations without the bean genetic map was performed mainly using RFLP need to perform phenotype tests that are inﬂuenced markers (Keim et al. 1990a). However, with the deby environmental factors. Compared to traditional velopment of PCR and the discovery of SSRs (Cregan breeding methods, marker-assisted breeding offers et al. 1994), the RFLP marker system was gradually great advantages by shortening the breeding time as replaced by microsatellites that have multiple alleles a result of (1) increased reliability, (2) increased efﬁ- and more advantages over RFLP. In the past decade ciency, and (3) reducing cost. Efﬁciency is increased the integrated linkage map of soybean has been unby the ability to determine the genotype at any de- der development and now contains over 1,800 linked velopmental stage. By selecting at the seedling stage, markers on 20 LGs with an average of one marker considerable amounts of time and space can be saved. per 1.4 cM (Song et al. 2004). This map consists priFor example, at the seedling stage, we can select lines marily of RFLP and SSR markers and has been used that have high oleic acid content in seeds based on to identify over 320 unique QTLs (Song et al. 2004). genotypes. This is especially advantageous when se- As for the other marker types, such as RAPD and lecting for traits that are expressed only at later stages AFLP, their applications to genetic analysis seem to of development, such as seed characteristics. With be limited due to the fact that they are dominant, but the application of high- throughput PCR assay and RAPDs and AFLPs have the great advantage of ease automated diagnostic technologies, the material and of use in the laboratory. Coupled with the bulked segconsumable cost for a PCR-based genotyping assay regant analysis (BSA) approach, they are extensively is much lower than the cost of phenotype evalua- used for quickly generating high-density maps in partion. Only plants that ﬁt breeding goals can be se- ticular regions when prior sequence information is lected before planting and will save a considerable lacking (Meksem et al. 2001). However, the drawback amount of money, ﬁeld space, and labor. The use of is that the markers are generally dominant and generDNA markers for indirect selection offers the greatest ated at random. One major problem with the RAPDs beneﬁts for quantitative traits with low heritability is their low reproducibility, depending highly on the as it is extremely difﬁcult and very costly to assess PCR conditions. Contrariwise, AFLP markers can still these traits in ﬁeld experiments. The success of MAS be a good choice for QTL mapping or diversity studies is dependent upon many factors, including availabil- in species devoid of dense marker maps. For highity of markers closely linked to the traits of interest, throughput genotype scoring in MAS, AFLP bands relative cost, and time required for MAS compared to can be converted into polymorphic (STS) markers by the conventional phenotypic selection. The efﬁciency sequencing AFLP bands and primer extension to rethat high-throughput MAS brings to the breeding pro- construct the polymorphism that generates the AFLP. cess is measured by the success in capturing desired Use of bacterial artiﬁcial chromosome (BAC) libraries traits in a large population throughout the breeding and physical maps can improve the efﬁciency of AFLP band conversion to STS. This approach was applied scheme. in the discovery of new markers linking to one of two

Chapter 1 Soybean

loci, rhg1 on LG-G and Rhg4 on LG-A2 (Meksem et al. 2001). SNPs are the most abundant form of sequence variability in the soybean genome (Zhu et al. 2003), and polymorphisms in gene coding are likely to contribute to the phenotypic differences between lines. As abundant DNA markers, SNPs are valuable for genetic association studied for complex traits. The expanding availability of soybean genome sequence data and the microarray-based technologies for high-throughput discovery of polymorphic markers around or within gene- coding regions is allowing the efﬁcient development of targeted SNP markers for important QTL or candidate genes. The use of SNPs in MAS will further reduce the cost of MAS and increase the accuracy of capturing targeted genes. Allele-speciﬁc polymerase chain reaction (ASPCR) assay was developed for the detection and genotyping of SNPs in the genomic DNA fragments tightly linked to two soybean mosaic virus resistance genes, Rsv1 and Rsv3. Using the modiﬁed procedure, many SNP loci in eight soybean parental lines and F2 individuals of three mapping populations could be genotyped, which could greatly facilitate small- to medium-scale MAS programs for agronomically important genes (Jeong and Maroof 2004). SNP genotyping assays that are high-throughput, accurate, and inexpensive are needed for MAS in plant- improvement programs. High-throughput SNP genotyping requires an accurate SNP identiﬁcation method and a compatible automated or semiautomated platform technology that allows rapid handling and scoring of the data. Luminex 100 ﬂow cytometer is used as a versatile and compatible platform for soybean SNP genotyping in several laboratories. Lee et al. (2004b) compared the effectiveness and robustness of different genotyping assays: single-base extension (SBE), allele-speciﬁc primer extension (ASPE), oligonucleotide ligation (OL), and direct hybridization (DH), performed on a Luminex 100 ﬂow cytometric platform, to compare the genotyping accuracy of these assays with that of the SNaPshot assay. The results showed that ASPE is more cost effective and simpler than SBE and would therefore be a good method for genetic mapping and diversity studies. DH is the most economical assay suitable for MAS, though optimization for DH would be required for some SNP markers. The conversion of AFLP bands into polymorphic STS markers is necessary for high-throughput genotyping. There are several technical hurdles that arise

41

from genome complexity (particularly sequence duplication), from the low- molecular-weight nature of the AFLP bands, and from the location of polymorphism within the AFLP band. The markers that are linked to one of two loci, rhg1 on LG-G and Rhg4 on LG-A2, were successfully converted, which confers resistance to the soybean cyst nematode (Meksem et al. 2001). When the polymorphic AFLP band sequence contained a duplicated sequence or could not be converted to a locus-speciﬁc STS marker, direct sequencing of BAC clones anchored to a physical map generated locus-speciﬁc ﬂanking sequences at the polymorphic locus. When the polymorphism was adjacent to the restriction site used in the AFLP analysis, single primer extension was performed to reconstruct the polymorphism. The converted AFLP markers could be insertions or deletions, microsatellites or SNPs. The polymorphic sequences are used to design a high-throughput assay for marker-assisted breeding for resistance to cyst nematode. Meksem et al. (2001) indicated that the converted AFLP markers contained polymorphism at a 10- to 20-fold higher frequency than expected for adapted soybean cultivars and that the efﬁciency of AFLP band conversion to STS could be improved using BAC libraries and physical maps. The method provides an efﬁcient tool for SNP and STS discovery suitable for marker-assisted breeding and genomics.

1.5.3 Marker-Assisted Introgression MAS is used to accelerate recovery of the elite parent genome in backcross programs. Marker-assisted backcrossing has made it possible to introduce two or more genes from different sources into the genetic background of one recipient genotype by recurrent backcrossing. Besides the transfer of the target genes, the main goal in such a breeding program is to recover the recurrent parent genome as rapidly and completely as possible. A core set of SSR markers with a good coverage of the entire genome can be used for background selection of individuals with a high proportion of recurrent parent genome. At the Soybean Genome Mapping Lab at the University of Missouri, microsatellite- based primer panels across the whole genome at approximately 20-cM intervals are used to select recurrent background genomes, and some polymorphic SSR ﬂanking markers associated with high oleic acid content are used to capture donor alleles that

42

G.-J. Lee et al.

contribute to the high oleic acid content of soybean seeds. In practice, we can perform marker-assisted backcrossing for six loci. Some general principles can be used for this project. First, when beginning a backcrossing project, the background of all parental lines (recurrent and donor) should be assessed. Second, two to four hybrids having the highest percentage of recurrent contribution, as determined by microsatellite analysis, will be used as males to make crosses with recurrent lines. The aim is to produce at least ten heterozygotes for subsequent background assessment. Third, this process of selected breeding will be repeated at each generation out until individuals contain greater than 98% of the recipient background. Near-isogenic lines (NILs) are widely used in QTL ﬁne mapping, map-based cloning, and novel variety development. They provide insight into gene structure and the contribution of the gene to their phenotypes by using a series of NILs that are identical at all genetic loci except for one region of interest. Utilizing traditional, random backcrossing methods it may take many years to develop a line containing 99% of the recurrent (elite line) genome combining the favorable genes from the donor. Selectively breeding individuals containing more of the recurrent genome from each generation will accelerate the procedure of producing an ideal genotype. Genetic diversity is limited in southern elite soybean. Soybean breeders in the United States are attempting to do introgression of high-yield alleles at three QTLs from the northern cultivar Archer into southern cultivars. The results indicate that it may be difﬁcult to capture the value assigned to QTL alleles derived from diverse parents with variable relative genetic value when the alleles are introgressed into populations with different genetic backgrounds or when tested in different environments (Reyna and Sneller 2001). Exotic germplasms such as wild soybean are valuable in broadening the genetic base of soybean cultivars. However, unwanted linkages often thwart the successful incorporation of beneﬁcial genes from exotic germplasms into elite lines. Thus, MAS can facilitate the utilization of exotic germplasms in soybeanbreeding programs, which makes the process of introgression easier and more accurate. The availability of closely linked molecular markers makes it possible only to isolate speciﬁc genomic regions and transfer them to elite lines with minimal linkage drag. A yieldenhancing QTL located on LG-B2 was found from G.

soja (PI 407305). This study demonstrates the potential of exotic germplasms for yield enhancement in soybean, though the yield effect was consistently observed in only two of six genetic backgrounds (Concibido et al. 2003).

1.5.4 Gene Pyramiding The purpose of gene pyramiding is to develop an ideotype that is homozygous for favorable alleles at different loci of interest that correspond to all target genes. Usually, these target genes exist in different donor genotypes so that the breeding scheme for pyramiding multiple genes is more complicated than that for a single gene. Servin et al. (2004) proposed that the best way to combine a series of target genes into a single genotype was through marker- assisted gene pyramiding. The success of gene pyramiding is dependent on the breeding scheme, selection strategy, and population size (Frisch and Melchinger 2001). MAS and gene introgression programs for the development of mid-oleic acid, low linolenic acid, and SCN-resistant soybean are being used by different groups. As an example, DNA markers were identiﬁed for three microsomal omega-3 FA desaturase genes and are being used in breeding for low linolenic acid content in soybean (Bilyeu et al. 2003).

1.6 Progress in Map-Based Cloning, Transformation, and Other Candidate Gene Approaches in Soybean 1.6.1 Map-Based Cloning The map-based-cloning approach has been extensively used for gene isolation in model plant systems. In soybean, map-based cloning is still tedious because of the lack of high-density genetic and physical maps. However, with the saturating marker technologies and gene golﬁng strategy, two SCN-resistant genes, rhg1 on LG-G and Rhg4 on LG-A2, have been cloned (Meksem et al. 2001). Advances in the methods used to detect DNA polymorphisms, the discovery

Chapter 1 Soybean

43

and mapping of a large number of SNP markers, and 1.6.3 the availability of an integrated genetic and physical Functional Genomics map will make the map-based cloning process much for Candidate Gene Discovery easier. Microarray analysis of gene expression is a promising technology to monitor the transcriptional machinery 1.6.2 of the organism. This technology has attracted the Soybean Transformation attention of many researchers interested in exploring the functional similarities or differences among Modern genetic and gene function analysis and thousands of genes simultaneously. As more tissues the manipulation of transgenic soybean (G. max) and developmental stages are proﬁled, a gene exwith desirable traits depend heavily on an ef- pression atlas can be created that describes the exﬁcient and dependable transformation process. pression pattern of every gene in the genome. Such Two transformation systems have been used most an atlas would be a tremendously useful tool to frequently in soybean over the past decade. One the biologist interested in the expression proﬁle of is biolistic-mediated transformation of soybean a speciﬁc gene (Borevitz and Chory 2004). An arembryogenic cultures (Finer and McMullen 1991; ray consists of immobilized biomolecules spatially Finer et al. 1992; Parrott et al. 1994; Hadi et al. 1996; arranged on a slide, which is used for identifying Stewart et al. 1996; Hazel et al. 1998; Santarem and known and unknown DNA samples based on base Finer 1999). The other is Agrobacterium-mediated pairing. These microarrays on the slides can be cretransformation of soybean cotyledonary nodes ated manually or by robots that deposit the samthrough direct organogenesis (Hinchee et al. 1988; ple spots, which are typically less than 200 μm in Di et al. 1996; Zhang et al. 1999; Clemente et al. diameter and are detected by specialized scanning 2000; Xing et al. 2000). The Plant Transformation and imaging equipment. The biomolecules include Core at the University of Missouri is working on oligonucleotides, PCR products, proteins, lipids, pepdeveloping a high- throughput transformation tides, and carbohydrates, but this section is limited to system for the Williams 82 genotype that has been DNA arrays. used as the subject for EST, genome mapping, There are two categories of microarrays, cDNA and functional genomics analyses. Zeng et al. microarray and oilgonucleotide array, depending (2004) optimized both Agrobacterium infection and on the property of arrayed sequence, cDNA or glufosinate selection in the presence of l-cysteine oligonucleotide (Zhu 2003; Venkatasubbarao 2004). for Williams 82 and recovered transgenic lines of GeneChip is a specialized oligonucleotide probe this genotype with an enhanced transformation array used to detect the sequence similarity and efﬁciency using this herbicide selection system. abundance of target DNA or RNA molecules through This work will greatly aid soybean functional complementary-sequence binding. High-density genomics and soybean transgenic technology in DNA probe arrays are powerful tools for a broad set general. of applications including gene expression monitorGenetic engineering of soybean is currently be- ing, sequence analysis, and genotyping (Zhu 2003; ing carried out using three distinct target explants, Lipshultz et al. 1999). cotyledonary node, embryonic axis, and somatic emThe soybean expressed sequence tag (EST) project bryos. Roundup Ready soybean is an input trait in- led by representatives of soybean commodity groups, troduced into soybean germplasm via genetic en- including public and private scientists and producgineering that provides growers with a convenient ers and growers, was initiated to develop 300,000 and effective weed-control tool. Ongoing research ef- cDNA libraries (gene messages) and gene sequences forts have expanded the targets of other novel in- of economic importance from many developmenput and output traits for soybean. Input traits be- tal stages or organs of soybean. Through collaboing investigated include alternative herbicide resis- rative efforts, currently over 80 libraries and over tance and tolerance to both biotic and abiotic stresses, 300,000 EST sequences have been generated from while output traits are primarily focused on modiﬁed roots, shoots, leaves, stems, pods, cotyledons, germioil and protein composition in the seed (Clemente nating shoot tips, ﬂower meristems, tissue-culture2004). derived embryos, and pathogen-challenged tissues

44

G.-J. Lee et al.

(Shoemaker et al. 2002). Cluster analysis revealed that the entire public EST collection yields 61,127 unigenes of which 36,357 are contigs and 24,770 are singletons (Vodkin et al. 2004). Similarly, The Institute for Genomic Research (TIGR) has a total of 63,676 unigenes consisting of 31,918 TC sequences and 31,748 singletons (http://www.tigr.org/tdb/tgi). To enable cDNA microarray experiments for expression proﬁles, the selected cDNA clones were processed into three sets of 9,216 cDNAs per array representing low-redundancy copy and at least once on an array (Vodkin et al. 2004). Recent GeneBank-submitted EST sequences from the subtracted library of droughtstressed soybean root tips contribute stress-speciﬁc unigenes for the further functional genomics approach. A long oligoarray featuring more strict and precise selection of transcripts is being developed and will be available commercially soon from Lila Vodkin’s group at the University of Illinois. Recently, soybean GeneChips have been made commercially available for studying the gene expression of over 37,500 G. max transcripts, over 15,800 Phytophthora root and stem rot transcripts, and over 7,500 SCN transcripts (http://www.affymetrix.com). Size of the soybean array on the GeneChip is an 11-probe, 11-μm pair. Proteomic analysis of soybean in responding to Bradyrhizobium was presented with 96 protein spots from root hairs inoculated, as well as 37 protein spots from the inoculated roots over different time points (Wan et al. 2004). Several proteins previously reported (peroxidase and phenylalanine-ammonia lyase) and novel proteins (phospholipase D, phosphoglucomutase) were identiﬁed, and further advances will be made. Protein analysis for seed proteins in soybean was conducted using cotyledon tissues, and nine basic proteins were isolated and are being analyzed (Woo et al. 2004). Several drought-stress-induced proteins were identiﬁed in soybean roots and leaves using novel proteomics technology, and the comparison of the tissue- speciﬁc transcriptome with proteome will help identify and reveal novel genes regulating the stresstolerance mechanism in soybean. Functional analysis of several genes of interest using RNAi silencing is in progress in soybean. Recently, RNAi suppression of some genes responsible for seed phospholipid degradation and defense genes to pathogen invasion (Phytophthora root and stem rot and SCN) was conducted; conﬁrmation of the ﬁndings is in progress (Graham et al. 2004; Lee et al. 2004a; Trick et al. 2004).

An integrated functional genomics approach will facilitate the identiﬁcation of novel genes and transcription factors for crop improvement.

1.7 Future Scope DNA markers are vital to leveraging knowledge from noncrop model systems to crop species of economic importance. They form the foundation for gene tagging and QTL mapping. They enable genome comparisons among different species and provide tools for assessing molecular variation within and between species. Efﬁcient sharing of genetic information between soybean and other legumes requires the availability of common DNA markers across all legume genomes. Comparative analysis among the legumes will enable advances in one crop to be applied to other species. Development of a high-resolution physical map of the soybean cultivar Williams 82 has been identiﬁed as a research priority in the USA (Stacey et al. 2004). A large EST resource was derived from this genotype and is targeted for whole genome sequencing in the future. Completion of a physical map with BAC end sequences will advance numerous soybean genomic goals, such as SNP marker development, comparative mapping, and genome sequencing, and will help to reveal ancient duplications within the soybean genome that are problematic for many genetic studies. Mapping cDNAs from the EST or unigenes onto the physical map will yield information useful to basic studies of genome structure as well as the discovery of candidate genes for QTLs. In addition, this approach will identify gene-rich regions that could be targeted for genome sequencing. Even though the virtue of genomic analysis privileges Arabidopsis as a model plant, soybean is considered a model species of the phaseoloid clade of the legume family. The tools of functional genomics such as transcriptomics, proteomics, and metabolomics and related bioinformatics approaches will lead to the identiﬁcation of a high degree of crosstalk and overlap between different response pathways and gene discovery in soybean. These comprehensive advances elevate the importance of a systems biology approach in soybean and integrate the comparative genomics analysis in the process. Comparative genomics analysis is the potential angle of in silico analysis to under-

Chapter 1 Soybean

stand biological systems and processes across plant species with the aim of identifying genes of agronomic interest. Discovery of novel genes and transcription factors need to be translated for better crop production using efﬁcient gene transformation technologies. Also, the scope of promoter elements is high for tissue-speciﬁc or stress-speciﬁc gene expression. Much attention should be given to identifying regulatory regions and investigating the structure that controls the transcription of genes of interest. Studies on crosstalk between pathways and the regulatory network and comparative genomics will help to correlate the effects of genetic background and environmental factors on soybean plants. The development of new markers and the identiﬁcation of candidate genes through a genomics approach will provide potential tools for soybean breeders for crop-improvement applications.

References Abel GH (1969) Inheritance of the capacity for chloride inclusion and chloride exclusion by soybeans. Crop Sci 9:697–698 Akkaya MS, Shoemaker RC, Specht JE, Bhagwat AA, Cregan PB (1995) Integration of simple sequence repeat DNA markers into a soybean linkage map. Crop Sci 35:1439–1445 Anderson TR, Buzzell RI (1992) Inheritance and linkage of the Rps7 gene for resistance to Phytophthora rot of soybean. Plant Dis 76:958–959 Apuya NR, Frazier BL, Keim P, Roth EJ, Lark KG (1988) Restriction fragment length polymorphisms as genetic markers in soybean, Glycine max (L.) Merrill. Theor Appl Genet 75:889–901 Arumuganathan K, Earle ED (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9:208–219 Ashﬁeld T, Saghai Maroof MA, Webb DM, Innes RW, Keim P, Danzer JR, Held D, Clayton K (1998) Rpg1, a soybean gene effective against races of bacterial blight, maps to a cluster of previously identiﬁed disease resistance genes. Theor Appl Genet 96:1013–1021 Athow KL, Probst AH (1952) The inheritance of resistance to frogeye leaf spot of soybeans. Phytopathology 42:660–662 Athow KL, Probst AH, Kurtzman CP, Laviolette FA (1962) A newly identiﬁed physiological race of Cercospora sojina on soybean. Phytopathology 52:712–174 Athow KL, Laviolette FA, Mueller EH, Wilcox JR (1980) A new major gene for resistance to Phytophthora megasperma var. sojae in soybean. Phytopathology 70:977–980 Athow KL, Laviolette FA (1982) Rps6, a major gene for resistance to Phytophthora megasperma f. sp. glycinea in soybean. Phytopathology 72:1564–1567

45

Ayala A, Ramirez CT (1964) Host-range, distribution, and bibliography of the reniform nematode, Rotylenchulus reniformis, with special reference to Puerto Rico. J Agric Univ Puerto Rico 48:140–160 Bachman MS, Tamulonis JP, Nickell CD, Bent AF (2001) Molecular markers linked to brown stem rot resistance genes, Rbs1 and Rbs2 in soybean. Crop Sci 41:527–535 Bailey MA, Mian MAR, Carter TE, Ashley DA, Boerma HR (1997) Pod dehiscence of soybean: identiﬁcation of quantitative trait loci. J Hered 88:152–154 Bernard RL (1972) Two genes affecting stem termination in soybean. Crop Sci 12:235–239 Bernard RL, Howell RW (1964) Inheritance of phosphorus sensitivity in soybeans. Crop Sci 4:298–299 Bernard RL, Cremeens CR (1971) A gene for general resistance to downy mildew of soybean (Peronospora manshurica). J Hered 62:359–362 Bernard RL, Smith PE, Kaufmann MJ, Schmitthenner AF (1957) Inheritance of resistance to phytophthora root and stem rot in the soybean. Agron J 49:391 Bianchi-Hall CM, Arellano C, Boerma HR, Parrott WA, Hussey RS, Ashley DA, Bailey MA, Carter TE Jr, Rufty TW, Mian MAR (2000) Aluminum tolerance associated with quantitative trait loci derived from soybean PI 416937 in hydroponics. Crop Sci 40:538–545 Bilyeu KD, Palavalli L, Sleper DA, Beuselinck PR (2003) Three microsomal omega-3-fatty-acid desaturase genes contribute to soybean linolenic acid levels. Crop Sci 43:1833–1838 Birt DF, Hendrick S, Alekel DL (2004) Soybean and the prevention of chronic human disease. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agronomy Monographs, 3rd ed. No. 16, ASA-CSSA-SSSA, Madison, WI, pp 1047–1117 Boerma HR, Kuhn CW (1976) Inheritance of resistance to peanut mottle virus in soybeans. Crop Sci 16:533–534 Boerma HR, Jones BG (1978) Inheritance of a second gene for brachytic stem in soybeans. Crop Sci 18:559–560 Bonato ER, Vello NA (1999) E6, a dominant gene conditioning early ﬂowering and maturity in soybeans. Genet Mol Biol 22:229–232 Borevitz JO, Chory J (2004) Genomics tools for QTL analysis and gene discovery. Curr Opin Plant Biol 7:132–136 Bravo JJ, Fehr WR, Welke GA, Hammond EG, Cianzio SR (1999) Family and line selection for elevated palmitate of soybean. Crop Sci 39:679–682 Brummer EC, Nickell AD, Wilcox JR, Shoemaker RC (1995) Mapping the Fan locus controlling linolenic acid content in soybean oil. J Hered 86:245–247 Brummer EC, Graef GL, Orf J, Wilcox JR, Shoemaker RC (1997) Mapping QTL for seed protein and oil content in eight soybean populations. Crop Sci 37:370–378 Burnham KD, Dorrance AE, Francis DM, Fioritto RJ, St. Martin SK (2003) A new locus in soybean for resistance to Phytophthora sojae. Crop Sci 43:101–105

46

G.-J. Lee et al.

Burzlaff JD, Palmer RG (1999) Soybean linkage studies: y18 and y20. Soybean Genet Newslett 26. http://www.soygenetics.org/articles/sgn1999-011.html Buzzell RI, Anderson TR (1981) Another major gene for resistance to Phytophthora megasperma var. sojae in soybean. Soybean Genet Newslett 18:30–33 Cairo CA, Stein J, Delgado L, Bortolotti S, Guelman SA, Ortiz JPA, Morandi EN (2002) Tagging the juvenile locus in soybean [Glycine max (L.) Merr.] with molecular markers. Euphytica 124:387–395 Caldwell BE, Brim CA, Ross JP (1960) Inheritance of resistance of soybeans to the cyst nematode, Heterodera glycines. Agron J 52:635–636 Carter TE Jr, Nelson RL, Sneller CH, Cui Z (2004) Genetic diversity in soybean. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agronomy Monographs 3rd ed. No. 16, ASA-CSSA-SSSA, Madison, WI, USA, pp 303–416 Chang SJC, Doubler TW, Kilo VY, AbuThredeih J, Prabhu R, Freire V, Suttner R, Klein J, Schmidt ME, Gibson PT, Lightfoot DA (1997) Association of loci underlying ﬁeld resistance to soybean sudden death syndrome (SDS) and cyst nematode (SCN) race 3. Crop Sci 37:965–971 Chen XF, Palmer RG (1998) Recombination and linkage estimation between the k2 and Mdh1-n y20 loci in soybean. Journal of Heredity 89:488–494 Chen Z, Shoemaker RC (1998) Four genes affecting seed traits in soybeans map to linkage group F. J Hered 89:211–215 Chung J, Cregan PB, Shoemaker RC, Specht JE, Lee DJ, Babka HL, Graef GL, Staswick PE (2003) The seed protein, oil, and yield QTL on soybean linkage group I. Crop Sci 43:1053–1067 Clark WJ, Harris FA, Maxwell FG, Hartwig EE (1972) Resistance of certain soybean cultivars to bean leaf beetle, striped blister beetile, and bollworm. J Econ Entomol 65:1669–1672 Clemente T (2004) Transgenes in soybean: methods and applications. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, 8–11 August 2004, Columbia, MO, p 2 Clemente TE, LaVallee BJ, Howe AR, Conner-Ward D, Rozman RJ, Hunter PE, Broyles DL, Kasten DS, Hinchee MA (2000) Progeny analysis of glyphosate selected transgenic soybean derived from Agrobacterium-mediated transformation. Crop Sci 40:797–803 Cober ER, Voldeng HD (2001) A new soybean maturity and photoperiod-sensitivity locus linked to E1 and T. Crop Sci 41:698–701 Concibido VC, Denny RL, Boutin SR, Hautea R, Orf JH, Young ND (1994) DNA marker analysis of loci underlying resistance to soybean cyst nematode (Heterodera glycines Ichinohe). Crop Sci 34:240–246 Concibido VC, Denny RL, Lange D, Danesh D, Orf J, Young N (1995) The soybean cyst nematode resistance gene on linkage group G is common among sources of resistance. Soybean Genet Newslett 22:269–272

Concibido VC, Young ND, Lange DA, Denny RL, Orf JH (1996) RFLP mapping and marker-assisted selection of soybean cyst nematode resistance in PI 209332. Crop Sci 36:1643–1650 Concibido VC, Lange D, Denny RL, Orf J, Young N (1997) Genome mapping on soybean cyst nematode resistance genes in ‘Peking’, PI 90763, and PI 88788 using DNA markers. Crop Sci 37:258–264 1997 Concibido VC, La Vallee B, McLaird P, Pineda N, Meyer J, Hummel L, Yang J, Wu K, Delannay X (2003) Introgression of a quantitative trait locus for yield from Glycine soja into commercial soybean cultivars. Theor Appl Genet 106:575–582 Concibido VC, Diers BW, Arelli PR (2004) A decade of QTL mapping for cyst nematode resistance in soybean. Crop Sci 44:1121–1131 Cornelious B, Chen P, Chen Y, de Leon N, Shannon JG, Wang D (2005) Identiﬁcation of QTLs underlying water-logging tolerance in soybean. Mol Breed 16:103–112 Cregan PB, Bhagwat AA, Akkaya MS, Jiang R (1994) Microsatellite ﬁngerprinting and mapping of soybean. Methods Mol Cell Biol 5:49–61 Cregan PB, Jarvik T, Bush AL, Shoemaker RC, Lark KG, Kahler AL, Kaya N, VanToai TT, Lohnes DG, Chung J, Specht JE (1999) An integrated genetic linkage map of the soybean. Crop Sci 39:1464–1490 Csanadi G, Vollmann J, Stift G, Lelley T (2001) Seed quality QTLs identiﬁed in a molecular map of early maturing soybean. Theor Appl Genet 103:912–919 Demirbas A, Cregan PB, Shoemaker RC, Specht JE, Graef GL, Rector BG, Lohnes DG, Fioritto RJ (2001) Simple sequence repeat markers linked to the soybean Rps genes for phytophthora resistance. Crop Sci 41:1220–1227 Devine TE (1998) Assignment of the Y17 locus to classical soybean linkage group 14. Crop Sci 38:696–697 Devine TE (2003) The Pd2 and Lf2 loci deﬁne soybean linkage group 16. Crop Sci 443:2028–2030 Devine TE, Kuykendall LD (1994) Rfg1, a soybean gene controlling nodulation with fast growing Rhizobium fredii strain 205. Plant Soil 158:47–51 Devine TE, Kuykendall LD (1996) Host genetic control of symbiosis in soybean (Glycine max L.). Plant and Soil 186:173–187 Devine TE, Kilen TC, O’Neill JJ (1991) Genetic linkage of the phtophthora resistance gene Rps2 and the nodulation response gene Rj2 in soybean. Crop Sci 31:713–715 Di R, Purcell V, Collins GB, Ghabrial SA (1996) Production of transgenic soybean lines expressing the bean pod mottle virus coat protein precursor gene. Plant Cell Rep 15:746–750 Diers BW, Shoemaker RC (1992) Restriction fragment length polymorphism analysis of soybean fatty acid content. J Am Oil Chem Soc 69:1242–1244 Diers BW, Imsande J, Shoemaker RC, Mansur L (1992a) Mapping phytophthora resistance loci in soybean with restric-

Chapter 1 Soybean tion fragment length polymorphism markers. Crop Sci 32:377–383 Diers BW, Keim P, Fehr WR, Shoemaker RC (1992b) RFLP analysis of soybean seed protein and oil content. Theor Appl Genet 83:608–612 Diers BW, Cianzio-Rodriguez S, Shoemaker RC (1992c) Possible identiﬁcation of quantitative trait loci affecting iron efﬁciency in soybean. J Plant Nutr 15:2127–2136 Fasoula DA, Stephens PA, Nickell CD, Vodkin LO (1995) Cosegregation of purple-throat ﬂower color with dihydrofalvonol reductase polymorphism in soybean. Crop Sci 35:1028–1031 Fasoula VA, Harris DK, Bailey MA, Phillips DV, Boerma HR (2003) Identiﬁcation, mapping, and conﬁrmation of a soybean gene for bud blight resistance. Crop Sci 43:1754–1759 Fehr WR (1991) A plant breeder’s response to changing needs in soybean oil quality. In: Wilkinson D (ed) Proc XXI Soybean Seed Res Conf, Chicago. American Seed Trade Association, Washington, DC, pp 79–87 Finer JJ, McMullen MD (1991) Transformation of soybean via particle bombardment of embryogenic suspension culture tissue. In Vitro Cell Dev Biol Plant 27:175–182 Finer JJ, Vain P, Jones MW, McMullen MD (1992) Development of the particle inﬂow gun for DNA delivery to plant cells. Plant Cell Rep 11:323–328 Frisch M, Melchinger AE (2001) The length of the intact donor chromosome segment around a target gene in markerassisted backcrossing. Genetics 157:1343–1356 Funatsuki H, Kawaguchi K, Matsuba S, Sato Y, Ishimoto M (2005) Mapping of QTL associated with chilling tolerance during reproductive growth in soybean. Theor Appl Genet 111:851–861 Graham MJ, Nickell C, Hoeft RG (1995) Inheritance of tolerance to manganese deﬁciency in soybean. Crop Sci 35:1007–1010 Graham TL, Yu O, Subramanian S, Graham M (2004) RNAi silencing as a tool for functional analysis of soybean defenserelated genes. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 146 Gunashinghe UB, Irwin ME, Kampmeir GE (1988) Soybean leaf pubescence affects aphid vector transmission and ﬁeld spread of soybean mosaic virus. Ann Appl Biol 112:259–272 Gunduz I, Buss GR, Chen P, Tolin SA (2002) Characterization of SMV resistance genes in Tousan 140 and Hourei Soybean. Crop Sci 42:90–95 Guo B, Sleper DA, Arelli PR, Shannon JG, Nguyen HT (2005) Identiﬁcation of QTLs associated with resistance to soybean cyst nematode races 2, 3 and 5 in soybean PI 90763. Theor Appl Genet 111:965–971 Guo B, Sleper DA, Nguyen HT, Arelli PR, Shannon JG (2006) Quantitative Trait Loci underlying Resistance to Three Soybean Cyst Nematode Populations in Soybean PI 404198A. Crop Sci 46:224–233

47

Hadi MZ, McMullen MD, Finer JJ (1996) Transformation of 12 different plasmids into soybean via particle bombardment. Plant Cell Rep 15:500–505 Hauge BM, Wang ML, Parsons JD, Parnell LD (2001) Nucleic acid molecules and other molecules associated with soybean cyst nematode resistance. US Pat Appl Publ No 20030005491 Hanson WD, Nickell CD, Gray LE, Sebastian SA (1988) Identiﬁcation of two dominant genes conditioning brown stem rot resistance in soybean. Crop Sci 28:41–43 Hartwig EE (1986) Identiﬁcation of a fourth major gene conferring resistance to soybean rust. Crop Sci 26:1135–1136 Hartwig EE, Bromﬁeld KR (1983) Relationship among three genes conferring speciﬁc resistance to rust in soybeans. Crop Sci 23:237–239 Hartwig EE, Johnes WF, Kilen TC (1991) Identiﬁcation and inheritance of inefﬁcient zinc absorption in soybean. Crop Sci 31:61–63 Hayes AJ, Ma GR, Buss GR, Maroof MAS (2000) Molecular marker mapping of RSV4, a gene conferring resistance to all known strains of soybean mosaic virus. Crop Sci 40:1434–1437 Hazel CB, Klein TM, Anis M, Wilde HD, Parrott WA (1998) Growth characteristics and transformability of soybean embryogenic cultures. Plant Cell Rep 17:765–772 Heer JA, Knap HT, Mahalingam R, Shipe ER, Arelli PR, Matthews BF (1998) Molecular markers for resistance to Heterodera glycines in advanced soybean germplasm. Mol Breed 4:359–367 Hegstad JM, Vodkin LO, Nickell CD (2000a) Genetic and agronomic evaluation of wp-m in soybean. Crop Sci 40:346–351 Hegstad JM, Tarter JA, Vodkin LO, Nickell CD (2000b) Positioning the wp ﬂower color locus on the soybean genome map. Crop Sci 40:534–537 Hinchee MA, Connor-Ward DV, Newell CA, McDonnell RE, Sato SJ, Gasser CS, Fischhoff DA, Re DB, Fraley RT, Horsch RB (1988) Production of transgenic soybean plants using Agrobacterium-mediated DNA transfer. Bio/Technology 6:915–922 Hnetkovsky N, Chang SJC, Doubler TW, Gibson PT, Lightfoot DA (1996) Genetic mapping of loci underlying ﬁeld resistance to sudden death syndrome (SDS). Crop Sci 36:393–400 Hymowitz T (2004) Speciation and cytogenetics. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agronomy Monographs, 3rd ed. No. 16, ASACSSA-SSSA, Madison, WI, pp 97–136 Hyten DL, Pantalone VR, Sams CE, Saxton AM, Landau-Ellis D, Stefaniak TR, Schmidt ME (2004) Seed quality QTL in a prominent soybean population. Theor Appl Genet 109:552–561 Ilarslan H, Skorupska HT, Horner HT, Palmer RG (1997) Genetics and cytology of a tissue-culture derived soybean genic male-sterile female-sterile. J Hered 88:129–138

48

G.-J. Lee et al.

Jeong SC, Maroof MAS (2004) Detection and genotyping of SNPs tightly linked to two disease resistance loci, Rsv1 and Rsv3, of soybean. Plant Breed 123:305–310 Jin W, Palmer RG, Horner HT, Shoemaker RC (1998) Molecular mapping of a male-sterile gene in soybean. Crop Sci 38:1681–1685 Johns CW, Palmer RG (1982) Floral development of a ﬂowerstructure mutant in soybeans, Glycine max (L.) Merr. (Leguminosae). Am J Bot 69:829–842 Johnson EOC, Stephens PA, Fasuola DA, Nickell CD, Vodkin LO (1998) Instability of a novel multicolored ﬂower trait in inbred and crossed soybean lines. J Hered 89:508–515 Joseph B, Specht J, Shoemaker RC (2004) Cloning the gene(s) underlying a major protein QTL on soybean linkage groupI. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 73 Karakaya HC, Tang Y, Cregan PB, Knap HT (2002) Molecular mapping of the fasciation mutation in soybean, Glycine max (Leguminosae). Am J Bot 89:559–565 Kassem MA, Meksem K, Iqbal MJ, Njiti VN, Banz WJ, Winters TA, Wood A, Lightfoot DA (2004) Deﬁnition of soybean genomic regions that control seed phytoestrogen amounts. J Biomed Biotechnol 2004(1):52–60 Keen NT, Buzzell RI (1991) New disease resistance genes in soybean against Pseudomonas syringae pv. glycinea: evidence that one of them interacts with a bacterial elicitor. Theor Appl Genet 81:133–138 Keim P, Diers BW, Olson TC, Shoemaker RC (1990a) RFLP mapping in soybean: Association between marker loci and variation in quantitative traits. Genetics 126:735–742 Keim P, Diers BW, Shoemaker RC (1990b) Genetic analysis of soybean hard seededness with molecular markers. Theor Appl Genet 79:465–469 Kiang YT (1990) Linkage analysis of Pgd 1, Pgi 1, pod color (L1), and determinate stem (dt1) loci on soybean linkage group 5. J Hered 81:401–404 Kilen TC, Hartwig EE, Keeling BL (1974) Inheritance of a second major gene for resistance to phytophthora rot in soybeans. Crop Sci 14:260–262 Kilen TC, Hartwig EE (1987) Identiﬁcation of single genes controlling resistance to stem canker in soybean. Crop Sci 27:863–864 Kim HK, Kang ST, Cho JH, Choung MG, Suh DY (2005) Quantitative trait loci associated with oligosaccharide and sucrose contents in soybean (Glycine max L). J Plant Biol 48:106–112 Komatsu OK, Okuda S, Takahashi M, Matsunaga R, Nakazawa Y (2005) QTL mapping of antibiosis resistance to common cutworm (Spodoptera litura Fabricius) in soybean. Crop Sci 45:2044–2048 Kosslak RM, Dieter JR, Ruff RL, Chamberlin MA, Bowen BA, Palmer RG (1996) Partial resistance to root borne infection by phyptophthora sojae in three allelic necrotic root mutants in soybean. J Hered 87:415–422

Kosslak RM, Chamberlin MA, Palmer RG, Bowen BA (1997) Programmed cell death in the root cortex of soybean root necrosis mutants. Plant J 11:729–745 Lambert L, Kilen TC (1984) Multiple insect resistance in several soybean genotypes. Crop Sci 24:887–890 Lark KG, Weisemann JM, Matthews BF, Palmer R, Chase K, Macalma T (1993) A genetic map of soybean (Glycine max L.) using an intraspeciﬁc cross of two cultivars: ‘Minsoy’ and ‘Noir 1’. Theor Appl Genet 86:901–906 Lee M (1995) DNA markers and plant breeding programs. Adv Agron 35:265–344 Lee SH, Bailey MA, Mian MAR, Carter TE, Shipe ER, Ashley DA, Parrott WA, Hussey RS, Boerma HR (1996a) RFLP loci associated with soybean protein and oil content across populations and locations. Theor Appl Genet 93:646–657 Lee SH, Bailey MA, Mian MAR, Shipe ER, Ashley DA, Parrott WA, Boerma HR (1996b) Identiﬁcation of quantitative trait loci for plant height, lodging, and maturity in a soybean population segregating for growth habit. Theor Appl Genet 92:516–523 Lee SH, Bailey MA, Mian MAR, Carter TE, Ashley DA, Hussey RS, Parrot WA, Boerma HR (1996c) Molecular markers associated with soybean plant height, lodging, and maturity across locations. Crop Sci 36:728–735 Lee JM, Bush AL, Specht JE, Shoemaker RC (1999) Mapping of duplicate genes in soybean. Genome 42:829–836 Lee SH, Park KY, Lee HS, Park EH, Boerma HR (2001a) Genetic mapping of QTLs conditioning soybean sprout yield and quality. Theor Appl Genet 103:702–709 Lee GJ, Rufty TW, Carter TE Jr, Boerma HR (2001b) A major QTL associated with Al tolerance in soybean. In: 2001 American Society of Agronomy-Crop Science Society of America-Soil Science of America Abstracts, ASA-CSSA-SSSA, Madison, WI Lee GJ, Carter TE Jr, Boerma HR, Shannon JG, Hood M, Hawbaker M (2002) Identiﬁcation of soybean yield QTL in irrigated and rain-fed environments. In: 2002 American Society of Agronomy-Crop Science Society of America-Soil Science of America Abstracts, ASA-CSSA-SSSA, Madison, WI Lee GJ, Walker DR, Boerma HR (2003) Conﬁrmational mapping of QTLs associated with bacterial pustule, bud blight, and soybean mosaic virus in soybean. In: 2002 American Society of Agronomy-Crop Science Society of America-Soil Science of America Abstracts, ASA-CSSA-SSSA, Madison, WI Lee JH, Schapaugh WT, Wang X, Trick HN (2004a) RNAi suppression of phospholipase D in soybean seed. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 138 Lee SH, Walker DR, Cregan PB, Boerma HR (2004b) Comparison of four ﬂow cytometric SNP detection assays and their use in plant improvement. Theor Appl Genet 110:167–174 Lee GJ, Carter TE Jr, Li Z, Gibbs MO, Boerma HR, Villagarcia MR, Zhou X (2004c) A major QTL conditioning salt tol-

Chapter 1 Soybean erance in S-100 soybean and descendent cultivars. Theor Appl Genet 109:1610–1619 Lersten RF, Carlson JB (2004) Vegetative morphology. In: Boerma HR, Specht JE (eds) Soybeans: Improvement Production and Uses. 3rd edn. Agron Monogr 16. ASA, CSSA and SSSA, Madison WI, pp 15–96 Lewers KS, St. Martin SK, Hedges BR, Widrlechner MP, Palmer RG (1996) Hybrid soybean seed production: comparison of three methods. Crop Sci 36:1560–1567 Lewers KS, St. Martin SK, Hedges BR, Palmer RG (1998) Effects of the Dt2 and S alleles on agronomic traits of F1 hybrid soybean. Crop Sci 38:1137–1142 Lewers KS, Crane EH, Bronson CR, Schupp JM, Keim P, Shoemaker RC (1999) Detection of linked QTL for soybean brown stem rot resistance in ‘BSR101’ as expressed in a growth chamber environment. Mol Breed 5:33–42 Li Z, Jakkula L, Hussey RS, Tamulonis JP, Boerma HR (2001) SSR mapping and conﬁrmation of the QTL from PI 96354 conditioning soybean resistance to southern root-knot nematode. Theor Appl Genet 103:1167–1173 Li Z, Wilson RF, Rayford WE, Boerma HR (2002) Molecular mapping genes conditioning reduced palmitic acid content in N87-2122-4 soybean. Crop Sci 42:373–378 Lightfoot D, Meksem K (2002) Isolated polynucleotides and polypeptides relating to loci underlying resistance to soybean cyst nematode and soybean sudden death syndrome and methods employing same. US Pat Appl Publ No. 2002144310 Lim SA (1989) Inheritance of resistance to Peronospora manshurica races 2 and 3 in soybean. Phytopathology 79:877–879 Lin SF, Cianzio SR, Shoemaker RC (1997) Mapping genetic loci for iron deﬁciency chlorosis in soybean. Mol Breed 3:219–229 Lincoln SE, Lander SL (1993) Mapmaker/exp 3.0 and Mapmaker/QTL 1.1. Whitehead Inst of Med Res Tech Rep, Cambridge, MA Lipshultz RJ, Fodor SPA, Gingeras TR, Lockhart DJ (1999) High density synthetic oligonucleotide arrays. Nat Genet 21:20–24 List GR, Steidley KR, Neff WE (2000) Commercial spreads formulation, structure, and properties. INFORM 11:980–986 Liu K (1997) Soybeans: Chemistry, Technology and Utilization. Aspen Publishers, Gaithersburg, MD, p 532 Lloyd JL, Wax LM (1984) Screening for soybean varieties sensitive to DPX-F6025. In: Proceedings of the North Central Weed Control Conference, 39:95 Lohnes DG, Schmitthenner AF (1997) Position of the phytophthora gene Rps7 on the soybean molecular map. Crop Sci 37:555–556 Lohnes DG, Specht JE, Cregan PB (1997) Evidence for homoeologous linkage groups in the soybean. Crop Sci 37:254–257 Luquez VM, Guiamet JJ (2001) Effects of the ’stay green’ genotype GGd1d1d2d2 on leaf gas exchange, dry matter accu-

49

mulation and seed yield in soybean (Glycine max L. Merr.). Ann Bot 87:313–318 Luzzi BM, Hussey RS, Boerma HR (1994) A gene for resistance to the southern root-knot nematode in soybean. J Hered 85:484–486 Mahalingam R, Shorupska HT (1995) DNA markers for resistance to Heterodera glycines race 3 in soybean cultivar Peking. Breed Sci 45:435–443 Mahama AA, Lewers KS, Palmer RG (2002) Genetic linkage in soybean: classical genetic linkage groups 6 and 8. Crop Sci 42:1459–1464 Mansur LM, Lark KG, Kross H, Oliveira A (1993) Interval mapping of quantitative trait loci for reproductive, morphological, and seed traits of soybean (Glycine max L.). Theor Appl Genet 86:907–913 Mansur LM, Orf JH, Chase K, Jarvik T, Cregan PB, Lark KG (1996) Genetic mapping of agronomic traits using recombinant inbred lines of soybean. Crop Sci 36:1327–1336 Matson AL, Williams LF (1965) Evidence of a fourth gene for resistance to the soybean cyst nematode. Crop Sci 5:477 Matthews BF, Devine TE, Weisemann JM, Beard HS, Lewers KS, McDonald MH, Park YB, Maiti R, Lin JJ, Kuo J, Pedroni MJ, Cregan PB, Saunders JA (2001) Incorporation of sequenced cDNA and genomic markers into the soybean genetic map. Crop Sci 41:516–521 Maughan PJ, Maroof MAS, Buss GR (1996) Molecular marker analysis of seed-weight: genomic locations, gene action, and evidence for orthologous evolution among three legume species. Theor Appl Genet 93:574–579 Maughan PJ, Maroof MAS, Buss GR (2000) Identiﬁcation of quantitative trait loci controlling sucrose content in soybean (Glycine max). Mol Breed 6:105–111 Mclean RJ, Byth DE (1980) Inheritance of resistance to rust (Phakopsora pachyrhizi) in soybeans. Aust J Agric Res, Melbourne, CSIRO 31:951–956 Meksem K, Doubler TW, Chancharoenchai K, Njiti VN, Chang SJC, Arelli APR, Cregan PE, Gray LE, Gibson PT, Lightfoot DA (1999) Clustering among loci underlying soybean resistance to Fusarum solanii, SDS and SCN in near-isogenic lines. Theor Appl Genet 99:1131–1142 Meksem K, Ruben E, Hyten D, Triwitayakorn K, Lightfoot D (2001) Conversion of AFLP bands into high-throughput DNA markers. Mol Genet Genom 265:207–214 Mian MAR, Mailey MA, Ashley DA, Wells R, Carter TE, Parrot WA, Boerma HR (1996) Molecular markers associated with water use efﬁciency and leaf ash in soybean. Crop Sci 36:1252–1257 Mian MAR, Shipe ER, Alvernaz J, Mueller JD, Ashley DA, Boerma HR (1997) RFLP analysis of chlorimuron ethyl sensitivity in soybean. J Hered 88:38–41 Mian MAR, Wells R, Carter TE, Ashley DA, Boerma HR (1998) RFLP tagging of QTLs conditioning speciﬁc leaf weight and leaf size in soybean. Theor Appl Genet 96:354–360

50

G.-J. Lee et al.

Mian MAR, Wang T, Phillips DV, Alvernaz J, Boerma HR (1999) Molecular mapping of the Rcs3 gene for resistance to frogeye leaf spot in soybean. Crop Sci 39:1687–1691 Monteros MJ, Burton JW, Boerma HR (2004) SSR analysis and conﬁrmation of oleic acid QTL in N00–3350. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 58 Mudge J, Cregan PB, Kenworthy JP, Kenworthy WJ, Orf JH, Young ND (1997) Two microsatellite markers that ﬂank the major soybean cyst nematode resistance locus. Crop Sci 37:1611–1615 Mueller EH, Athow KL, Laviolette FA (1978) Inheritance of resistance to four physiologic races of Phytophthora megasperma var. sojae. Phytopathology 68:1318–1322 Mukherjee D, Lambert JW, Cooper RL, Kennedy BW (1966) Inheritance of resistance to bacterial blight in soybeans. Crop Sci 6:324–326 Narvel JM, Lee SH, Boerma HR, Wang T, Jakkula LR, Phillips DV (2001a) Molecular mapping of Rxp conditioning reaction to bacterial pustule in soybean. J Hered 92:267–270 Narvel JM, Walker DR, Rector BG, All JN, Parrot WA, Boerma HR (2001b) A retrospective DNA marker assessment of the development of insect resistant soybean. Crop Sci 41:1931–1939 Nichols DM, Carlson SR, Diers BW (2004) Fine mapping a seed protein QTL on LG I in soybean. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 64 Nickell AD, Wilcox JR, Lorenzen LL, Cavins JF, Guffy RG, Shoemaker RC (1994) The Fap2 locus in soybean maps to linkage group D. J Hered 85:160–162 Njiti VN, Meksem K, Iqbal MJ, Johnson JE, Kassem MA, Zobrist KF, Kilo VY, Lightfoot DA (2002) Common loci underlie ﬁeld resistance to soybean sudden death syndrome in Forrest, Pyramid, Essex, and Douglas. Theor Appl Genet 104:294–300 Orellana RG (1981) Resistance to bud blight in introductions from the germplasm of wild soybean. Plant Dis 65:594–595 Orf JH, Chase K, Jarvik T, Mansur LM, Cregan PB, Adler FR, Lark KG (1999) Genetics of soybean agronomic traits. I. Comparison of three related recombinant inbred populations. Crop Sci 39:1642–1651 Orf JH, Diers BW, Boerma HR (2004) Genetic improvement: Conventional and molecular strategies. In: Boerma HR, Specht JE (eds) Soybeans: improvement, production, and uses. Agron Monogr, 3rd edn. No. 16, ASA-CSSA-SSSA, Madison, WI, pp 417–480 Palmer RG (2000) Genetics of four male-sterile, female-sterile soybean mutants. Crop Sci 40:78–83 Palmer RG, Kilen TC (1987) Qualitative genetics and cytogenetics. In: Wilcox JR (ed) Soybeans: Improvement, Production, and Uses. Agronomy Monographs, 2nd edn. ASA-CSSASSSA, Madison, WI, pp 135–209 Palmer RG, Kiang YT (1990) Linkage map of soybean (Glycine max L. Merr.). In: O’Brien SJ (ed) Genetic Maps: Locus

Maps of Complex Genomes. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, pp 668–693 Palmer RG, Horner HT (2000) Genetics and cytology of a genic male-sterile, female-sterile mutant from a transposoncontaining soybean population. J Hered 91:378–383 Palmer RG, Pfeiffer TW, Buss GR, Kilen TC (2004) Qualitative genetics. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agronomy Monographs, 3rd edn. No. 16, ASA-CSSA-SSSA, Madison, WI, pp 137–233 Pantalone VR, Wilson RF, Novitzky WP, Burton JW (2002) Genetic regulation of elevated stearic acid concentration in soybean oil. J Am Oil Chem Soc 79:549–553 Panthee DR, Pantalone VR, West DR, Saxton AM, Sams CE (2005) Quantitative trait loci for seed protein and oil concentration, and seed size in soybean. Crop Sci 45:2015–2022 Panthee DR, Pantalone VR, Sams CE, Saxton AM, West DR, Orf JH, Killam AS (2006) Quantitative trait loci controlling sulfur containing amino acids, methionine and cysteine, in soybean seeds. Theor Appl Genet 112:546–553 Parrott WA, All JN, Adang MJ, Bailey MA, Boerma HR, Stewart CN Jr (1994) Recovery and evaluation of soybean plants transgenic for a Bacillus thuringiensis var. kurstaki insecticidal gene. In Vitro Cell Dev Biol Plant 30:144–149 Passioura JB (1977) Grain yield, harvest index, and water use of wheat. J Aust Inst Agric Sci 43:1093–1190 Patzoldt ME, Grau CR, Stephens PA, Kurtzweil NC, Carlson SR, Diers BW (2005) Localization of a quantitative trait locus providing brown stem rot resistance in the soybean cultivar bell. Crop Sci 45:1241–1248 Phillips DV, Boerma HR (1982) Two genes for resistance to race 5 of Cercospora sojina in soybeans. Phytopathology 72:764–766 Poehlman JM, Sleper DA (1995) Breeding Field Crops. Iowa State University Press, Ames, IA Polzin KM, Shoemaker RC, Nickell CD, Lohnes DG (1994) Integration of Rps2, Rmd, and Rj2 into linkage group J of the soybean molecular map. J Hered 85:300–303 Pracht JE, Nickell CD, Harper JE (1993) Genes controlling nodulation in soybean: Rj5 and Rj6. Crop Sci 33:711–713 Primomo VS, Falk DE, Ablett GR, Tanner JW, Rajcan I (2002) Inheritance and interaction of low palmitic and low linolenic soybean. Crop Sci 2002 42:31–36 Primomo VS, Poysa V, Ablett GR, Jackson CJ, Gijzen M, Rajcan I (2005) Mapping QTL for individual and total isoﬂavone content in soybean seeds. Crop Sci 45:2454–2464 Probst AH (1950) The inheritance of leaf abscission and other characters in soybeans. Agron J 42:35–45 Qui BX, Arelli PR, Sleper DA (1999) RFLP markers associated with soybean cyst nematode resistance and seed composition in a ‘Peking’ × ‘Essex’ population. Theor Appl Genet 98:356–364 Rafalski A, Tingey S (1993) RFLP map of soybean (Glycine max). In: O’Brien SJ (ed) Genetic Maps: Locus Maps of Complex Genomes. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, pp 149–156

Chapter 1 Soybean Rahman SM, Takagi Y, Kinoshita T (1996) Genetic control of high oleic acid content in seed oil of two soybean mutants. Crop Sci 36:1125–1128 Rahman SM, Takagi Y, Kinoshita T (1997) Genetic control of high stearic acid content in seed oil of two soybean mutants. Theor Appl Genet 95:772–776 Rao-Arelli AP (1994) Inheritance of resistance to Heterodera glycines race 3 in soybean accessions. Plant Dis 78:898–900 Rector BG, All JN, Parrot WA, Boerma HR (1999) Quantitative trait loci for antixenosis resistance to corn earworm in soybean. Crop Sci 39:531–538 Rector BG, All JN, Parrot WA, Boerma HR (2000) Quantitative trait loci for antibiosis resistance to corn earworm in soybean. Crop Sci 40:233–238 Reyna N, Sneller CH (2001) Evaluation of marker-assisted introgression of yield QTL alleles into adapted soybean. Crop Sci 41:1317–1321 Reyna N, Cornelious B, Shannon JG, Sneller CH (2003) Evaluation of a QTL for waterlogging tolerance in southern soybean germplasm. Crop Sci 43:2077–2082 Ryan SA, Nelson RS, Harper JE (1983) Soybean mutants lacking constitutive nitrate reductase activity. II. Nitrogen assimilation, chlorate resistance, and inheritance. Plant Physiol 72:510–514 Santarem ER, Finer JJ (1999) Transformation of soybean [Glycine max (L.) Merrill] using proliferative embryogenic tissue maintained on semisolid medium. In Vitro Cell Dev Biol Plant 35:451–455 Schuster I, Abdelnoor RV, Marin SRR, Carvalho VP, Kiihl RAS, Silva JFV, Sediyama CS, Barros EG, Moreira MA (2001) Identiﬁcation of a new major QTL associated with resistance to soybean cyst nematode (Heterodera glycines). Theor Appl Genet 102:91–96 Sebolt AM, Shoemaker RC, Diers BW (2000) Analysis of a quantitative trait locus allele from wild soybean that increases seed protein concentration in soybean. Crop Sci 40:1438–1444 Senda M, Jumonji A, Yumoto S, Ishikawa R, Harada T, Niizeki M, Akada S (2002) Analysis of the duplicated CHS1 gene related to the suppression of the seed coat pigmentation in yellow soybeans. Theor Appl Genet 104:1086–1091 Servin B, Martin OC, Mezard M, Hospital F (2004) Toward a theory of marker-assisted gene pyramiding. Genetics 168:513–523 Sexton R, Roberts JA (1982) Cell biology of abscission. Ann Rev Plant Physiol 33:133–162 Shipe ER, Buss GR, Tolin SA (1979) A second gene for resistance to peanut mottle virus in soybeans. Crop Sci 19:656–658 Shoemaker RC, Olson TC (1993) Molecular linkage map of soybean (Glycine max L. Merr.). In: O’Brien SJ (ed) Genetic Maps: Locus Maps of Complex Genomes. Cold Spring Harbor Press, Cold Spring Harbor, NY, pp 6131–6138 Shoemaker RC, Specht JE (1995) Integration of the soybean molecular and classical genetic linkage groups. Crop Sci 35:436–446

51

Shoemaker RC, Polzin K, Labate J, Specht J, Brummer EC, Olson T, Young N, Concibido V, Wilcox J, Tamulonis JP, Kochert G, Boerma HR (1996) Genome duplication in soybean (Glycine subgenus soja). Genetics 144:329–338 Shoemaker R, Keim P, Vodkin L, Retzel E, Clifton SW, Waterston R, Smoller D, Coryell V, Khanna A, Erpelding J, Gai X, Brendel V, Raph-Schmidt C, Shoop EG, Vielweber CJ, Schmatz M, Pape D, Bowers Y, Theising B, Martin J, Dante M, Wylie T, Granger C (2002) A complication of soybean ESTs: generation and analysis. Genome 45:329–338 Shoemaker RC, Cregan PB, Vodkin LO (2004) Soybean genomics. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agron Monogr, 3rd edn. No. 16, ASA-CSSA-SSSA, Madison, WI, pp 235–263 Sills G, Nienhuis J (1998) Changes in DNA-marker frequencies associated with response to contrasting selection methods in Arabidopsis. Theor Appl Genet 97:275–282 Singh BB, Jha AN (1978) Abnormal differentiation of ﬂoral parts in a mutant strain of soybean. J Hered 69:143–144 Skorupska HT, Choi IS, Rao-Arelli AP, Bridges WC (1994) Resistance to soybean cyst nematode and molecular polymorphism in various sources of Peking soybean. Euphytica 75:63–70 Sneller CH, Isleib TG, Carter TE Jr (1992) Isozyme screening of near-isogenic male-strile soybean lines to uncover potential linkages: linkage of the Pgm1 and ms2 loci. J Hered 83:457–459 Song QJ, Marek LF, Shoemaker RC, Lark KG, Concibido VC, Delannay X, Specht JE, Cregan PB (2004) A new integrated genetic linkage map of the soybean. Theor Appl Genet 109:122–128 Soybean Genetics Committee (1995) Soybean genetics committee report. Soybean Genet Newslett 22:11–14 Specht JE, Hume DJ, Kumundini SV (1999) Soybean yield potential – a genetic and physiological perspective. Crop Sci 39:1560–1570 Specht JE, Germann M, Markwell JP, Lark KG, Orf JH, Macrander M, Chase K, Chung J, Graef GL (2001) Soybean response to water: a QTL analysis of drought tolerance. Crop Sci 41:493–509 Spencer MM, Pantalone VR, Meyer EJ, Landau-Ellis D, Hyten DL (2003) Mapping the Fas locus controlling stearic acid content in soybean. Theor Appl Genet 106:615–619 Stacey G, Vodkin L, Parrott WA, Shoemaker RC (2004) National Science Foundation-sponsored workshop report. Draft plan for soybean genomics. Plant Physiol 135:59–70 Stelly DM, Palmer RG (1980) A partially male-sterile mutant line of soybeans Glycine max (L.) Merr.: characterization of msp phenotype variability. Euphytica 29:539–546 Stephens PA, Nickell CD, Kolb FL (1993) Genetic analysis of resistance to Fusarium solanii in soybean. Crop Sci 33:929–930 Stewart CN Jr, Adang MJ, All JN, Boerma HR, Cardineau G, Tucker D, Parrott WA (1996) Genetic transformation, recovery, and characterization of fertile soybean trans-

52

G.-J. Lee et al.

genic for a synthetic Bacillus thuringiensis cryIAc gene. Plant Physiol 112:121–129 Stoltzfus DL, Fehr WR, Welke GA, Hammond EG, Cianzio SR (2000) A fap7 Allele for elevated palmitate in soybean. Crop Sci 40:1538–1542 Takahashi R, Abe J (1994) Genetic and linkage analysis of low temperature-induced browning in soybean seed coat. J Hered 85:447–450 Takahashi R, Asanuma S (1996) Association of T gene with chilling tolerance in soybean. Crop Sci 36:559–562 Tamulonis JP, Luzzi BM, Hussey RS, Parrot WA, Boerma HR (1997a) DNA marker analysis of loci conferring resistance to peanut root-knot nematode in soybean. Theor Appl Genet 95:664–670 Tamulonis JP, Luzzi BM, Hussey RS, Parrot WA, Boerma HR (1997b) RFLP mapping of resistance to southern root- knot nematode in soybean. Crop Sci 37:1903–1909 Tamulonis JP, Luzzi BM, Parrot WA, Hussey RS, Boerma HR (1997c) DNA markers associated with Javanese root-knot nematode resistance in soybean. Crop Sci 37:783–788 Tang Y, Knap HT (1998) Fasciation mutation enhances meristematic activity and alters pattern formation in soybean. Int J Plant Sci 159:249–260 Tanya P, Srinives P, Toojinda T, Vanavichit A, Lee SH (2005) Identiﬁcation of SSR markers associated with N-2- ﬁxation components in soybean [Glycine max (L.) Merr.]. Korean J Genet 27:351–359 Tasma IM, Shoemaker RC (2003) Mapping ﬂowering time gene homologs in soybean and their association with maturity (E) loci. Crop Sci 43:319–328 Tasma IM, Shoemaker RC, Green DE, Lorenzen LL (2001) Mapping genetic loci for ﬂowering time, maturity, and photoperiod insensitivity in soybean. Mol Breed 8:25–35 Terry LI, Chase K, Jarvik T, Orf J, Mansur L, Lark KG (2000) Soybean quantitative trait loci for resistance to insects. Crop Sci 40:375–382 Thompson JA, Bernard RL, Nelson RL (1997) A third allele at the soybean dt1 locus. Crop Sci 37:757–762 Todd JJ, Vodkin LO (1998) Duplications that suppress and deletions that restore expression from a chalcone synthase multigene family. Plant Cell 8:687–699 Trick HN, Steeves R, Todd TC (2004) Utilization of RNA interference to confer resistance to the soybean cyst nematode, Heterodera glycines. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 40 VanToai TT, Martin SK, Chase K, Boru G, Schnipke V, Schmitthenner AF, Lark KG (2001) Identiﬁcation of a QTL associated with tolerance of soybean to soil waterlogging. Crop Sci 41:1247–1252 Venkatasubbarao S (2004) Microarray-status and prospects. Trends Biotechnol 22:630–637 Verma DPS (1992) Signals in root nodule roganogenesis and endocytosis of Rhizobium. Plant Cell 4:373–382

Vest G (1970) Rj3-a gene conditioning ineffective nodulation in soybean. Crop Sci 10:34–35 Vierling RA, Faghihi J, Ferris VR, Ferris JM (1996) Association of RFLP markers conferring broad-based resistance to the soybean cyst nematode (Heterodera glycines). Theor Appl Genet 92:83–86 Vodkin LO, Khanna A, Shealy R, Clough SJ, Gonzalez DO, Philip R, Zabala G, Thibaud-Nissaen F, Sidarous M, Stromvik MV, Shoop E, Schmidt C, Retzel E, Erpelding J, Shoemaker RC, Rodriguez-Huete AM, Polacco JC, Coryell V, Keim P, Gong G, Liu L, Pardinas J, Schweitzer P (2004) Microarrays for global expression constructed with a low redundancy set of 27,500 sequenced cDNAs representing an array of developmental stages and physiological conditions of the soybean plant. BMC Genomics 5 (Published online 29 September 2004) Wan J, Torres M, DaGue B, Mooney B, Xu D, Stacey G (2004) Proteomic analysis of soybean root hairs after infection by Bradyrhizobium japonicum. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 117 Wang D, Arelli PR, Shoemaker RC, Diers BW (2001) Loci underlying resistance to race 3 of soybean cyst nematode in Glycine soja plant introduction 468916. Theor Appl Genet 103:561–566 Webb DM, Baltazar BM, Rao-Arelli PA, Schupp J, Clayton K, Keim P, Beavis WD (1995) Genetic mapping of soybean cyst nematode race-3 resistance loci in soybean PI437.654. Theor Appl Genet 91:574–581 Weiss MG (1943) Inheritance and physiology of efﬁciency in iron utilization in soybeans. Genetics 28:253–268 Weiss MG (1970) Genetic linkage in soybeans. Linkage group II and III. Crop Sci 10:300–303 Wells R, Ashley DA, Boerma HR, Reger BJ (1986) Physiological comparisons of two soybean cultivars differing in canopy photosynthesis. II. Variation in speciﬁc leaf weight, nitrogen, and protein components. Photosyn Res 9:295–305 Weng C, Yu K, Anderson TR, Poysa V (2001) Mapping genes conferring resistance to Phytophthora root rot of soybean, Rps1a and Rps7. J Hered 92:442–446 Werner BK, Wilcox JR, Housley TL (1987) Inheritance of an ethyl methanesulfonate-induced dwarf in soybean and analysis of leaf cell size. Crop Sci 27:665–668 Wiebold WR, Kenworthy WJ (1985) Leaﬂet expansion rates for 15 soybean cultivars. Field Crops Res 12:271–279 Williams C, Gilman DF, Fontenot DS, Birchﬁeld WB (1981) Inheritance of reaction to the reniform nematode Rotylenchulus reniformis in soybean. Crop Sci 21:93–94 Willmot DB, Nickell CD (1989) Genetic analysis of brown stem rot resistance in soybean. Crop Sci 29:672–674 Wilson RF (2004) Seed composition. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agron Monogr, 3rd edn. No. 16, ASA-CSSA-SSSA, Madison, WI, pp 621–677

Chapter 1 Soybean Woo SH, Chung KY, Kim HS, Song BH, Hirano H, Jong SK (2004) Proteome approaches as an efﬁcient means for the separation of basic seed proteins in soybean Glycine max L. Merr. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 122 Wrather JA, Stienstra WC, Koenning SR (2001) Soybean disease loss estimates for the United States from 1996 to 1998. Can J Plant Pathol 23:122–131 Xing A, Zhang Z, Sato S, Staswick PE, Clemente TE (2000) The use of the two T-DNA binary system to derive markerfree transgenic soybeans. In Vitro Cell Dev Biol Plant 36:456–463 Xu SJ, Singh RJ, Kollipara KP, Hymowitz T (2000) Primary trisomics in soybean: origin, identiﬁcation, breeding behavior, and use in linkage mapping. Crop Sci 40:1543–1551 Yamanaka N, Ninomiya S, Hoshi M, Tsubokura Y, Yano M, Nagamura Y, Sasaki T, Harada K (2001) An informative linkage map of soybean reveals QTLs for ﬂowering time, leaﬂet morphology and regions of segregation distortion. DNA Res 8:61–72 Yates JL, Harris DK, Boerma HR (2004) Marker-assisted selection around a major QTL on LG-I increases seed protein content in backcross-derived lines of soybean. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 67 You MG, Yen Y, Gai JY, Zhao TJ (1998) Genetic analysis of short petiole and abnormal pulvinus in soybean. Euphytica 102:329–333 Yu YG, Saghai Maroof MA, Buss GR, Maughan PJ, Tolin SA (1994) RFLP and microsatellite mapping of a gene for soybean mosaic virus resistance. Phytopathology 84:60–64 Yue P, Sleper DA, Arelli PR (2001a) Mapping resistance to multiple races of Heterodera glycines in soybean PI 89772. Crop Sci 41:1589–1595

53

Yue P, Arelli PR, Sleper DA (2001b) Molecular characterization of resistance to Heterodera glycines in soybean PI 438489B. Theor Appl Genet 102:921–928 Zeng P, Vadnais DA, Zhang Z, Polacco JC (2004) Reﬁned glufosinate selection in agrobacterium-mediated transformation of soybean [Glycine max (L.) Merrill]. Plant Cell Rep 22:478–482 Zhang J, Specht JE Graef GL, Johnson BE (1992) Pubescence density effects on soybean seed yield and other agronomic traits. Crop Sci 32:641–648 Zhang Z, Xing A, Staswick PE, Clemente TE (1999) The use of glufosinate as a selective agent in Agrobacteriummediated transformation of soybean. Plant Cell Tiss Org Cult 56:37–46 Zhang WK, Wang YJ, Luo GZ, Zhang JS, He CY, Wu XL, Gai JY, Chen SY (2004) QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers. Theor Appl Genet 108:1131–1139 Zhu T (2003) Global analysis of gene expression using genechip microarrays. Curr Opin Plant Biol 6:418–425 Zhu YL, Song QJ, Hyten DL, Van Tassell CP, Matukumalli LK, Grimm DR, Hyatt SM, Fickus EW, Young ND, Cregan PB (2003) Single-nucleotide polymorphisms in soybean. Genetics 163:1123–1134 Zhu S, Walker DR, Boerma HR, All JN, Parrott WA (2004) Conﬁrmation and ﬁne-mapping of an insect resistance QTL that enhances and broadens the effect of Bt in soybean. In: Soy2004 10th Biennial Conference on the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 83 Zou JJ, Singh RJ, Hymowitz T (2003) Association of the yellow leaf (y10) mutant to soybean chromosome 3. J Hered 94:352–355

CHAPTER 2

2 Oilseed Rape Rod Snowdon, Wilfried Lühs, and Wolfgang Friedt Department of Plant Breeding, Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University of Giessen, Heinrich-Buff-Ring 26–32, 35392Giessen, Germany e-mail: [email protected]

2.1 Introduction 2.1.1 Origin and History of Oilseed Rape Rapeseed (Brassica napus L.; genome AACC, 2n = 38) is today the most widely cultivated crop species in the crucifer family (Brassicaceae). The species is now divided into two subspecies, comprising on the one hand the swedes (B. napus ssp. napobrassica) and on the other hand B. napus ssp. napus, which includes winter and spring oilseed, fodder and vegetable rape forms. The latter include the distinct leaf rape forms (B. napus ssp. napus var. pabularia), which used to be common as a winter-annual vegetable in many parts of the world (Siberian kale, Hanover salad; German: Schnittkohl; French: chou à faucher; Chinese: xi yang you cai). The species originated through spontaneous interspeciﬁc hybridization between turnip rape (Brassica rapa L., syn. campestris; genome AA, 2n = 20) and cabbage (Brassica oleracea L.; genome CC, 2n = 18), resulting in an amphidiploid genome comprising the full chromosome complements of its two progenitors. Because no wild B. napus forms are known, it is assumed that the species arose relatively recently, in the Mediterranean region, where both of its two parental species concurred. The occurrence of spontaneous chromosome doubling in crosses among closely related Brassica diploid species is well documented; the related amphidiploids Indian or brown mustard (B. juncea; genome AABB, 2n = 36) and Abyssinian or Ethiopian mustard (B. carinata; genome BBCC, 2n = 34) arose in the same manner after crosses of black mustard (B. nigra, genome BB, 2n = 16) with B. rapa and B. oleracea, respectively. Brassica vegetables and oilseeds were among the earliest plants to be systematically cropped by mankind. There are indications that a vegetable crucifer was widely cultivated as early as 10,000 years

ago. In India records have been identiﬁed which suggest that oilseed brassicas (probably B. rapa) were being used as early as 4000 BC, and 2000 years ago their use had spread into China and Japan. Swedes (B. napus ssp. napobrassica) were known in Europe at the time of the Romans, and utilisation (probably of B. rapa) for oil purposes in northern Europe is thought to have begun around the 13th century. By the 16th century, rapeseed was the major source of lamp oil in Europe, although it was not until the 18th century that signiﬁcant cultivation areas of the crop were recorded (Kroll 1994; Kimber and McGregor 1995). Today, oilseed rape is the most heavily produced oilseed crop in Europe and only soybean has a greater importance worldwide. Oilseed rape production is dominated by North America (particularly Canada), Western Europe and China; however the crop is also signiﬁcant in Eastern Europe, the Indian subcontinent and Australia. Oilseed rape has become a major international crop only in the past three decades, however (Lühs and Friedt 1994a). The use of rapeseed oil for lamp fuel was largely superseded by petroleum from the end of the 19th century onwards, and only the high quality of rapeseed fats as lubricants for industrial machinery guaranteed continued production of the crop throughout the 20th century. Oil from early rapeseed varieties contained a high quantity of erucic acid (cis 13-docosenoic acid, 22:1n-9), which in high doses can lead to cardiac damage and related health problems. Erucic acid also has a bitter taste, meaning that the oil was generally used only by the poor as a food oil. In times of poverty and crisis, of course, such negative aspects tended to be outweighed by necessity; hence rapeseed production peaked signiﬁcantly during the wars in Europe in the 20th century, particularly in World War II when rapeseed oil was used especially for the production of margarine. The poor reputation of rapeseed oil as a foodstuff was overcome only by the development of 0 and

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

56

R. Snowdon, W. Lühs, W. Friedt

00 rapeseed varieties in the 1970s (Stefansson 1983; Downey and Röbbelen 1989; Downey 1990). The ﬁrst major breakthrough came with the initial 0-quality cultivars with erucic acid levels of less than 1% (Stefansson and Hougen 1964). Earlier rapeseed cultivars contained up to 50% erucic acid in the seed oil. The identiﬁcation of the fatty acid mutants from which the ﬁrst 0-rapeseed derived was made possible by major improvements in high-throughput seed analysis techniques, in particular gas chromatography. The ﬁrst erucic acid-free variety, derived from a spontaneous mutant of the German spring rapeseed cultivar Liho, was released in Canada in the early 1970s. The value of the crop was still suppressed by the presence in the seed of high quantities of glucosinolates, however, which made rapeseed meal unsuitable as a livestock feed. In monogastric animals the digeston of glucosinolates results in the release of toxic byproducts that can cause liver and kidney damage along with lymph dysfunction. In 1969 the Polish spring rape variety Bronowski was identiﬁed as a low glucosinolate form, and this cultivar provided the basis for an international backcrossing program to introduce this polygenic trait (Bronowski was found to possess at least three recessive genes for low glucosinolate content) into high-yielding erucic acid-free material. The result was the release in 1974 of the ﬁrst 00-quality spring rapeseed variety, Tower, with zero erucic acid and low glucosinolate content, and thus began the advance of oilseed rape (canola) in the following decades to one of the most important oil crops in temperate regions. The particular value of rapeseed oil lies in its diverse range of uses. Besides its use as a highly nutritional food oil, rapeseed oil also provides a raw material for an astounding array of products ranging from rapeseed methyl ester (biodiesel) to industrial lubricants, tensides for detergent and soap production and biodegradable plastics. 2.1.2 Botanical Description Oilseed rape is cultivated in Europe and Asia predominantly as winter rapeseed, whereas in Canada, northern Europe and Australia only spring forms are suitable. The differentiation into winter and spring forms is governed by a genetic mechanism controlling the requirement for vernalisation to promote the onset of ﬂowering. Spring oilseed rape does not require vernalisation and is not winter-hardy, hence the crop is sown in spring and stem development begins

immediately after germination. Winter oilseed rape on the other hand is sown in autumn and survives the winter in a leaf rosette form on the soil surface. In the following spring a long vertical stem develops, and shortly before the ﬂoral development lateral branches are formed. Flowering generally occurs in late spring, with pod development and ripening taking place over a period of around 6 to 8 weeks until mid-summer. As a member of the family Brassicaceae (Cruciferae), B. napus possesses a Brassica-typical radial ﬂower comprised of four petals in the typical crucifer cross-form, alternating with four sepals. The inﬂorescence is racemose, with indeterminate ﬂowering beginning at the lowest bud on the main raceme and continuing upward during the following days. The stigma is receptive from about 3 d prior until 3 d after the opening of the ﬂower. The normally yellow ﬂowers have one pair of lateral stamens with short ﬁlaments and four median stamens with longer ﬁlaments. Oilseed rape anther sutures are introrse in the bud stage; however the anthers of the four long stamens become extrorse after the ﬂowers open. In contrast to the majority of B. rapa and B. oleracea, its diploid progenitors, B. napus is a facultative outcrossing species with a high degree of self-pollination. When insect pollinators are abundant, a greater proportion of cross-pollination can occur, although through targeted fertilization-direction (e.g. using male-sterility systems for hybrid varieties, see below) it is possible to obtain up to 100% outcrossing. Brassica ﬂowers possess two functional nectaries at the base of the short stamens, along with two nonfunctional nectaries at the base of the pairs of long stamens. The synocarpous ovary, consisting mainly of two but sometimes up to four carpels, develops after fertilization into a bivalved silique with a longitudinal septum. 2.1.3 Economic Importance of Oilseed Rape Rapeseed/canola (mainly B. napus) has become a signiﬁcant agricultural product during the past 30 years and is now the world’s third leading source of both vegetable oil (after soybean and oil palm) and oil meal (after soybean and cotton). In 2005 world production of the seven major oilseeds was 371 million metric tonnes (MMT), with soybeans dominating (210 MMT) followed by rapeseed/canola with 46 MMT (Table 1; FAOSTAT data 2006). This annual production comes mainly from the European

Chapter 2 Oilseed Rape Table 1. World production of major oilseed crops in millions of metric tons (FAOSTAT data, 2006: http://faostat.fao.org/) Crop

2003

2004

2005

Soybeans Rapeseed Cottonseed Groundnuts in shell Sunﬂower seed Sesame seed Linseed Total

190.60 36.61 34.36 36.43

206.41 46.17 43.33 36.42

209.98 46.41 41.65 35.91

27.80 3.20 2.13 331.13

26.47 3.28 1.96 364.04

31.07 3.32 2.71 371.04

Union (EU-25: 14.98 MMT), China (13.05 MMT), Canada (8.44 MMT), India (6.40 MMT), and Australia (1.13 MMT). The world average yield of 2.71 metric tonnes per hectare in 2005 covered a wide range, with up to 4.14 t/ha in Western Europe, 1.81 in China, 1.63 in Canada and 1.04 in Australia to 0.90 t/ha in India (FAOSTAT data 2006). These differences are due to varieties (e.g. winter vs. spring type), climate and soil as well as the use of agricultural inputs (seed quality, fertilizers and agrochemicals) and different agronomic techniques.

2.1.4 Nutritional and Chemical Composition of Rapeseed Oil Oilseeds generally are crushed to yield oil (40 to 45% in the case of rapeseed) and residual meal that is rich in protein and used mainly for animal feed and, to a lesser extent, for human food. In contrast to soybean meal, rapeseed meal is not widely used for human consumption (Lühs and Friedt 1994a; Friedt et al. 2004). Seed oils, which are composed mainly of triacylglycerols (TAGs), are an important source of fatty acids for human nutrition and hydrocarbon chains for industrial products. Commodity rapeseed oil is now produced mainly from low erucic low glucosinolate varieties (canola), but there are also high-erucic cultivars and newer varieties with modiﬁed fatty acid composition furnishing specialty uses and niche markets, such as oleochemistry or frying purposes (Friedt and Lühs 1998). Over the whole range of oils and fats about 80% is used as human food, a further 6% goes into animal feed, and the balance (14%) provides the

57

basis of the oleochemical industry (Lühs and Friedt 1994b).

Rapeseed oil for human nutrition Today, the oil of modern rapeseed varieties, which is almost entirely lacking in nutritionally undesirable long-chain fatty acids, is highly appreciated due to its fatty acid proﬁle, which meets new health recommendations to reduce total dietary saturated fat intake, mainly derived from animal sources. The current interest in the nutritional and health effects of fatty acids, such as laurate (12:0), myristate (14:0), palmitate (16:0) and to a certain extent also stearate (18:0), relates to evidence associating high intake of these fatty acids in the diet with increased levels of blood cholesterol, arteriosclerosis and high coronary heart disease risk (Grundy and Denke 1990; Gurr 1992; Hu et al. 1997). With regard to commonly consumed vegetable oils and fats, low-erucic-acid-content rapeseed or canola oil contains the lowest level (ca. 6 to 7%) of saturated fatty acids. Furthermore, with 58 to 60% of its total fatty acids as oleic acid (18:1n-9), canola oil is an important source of this monounsaturated fatty acid (MUFA) whose nutritional signiﬁcance has increased during the last 20 years (Mattson and Grundy 1985; Dupont et al. 1989; Marsic et al. 1992; Valsta et al. 1992; McDonald 1995; Trautwein 1997). Canola oil received GRAS (Generally Recognized as Safe) status on the United States market in 1985, and its high nutritional quality is being further enhanced today by the admission to the market of low-saturated-fatty-acid-content oilseeds, often accompanied by low linolenic acid and/or high oleic acid content. Contrary to the nutritional necessity of certain n-6 and n-3 polyunsaturated fatty acids (PUFA), canola or soybean oils containing 8 to 10% α-linolenic acid (18:3n-3) are more liable to rapid oxidative damage than oils with little or no 18:3n-3. For example oxidation of linoleate (18:2n-6) and linolenate is ca. 10 and 25 times higher, respectively, than that of oleic acid (Frankel 1991; Kinsella 1991; Carlson 1995; Horrobin 1995; Lands 1997). Vegetable oils with a high 18:1n-9 and/or low 18:3n-3 content are marketed for bottled salad oil and salad dressings as well as for food applications requiring high cooking and frying temperature stability, including extended shelf-life products (such as snack foods). Increasing the 18:1n-9 content while reducing PUFA levels decreases the development of unpleasant ﬂavors and off-odors indicating oil ran-

58

R. Snowdon, W. Lühs, W. Friedt

cidity (Warner and Mounts 1993). Providing oxidative stability without extensive hydrogenation, high-oleiccontent and low-linolenic-content oils are being developed primarily to reduce trans fatty acids formed during the hydrogenation of vegetable oils. Nutritional research suggests that these stereo-isomers of cis-fatty acids may have negative nutritional effects. Trans fatty acids appear to increase serum low-density lipoprotein (LDL) cholesterol levels and may reduce serum high-density lipoprotein (HDL) cholesterol levels to a greater extent than saturated fatty acids (Mensink and Katan 1993). These concerns led to a recommendation to reduce their amount in the diet. Since trans fatty acids are unavoidable during oil processing, demand is increasing for MUFA oils, which do not require extensive hydrogenation (Hu et al. 1997). The nutritional advantages of canola as a cooking oil or margarine are rivalled only by olive oil, where rapeseed oil exhibits a higher content of essential fatty acids, especially α-linolenic acid, and is the only plant oil with an optimal 2:1 proportion of linoleic to α-linolenic acid. There is growing evidence that n-3 fatty acids play a role in the prevention and therapy of a number of chronic diseases, such as some forms of cancer, inﬂammatory diseases like rheumatoid arthritis and possibly abnormal cognitive functions such as depression. Most evidence, however, relates to the reduction of the risk for coronary heart disease (Trautwein 2001). The scientiﬁc rationale for ‘dietary fat strategies’ like the Lyon Diet Heart Study (cf. De Lorgeril et al. 2001) is to adopt a beneﬁcial so-called ‘Mediterranean’ diet. In this study patients had to drastically reduce the consumption of foods rich in saturated (essentially animal) fat. Among vegetable oils only olive oil, despite its lack of 18:3n-3, and rapeseed oil (canola), despite a relatively high amount of linoleic acid (18:2n-6), have the necessary fatty acid composition that reﬂects the beneﬁcial effects of the Mediterranean diet. The best combination between dietary saturated, polyunsaturated and monounsaturated fatty acids (essentially 18:1n-9) is obtained with olive and canola oil. Thus, rapeseed (canola) oil and canola-oil-based margarine are recommended as a major source of 18:3n-3 in the secondary prevention of coronary heart disease (cf. De Lorgeril et al. 2001).

and Foglia 2001). Historically, rapeseed oil was used mainly for industry and for domestic lighting. However, the industrial applications were limited until steam power came into use. Until the replacement of steam power by diesel engines, the use of rapeseed oil as a lubricant or additive to petroleum-based lubricants was developed because rapeseed oil clings to water-treated metal surfaces better than other lubricants. Besides being used industrially in many applications in which almost any vegetable oil can be used, rapeseed oil with high content of erucic acid (22:1n-9) in particular has considerable advantages in speciﬁc applications due to the properties of this longchain fatty acid. High-erucic-acid rapeseed (HEAR) oil contains ca. 50% erucic acid (00-quality rapeseed contains less than 2% erucic acid in the seed oil). The special properties of HEAR oil include high smoke and ﬂash points, stability at high temperatures, durability and the ability to remain ﬂuid at low temperatures. The principal end use of HEAR is to produce erucamide, which is used as a slip additive in polyethylene and polypropylene manufacture to reduce surface friction and prevent adhesion between ﬁlm surfaces. Erucamide is a relatively large complex molecule, making it difﬁcult and expensive to produce synthetically from petrochemicals. HEAR oil is also used in printing inks and lubricants and has a range of other applications (Lühs and Friedt 1994b; Piazza and Foglia 2001). Rapeseed oil is currently also incorporated into lubricants for two-stroke petrol engines, and methyl esters derived from rapeseed oil are widely used as a diesel substitute (biodiesel). Commercial biodiesel production occurs in several countries worldwide. In Europe low-erucic-acid-content rapeseed oil is the primary feedstock. The driving forces behind the use of biodiesel fuels are mainly environmental and energy concerns. In general, vegetable oil biodiesel fuels, being simple alkyl esters, have the following advantages over diesel fuel: as a neat fuel or in blends with diesel fuel, they produce less smoke and particulates, have higher octane values, produce lower carbon monoxide and hydrocarbon emissions, and are biodegradable and non-toxic. Biodiesel is less volatile than petroleum diesel, which is reﬂected by its higher ﬂash point, resulting in its safer handling and storage (Körbitz 1995). Conversely, biodiesel fuels present technical challenges of their own, such as higher pour, Non-food uses of rapeseed oil and protein cloud and cold ﬁlter plugging points and slow lowRapeseed oil has many potential uses other than as temperature ﬂow, possibly reduced oxidative stability an oil for nutrition (Lühs and Friedt 1994b; Piazza in storage, and incomplete combustion. The advan-

Chapter 2 Oilseed Rape

tages and disadvantages of fat- and oil-derived alkyl ester diesel fuels with respect to fuel properties, engine performance and emissions have been reviewed in detail by Graboski and McCormick (1998). The use of oilseed rape oils as industrial lubricants has considerable environmental beneﬁts because they are inherently biodegradable, of low ecotoxicity and toxicity towards humans, derived from renewable resources and have no net carbon dioxide contribution to the atmosphere. The cost of rapeseed oil falls in the range between mineral and synthetic oils. Furthermore, rapeseed oil has a high viscosity index and the oil structure endures mechanical stresses well. Its low friction coefﬁcient reduces heat development during use and the freezing temperature is also very low. Due to its polarity, the oil adheres well to metal surfaces and provides good protection against corrosion. Because of these characteristics it is not surprising that considerable efforts are being directed towards increased use of rapeseed oil and oil derivatives in designing environmental fuels and lubricants (Piazza and Foglia 2001). Protein-based plant products for non-food markets are another option for utilisation of rapeseed meal. Currently, soybean and wheat proteins represent the most important resource for protein production, with some emerging availability of pea proteins and cottonseed proteins and, very recently, rapeseed proteins. Vegetable proteins are already used in a number of products including plastics and adhesives, and the potential for expansion of such applications is signiﬁcant. The thermoplastic properties and good biodegradation properties predispose rapeseed oil for use in bioplastic production.

2.2 Breeding of Oilseed Rape 2.2.1 Breeding Methods The majority of oilseed rape cultivars are pure lines derived from breeding schemes designed for selffertilizing crops, i.e. pedigree selection or modiﬁcations thereof. Backcrossing has been successfully used to transfer simply inherited traits such as low erucic acid and glucosinolate content into adapted breeding material. B. napus is also one of the most amenable crop species to improvement through biotechnology.

59

For instance, it is possible to reproducibly obtain haploid and subsequently doubled-haploid (DH) plants through anther and/or microspore culture (e.g. Weber et al. 2005). The principal advantage of haploid techniques is the rapid ﬁxation of segregating genotypes, occurring in lower frequency, in which recessive genes coding for speciﬁc traits are combined in the homozygous condition. Thus, utilisation of microspore culture can allow a substantial acceleration of the breeding cycle. Due to the generally high response of B. napus genotypes, the use of DH production has become common practice in commercial breeding programs and has already resulted in numerous licensed cultivars. Besides haploid techniques, wide hybridizations using embryo rescue techniques or protoplast fusion can also be used to create novel genetic variation. However, once a useful property has been identiﬁed in a basic breeding stock, e.g. a mutant line or germplasm from a wild relative, it may take many years to accomplish the development of cultivars possessing this novel desirable trait. Marker-assisted selection (MAS) has had a signiﬁcant impact on the efﬁciency of plantbreeding routines such as backcrossing programs. In cases where conventional approaches have not been sufﬁcient, further improvements can be achieved by genetic engineering. Genetic engineering for herbicide tolerance and modiﬁed oil quality will be described in more detail later in this chapter.

2.2.2 Breeding for Improved Productivity Unlike soybeans, peanut and most other oilseeds, breeding progress in Brassica oil crops has always been connected with drastic improvements in seed quality, followed by relatively quick acceptance by growers and the processing industry. However, research on seed quality does not ignore the importance of high yields. According to the morphological and agronomical characteristics, the yield of rapeseed consists of the number of siliques per unit area, the number of seeds per silique and the 1000-seed weight (Diepenbrock 2000). Furthermore, improvement in productivity includes several agronomic parameters, such as early maturing, resistance to lodging and shattering and resistance to weeds, insects and, particularly, to the major diseases. A recent review of oilseed rape diseases was presented by Rimmer and Buchwaldt (1995). Their

60

R. Snowdon, W. Lühs, W. Friedt

conclusions are summarised here: Sclerotinia stem rot (Sclerotinia sclerotiorum) and stem canker (Leptosphaeria maculans, anamorph Phoma lingam), also known as blackleg disease, are the major diseases of oilseed rape in Canada. These are also commonly found in Europe, where there are a number of additional diseases of local importance. Verticillium wilt caused by Verticillium longisporum is a particular problem in Sweden and Germany, light leaf spot (Pyrenopeziza brassicae) in northern parts of Europe and clubroot (Plasmodiophora brassicae) in Scandinavian countries and the northern United Kingdom. In China, sclerotinia stem rot is a major disease, and viral disease also causes substantial yield losses. Alternaria (Alternaria brassicae) causes problems for B. rapa in northern parts of India and simultaneous infection of white rust (Albugo candida) and downy mildew (Perenospora parasitica) is common on both B. rapa and B. juncea in this region. Control of disease has involved a range of strategies. It has been possible to utilise natural variation to breed varieties, particularly in B. napus, with improved resistance to a number of diseases, including light leaf spot and blackleg disease. For other diseases, like alternaria, sclerotinia stem rot, white rust and verticillium wilt, availability of resistant lines is more limited. Cultural control methods, particularly rotation, are important means of controlling diseases such as clubroot and sclerotinia, and good agronomic practice will limit the number of susceptible crops in the rotation (Walker and Booth 2001). In some cases, transfer of resistant germplasm to B. napus from other Brassica species and related crucifers has been successfully applied, as described later in this chapter.

2.2.3 Improvement of Seed Components The gross composition of Brassica oil crop species varies widely depending on both genetic (i.e. species, variety, cultivar) and environmental (e.g. temperature, water and nutrient supply) factors. The oil content ranges from 36 to 50% (on a dry-matter basis), while the oil-free meal contains 33 to 48% protein (Canvin 1965; Appelqvist and Ohlson 1972; Arnholdt and Schuster 1981; Marquard and Schuster 1981; Salunkhe et al. 1992). The improvement of oil content is an important goal due to the primary economic value of the oil component and its relatively high heritability (Grami et al. 1977), and it has to date been

quite successful due to the ease and speed with which oil content can be measured by non-destructive NMR (nuclear magnetic resonance) techniques. Although oil and protein content are negatively correlated, improvements can be achieved through selecting for the sum of the two seed components (Grami et al. 1977; Arnholdt and Schuster 1981; Stefansson 1983). Low-glucosinolate rapeseed meal still presents several problems, however. Besides the presence of undesirable compounds like sinapic acid esters, phytic acid and phytates, phenolic acids and tannins, the comparatively high crude ﬁber content (approx. 15% of dry oil-free meal) is disadvantageous (Shahidi 1990; Thies 1991; Salunkhe et al. 1992). Due to the small size of the seeds the hull, accounting for about 10 to 20% of the seed weight, imparts most of the ﬁber content to the meal (Appelqvist and Ohlson 1972; Anjou et al. 1977). Currently, the most promising route to reducing ﬁber and hull content genetically is to breed cultivars with a yellow (light) seed coat, like pure yellow-seeded cultivars occurring in the sarson subspecies of B. rapa or in B. juncea. Since yellow seed coats are signiﬁcantly thinner than brown or black ones, the development of pure yellow-seeded B. napus cultivars with agronomically acceptable performance remains an important goal in quality breeding towards increased oil and protein content. The value and suitability of rapeseed oil for nutritional or industrial purposes are again determined by its fatty acid composition. The identiﬁcation of naturally occurring zero-erucic mutants in both B. napus and B. rapa was indeed the ﬁrst discovery opening the era of mutant-derived quality improvement in oil crops (Downey 1964; Stefansson and Hougen 1964; Röbbelen 1990). Canola-quality rapeseed low in saturated fatty acids and almost lacking nutritionally undesirable very long-chain fatty acids meets all the requirements of a prime edible oil (Ackman 1990; Downey and Bell 1990; Trautwein 1997). Despite the beneﬁcial nutritional properties of α-linolenic acid (18:3n-3), the oxidative stability of the oil can be improved by decreasing the linolenate content from average 10% to less than 3%, which results in enhanced shelf life (Rakow et al. 1987; Pleines and Friedt 1988, 1989; Downey and Bell 1990). In the last three decades improvements in the C18 fatty acid composition in rapeseed (B. napus) have been achieved by selecting altered linoleate/linolenate genotypes after chemical mutagenesis. The fatty acid proﬁles of these lines indicated that nearly all of the linolenic acid was being directed to linoleic acid and that the level of oleate

Chapter 2 Oilseed Rape

increased only insigniﬁcantly (Rakow 1973; Röbbelen and Nitsch 1975; Röbbelen and Thies 1980; Rakow et al. 1987; Röbbelen 1990). In 1988, the spring rapeseed cultivar Stellar, which produces oil containing less than 3% linolenate, was released for commercial production in Canada, although its agronomic performance was less than satisfactory (Scarth et al. 1988). Due to strong environmental and marked maternal inﬂuences, only low correlations have been found between the contents of polyenoic fatty acids determined in half-seeds and their progenies. The most important factor inﬂuencing the biogenesis of the unsaturated fatty acids is the prevailing temperature during seed development (Pleines and Friedt 1988, 1989). Recent reports describe mutants with reduced levels of polyunsaturated fatty acids (PUFAs) obtained by blocking oleic acid desaturation. The development of canola cultivars with reduced levels of PUFAs accompanied by higher oleate content would produce a dietary oil with additional markets (Marsic et al. 1992). For industrial applications a very high content of oleic acid (80 to 90%) is preferred because this is most suitable for certain consecutive chemical reactions (Lühs and Friedt 1994b). The low-linolenic-acid-content trait was created in rapeseed (B. napus) using chemical mutagenesis and subsequent selection for altered ratios of linoleic/linolenic acid facilitated by rapid screening methods like the thiobarbituric acid (TBA) test. Several low-linolenic-content Canadian spring rapeseed varieties have been released (e.g. Stellar, Apollo and Allons), and later this trait was also transferred into suitably adapted winter rapeseed lines (Kräling and Röbbelen 1991; Rücker and Röbbelen 1996; Scarth et al. 1997). To increase the oleic acid content to above 80% and, concomitantly, to lower the level of PUFAs, different breeding procedures have been utilised. These include mutagenesis applied to seeds (Auld et al. 1992; Rücker and Röbbelen 1995) or microsporederived embryos (Wong et al. 1991). Furthermore, relevant genes of Δ12 or Δ15 desaturases, responsible for the biosynthesis of 18:2n-6 and 18:3n-3, have been isolated, and subsequently rapeseed was genetically engineered, leading either to high-oleic-acid or highlinoleic-acid proﬁles (Hitz et al. 1995; Schefﬂer et al. 1997). Traditional varieties of Brassica oilcrop species typically contain appreciable levels of long-chain fatty acids like eicosenoic (20:1n-9), erucic (22:1n-9) and nervonic acid (24:1n-9). Studies on the inheritance of erucic acid in Brassica oil crops revealed that erucic

61

acid is under the control of the embryonic genotype. In monogenomic species like turnip rape (B. rapa), the erucic acid synthesis is governed by one gene locus. Consequently, in the amphidiploid B. napus two major gene loci participate. Multiple alleles occur at each locus, acting in a largely additive manner. Homozygous genotypes with various alleles produce levels of erucic acid ranging from less than 0.1 to about 60%. In the winter forms of B. napus, alleles are present which in a single dose give about 15 to 18% erucate (cf. Jönsson 1977). The accumulation of fatty acids of the preceding chain elongation steps, namely oleate and eicosenoate, is probably inﬂuenced by the same major gene loci (Zhou and Liu 1987; Chen and Beversdorf 1990). With regard to oleic acid operating simultaneously as the substrate for desaturation, it is assumed that in HEAR at least two minor gene loci are also involved that control the desaturation of oleic acid to form linoleate and linolenate, respectively (Jönsson 1977; Chen and Beversdorf 1990). In resynthesised rapeseed produced with the aim of genetic modiﬁcation of oil quality (Chen and Heneen 1989; Friedt and Lühs 1998), the effect of alleles responsible for a speciﬁc erucic acid content is probably inﬂuenced additionally by the level of ploidy and the genetic background. Due to the procedure of resynthesis commonly used, i.e. hybridization of the monogenomic parents followed by artiﬁcial diploidisation, synthetic B. napus forms are homozygous lines, so that a change in their fatty acid composition, as compared to that of their parents, could also be explained by interactions between the non-homologous genes of the A and C genomes. These interactions have to be attributed to epistasis (non-allelic gene actions) rather than dominance (Chen and Heneen 1989; Chopra and Prakash 1991). Lühs and Friedt (1995) suggested that it might be possible to achieve recombinants with new allele combinations via introgression of resynthesised germplasm into conventional high erucic acid breeding material. However, the possibility of increasing erucic acid synthesis by accumulation and combination of desirable alleles is obviously limited due to the restriction of the subsequent TAG synthesis. Cruciferous seed oils seem to contain erucic acid almost exclusively in the sn-1 and sn-3 positions of the glycerol backbone, so that mutants in rapeseed with a desirable erucic content beyond the theoretical limit (>66%) are unlikely, and a maximum of ca. 60 to 63% erucic acid is currently achievable (Lühs and Friedt 1995). With regard to well-known industrial and non-food uses, trierucoylglycerol (trierucin)

62

R. Snowdon, W. Lühs, W. Friedt

would make the processing of comparatively pure erucic acid much easier and more commercially attractive. However, in the near future it might be possible to introduce the property for producing trierucinenriched seed oil into B. napus by genetic engineering (Friedt and Lühs 1998; Han et al. 2001).

2.2.4 Hybrid Breeding and Cytoplasmic Male Sterility Systems Although for many years the emphasis in oilseed rape breeding was strongly focussed on open-pollinating varieties, up to 30% heterosis for seed yield has been reported for B. napus (e.g. Schuster 1969; Grant and Beversdorf 1985; Lefort-Buson et al. 1987; Brandle and McVetty 1989), and for both winter rapeseed and spring canola hybrid varieties have rapidly gained in importance over the past decade as effective systems for controlled pollination were developed. The ﬁrst restored winter rapeseed hybrids were released in 1995. In current European winter rapeseed material yield improvements of up to 15% have been reported for F1 hybrids compared to non-hybrid openpollinating varieties. This has led to a major increase in production of hybrid rapeseed in the leading producing countries. For example, although only 14 of the 53 approved German 00 winter rapeseed cultivars listed by the German Plant Variety Ofﬁce in 2004 were hybrids (Bundessortenamt 2004), more than 50% of the 1.3 million hectares of German winter rape in 2003/2004 were planted with hybrid varieties. Furthermore, in 2003/2004 the hybrid cultivar Talent replaced the open-pollinating Express as the most widely cultivated winter oilseed rape variety in Germany, the ﬁrst time a hybrid cultivar has achieved the top position. One of the most important reasons for the upsurge in interest in hybrid varieties is that they tend to have higher yield stability and better adaptation to lowinput cropping systems than conventional cultivars (Budewig and Léon 2003; Friedt et al. 2003). Numerous cytoplasmic male sterility (CMS) systems have been discovered and are used in crop brassicas. Because CMS arises from speciﬁc interactions between the mitochondrial and nuclear genomes, the combination of cytoplasm and nucleus from different species often results in complete or partial male sterility and in many cases functional mutations of ﬂoral structure. Two spontaneous male sterile cytoplasms, nap and pol, are found in B. napus. The nap system was

the ﬁrst to be identiﬁed, originating from intraspeciﬁc crosses using Bronowski (Thompson 1972) or Hokuriku 23 (Shiga and Baba 1973) as the male parent. As with many other forms of CMS, the mtDNA regions implicated in specifying the nap and pol forms of male sterility contain novel open reading frames (ORFs). Unlike other CMS-associated ORFs, however, a high level of sequence similarity extends over the entire length of the nap and pol CMS-associated ORFs (Brown 1999). In other plant species where more than one form of CMS is found, the nuclear genes that restore fertility to various male sterile cytoplasms represent distinct genes that map to different nuclear loci. The restorers for the nap and pol cytoplasms (Rfn and Rfp, respectively), however, were found to represent different alleles or haplotypes of a single nuclear locus. Both alleles specify factors that inﬂuence mtRNA processing events, but the speciﬁc processing events conditioned by the two alleles are different, suggesting that the factors encoded by these genes recognize distinct RNA structural features. Unlike other nuclear genes that affect mitochondrial gene expression, Rfn is capable of modifying the expression of multiple mtDNA regions, some of which are not associated with CMS (Brown 1999). Most other B. napus CMS systems result from interspeciﬁc or intergeneric crosses, often using known sterility-inducing systems from other species. The best example for this is the widely used INRAOgura CMS originating from Raphanus sativus (Ogura 1968), which was transferred to oilseed rape by French scientists some 30 years ago (Bannerot et al. 1974). Although this system was described by Tokumasu (1951) as a genic male sterility, in B. napus it is expressed as CMS. The Ogura CMS in radish (Raphanus sativus) is caused by an aberrant mitochondrial gene, orf138, that prevents the production of functional pollen without affecting female fertility. Rfo, a nuclear gene from radish that restores male fertility, alters the expression of orf138 at the posttranscriptional level. In recent years a large effort has been made in the genetic characterization of the Ogura CMS system for generation of effective selection markers for fertility restoration and cloning of the genes involved. Using bulked segregant analysis and comparative mapping, Delourme et al. (1998) identiﬁed molecular markers linked to the Rfo restorer gene. These markers were then used to localise the radish introgression on the B. napus genetic map constructed from the cross Darmor-bzh × Yudal. From the comparison

Chapter 2 Oilseed Rape

of the linkage group (LG) containing the introgression with the corresponding LG constructed from an F2 progeny segregating for the radish introgression, it was concluded that the introgression was derived from a homoeologous recombination, that it was not distal and that it had replaced a B. napus region of around 50 cM. A QTL involved in aliphatic seed glucosinolate content was located on the same LG at a position corresponding to one end of the introgression. Bulked segregant analysis was also used by Hansen et al. (1997) to identify random ampliﬁed polymorphic DNA (RAPD) markers linked to the Ogura CMS fertility restorer gene in oilseed rape. After screening for polymorphisms using 960 primers, 14 randomly ampliﬁed polymorphic DNA (RAPD) markers were mapped in a population of 242 F2 individuals to a 25cM region including the restorer locus. The map was used to select 11 markers that were investigated for polymorphisms between the restorer donor line and 46 recipient lines. A set of four RAPD markers were used in MAS of plants homozygous for the restorer allele. One marker in attraction phase with the restorer allele and three in attraction to the non-restorer allele were informative in all 46 combinations and allowed identiﬁcation of 906 homozygous restored plants among 4,605 BC1 F2 plants analysed. Bellaoui et al. (1999) analysed the effect of the restorer gene on the expression of the ORF138 protein associated with Ogura CMS. It was shown that the presence of the Rfo gene in the genome of the restored plants decreased the amount of ORF138 protein in ﬂoral buds and that this effect was most dramatic in anthers at the stage of development when the sterile phenotype is normally expressed. However, the amount of orf138 transcripts was not affected by the Rfo gene in the same organs at the same stages. Total polysome analyses of buds and anthers showed that the orf138 transcripts are translated with the same efﬁciency in sterile and restored plants. It was concluded that the Rfo gene product acts on the posttranslational stability of the ORF138 protein, leading to a decrease in the accumulation of the protein and a restoration of fertility. Giancola et al. (2003) used the region of the Arabidopsis genome syntenic to the Rfo gene to characterize the radish introgression in restored rapeseed. Ampliﬁed consensus genetic markers (ACGM) in restored rapeseed plants were employed, alongside the construction of a precise genetic map around the Rfo gene in a segregating radish population. The use of ACGMs made it possible to detect radish orthologues of Ara-

63

bidopsis genes in the restored rapeseed genome. The positions of markers linked to the Rfo gene were used to produce a schematic diagram of the radish introgression in rapeseed. Markers which could be mapped both on radish and restored rapeseed indicated that at least 50 cM of the radish genome was integrated in restored rapeseed. Using markers closely linked to the Rfo gene in rapeseed and radish, a contig was identiﬁed spanning six bacterial artiﬁcial chromosome (BAC) clones carrying the orthologous Rfo gene on A. thaliana chromosome 1. Desloire et al. (2003) also applied a combination of positional cloning and microsynteny analysis between A. thaliana and radish to delimit the Rfo locus, in this case to a 15-kb DNA segment. Analysis of this segment conﬁrmed that the Rfo gene is a member of the pentatricopeptide repeat (PPR) family. Ultimately, Brown et al. (2003) used a map-based cloning approach relying on this synteny between the corresponding genome regions of radish and Arabidopsis to clone Rfo. A radish gene encoding a 687-amino-acid protein with a predicted mitochondrial targeting presequence was found to confer male fertility upon transformation into Ogura CMS B. napus. This gene codes for a PPR-containing protein with multiple PPR domains. Two similar genes that did not appear to function as Rfo were found to ﬂank this gene. Furthermore, comparison of the Rfo region with the syntenic Arabidopsis region indicated that no PPR gene was present at the equivalent Rfo locus in Arabidopsis, although a smaller and related PPR gene was found about 40 kb from this site. In Arabidopsis, the PPR gene family contains more than 450 members of unknown function, most of which are predicted to be targeted to mitochondria and chloroplasts and are thought to have roles in organellar gene expression. Bett and Lydiate (2004) used three R. sativus populations (BC1 , F2 and R8 ) segregating for the restoration of Ogura CMS to map restorer loci in the donor species. Three restorer loci, Rf1, Rf2 and Rf3, were localised, whereby each exhibited dominant restoring alleles and together the loci were mutually epistatic. The complex genetic control of the restoration of Ogura CMS in Raphanus was compared with the more simple genetic control of this trait previously described in B. napus. Markers linked to each of the three restorer loci will now allow the routine generation and veriﬁcation of deﬁned restorer and maintainer lines for various combinations of deﬁned restorer loci.

64

R. Snowdon, W. Lühs, W. Friedt

The Japanese radish cv. Kosena has also been used to transfer CMS to B. napus (Sakai and Imamura 1990). Iwabuchi et al. (1999) showed that the CMS-associated gene orf125 from Kosena radish has a sequence homologous to that of the Ogura CMSassociated gene orf138. Only two amino acid substitutions and a 39-bp deletion in the orf138 coding region distinguish the two gene sequences. In Kosena radish, orf125 was found to be linked to orfB, whereas the orf125 locus differed in a B. napus CMS cybrid derived from protoplast fusion between Kosena radish and B. napus. A novel mtDNA sequence was identiﬁed in the 3’ ﬂanking region of orf125 in the B. napus Kosena CMS cybrid. The orf125 was expressed both in radish and the B. napus Kosena CMS cybrid, and its accumulation was found to be strongly associated with the CMS phenotype in B. napus, whereas fertility restoration was accompanied by a decrease in the amount of ORF125 protein. Imai et al. (2003) used a positional cloning strategy to isolate the fertility restoration gene Rfk1 from radish. RAPD-sequence tagged site (STS) markers tightly linked to the gene in radish were isolated, and a RAPD map surrounding the Rfk1 locus was constructed. Recombinants for bulk segregant analysis were identiﬁed among 948 F2 plants with adjacent RAPD-STS markers. This enabled isolation of tightly linked ampliﬁed fragment length polymorphism (AFLP) markers surrounding the gene of interest. Ten linked AFLP markers were obtained and used to construct a high-resolution map of the genome region involved in fertility restoration. The closest AFLP-STS markers ﬂanking Rfk1 were 0.1 cM and 0.2 cM away. Screening of lambda and cosmid libraries with the four adjacent AFLP markers enabled the identiﬁcation of genomic clones that were aligned by examination of end sequences and restriction fragment length polymorphism (RFLP) patterns for each clone and by hybridization to the DNA isolated from recombinants. This led to construction of a 198-kb contig, comprised of 20 lambda and two cosmid clones, that spanned the Rfk1 gene. By analysis of the breakpoints in recombinants with the rfk1/rfk1 or Rfk1/- genotype, the Rfk1 locus could be assigned to a 43-kb region comprising four lambda clones and one cosmid clone. This exact localisation in the radish genome made it possible to identify the gene by sequence analysis, giving rise to the possibility of genetically transforming cytoplasmic male-sterile B. napus plants for fertility restoration.

2.2.5 Use of Male Sterility Systems in Oilseed Rape Breeding Although numerous CMS systems are available from different sources, their use in oilseed rape breeding is often inhibited by instability, the absence of suitable restorer or maintainer lines, or negative effects of the cytoplasm used to induce the male sterility. Environmental instability of the expression of nap male sterility means this system is unsuitable for hybrid production, and the Polima (pol) system was only made workable by screening of huge numbers of lines in different environments (Bartkowiak-Broda et al. 1991) in order to identify stable maintainer genotypes. The monogenically inherited restorer genes for B. napus Polima CMS can be readily introduced into elite lines, and pol is therefore now effectively used to produce registered F1 hybrid spring canola varieties in numerous countries. Male-sterile-inducing cytoplasm can also have negative effects on ﬂower morphology, nectar production or yield, and sometimes chlorophyll deﬁciencies also need to be overcome. In some cases, suitable B. napus restorer lines have been produced for B. tournefortii CMS (Banga et al. 1995; Stiewe et al. 1995a,b). Restored F1 hybrids based on the Ogura CMS system are under increasing production in France and other European countries, and hybrid cultivars based on the commercial Male-Sterility Lembke (MSL) system are currently among the best-selling winter oilseed rape varieties in Germany.

2.2.6 Genetic Diversity for Heterosis and Hybrid Breeding The relative lack of genetic variability within oilseed rape breeding material can to a large extent be attributed to the limited geographical area, the Mediterranean region, where the natural habitats of the progenitor species overlap. For winter oilseed rape only three distinct local landraces are known. These evolved in different European climate zones and hence display variation in vegetative growth and winter hardiness. The ﬁrst released cultivar, Lembkes, selected in Germany from a Mecklenburg landrace in the early 20th century, was extensively exploited in French, Swedish, German and Polish breeding programs. The genetic base of oilseed rape is today even narrower because the introduced double-low (00) quality again

Chapter 2 Oilseed Rape

originates from single sources, the spring cultivars Liho and Bronowski. Consequently, there is a need to introduce new genetic variation to breeding material since most cultivars share a more or less common parentage (Thompson 1983; Downey and Rakow 1987). Compared to the narrow gene pool of present-day 00-quality oilseed-rape-breeding material, which severely limits the formation of heterotic pools, erucic-acid- and glucosinolate-containing plant material represents a comparatively genetically divergent source for the development of heterotic rapeseed forms (Röbbelen 1975; Thompson 1983; Schuster 1987). Because of the emphasis on oil quality, such material has found only limited use in practical rapeseed breeding in the past few decades. However, strong heterotic effects are observed in experimental crosses between material of distant geographical and genetic origin (Lefort-Buson et al. 1987; Brandle and McVetty 1990), and efforts are increasing to develop new cytoplasmic-genetic male-sterile and restorer lines as the most promising system for the production of new hybrid cultivars. Following appropriate quality conversion, inbred lines and DH lines with a high genetic distance to existing 00-quality varieties have the potential to become an important resource for the development of high-performance pools with improved combining ability compared to existing 00-rapeseed material.

2.2.7 Expanding the Genetic Variability in Oilseed Rape by Interspecific Hybridization One strategy to broaden the genetic basis of oilseedrape-breeding material is the production of resynthesised rapeseed by crossing the original ancestors, B. oleracea and B. rapa. This has the potential not only to increase genetic variability with a view to hybrid breeding but also to broaden the genetic base with respect to pest and disease resistances, which in some cases is severely eroded in B. napus. For such interspeciﬁc hybridizations a variety of biotechnological tools, for example embryo rescue techniques or protoplast fusion, are used to circumvent existing incompatibility barriers. In some cases resynthesised rape forms have resulted in successful release of cultivars carrying novel resistance genes from the diploid species. For example, Diederichsen and Sacristan (1996) successfully used protoplast fusion to transfer resistance to

65

clubroot (Plasmodiophora brassicae) from B. oleracea to B. napus. Through advanced backcrossing a racespeciﬁc resistance was subsequently transferred from resynthesised rapeseed progeny to elite winter oilseed rape material, and the winter oilseed rape varieties Mendel and Tosca derived from this material were released in the early 2000s to speciﬁcally combat this disease in affected areas of Britain and Germany. In another example, Mithen and Magrath (1992) generated synthetic lines of B. napus carrying resistance to blackleg disease (Leptosphaeria maculans, anamorph: Phoma lingam) derived from B. rapa via embryo culture. The resistance was then integrated successfully into spring canola, resulting in the release of the cv. Surpass in the late 1990s and subsequent efforts to introgress this resistance into winter oilseed rape material. Although this Phoma resistance from B. rapa has in the meantime been overcome by virulent L. maculans isolates in Australia, and the clubroot resistance from B. oleracea is also race-speciﬁc and hence not durable without careful agronomic management, these examples nevertheless demonstrate the potential utility of B. oleracea and B. rapa for the identiﬁcation and combination of novel resistance genes to important oilseed rape pathogens. This strategy has the potential to prove particularly valuable for development of resistance to Verticillium wilt. This disease, caused by the host-adapted pathogen V. longisporum, causes grave yield losses in affected areas of Sweden, Denmark, Great Britain and the north of Germany. The fungus forms microsclerotia, which can persist in the soil for more than a decade, and because accredited fungicides are not available, the only current alternative for effective control of the disease in short crop rotations is the breeding of resistant cultivars. Very little resistance is available in either winter or spring rapeseed, however, necessitating a search for resistance sources in related species. Transfer of resistance from B. oleracea to B. napus was reported by Happstadius et al. (2003), and in our own work we have identiﬁed further resistance donors in ongoing screening of diverse turnip rape and cabbage accessions. In order to develop durable polygenic resistance to Verticillium wilt, we aim to combine resistances from B. oleracea and B. rapa in novel resynthesised B. napus genotypes by interspeciﬁc hybridization, assisted by embryo rescue (ovule culture). After characterising the resistance by genetic mapping it should be possible using marker-assisted backcrossing to simultaneously transfer A- and Cgenome resistance genes into elite rapeseed lines as

66

R. Snowdon, W. Lühs, W. Friedt

a starting point for the development of new varieties with combined resistance from the diploid progenitors. Resynthesised rapeseed also represents an interesting source of genetic variation for quality improvement in oilseed rape. For example, we have made crosses between B. rapa ssp. trilocularis (Yellow Sarson) and several selected cauliﬂowers (B. oleracea convar. botrytis var. botrytis) to create new oilseed rape germplasm with a high erucic acid content. The offspring displayed desirable variation in the content of major fatty acids, raising the possibility of producing breeding lines with an erucic acid content of 60% or even more. Furthermore, the high genetic distance of these lines from conventional cultivars (see Seyis et al. 2003a) also makes them potential candidates for improving heterosis. In ﬁeld trials at two locations experimental hybrids based on these resynthesised lines gave a higher yield potential compared to check cultivars (Seyis et al. 2003b). Interspeciﬁc crosses are also an important source of seed-color variants for breeding of light-seeded rape. Brown or yellow seeds are of particular interest for breeding of oilseed rape because of their association with a thinner seed coat that results in reduced dietary ﬁber content. This considerably improves the feed quality of rapeseed meal after oil extraction (Shirzagedan and Röbbelen 1985; Slominski et al. 1994, 1999). Light seed color and low ﬁber content are considered to coincide because the biochemical pathways leading to lignin (ﬁber) and pigment synthesis have common precursors, such as p-cumarate (Theander et al. 1977; Whetten et al. 1998). Furthermore, the reduction in testa thickness in yellowseeded oilseed rape has also been found to be associated with increased seed oil and/or protein content per dry weight (Xiao and Liu 1982, Piotrowska et al. 2003). A variety of different yellow-seeded rapeseed material has been generated by interspeciﬁc crosses between yellow-seeded B. rapa and brown-seeded B. oleracea (Schwetka 1981) or B. alboglabra (Chen et al. 1988; Rahman 2001, 2003). The yellow-seed trait has also been introduced to B. napus from B. chinensis (Liu 1983), B. juncea (Rashid et al. 1994) and B. carinata (Rashid et al. 1994; Meng et al. 1998; Rahman et al. 2001, 2003). We have studied the genetics of yellow seed color and raw ﬁber content in crosses involving yellow-seeded lines from two genetically divergent B. rapa sources, and found that in each case the trait was controlled by a major dominant gene along with either one or two epistatic loci (Badani et al., in press). This

ﬁnding, corroborated by QTL localisation and segregation analyses, supports results published by Somers et al. (2001) and Liu et al. (2005) for two further independent yellow-seed sources. Viewed together, these studies suggest that the same genetic loci appear to control seed color in genetically diverse B. napus material, even though more than 20 different genes are known in A. thaliana that give rise to an analogous transparent testa phenotype in the corresponding mutants. Other Brassica species and even less closely related genera are also important as potential sources of disease resistance for oilseed rape breeding. A prime example for this is the use of interspeciﬁc and intergeneric hybrids as a source for new resistance against blackleg disease. The genetic basis of blackleg resistance in B. napus in European cultivars originates for the most part from the French cultivar Jet Neuf, which possesses a partial, polygenically controlled adult plant resistance not expressed at the seedling stage (Cargeeg and Thurling 1980). In contrast, all Brassica species containing the B genome exhibit an absolute and stable resistance to most of the aggressive pathogen isolates studied to date. B-genome resistance is mono- or oligogenically controlled (Rimmer and van den Berg 1992; Dixelius 1999) and efﬁcient from the seedling stage onwards. Thus, B-genome donors like B. nigra (L.) Koch (BB, 2n = 16) and B. juncea (L.) Czern (BBCC, 2n = 36) have been extensively used as a genetic pool in an attempt to develop resistant oilseed rape (e.g. Roy 1978; Sacristán and Gerdemann 1986; Sjödin and Glimelius 1989; Chèvre et al. 1996; Struss et al. 1996; Plieske et al. 1998; Dixelius 1999). On the other hand, some aggressive isolates of the pathogen have been shown to overcome the resistance of B. juncea (Purwantara et al. 1998; Winter et al. 1999). Leptosphaeria maculans exhibits a broad variation in virulence, giving it the potential to adapt quickly to a given resistance (Kuswinanti et al. 1999). Generation of durable resistance therefore necessitates the application of a broad spectrum of resistance sources in oilseed rape breeding. For this reason, interspeciﬁc and intergeneric transfer of blackleg resistance from wild crucifers is an interesting alternative, and in recent years progress has been made in introgressing resistance into oilseed rape from different sources, including Sinapis arvensis (Snowdon et al. 2000; Winter et al. 2003) and Coincya monensis (Winter et al. 2003). Other examples of intergeneric hybridization for resistance gene transfer into B. napus include resistance to beet cyst ne-

Chapter 2 Oilseed Rape

matodes on Raphanus sativus addition chromosomes (Thierfelder and Friedt 1995; Voss et al. 2000; Peterka et al. 2004), whereas Klewer et al. (2003) used sexual and somatic hybridization in an attempt to transfer resistance to Alternaria blackspot into B. napus from B. elongata, Sinapis alba, Diplotaxis tenuifolia and D. erucoides. In such broad intergeneric hybrids ovary culture techniques are absolutely necessary to overcome incompatibility barriers; however, a successful transfer of the desired trait is often achieved. The prerequisite for this is that intergenomic chromosome recombination takes place in early backcross generations before the loss of non-homologous donor chromosomes.

2.3 Cytogenetic Studies of Brassica Crops and Interspecific Hybrids 2.3.1 History of Cytogenetic Studies in Brassica After Morinaga and U discovered through cytogenetic studies in the early 1930s that amphidiploid Brassica species originate from diploid progenitors and contain the complete chromosome sets of their parental species, chromosome studies came to play a leading role in genome analysis among the Brassicaceae. The age of classical cytogenetics has, however, been largely superseded by the implementation of DNA techniques during the past few decades, and the difﬁculties associated with Brassica chromosomes as a cytological object – in particular their small size and lack of distinctive cytological landmarks – have made Brassica cytogenetics a rare art amongst the proliferating molecular marker technologies. For many years little more could be achieved than simple chromosome counts or meiotic studies of the offspring from interspeciﬁc or intergeneric crosses, giving insight into genome homologies amongst the various Brassica relatives. In recent years, however, advances in the molecular cytogenetic technique of ﬂuorescence in situ hybridization (FISH), which enables the direct chromosomal localisation of labelled DNA probes, have enabled a resurgence of cytogenetic analyses in plant genome research and molecular breeding. The ﬁeld of Brassica cytogenetics dates back to the early decades of the 20th century, when a number of

67

predominantly Asian scientists began with detailed investigations of chromosome numbers and chromosome pairing in some of the important crucifer species. The ﬁrst major achievement was the publication of the chromosome number for B. rapa by Takamine (1916), followed 8 years later by the synthesis and analysis of Raphanobrassica by Karpechenko (1927) and the experiments of Morinaga, who began working intensively on interspeciﬁc hybrids during the 1920s and investigating chromosome pairing and homology in detail. It was during this period that others began to publish surveys of chromosome counts for large numbers of crucifer species – in particular Manton (1932), who was one of the pioneers in this area – and to investigate the somatic chromosomes in more detail. However it was the work of Morinaga (1934) and U (1935) that gave rise to another generation of researchers who began to look more deeply into genome homology in the Brassicacaeae. The development of ovary culture and embryo rescue techniques in the 1950s enabled enormous progress in the study of genome homologies based on chromosome pairing analyses. Additionally, technological advances in optical equipment and microscopy brought great improvement in cytological techniques in general. Based on these techniques Röbbelen (1960) was the ﬁrst to publish detailed cytological descriptions of Brassica somatic chromosome structure. From a cytogenetics perspective the period between the 1960s and the end of the 1980s was dominated by an intensive effort to collect and classify botanical representatives of the crucifer tribe and to study the evolutionary and genomic relationships among this array of species. One of the major personalities in this movement was Harberd (1972), whose study of chromosome pairing among a huge number of species eventually led to the classiﬁcation of cytodemes describing homologous genomes. Based largely upon this work we know now that there is extensive genome homology or homoeology throughout the entire Brassica coenospecies, and from a plantbreeding perspective in particular it has become well known that we consequently have the possibility to broaden gene pools for the introgression of novel genes or alleles, well beyond the species boundary. Related Brassica species and their relatives among the Brassicaceae represent a huge pool of potential gene donors for agronomically relevant traits in oilseed rape. Table 2presents an overview of sexual and somatic interspeciﬁc and intergeneric crosses between B. napus and other crucifers, with examples of rele-

68

R. Snowdon, W. Lühs, W. Friedt

Table 2. Examples of sexual (x) and somatic (+) Brassica napus interspeciﬁc and intergeneric hybrids with genome donors from family Brassicaceae Cross

Genome donor

Character transferred or studied

Reference

B. napus x

Brassica carinata Brassica chinensis Brassica cossoniana Brassica elongata

Yellow seed color Yellow seed color Genome homology Blackleg resistance

Brassica juncea Brassica juncea Brassica juncea Brassica juncea Brassica gravinae Brassica nigra Brassica rapa Coincya monensis Diplotaxis erucoides Eruca sativa Hirschfeldia incarna Orychophragmus violaceus Raphanus raphinistrum Raphanus sativus Raphanus sativus

Yellow seed color Blackleg resistance Earliness Shattering resistance Alloplasmy Blackleg resistance Blackleg resistance Blackleg resistance Genome homology Altered oil quality Potential transgene transfer Pigmentation Potential transgene transfer Ogura CMS Nematode resistance

Sinapis alba Sinapis alba Sinapis arvensis Sinapis arvensis Sinapis arvensis

Nematode resistance Alternaria resistance Potential transgene transfer Genome homology Blackleg resistance

Sinapis pubescens Arabidopsis thaliana Arabidopsis thaliana Barbarea vulgaris Crambe abyssinica Lesquerella fendleri Raphanus sativus Sinapis alba

Resistance Acetolactate synthase Blackleg resistance Cold tolerance High erucic acid Lesquerolic acid Kosena CMS Alternaria resistance

Thlaspi perfoliatum

Nervonic acid

Rashid et al. (1994) Liu (1983) Harberd and McArthur (1980) Plümper and Sacristán (1995), Klewer et al. (2003) Rashid et al. (1994) Roy (1984), Chèvre et al. (1997) Rao et al. (1993) Prakash and Chopra (1988) Nanda-Kumar et al. (1989) Chèvre et al. (1991b, 1996) Chèvre et al.(2003) Winter et al. (2003) Harberd and McArthur (1980) Bijral and Sharma (1996) Kerlan et al. (1993) Li and Luo (1993) Kerlan et al. (1993) Takeshita et al. (1980) Lelivelt and Krens (1992), Peterka et al. (2004) Lelivelt et al. (1993) Klewer et al. (2003) Kerlan et al. (1993) Mizushima (1950) Snowdon et al. (2000), Winter et al. (2003) Inomata (1994) Bauer-Weston et al. (1993) Forsberg et al. (1994) Fahleson et al. (1994b) Wang et al. (2004) Skarzhinskaya et al. (1996) Sakai and Imamura (1990) Plümper and Sacristán (1995), Klewer et al. (2003) Fahleson et al.(1994a)

B. napus +

vant traits that in numerous cases could be successfully introgressed from the donor into the oilseed rape genome. FISH techniques offer the potential not only for more reliable chromosome identiﬁcation in Brassica, but also in terms of the information they might be able to offer regarding the integration of genetic and physical maps, for ordering molecular markers and measuring physical genome distances, and

for structural and functional chromosome analysis. FISH methods for the accurate localisation of repetitive DNA sequences at chromosomal subarm level, particularly ribosomal DNA sequences, have enabled the elucidation of karyotypes for B. napus and its progenitor species and the identiﬁcation of A- and C-genome chromosomes in the amphidiploid species (Armstrong et al. 1998; Fukui et al. 1998; Snowdon et al. 2002; our Fig. 1). FISH hybridization of BAC

Chapter 2 Oilseed Rape

69

Fig. 1. Karyotypes based on ﬂuorescence in situ hybridization patterns with 5S (green) and 25S (red) rDNA probes and DAPI staining (blue), for Brassica rapa L., B. oleracea L. and their amphidiploid B. napus L. Closed arrowheads: co-localisation of 5S and 25S loci; open arrowheads: weak 5S locus and weak 25S locus on B. napus chromosomes C5 and C7, respectively. Red asterisks: position of large 25S rDNA locus located on satellite of B. napus chromosome A2, which in this spread was lost during chromosome preparation. The B. napus karyotype is divided into two sets of chromosomes with differing chromatin condensation patterns resembling, respectively, those of B. rapa (a) and B. oleracea (c). Each B. napus chromosome is aligned and numbered in accordance with its putative homologue in the B. rapa or B. oleracea genome. [Reproduced with permission from Snowdon et al. (2002)]

clones to B. oleracea (Howell et al. 2002) and B. rapa (Jackson et al. 2000) chromosomes represents a ﬁrst step towards integration of physical and genetic maps with the karyograms of the diploid species and their amphidiploid hybrid B. napus. In the latter study, FISH techniques were adapted for comparative physical mapping between A. thaliana and B. rapa. Six BAC clones representing a 431-kb contiguous region of chromosome 2 of A. thaliana were mapped on both chromosomes and DNA ﬁbers of B. rapa. Although the DNA fragment investigated is single-copy in A. thaliana, it hybridized on up to six B. rapa chromosomes, indicating multiple duplications in the B. rapa genome. The ﬁber-FISH signals in B. rapa were similar to those in A. thaliana for each BAC, suggesting that the genomic region investigated is duplicated but not expanded in the B. rapa genome. Such comparative ﬁber-FISH mapping results support other evidence that chromosomal duplications, rather than regional expansion due to accumulation of repetitive

sequences in the intergenic regions, played the major role in the evolution of the diploid Brassica genomes. The use of total genomic DNA as a FISH probe (genomic in situ hybridization, or GISH; HeslopHarrison and Schwarzacher 1996) is especially useful for diagnostic studies of the amount and integration of foreign chromatin in interspeciﬁc and intergeneric plant hybrids. Hybrids between high-yielding rapeseed cultivars and related species are relatively easily produced and have often been used to develop new lines containing introgressed traits like novel pest or disease resistances. Great advances in interspeciﬁc hybridization have resulted from the application of in vitro techniques for the generation of viable offspring from interspeciﬁc and intergeneric hybrids (Lühs et al. 2002). Identiﬁcation of alien DNA in wide crosses has been achieved by quantiﬁcation of chromosome content by ﬂow cytometry (Sabharwal and Dolezel 1993) and by tracing chromosome and DNA transfer using molecular markers. Visu-

70

R. Snowdon, W. Lühs, W. Friedt

alisation of alien chromatin in interspeciﬁc hybrids using in situ hybridization techniques, on the other hand, potentially enables pinpointing of introgressions to speciﬁc chromosomes (Heslop-Harrison and Schwarzacher 1996; Snowdon et al. 1997). As an example, Skarzhinskaya et al. (1998) studied the chromosome complements of somatic hybrids produced between B. napus and Lesquerella fendleri with novel fatty acid compositions by analysing their karyotypes and performing GISH. Symmetric fusions of protoplasts fused with no pretreatment resulted in hybrids containing L. fendleri chromosomes in numbers varying from two chromosomes to two full chromosome complements. Asymmetric hybrids were also generated by irradiating L. fendleri protoplasts before fusion. In this case plants with 38 to more than 76 chromosomes were obtained. In the hybrids with 38 chromosomes, the presence of L. fendleri chromosomes was not detected by GISH analysis, even though L. fendleri DNA was revealed by Southern blotting. Intra- and intergenomic recombinations were observed in hybrids from both symmetric and asymmetric fusions, but particularly in plants resulting from asymmetric fusions. Intergeneric sexual bybrids between B. napus and Sinapis arvensis containing novel genes for resistance against blackleg disease on chromosome additions and introgressions were analysed via GISH by Snowdon et al. (2000). Selfed BC3 progenies included fertile plants exhibiting high seedling and adult plant resistance associated with the presence of an acrocentric addition chromosome from S. arvensis. Furthermore, some individuals with adult plant resistance but cotyledon susceptibility were observed to have a normal B. napus karyotype with no visible GISH signals, indicating introgression lines carrying at least a subset of the S. arvensis resistance genes. Schelfhout et al. (2004, 2006) used a B-genome-speciﬁc centromeric repeat sequence as a PCR and FISH marker to characterize B-genome introgressions in sexual progeny from B. napus × B. juncea crosses exhibiting various traits of agronomic interest, including resistance against blackleg disease and pod shattering. Genotypes with normal B. napus karyotype were identiﬁed in which the minisatellite sequence could be detected by PCR, although no FISH signals were observed, indicating small chromosomal introgressions that carried the gene of interest. Voss et al. (2000) generated intergeneric crosses between spring oilseed rape and nematode-resistant oil radish (R. sativus) genotypes, using embryo res-

cue to overcome incompatibility barriers. In three backcross generations, highly resistant progeny with a minimal number of R. sativus chromosomes were selected by resistance testing accompanied by GISH analysis. This strategy led to the identiﬁcation of a resistant BC3 plant with a monosomic, acrocentric addition chromosome. This individual was backcrossed once again to produce a stable disomic addition line; however, efforts to introgress the resistance on a stable introgression failed. Similarly, Peterka et al. (2004) also generated oilseed rape interspeciﬁc hybrid lines containing nematode resistance on a monosomic R. sativus addition chromosome. Here, R. sativus chromatin was identiﬁed by PCR and FISH with a Raphanus-speciﬁc centromeric repeat sequence. In this case also, however, no intergenomic transfer of the resistance was reported. Fahleson et al. (1997) analysed somatic hybrids between Eruca sativa and B. napus using in situ hybridization with two E. sativa-speciﬁc repetitive DNA sequences accompanied by GISH. One of the repetitive sequences showed 100% similarity with a part of the E. sativa rDNA intergenic spacer and localised to the three pairs of E. sativa rDNA loci, whereas the other clone was a tandemly repeated element located close to the telomeres on at least 10 E. sativa chromosomes. Analysis of progenies derived from the somatic hybrids revealed the presence of E. sativa DNA; however, no intergenomic translocations could be detected by GISH, although the somatic hybrid progeny contained one or two complete E. sativa chromosomes. Together these results emphasise the fact that chromosome translocations among non-homologous genomes are more likely in the presence of homoeologous chromosome pairing allowing intergenomic recombination. Genome homoeology at the chromosomal level is expected to be more extensive between oilseed rape and its closer relatives, and this is conﬁrmed by the relative ease with which agronomic traits have been transferred to B. napus from B. nigra, B. juncea, B. carinata and Sinapis species in comparison with the difﬁculties observed in more distant crosses. On the other hand, successful transfer of genes of interest in intertribal asymmetric hybrids has also been demonstrated on a number of occasions and may indicate unknown or partial genome homologies. Interesting results in this respect were obtained by Wang et al. (2004), who produced sexual progenies of asymmetric somatic hybrids between B. napus and Crambe abyssinica in an effort to improve the fatty acid composition of oilseed rape seed. Through

Chapter 2 Oilseed Rape

meiotic GISH these authors were able to identify intergenomic chromatin bridges and detect asynchrony between the B. napus and C. abyssinca meiotic cycles. Lagging, bridging and late disjunction of univalents derived from C. abyssinica were observed, whereas analysis of cleaved ampliﬁed polymorphic sequence (CAPS) markers derived from the FAE1 gene showed novel patterns different from the B. napus recipient in some hybrid offspring. This indicated the existance of novel allelic variation in the interspeciﬁc hybrids that presumably arose from introgression of Crambe chromatin to one or more B. napus chromosomes. Some of the recombinant offspring contained signiﬁcantly higher amounts of seed erucic acid than the B. napus parent, demonstrating that it is possible to introgress agronomic traits from distantly related crucifers into elite oilseed rape material. In another example, Winter et al. (2003) used mitotic GISH to characterize recombination lines containing genes for blackleg resistance from Moricandia arvensis. Resistant lines were identiﬁed which exhibited a normal B. napus karyotype but carried the Moricandia resistance genes on putative chromosome introgressions. Although such crosses can exhibit signiﬁcant linkage drag and hence must be viewed as extremely basic material from a breeding perspective, such prebreeding is of enormous interest in terms of broadening the genetic variability for particular traits where little variation is available within B. napus itself.

2.4 Genetic Diversity Studies in Brassica napus Besides spring and winter oilseed rape types, B. napus is often also grown as a fodder crop or as green manure. Swede cultivars (B. napus ssp. napobrassica) are also relatively common, particularly in Great Britain and Scandinavia, and a small number of rape kale vegetable forms (B. napus ssp. napus var pabularia) are also known, predominantly in Asia. Owing to their generally unsuitable seed characters, however, in particular high content of seed erucic acid, glucosinolates, and other anti-nutritive substances, fodder and vegetable rape forms have been generally overlooked for breeding of oilseed cultivars in recent decades. This emphasis on speciﬁc oil quality traits has led to a considerable narrowing of the gene pool of elite oilseed rape breeding material in recent

71

decades. On the other hand, genetically diverse material among vegetable and fodder rape represents a potentially valuable source for improved pathogen and pest resistance (Lühs et al. 2003a,b), and introduction of untapped germplasm into breeding lines also has the potential to improve heterotic potential. However, the construction of genetic pools, as used for example in maize hybrid breeding, has not been achieved for oilseed rape to date. Because of linkage drag for seed yield and quality traits associated with non-oilseed rape morphotypes, identiﬁcation of exotic germplasm amongst the respective gene pools of winter and spring oilseed forms is of particular interest in this respect. Due to the genetic bottleneck introduced in the 1970s and 1980s by the focus on breeding for material with double-low seed quality, genetic variability in oilseed rape is restricted with regard to many characters of value for breeding purposes. On the other hand, considerable genetic variation has been discovered within the species as a whole using molecular marker analyses. Becker et al. (1995) compared cultivars and resynthesised lines using allozyme and restriction fragment length polymorphism (RFLP) markers and concluded that resynthesised forms are a suitable resource for broadening the genetic base of rapeseed. Song et al. (1995) described the rapid genome changes that occur in synthetic Brassica polyploids and discussed the evolutionary implications arising from the ability of polyploid species to generate extensive genetic diversity in a short period of time. Thormann et al. (1994) used RFLP and RAPD markers to determine genetic distances in and between cruciferous species. Halldén et al. (1994) compared B. napus breeding lines with RFLPs and RAPDs, while Diers and Osborn (1994) compared RFLP patterns in winter and spring rapeseed genotypes and concluded that the two forms constitute two genetically different groups. The relationship between genetic distance and heterosis in oilseed rape was investigated by Diers et al. (1996) using RFLP markers and by Riaz et al. (2001) with sequence-related ampliﬁed polymorphisms (SRAPs). Plieske and Struss (2001) were able to clearly differentiate winter and spring rapeseed in a cluster analysis using simple sequence repeat (SSR, microsatellite) markers. The use of RAPDs for discrimination among rapeseed cultivars was also described by Mailer et al. (1994), whereas Lombard et al. (2000) utilised ampliﬁed fragment length polymorphisms (AFLPs; Vos et al. 1995) to genotype winter rapeseed cultivars and estimate

72

R. Snowdon, W. Lühs, W. Friedt

genetic similarities. Using SSR markers Hasan et al. (2006) found remarkable genetic variation in exotic vegetable and fodder rape genotypes compared to the gene pools of conventional spring and winter oilseed material. Similar extreme genetic variation compared to conventional rapeseed cultivars was also found in resynthesised rapeseed lines analysed by Becker et al. (1995) using allozyme and RFLP markers, and in other resynthesised rapeseed material investigated by Seyis et al. 2003a) using AFLP markers. In the latter study the genetic differences were correlated to heterotic yield potential in experimental hybrids (Seyis et al. 2003b). Such exotic material must obviously be viewed from a long-term perspective with regard to use in oilseed rape breeding; however, genetic diversity analyses using molecular markers have the potential to identify novel genetic variation that might assist in future improvement of heterotic potential in B. napus. Furthermore, the analysis of large sets of genetically diverse material using mapped markers in linkage disequilibrium can potentially provide a valuable data basis for the creation of so-called ‘graphical genotypes’ for allele-trait association studies.

2.5 Genetic Modification: Status and Potential of Transgenic Brassica napus Genetic engineering is considered a powerful tool for practical plant breeding since the transfer of speciﬁc traits to a target genotype is possible without changing the phenotype and agronomic performance of the recipient plant. Oilseed rape is particularly amenable to Agrobacterium tumefaciensmediated transformation, and during the last two decades considerable progress has been made in the development of transgenic varieties. Consequently, the global area of transgenic crops has grown continuously. The area planted in GM crops increased dramatically from 1.7 million ha in 1996 to almost 90 million ha in 2005 (James 2005). In 2003, four countries accounted for 98% of the global genetically modiﬁed crop area. The United States grew 42.8 million ha (63% of global total), followed by Argentina with 13.9 million ha (21%), Canada 4.4 million ha (6%), Brazil 3.0 million ha (4%) and China 2.8 million ha (4%). Amongst the different crops, soybean, cotton, corn and canola are the four principal crops in which

transgenic technology is utilized. Herbicide tolerant soybean is the dominant transgenic crop commercially grown, representing 61% of the global transgenic crop area in 2003. The second most dominant crop is insect-tolerant Bt-corn, which occupied 9.1 million ha (13% of global total) in 2003. After Btcotton (7.2 million ha) the fourth most dominant crop was herbicide tolerant canola, which was grown on 3.6 million ha, equivalent to 5% of the global transgenic area in 2003. The huge success of genetically modiﬁed crop plants in America and China has not been continued in the European Union member states. This is mainly due to limited public acceptance and unclear administrative legislation (cf. Friedt and Lühs 1998). One of the major markets for transgenic canola is Canada, where today the vast majority of the crop comprises herbicide-tolerant varieties, and a significant proportion of the continually expanding production of oilseed rape in China is also comprised of genetically modiﬁed varieties. The ﬁrst generation of transgenic varieties showed a strong emphasis on herbicide tolerance and hybrid breeding systems; however, efforts are increasing in the areas of genetically modiﬁed fatty acid biosynthesis and to a certain extent in the introgression of transgenic pest and disease resistance (e.g. National Research Council Committee on Genetically Modiﬁed Pest-Protected Plants, Board on Agriculture and Natural Resources 2000).

2.5.1 Herbicide Tolerance Making crop plants tolerant to herbicides allows farmers to use these herbicides to control weeds without harming the crop. This has been achieved in some crops for certain narrow-spectrum herbicides, using tissue culture techniques. The two most promising developments produced by genetic modiﬁcation to date are oilseed rape varieties tolerant to the postemergent broad-spectrum herbicides glyphosate [N-(phosphonomethyl)glycine] and glufosinate-ammonium [2-amino-4-(hydroxymethylphosphinyl)butanoic acid] (Oelck et al. 1991). Since its introduction in 1974, glyphosate, the active ingredient in the herbicide RoundUp, has increased dramatically in use, particularly with the advent in the 1990s of crops genetically engineered to be tolerant to this herbicide, e.g. Roundup Ready (RR) canola, soybeans, cotton and corn (Shaner 2000). The

Chapter 2 Oilseed Rape

cellular target of glyphosate in plants is the enzyme 5enolpyruvyl-3-shikimate phosphate (EPSP) synthase, which catalyses the formation of EPSP from phosphoenolpyruvate (PEP) and shikimate-3-phosphate. Inhibition of this step of the shikimate pathway causes starvation of aromatic amino acids, accumulation of shikimate and, eventually, cellular death. Herbicide tolerance in plants was mediated by overexpression of an Agrobacterium-derived EPSP synthase with decreased afﬁnity for glyphosate, but unaffected kinetic efﬁciency and tight binding of PEP (cf. Padgette et al. 1995). Glufosinate-ammonium is the chemically synthesised form of the bacterial product phosphinothricin (PPT) and is used worldwide as a non-selective herbicide. It is a foliage-applied contact herbicide that controls or suppresses most annual and perennial weeds. Glufosinate-ammonium inhibits glutamine synthetase (GS), an enzyme critical to the metabolism of nitrogen by plants, causing a greatly reduced availability of amino acids required for photorespiratory glyoxylate transamination. Thus accumulation of glyoxylate in GS-inhibited plants produces phytotoxic effects that result in rapid cessation of photosynthesis and death (Dekker and Duke 1995). Two genes that confer tolerance to PPT have been isolated from bacteria and cloned: the bar gene from Streptomyces hygroscopicus, which confers resistance to bialaphos (Murakami et al. 1986), and the pat gene from S. viridochromogens, which confers resistance to phosphinothricin (Strauch et al. 1988). Because of their stable expression, both of these genes, which behave in a dominant fashion, have been used extensively as selectable markers in transformation experiments. The enzyme phosphinothricin acetyltransferase, encoded by the bar and pat genes, deactivates the active ingredient phosphinothricin by acetylation of its free NH2 group, thereby neutralising its toxic effect on plant tissue (Botterman and Leemans 1988; Yoder and Goldsbrough 1994). Several studies have demonstrated that the pat gene conferring glufosinate-ammonium tolerance, when introgressed into B. napus via Agrobacterium-mediated transformation, behaves as a dominant gene and shows Mendelian inheritance (Budar et al. 1986; De Block et al. 1987, 1989; Kumar et al. 1998). The ﬁrst glufosinate-ammonium-tolerant B. napus spring cv. Innovator was registered for production in Canada in 1995 (Oelck et al. 1995). Although weed control in canola is possible with available herbicides, multiple treatments with chemicals of dif-

73

ferent herbicide families are often required for control of all weeds. Certain cruciferous weeds such as wild mustard (Sinapis arvensis) and stinkweed (Thlaspi arvense) are difﬁcult to control, and the use of specialty herbicides for cruciferous weed control is sometimes required. In addition, the use of multiple herbicides increases production costs and the chemical load on soils. The availability of several types of herbicide-tolerant plants allows for rotation of herbicides, minimising the risk of weeds becoming resistant to any particular one. Several varieties of transgenic herbicide-tolerant oilseed rape are grown and processed in the USA, Canada and China. In 2004 herbicide-tolerant varieties comprised more than 85% of the canola crop in Canada, with the majority of the herbicide tolerance being of transgenic origin (http://www.canola-council.org/).

2.5.2 Genetic Engineering of Fatty Acid Biosynthesis Rapeseed is an important source of energy both for human consumption and for feeding of livestock, and also provides raw material for a wide range of industrial products for many non-food purposes. Modiﬁcation of the fatty acid composition to make rapeseed oil more competitive in various segments of the food and industrial oil markets has become an important objective of oilseed rape molecular genetics and breeding. One of the central objectives in this context is the genetic modiﬁcation of the seed storage oil by maximising the proportion of speciﬁc or functional fatty acids in order to obtain tailor-made raw materials suited for various industrial purposes (Friedt and Lühs 1998; Biermann et al. 2000). However, the quality of vegetable food products has increased in relevance for human nutrition in recent decades, with the advent of so-called ‘functional foods’. With regard to speciﬁc properties of such nutritive substances, genetic engineering offers the possibility of adapting plant storage lipids to meet speciﬁc nutritional and even therapeutic requirements (Leckband et al. 2002; Friedt et al. 2004). Rapeseed oil is unique in having a large spectrum of usability and positive properties for food and non-food applications. Genetic engineering of lipid biosynthesis in rapeseed has already led to commercialisation, with transgenic varieties expressing genetically modiﬁed fatty acid patterns being available since 1995 (cf. Friedt and Lühs 1998).

74

R. Snowdon, W. Lühs, W. Friedt

The isolation of the majority of genes encoding enzymes of storage oil synthesis made this basic metabolism one of the ﬁrst targets for gene transfer to plants. In the following section we will highlight both the achievements made and the impediments observed with regard to the modiﬁcation of oilseed quality by genetic transformation. Technical aspects and efforts, ranging from regeneration of fertile plants from tissue cultures to the ﬁnal selection and ﬁeld evaluations of transformants with the desired phenotype, are discussed in more detail by Weber et al. (2000). The biochemical pathways of plant lipid synthesis are well understood and have been comprehensively reviewed by a number of authors (Ohlrogge et al. 1991; Töpfer et al. 1995; Murphy 1999; Drexler et al. 2003). In plants consecutive steps of de novo fatty acid biosynthesis take place in different cell compartments. Fatty acid synthesis starts in the stroma of the plastids where malonyl-CoA is synthesised from acetyl-CoA and carbonate by an acetyl-CoA carboxylase (ACCase) enzyme. Condensing enzymes (ketoacyl-ACP synthases, KAS), as part of the FAS (fatty acid synthase) multienzyme complex, elongate the initial malonyl-CoA consecutively by adding C2 units derived from malonyl-CoA under release of CO2 . During this process the growing fatty acid chain is bound to an acetyl carrier protein (ACP). After seven condensation cycles, a C16 acyl thioester (palmitoyl-ACP) is elongated by another KAS speciﬁc to stearoyl-ACP, which is then desaturated to oleoyl-ACP by the action of a Δ9-stearoyl-ACP desaturase. The three latter acyl residues are released from ACP via hydrolisation by acyl-ACP thioesterases (TE), resulting in palmitic (16:0), stearic (18:0) and oleic (18:1n-9) as the primary fatty acids. Acyl-ACPTEs are distinguished by their evolutionary origin into FatA and FatB TEs. The FatA types have unsaturated acyl groups as substrates, while FatB types prefer saturated acyl ACPs (Jones et al. 1995). In certain plant species, e.g. Cuphea sp., speciﬁc FatB TEs terminate the chain length of storage lipid fatty acids by hydrolysing acyl-ACPs before they reach a length of 18 carbons, resulting in short- to medium-chain fatty acids: capric (8:0), caprylic (10:0), lauric (12:0) and myristic (14:0) acid, respectively. However, fatty acids hydrolysed from ACP are exported into the cytoplasm, where they are activated with CoA by a membranebound acyl-CoA synthase (ACS). In Brassicas and some other plant species, oleoyl-CoA is subject to further elongation, which results in long-chain fatty

acid residues: eicosenoyl (20:1n-9), erucoyl (22:1n-9) and nervonoyl (24:1n-9)-CoA, which complete the acyl-CoA. At the endoplasmic reticulum the assembly of TAGs is catalysed by acyltransferases (AT) which convert CoA-activated acyls and glycerol-3-phosphate (G3P) under the release of inorganic phosphate to TAGs. In a ﬁrst step, lysophosphatidic acid (LPA) is formed by glycerol-3-phosphate acyltransferase (G3PAT), which is then converted into diacylglycerol (DAG)-phosphate by the action of a substrate-speciﬁc lysophosphatidic acid acyltransferase (LPAAT). The substrate speciﬁcity of LPAAT results in a distinct occupation of the central sn2 position of the resulting TAG with a speciﬁc fatty acid, which has an impact on the fatty acid pattern of the seed oil. For complementation of TAG synthesis, the phosphate group bound to the diacylglycerol is released by phosphatidic acid phosphatase (PAP) prior to conversion of the diacylglycerol by diacylglycerol acyltransferase (DAGAT), the only enzyme unique to storage lipid synthesis (Murphy 1999), to the ﬁnal TAG. Alternatively, DAG can be converted reversibly to phosphatidylcholine (PC), which may enter membrane lipid synthesis or is used as substrate for membrane-bound desaturases that in turn desaturate the acyl residues at positions Δ9, Δ12 and Δ15 or Δ6. Additionally, in some plant species, functional groups, e.g. hydroxy residues in castor seeds (Ricinus communis L.), are added to the PC-bound fatty acid residue prior to desaturation. The PCs with such altered fatty acid residues can then, by acyl exchange, be reconverted to various diacylglycerols (DAG) which complement the substrate pool for DAG acyltransferase for triacylglycerid synthesis of storage lipids. A wide range of genes encoding various enzymes involved in plant storage lipid synthesis have been isolated and cloned (cf. Martínez de Ilárduya et al. 1999; Mekhedov et al. 1999; Drexler et al. 2003) and thus are available for genetic engineering of fatty acid composition of seed oils. The quality of oils and fats is determined primarily by the composition of fatty acids, their chain length, their degree of desaturation and their functional groups. The latter can be divided into two general groups. On the one hand, there is the nutritional sector in which saturated fatty acids are not desired because of their unfavorable effects (e.g. cardiovascular diseases, arteriosclerosis, etc.). On the other hand is the industrial sector, in which saturated fatty acids are valuable raw materials for the produc-

Chapter 2 Oilseed Rape

tion of cosmetics, softeners, lubricants, pesticides and related products. An increase in the content of an existing or a new fatty acid in order to facilitate industrial processing of the raw material is one of the most important objectives of genetic engineering in oil crops (Friedt and Lühs 1998; Biermann et al. 2000). Each plant has a typical fatty acid pattern that is predetermined by its enzyme conﬁguration. A modiﬁcation of typical fatty acid patterns may be derived by either increasing or reducing the expression of enzyme activities. Previous approaches to alter the fatty acid composition in oilseed rape by modifying C18 desaturation were accomplished by Knutzon et al. (1992) and Zarhloul et al. (1999). For this purpose, a construct of Δ9stearoyl-ACP desaturase (Δ9 DES) was expressed in antisense orientation, which resulted in an increased level of stearic acid of up to 40% total fatty acids. It was concluded that the antisense Δ9-stearoyl-ACPdesaturase transcript hybridized with the RNA of the native Δ9-desaturase, which results in a suppression of the endogenous desaturase expression followed by an accumulation of stearoyl-ACP from which stearic acid can be released by the activity of the stearoyl-ACP-TE (Töpfer et al. 1995). The alteration of fatty acid chain length has been a further aim of modifying lipid composition of rapeseed. In a ﬁrst step, a major breakthrough was achieved when seeds of transgenic Arabidopsis thaliana plants engineered with a TE gene from California bay (Umbellularia californica), a plant containing 70% lauric acid in its oil (Pollard et al. 1991), showed an accumulation of up to 25% of this fatty acid (Voelker et al. 1992). The expression of the same gene in rapeseed led to an accumulation of 40% lauric acid (Voelker et al. 1996). Some Cuphea species include more than 85% (Crane et al. 2003) of one single saturated medium-chain fatty acid (MCFA), for example the Mexican shrub C. hookeriana with seed oil containing up to 75 mol % caprylic acid (8:0) and capric acid (10:0). Therefore, the presence of medium-chain speciﬁc enzymes could be assumed. Jones et al. (1995) isolated a cDNA of C. hookeriana (ChFatB1), expressed in the whole plant, which was active on the 14:0- and 18:1(n-9)-acyl-ACP with strong preference for 16:0acyl-ACP. In contrast, ChFatB2, which is expressed only in seeds possessing a substrate speciﬁcity for 8:0- and 10:0-acyl-ACP, has maximum values of 11% and 27% C8:0 and 10:0 in B. napus, respectively (Dehesh et al. 1996). For another species belonging to

75

the genus Cuphea, namely C. lanceolata, speciﬁc acylACP TE genes have been cloned (Töpfer and Martini 1994). Among these, ClFatB3 and ClFatB4 have been used in transformation experiments with rapeseed, resulting in an increase in MCFA to 1% and 3% of caprylic and capric acid, respectively, whereas ClFatB4 led to the formation of 7% myristic and 15% palmitic acid in the storage oil (Töpfer et al. 1995). A further achievement was reported by Voelker et al. (1997), who described an enrichment of C14-18 and C10-18 saturated fatty acids in rapeseed by expression of FatB TEs from nutmeg (Myristica fragrans) and elm (Ulmus americana), respectively. The expression of a TE from the tropical tree mangosteen (Garcinia manostana) in transgenic rapeseed resulted in an accumulation of ca. 20% stearic acid (Hawkins and Kridl 1998). Another approach is the heterologous expression of different β-ketoacyl-acyl carrier protein synthase (KAS) isoforms, which catalyse the condensation of acetyl-CoA with a speciﬁc acyl-carrier protein (ACP). These condensing enzymes are available and possess different speciﬁcities: KAS III, the starting enzyme of the fatty acid synthesis, catalyses the condensation of acetyl-CoA and malonyl-ACP. KAS I is speciﬁc for elongation of the substrates from 4:0-ACP to 16:0ACP, KAS II is responsible for the condensation of the substrates from 16:0-ACP to 18:0-ACP, and KAS IV directs its condensing activity on medium-chain acyl substrates (Dehesh et al. 1998, 2001). This suggests that the enzymes in the fatty acid synthesis may be coordinately regulated to counteract changes brought by genetic overexpression and thus maintain the ﬂux of fatty acid production (Dehesh et al. 2001). Different studies reveal that an overexpression of a mediumchain speciﬁc KAS III from Cuphea lanceolata leads to an increased level of palmitic acid in the seed oil of rape (Dehesh et al. 2001; Stoll et al. 2003). It is obvious that KAS enzymes in combination with a speciﬁc TE, overexpressed in rapeseed, strongly enhance the levels of the respective fatty acids in contrast to a TE expressed alone (Dehesh et al. 1998). KAS IV from Cuphea hookeriana (ChKASIV) in combination with the respective medium-chain speciﬁc TE (ChFatB2) resulted in a 30 to 40% increase in the levels of MCFA compared with lines expressing ChFatB2 alone (Dehesh et al. 1998). The same group created Brassica plants expressing ChKASIV, and a TE from C. palustris (CpFatB1) contained on average 40% more C8:0 and 10:0 fatty acids than plants expressing the TE alone.

76

R. Snowdon, W. Lühs, W. Friedt

We also were able to modify the fatty acid pattern of rapeseed by coexpression of a modiﬁed KASIII originating from C. lanceolata (Abbadi et al. 2000) with TEs from C. lanceolata (ClFatB3, Töpfer et al. 1995) and C. hookeriana (ChFatB2, Dehesh et al. 1996). This resulted in an increase of MCFA to 2.9% capric and 9.8% palmitic acid for the ﬁrst combination and 1% caprylic and 6% capric acid for the second combination, respectively (unpublished data). In order to explore the regulation site of KASIII, which is sensitive for regulatory acyl-ACPs (predominantly decanoylACP) inhibiting enzyme activity, a mutant KASIIIN291D was designed by site-speciﬁc mutagenesis (Abbadi et al. 2000). This mutant showed a slightly reduced enzymatic activity but is not inhibited by acyl-ACP. A further approach to increase single fatty acids or to modify the fatty acid pattern takes place in the cytosol. At the endoplasmic reticulum the assembly of TAGs is catalysed by acyltransferases (ATs), which attach CoA-activated acyls on the glycerol backbone under the release of inorganic phosphate. In a ﬁrst step, lysophosphatidic acid (LPA) is formed by glycerol3-phosphate acyltransferase (G3PAT), which is then converted into diacylglycerol (DAG) phosphate by the action of a substrate speciﬁc LPA acyltransferase (LPAAT). The substrate speciﬁcity of LPAAT results in a distinct occupation of the central sn-2 position of the resulting TAG with a speciﬁc fatty acid, which has an impact on the fatty acid pattern of the seed oil. For complementation of TAG synthesis, the phosphate group bound to the DAG is released by phosphatidic acid phosphatase (PAP) prior to conversion of the DAG by DAG acyltransferase (DAGAT), the only enzyme unique to storage lipid synthesis (Murphy 1999), to the ﬁnal TAG. Rapeseed bearing the California bay TE contains up to 40% lauric acid, while only the sn-1 and the sn-3 positions of the TAG are occupied (Voelker et al. 1996). In order to further improve the amount of lauric acid, a LPAAT from coconut (Cocos nucifera) (Knutzon et al. 1999), which enables a laurate deposition at the sn-2 position and leading to trilaurin, was co-expressed in the bay TE rapeseed (Knutzon et al. 1992). These authors were able to increase the content of laurate above 50% in the seed oil. Despite the success of some research programs in utilising parallel mutagenesis and lipid proﬁlingtechniques

to demonstrate novel fatty acid synthesis, commercially viable increases in oil yields or speciﬁc designer fatty acids have for the most part not been realised. In the context of the modiﬁcation of the fatty acid structure some effort has been spent on determining the limiting factors. Wiberg et al. (1997) studied the partitioning of laurate between membrane and storage lipids in developing seeds of laurateproducing transgenic rape. They revealed that a substantial decrease of laurate in mature seeds follows the accumulation of laurate in the TAG during seed development, in accordance with a rising amount of lauroyl-phosphatidyl-choline-metabolising enzymes. It was suggested that there is only one substrate pool for both phospholipids and TAGs, and that rapeseed ATs speciﬁcally remove or exclude lauroyl moieties and, therefore, differ from corresponding enzymes of plants naturally accumulating laurate. The same effect could be true for caprate-producing rapeseed lines. If the high amount of MCFA in the membrane lipids is a limit for accumulating caprate, traditional breeding methods should be used to select germplasm with a higher capacity for the exclusion of MCFA from the membranes (Wiberg et al. 1997). One future alternative could be the identiﬁcation of genes encoding enzymes responsible for the exclusion mechanism, and their co-expression with medium-chain speciﬁc TEs and condensing enzymes to produce oilseed rape with very high levels of MCFA (Wiberg et al. 1997). On the other hand, the importance of the genetic background should not be underestimated. According to Tang and Scarth (2004), selection for appropriate genetic backgrounds is vital to maximising the expression of the target fatty acids. In the case of MCFA-producing rapeseed, acylCoA proﬁling has been conducted to determine limiting factors (Larson et al. 2002; Graham et al. 2002), and the lack of the necessary enzymatic repertoire to incorporate MCFA efﬁciently into seed oils was highlighted as a general problem. In conclusion, it has become obvious that the genetic modiﬁcation of fatty acid synthesis is still poorly understood, and changes in the content of speciﬁc fatty acids are often unpredictable. Nevertheless a great potential exists for genetic engineering of novel seed oil compositions in oilseed rape beyond intraspeciﬁc boundaries.

Chapter 2 Oilseed Rape

2.6 Molecular Markers and Genetic Mapping

2.6.1 Use of Isoenzymes in Oilseed Rape Breeding Before the advent of DNA marker systems, isoenzyme analysis was the quickest and most effective method for characterization and identiﬁcation of morphologically difﬁcult-to-distinguish rapeseed varieties, populations and individuals. In the late 1980s isoenzymes were still being widely used in Brassica vegetable and oilseed breeding for veriﬁcation of hybrids and characterization of interspeciﬁc sexual and somatic cross products. Gupta and Röbbelen (1986), Mündges-Christmann and Köhler (1990) and Chèvre et al. (1991a) applied isoenzymes for varietal identiﬁcation in oilseed rape cultivars, whereas Sundberg et al. (1987), Jourdan et al. (1989) and Mündges et al. (1989) used isoenzymes separated by starch gel electrophoresis to conﬁrm the genomic composition of resynthesised rapeseed generated by somatic hybridization. A number of groups working on somatic interspeciﬁc hybridization for transfer of characters agronomically important to oilseed rape used isoenzyme analysis for characterization of the resulting hybrid and backcross offspring (Sacristan et al. 1989; Sjödin and Glimelius 1989; Sundberg and Glimelius 1991). Isoenzyme banding patterns were also a useful tool for estimation of outcrossing rates (Jain 1979; Hackenberg et al. 1990; Becker et al. 1992). The utility of isoenzyme markers for markerassisted selection (MAS) in crop Brassicas was limited by the lack of diagnostic markers for morphological traits. One notable exception was the discovery by Hinata and Nishio (1978) of allele speciﬁcity of stigma glycoproteins in B. oleracea and B. rapa. In oilseed rape breeding, moreover, one of the best examples of successful MAS for an agronomically important trait involves isoenzyme markers. Selection of glucose-6phosphate isomerase (GPI) alleles from radish present on the oilseed rape chromosome introgression carrying the Ogura CMS fertility-restorer gene can assist in early identiﬁcation of restorer plants (Delourme and Eber 1992; Horacek and Acanova 2003), and this GPI marker is still used today in hybrid breeding programs utilising the Ogura CMS system.

77

As the ﬁrst Brassica RFLP markers (Song et al. 1988) and the resulting genetic maps (see below) became available in the 1990s, interest in isoenzymes rapidly dwindled. Due to the low cost, simple equipment and ease of use, however, use of isoenzymes for homogeneity tests or conﬁrmation of hybrids can still play a role in commercial breeding programs and variety registration today, e.g. in smaller breeding companies without more sophisticated DNA laboratories.

2.6.2 Brassica napus Genetic Maps: From RFLP to PCR Markers As mentioned above, protein and isoenzyme markers still play a role for speciﬁc questions in commercial oilseed rape breeding, but genome research and marker-assisted applications in Brassica ﬁrst began to ﬂourish in the late 1980s with the development of the ﬁrst restriction fragment length polymorphism (RFLP) linkage maps for B. oleracea (Slocum et al. 1990), B. rapa (Song et al. 1991) and B. napus (Landry et al. 1991). In the meantime, a large number of B. napus genetic maps have been generated from a large number of different rapeseed crosses, and considerable efforts have been invested in the localisation of genes and QTL controlling agronomically relevant traits. Figure 2 shows a detailed consensus genetic map, aligned using common RFLP markers mapped in four mapping populations by the group of Thomas C. Osborn at the University of Wisconsin at Madison. Table 3 presents an overview outlining details of a number of published B. napus genetic maps, including cross parents, number and types of markers, map size and the traits that have been studied in the respective crosses. The following section highlights some of the major genetic mapping efforts of the past one and a half decades. While this overview gives a good impression of the multitude of data that have been collated by different oilseed rape researchers during this time, it also underlines the difﬁculties in collating and comparing this information amongst different genetic maps. In particular, for comparison of QTLs, but also for integration of genetic, physical and karyotpe maps, it is essential that all available mapping data be integrated as much as possible in future using standardised chromosome nomenclature and common markers. Fortunately such initiatives have slowly begun to gain momentum in recent years, and the genetic tools

78

R. Snowdon, W. Lühs, W. Friedt

Fig. 2. Consensus genetic linkage map of molecular markers compiled from individual maps constructed for four segregating populations of Brassica napus doubled-haploid lines (see Udall et al. 2005; map populations are described in Table 3). Linkage groups N1–N10 correspond to B. rapa A genome LGs A1–A10, whereas LGs N11–N19 correspond to B. oleracea C genome LGs C1–C9, respectively. Marker locus names and map positions (in cM) are in the ﬁrst two columns of each LG. Individual maps contributed complementary sets of polymorphic loci to the consensus map, as shown by bars in four columns (SYN, HUA, MF, RV) aligned with loci in each LG. Circles: loci that had different orders (greater than 2 cM) in individual DH maps compared to consensus map. Linkage group N11 of MF map was not included in consensus map due to a very different locus order. HNRT indicates loci that were part of a homoeologous non-reciprocal transposition on N11 for which genetic distances could not be estimated. Loci on N7 and N16 having P1804 alleles the same size as fragments found in B. rapa are in italics. Loci on N7 and N16 that had segregating monomorphic loci in the SYN population are underlined. Map image kindly provided by Tom Osborn, University of Wisconsin, Madison, WI, USA, and is used with permission from the Genetics Society of America. For further details see Udall et al. (2005)

Chapter 2 Oilseed Rape

Fig. 2. (continued)

79

80

R. Snowdon, W. Lühs, W. Friedt

Fig. 2. (continued)

Chapter 2 Oilseed Rape

Fig. 2. (continued)

81

277

RFLP

RFLP

50 DH lines

92 DH lines 82 DH lines (reciprocal crosses)

50 DH lines 92 DH lines 2 × 90 lines (complementary backcross populations) 156 DH lines

SYN1 × N-o-9 (resynthesised rapeseed × winter DH cultivar) N-o-1 × N-o-9 (DH from Westar × DH from British biennial breeding line) SYN1 × N-o-9 N-o-1 × N-o-9 N-o-93 × N-o-1 (Spring canola line × DH from Westar)

Mansholts Hamburger Raps × Samourai (European landrace × French 00-quality winter rapeseed)

392

RFLP

105 DH lines

RFLP RAPD SSR

RFLP

RFLP (later expanded with RFLP, AFLP, isozyme and gene loci)

204 2 4

201 common markers 189 and 176, respectively

132 (expanded to a total of 480 loci)

120

Major × Stellar (Blackleg-resistant French winter rapeseed × blackleg-susceptible Canadian spring canola variety)

RFLP

90 F2:F3-families

Number of markers

Westar × Topas (both Canadian spring canola varieties)

Marker type(s)

Population type and size

Map parents

Comparison of A and C genome organisation, observation of locus duplications; microspore culture responsiveness Blackleg resistance, white rust resistance, growth habit, ﬂowering time, winter tolerance, glucosinolate content, erucic and linolenic acid contents Comparative genome analysis, identiﬁcation of A and C genome LGs Comparative genome analysis, reciprocal crosses

Traits investigated

1441

Seed erucic acid, seed glucosinolates, oil content, tocopherol composition

1512 (aligned) Integrated map from two crosses 1544 and 1577, Comparison of meiotic respectively recombination in reciprocal crosses

1741 and 1606, respectively

1656

1016

1413

Map size (cM)

Table 3. Selected Brassica napus genetic mapping studies: Map parents, segregating populations and markers used, map size and investigated traits

Ecke et al. (1995), Uzunova et al. (1995), Uzunova and Ecke (1999), Marwede et al. (2005)

Kelly et al. (1997)

Parkin and Lydiate (1997)

Sharpe et al. (1995)

Sharpe et al. (1995), Parkin et al. (1995)

Ferreira et al. (1994, 1995a–c), Toroser et al. (1995), Thormann et al. (1996), Osborn et al. (1997), Kole et al. (2002b)

Landry et al. (1991), Cloutier et al. (1995)

Reference

82 R. Snowdon, W. Lühs, W. Friedt

134 DH lines

94 DH lines

152 DH lines 134 DH lines 94 DH lines

95 DH lines

235 DH lines

Stellar × Drakkar (Canadian low-linolenic canola × French spring type)

Darmor-bzh × Yudal Darmor × Samourai Stellar × Drakkar

90-DHW-1855-4 × 87-DHS-002 (winter × spring breeding lines)

Express × R54 (German 00-quality winter variety × resynthesised rapeseed)

RAPD AFLP RFLP Isozyme SSR SCAR Bzh gene RAPD AFLP RFLP SCAR Isozyme RAPD AFLP Isozyme SCAR SSR Isozyme RAPD RFLP AFLP RFLP RAPD STS AFLP

152 DH lines

Darmor × Samourai (French winter oilseed rape varieties)

RFLP

200 backcross lines

Tapidor × Victor French 00-quality winter rapeseed × ++-quality Swedish winter rapeseed Darmor-bzh × Yudal (French dwarf winter type × Korean spring type)

Marker type(s)

Population type and size

Map parents

Table 3. (continued)

274 66 2 143

1141

2125

2429

1912

Resistance to TuMV

Genome organisation

Consensus map from three populations

Linolenic acid

Blackleg resistance

Dwarf gene, resistance to blackleg and light leaf spot, erucic acid content, linolenic acid content

2023

271 219 79 10 7 3 1 150 122 68 1 3 167 163 6 3 1 540 1574

Seed glucosinolates, oil content, fatty acid composition

Traits investigated

1238

Map size (cM)

158

Number of markers

Dreyer et al. (2001)

Cheung et al. (1997)

Lombard and Delourme (2001)

Jourdren et al. (1996b), Lombard and Delourme (2001)

Pilet et al. (2001), Lombard and Delourme (2001)

Foisset et al. (1995, 1996), Jourdren et al. (1996a), Pilet et al. (1998a,b), Lombard and Delourme (2001)

Howell et al. (1996, 2003), Burns et al. (2003)

Reference

Chapter 2 Oilseed Rape 83

MF216 × P1804 (DH line from (Major × Stellar) × male-fertility restorer line)

170 DH lines

RFLP

RFLP SSR

162 DH lines

202 DH lines

RFLP

SSR RFLP AFLP SNP

166 DH lines

162 DH lines

AFLP SSR

105 DH lines

25629-3 × K26-96 (yellow-seeded 00-quality inbred line × black-seeded ++-quality DH line) Express × 1012/98 (German winter oilseed rape variety × yellow-seeded breeding line, both 00-quality) Tapidor × Ningyou 7 (French 00-quality winter rapeseed × ++ quality old Chinese semi-winter variety)

RV289 × P1804 (derivate from Chinese winter rape cultivar × male-fertility restorer line) TO1147 × P1804 (Resynthesised B. napus × male-fertility restorer line)

RFLP AFLP SSR RAPD AFLP SSR

123 F2:F3-families

H5200 × Ning RS1 (MS restorer line × partial Sclerotinia resistant)

Marker type(s)

Population type and size

Map parents

Table 3. (continued)

218

309 2

1398

1668

1460

Comparative mapping for analysis of homoeologous chromosome recombination Comparative mapping for analysis of homoeologous chromosome recombination Comparative mapping for analysis of homoeologous chromosome recombination

Yield and yield components, seed oil, erucic acid, glucosinolate and protein contents

1532

124 18 15 350

239

Seed color, dietary ﬁber

Seed color, dietary ﬁber

Resistance to Sclerotinia sclerotiorum

Traits investigated

1721

1397

1625

Map size (cM)

262 85

72 30 3 2 211 35

Number of markers

Udall et al. (2005)

Udall et al. (2005)

Udall et al. (2005)

Pers. comm., Dan Qiu and Jinling Meng, Huazhong Agricultural Unversity, Wuhan, China; see http://brassica.bbsrc.ac.uk/ IMSORB

Badani et al., (in press)

Badani et al., (in press)

Zhao and Meng (2003)

Reference

84 R. Snowdon, W. Lühs, W. Friedt

RFLP

SSR SCAR

164 DH lines

574 F2 plants from unbalanced diallel cross between six parental lines

RV128 × P1804 (Westar × Samourai introgression line × male-fertility restorer line) Comet, Jaguar, Vivol (French winter rapeseed cultivars) and three French commercial breeding lines

Marker type(s)

Population type and size

Map parents

Table 3. (continued)

304 59

205

Number of markers

2619

1453

Map size (cM) Comparative mapping for analysis of homoeologous chromosome recombination SSR consensus map from six segregating populations

Traits investigated

Piquemal et al. (2005)

Udall et al. (2005)

Reference

Chapter 2 Oilseed Rape 85

86

R. Snowdon, W. Lühs, W. Friedt

necessary to realise them are beginning to be made available in the public domain. It remains to hope that a common, integrated, public oilseed rape map with extensive marker coverage, QTL data and alignment to physical maps will be available to international Brassica researchers in coming years. The ﬁrst B. napus map, published by Landry et al. (1991), was based on F2 segregation analyses from a cross between the canola cultivars Westar and Topas, using RFLPs from a seedling-speciﬁc cDNA library and independent digestions with BamHI, EcoRI, EcoRV and HindIII. A total of 120 RFLP loci were mapped on 19 LGs covering a total of 1,413 cM. Due to low polymorphism among the RFLP markers in this cross the number of loci that could be mapped was relatively low; however, it was nevertheless possible to detect considerable locus duplication corresponding to the amphidiploid genome organisation, and the ﬁrst evidence was observed for extensive rearrangements of the linear order of the duplicated loci. Comparisons of this map with the corresponding B. oleracea and B. rapa maps enabled the ﬁrst detailed investigations of genome organisation among the respective Brassica genomes. Furthermore, the fact that functional DNA sequences were used as markers meant that the results were immediately relevant for applications in canola breeding. A further map was reported by Hoenecke and Chyi (1991) shortly afterwards, this time based on a cross between two breeding lines. In this case 125 RFLP markers covering 1,350 cM were mapped to 19 LGs. Subsequently a number of groups produced RFLP maps using segregating F2 or doubled-haploid (DH) populations derived from homozygous DH lines as parents. The map made by Ferreira et al. (1994), developed from a cross between a winter oilseed rape cv. Major and the spring canola variety Stellar, was used initially to localise QTLs associated with the annual/biennial growth habit and subsequently for extensive studies of ﬂowering time genes (Ferreira et al. 1995b; Osborn et al. 1997). In the initial study a total of 132 RFLP loci were grouped on 22 LGs covering 1,016 cM. The map positions for a subset of the mapped markers were compared with the locus ordering in F2 progeny from the same cross, and no signiﬁcant differences could be established between the two maps. Comparisons of this map with the maps for B. rapa and B. oleracea containing the same markers suggested that less recombination was observed in the B. napus cross than would be expected from the combined map distances of the two diploid progenitors.

A high percentage (32%) of segregating marker loci were duplicated in the DH map, and conserved linkage arrangements of some duplicated loci indicated intergenome homoeology in the amphidiploid, or intragenome duplications from the diploid progenitors. Toroser et al. (1995) also used the Major × Stellar mapping population to localise QTLs which regulate the total seed aliphatic-glucosinolate content in B. napus. A population of 99 F1 -derived DH recombinant lines were used for single-marker analysis and interval mapping of QTL associated with total seed glucosinolates. In further studies Ferreira et al. (1995a,c) localised loci contributing to blackleg and white rust resistance using the same cross, and genes controlling erucic and linolenic acid biosynthesis were localised in this map by Thormann et al. (1996). The ﬁrst integrated map constructed using two different segregating populations was developed by Sharpe et al. (1995). This study revealed considerable chromosome instability in one of the crosses, which involved a resynthesised rapeseed crossed with a normal rapeseed variety. Parkin et al. (1995) mapped this cross with 399 RFLP markers and discovered that the majority of loci exhibited disomic inheritance of parental alleles. This provided evidence demonstrating that B. rapa chromosomes were each pairing exclusively with recognisable A genome homologues in B. napus and that B. oleracea chromosomes were pairing similarly with C-genome homologues. That enabled identiﬁcation of the ten A genome and nine C genome LGs of B. napus and showed that the nuclear genomes of B. napus, B. rapa and B. oleracea have remained essentially unaltered since the formation of B. napus. This result was conﬁrmed in a comparative mapping study including natural and resynthesised rapeseed described by Udall et al. (2005). According to Sharpe et al. (1995), the chromosome abnormalities they observed were probably caused by associations between homoeologous chromosomes in the resynthesised parent and the F1 plant at meiosis, leading to non-disjunction and homoeologous recombination. Kelly et al. (1997) used an F1 individual derived from a cross between two distinct lines of spring oilseed rape to produce a pair of complementary backcross populations comprising 90 individuals each. Genetic maps were generated from both populations and aligned using 117 common loci to produce an integrated genome map of B. napus with 243 RFLP loci. No differences could be observed between the frequencies and distributions of crossovers in the two male and female populations of F1 gametes, and the respec-

Chapter 2 Oilseed Rape

tive genetic maps each consisted of 19 LGs spanning 1,544 and 1,577 cM. The maps were found to be comparable with other B. napus maps in terms of their genetic size and general organisation. With the discovery of the polymerase chain reaction (PCR; Mullis and Faloona 1987) the potential arose to greatly increase the marker density in existing genetic maps through ampliﬁcation of highly polymorphic anonymous PCR fragments, ﬁrst with RAPD markers (Williams et al. 1990) and more recently with AFLPs (Vos et al. 1995) and intersimple sequence repeats (ISSRs; Zietkiewicz et al. 1994). Foisset et al. (1995, 1996) integrated RAPD and isozyme markers into an RFLP map based on DH offspring from the F1 of a cross between the French dwarf winter oilseed rape variety Darmor-bzh and the Korean spring rapeseed Yudal. Using 153 DH lines a total of 254 markers were mapped to 19 LGs covering 1,765 cM, and a number of agronomically relevant genes could be localised. In particular, the dwarf gene Bzh was localised and linked markers were identiﬁed by a combination of genetic mapping, on the one hand, and targeting of the dwarf gene using near-isogenic lines (NILs) and bulked segregant analysis (BSA) on the other hand. The BSA approach was found to be more efﬁcient in ﬁnding DNA markers linked to Bzh, whereas the efﬁciency of the NIL approach was limited by the similarity of the genetic background between the dwarf donor parent and the recurrent lines. In the late 1990s RAPD and subsequently AFLP markers began to be more broadly incorporated in new and existing B. napus genetic maps and provided relatively cheap and less labour-intensive alternatives for saturation of genome regions containing genes of interest. These high-throughput, highly polymorphic marker systems also improved the application of BSAs for quick identiﬁcation of markers linked to qualitative and also quantitative traits of interest. For example, Delourme et al. (1994) used BSA to identify RAPD markers linked to the restorer gene Rfo used in the Ogura Raphanus sativus cytoplasmic male sterility of rapeseed. DNA polymorphisms generated by four RAPD primers were found to be completely linked to the restorer gene, with the polymorphic DNA fragments being associated either with the fertility restorer allele or with the sterility maintainer allele. Southern hybridization of labelled RAPD fragments on digested genomic DNA from the same three pairs of bulks revealed fragments speciﬁc to either the male sterile bulks or the restored bulks, and a few fragments common to all bulks. The four RAPD frag-

87

ments which were completely linked to the restorer locus were cloned and sequenced to develop sequence characterized ampliﬁed region (SCAR) markers. SSR markers (microsatellites) represent a class of variable-number tandem repeats comprised of a short (1 to 6 bp) nuceotide motif repeated up to 100 times or more. SSRs exist in eukaryotic nuclear genomes and in the chloroplast genome of some plants (Powell et al. 1995) and are of great interest to population geneticists because of their high mutation rate, which in plants has been estimated to be as high as 10−6 (Udupa and Baum 2001). SSR markers are generated by PCR ampliﬁcation of speciﬁc microsatellite loci using primers developed from the sequences ﬂanking the simple repeat. Because of their robust nature, generally codominant inheritance and relatively high level of polymorphism, SSR markers are an extremely valuable tool for genetic mapping; along with RFLPs they have become the markers of choice for alignment of oilseed rape genetic maps from different crosses. Uzunova and Ecke (1999) screened some 45,000 clones of a small insert library of B. napus genomic DNA and estimated that GA/TC and CA/TG SSRs occurred at a rate of approx. one repeat every 100 kb and 400 kb, respectively. This high abundance suggests that SSR sequences are also common within coding regions, which can make SSR markers particularly effective tools for MAS and map-based gene cloning. The crosstaxa ampliﬁcation of SSRs among the Brassicaceae was demonstated by Westman and Kresovich (1998), who showed that primer pairs designed to amplify singlelocus SSRs in A. thaliana could in some cases be used to amplify multiple marker loci in different Brassica crop species. Sets of substitution lines have advantages over segregating populations for the analysis of loci inﬂuencing quantitative traits because the effects of individual QTLs can be compared in a uniform genetic background. Howell et al. (1996) developed a strategy for the rapid production of B. napus substitution lines, involving the systematic application of MAS over two to four backcrossing generations. A genetic map containing 158 loci was generated from a population of 200 ﬁrst-backcross (B1) individuals. Six complementary B1 individuals enriched for recurrent genotype and collectively carrying almost all of the donor genome were selected. A total of 288 B2 plants derived from the selected B1 individuals were analysed, and complementary individuals carrying ﬁve or fewer donor segments were identiﬁed. Similar selection, carried out on 250 B3 plants from two distinct B1

88

R. Snowdon, W. Lühs, W. Friedt

lineages, identiﬁed 74 B3 individuals carrying one or two donor segments. Together, 12 of these isolated segments represented 33% of the mapped genome. Lines homozygous for single substituted segments were derived from selfed progeny of selected B3 plants. Full sets of substitution lines can be used to elucidate the genetic control of quantitative production traits in oilseed rape over several environments. Besides representing ‘immortal’ populations that can be continually tested in different years and environments, substitution lines also enable an exact dissection of quantitative traits by enabling observation and comparison of effects from individual QTLs in a uniform genetic background. Butruille et al. (1999) developed four populations of inbred backcross lines of oilseed rape and used them to map genomic regions from the donor parent (a winter-type cultivar) which inﬂuenced agronomic traits in spring-type inbreds and hybrids. RFLP markers identiﬁed among the introgression lines were used to enrich a composite genetic map of B. napus with 72 new RFLP loci. The selfed and hybrid progenies of the inbred backcross lines were evaluated during two growing seasons for several agronomic traits. Both pedigree structure and map information were incorporated into the QTL analysis by using a regression approach and shown to be valuable for both genetic mapping and QTL analysis. Comparison of gene loci and particularly QTL among different mapping populations can only be achieved when consensus markers are available for different crosses. In one example, Lombard and Delourme (2001) developed a framework B. napus consensus map by integrating three DH mapping populations derived from crosses between or within spring and winter rapeseed parents. A total of 992 isozyme, RFLP, RAPD and AFLP markers were mapped to at least one population, and 540 markers could be included in the consensus map. A total of 253 markers were common to at least two populations. Markers were distributed over 19 LGs and covered 2,429 cM. The markers were more or less evenly spaced on the entire genome, although on several LGs RAPD and AFLP markers were not uniformly distributed. The growing availability of Arabidopsis EST collections and their integration into comparative Brassica genome maps (e.g. Fourmann et al. 2002; Babula et al. 2003) enables the ﬁne mapping of genome rearrangements and the delineation of gene-coding regions in crop genomes, enabling the correlation of traits in Brassica crops with Arabidopsis candidate

genes and development of genetic markers considerably more closely linked to the relevant genes. In some cases, however, it is not even necessary to know the gene sequence. For example, Li and Quiros (2001) developed sequence-related ampliﬁed polymorphic (SRAP) markers that preferentially amplify and detect polymorphisms in open reading frames (ORFs). Primers containing CCGG motifs were found to preferentially anneal in ORFs due to the predominance of GC bases in coding sequences. Because exonic sequences generally exhibit few or no length polymorphisms, the ORF primers were combined with primers containing an AATT core near their 3 end. These sequences occur more frequently in promoter regions and introns which are prone to sequence variation, meaning that the resulting anonymous exon-intron markers showed relatively high polymorphism. After sequencing the SRAP ampliﬁcation products it was found that a large proportion of the markers matched known genes, and their utility for gene tagging was demonstrated by localisation of a glucosinolate desaturation gene in B. oleracea. In future, methods that combine candidate gene information with highthroughput technologies for detection and screening of closely linked markers are likely to have the best success in the study and isolation of gene loci involved in important quantitative traits in rapeseed.

2.6.3 Mapping of Genes and QTLs for Morphological and Quality Traits A large amount of work over the past decade has been done to investigate the genetic basis of morphological and quality traits in oilseed rape. Teutonico and Osborn (1994) constructed an RFLP linkage map for oilseed B. rapa, using anonymous genomic DNA and cDNA clones from Brassica along with cloned genes from A. thaliana, and used this map to localise genes controlling simply inherited traits including yellow seeds, seed erucic acid and pubescence. The map included 139 RFLP loci organised into ten LGs and one small group, covering a total of 1,785 cM. Comparisons of the linkage arrangements between B. rapa and B. oleracea revealed extensive colinearity among the Brassica A and C genomes, and nine of the ten B. rapa LGs had conserved linkage arrangements with B. napus LGs. The majority of loci in common were in the same order among the three species, although the distances between loci were largest on the B. rapa map.

Chapter 2 Oilseed Rape

Genome organisation was also compared between B. rapa and A. thaliana using RFLP loci detected with 12 cloned genes in the two species. This provided some of the ﬁrst evidence that linkage arrangements are to a certain extent conserved among even distantly related crucifer genomes. Considerable knowledge has been gained on the genetic control of ﬂowering time through QTL analysis and comparative mapping of genes associated with this trait in ortholgous Brassica and Arabidopsis genome regions. Osborn et al. (1997) found that a major QTL for ﬂowering time, VFR2, occurs in a region homologous to a region in B. napus which controls the same trait, and that this region is also homologous to the top of chromosome 5 in Arabidopsis, where several ﬂowering-time genes are located. After backcrossing, VFR2 was found to segregate as a discrete character, and by comparative ﬁne mapping it was shown to co-segregate with the Arabidopsis gene ﬂc, which regulates ﬂowering time (Kole et al. 2001). Thus, VFR2 appears to be homologous to FLC and may control ﬂowering time though a similar mechanism as in A. thaliana. Uzunova et al. (1995) constructed a linkage map of the rapeseed genome comprising 204 RFLP markers, two RAPD markers and one phenotypic marker using an F1 -derived DH population from a cross between the old German winter rapeseed landrace Mansholt’s Hamburger Raps and the French winter rapeseed cv. Samourai. The mapped markers were distributed on 19 LGs covering 1,441 cM. About 43% of the markers proved to be of dominant nature, 36% of the mapped marker loci were duplicated, and conserved linkage arrangements reﬂected extensive duplicated regions in the rapeseed genome. Using cDNA probes for the genes of acyl-carrier-protein (ACP) and beta-ketoacyl-ACP-synthase I (KASI) the respective homoeologous loci of these genes were localised, and the linkage map was also used to localise QTL for seed glucosinolate content by interval mapping. Four QTLs were mapped on different LGs. This map was used by Ecke et al. (1995) to localise the genes controlling the synthesis of erucic acid and loci involved in variation for oil content. The observed three-class segregation for erucic acid conﬁrmed the inheritance of this trait by two erucic acid genes, which were mapped to two different LGs on the RFLP map. Although the parents of the segregating DH population showed no signiﬁcant difference in seed oil content, in the DH population a transgressive segregation in oil content was observed. This segregation followed a normal dis-

89

tribution, characteristic of a quantitative trait. Three QTLs for seed oil content were localised on three different LGs. Their additive effects together explained about 51% of the phenotypic variation for oil content and two of the QTLs co-localised with the two erucic acid genes, indicating a direct effect of these genes on oil content. Based on a candidate gene approach, two DNA sequences homologous to the FAE1 gene involved in erucic acid synthesis were isolated from a B. napus immature embryo cDNA library by Barret et al. (1998). The sequences of the two cDNA clones were highly homologous, yet distinct, sharing 97% nucleotide identity and 98% identity at the amino acid level. Southern hybridization showed the rapeseed beta-ketoacyl-CoA synthase to be encoded by a small multigene family, whereas Northern hybridization showed the expression of the rapeseed FAE1 gene(s) to be restricted to the immature embryo. One of the genes was found to be tightly linked to one of two loci controlling erucic acid content in rapeseed. Fourmann et al. (1998) developed polymorphic markers within the respective B. napus FAE1 gene loci from the A and C genomes and showed that the two genes co-segregated with the erucic acid loci identiﬁed by Ecke et al. (1995). Finally, Das et al. (2002) cloned the fatty acid elongase genes (FAE1) from B. campestris and B. oleracea and demonstrated their effects on erucic acid levels. Jourdren et al. (1996a) also mapped the gene loci responsible for expression of erucic acid in DH progeny derived from a low × high erucic acid F1 hybrid. In this case the aim was to identify genetic markers closely linked to the genes that could be used to assist selection for high erucic acid levels, which is complicated by an inability to clearly distinguish between homozygous and heterozygous genotypes with high levels of this fatty acid. RAPD markers were used to map the two genes to two independent LGs through a QTL approach. A close association was found between individual plant genotypes and the erucic acid content of the doubled-haploid (DH) progeny, and it was shown that the two genes do not contribute uniformly to the erucic acid level. A gene determining alpha-linolenic acid (18:3) content was localised by Hu et al. (1995), whereas Jourdren et al. (1996b) identiﬁed speciﬁc polymorphisms within one of the B. napus FAD3 delta-desaturase gene copies which were also associated with variations in linolenic acid concentration. Schierholt et al. (2000) mapped a high oleic acid mutation in B. napus and showed that linked AFLP markers localised near

90

R. Snowdon, W. Lühs, W. Friedt

a copy of the FAD2 gene. Previously, Tanhuanpaa et al. (1998) had identiﬁed an association between the FAD2 gene, which codes 18:1 desaturase, and a QTL for oleic acid in B. rapa. Allele-speciﬁc PCR markers for this locus were developed based on a single nucleotide polypmorphism (SNP) in the B. rapa FAD2 gene which resulted in a functionally relevant amino acid substitution. Somers et al. (1998) identiﬁed RAPD markers associated with linoleic acid desaturation in B. napus and found that the gene FAD3 localised near one of the identiﬁed QTLs for this trait. Markers associated with low linolenic acid loci were identiﬁed in a DH population derived from a cross between the B. napus lines Apollo (low linolenic) and YN90-1016 (high linolenic) using RAPDs and BSA. A total of 16 markers were distributed over 3 LGs, which individually accounted for 32, 14 and 5% of the phenotypic variation in 18:3-content. The rapeseed FAD3 gene was mapped near the locus controlling 14% of the variation. The mode of inheritance appeared to be additive, and a QTL analysis showed that collectively the three loci explained 51% of the phenotypic variation within this population. PCR fragments for low linolenic acid Apollo alleles were identiﬁed at all three loci, and simultaneous selection for these alleles at all three loci resulted in a group of DH lines with 4.0% linolenic acid. Hu et al. (1999) also identiﬁed two RAPD markers linked to 18:3-content in rapeseed oil and furthermore developed sequence-tagged markers associated with low linolenic acid content that appeared to be linked to an omega-3 desaturase gene. A SCAR marker was developed that ampliﬁed an allele associated with low linolenic acid content. A different allele was associated with high linolenic acid content in the cross investigated. This marker, which explained some 25% of the genetic variation for the trait, was mapped and found to co-localise with an omega-3 desaturase gene in B. napus. Howell et al. (2003) localised loci controlling seed glucosinolate content in aligned oilseed rape maps generated from their two intervarietal backcross populations. Four QTLs were localised in a population derived from the cross Victor × Tapidor, together accounting for 76% of the phenotypic variation. Three of these loci also appeared to control the accumulation of seed glucosinolates in the cross Bienvenu × Tapidor, where they accounted for 86% of the phenotypic variation. The three QTLs common to both mapping populations mapped to homoeologous genomic regions, suggesting that seed glucosinolate ac-

cumulation is controlled by duplicate genes. A comparative analysis of QTLs controlling seed glucosinolate accumulation was performed by aligning these two maps with published genetic maps generated by other research groups. This demonstrated that highglucosinolate varieties often carry low-glucosinolate alleles at one or more of the loci controlling seed glucosinolate accumulation. Such information could be particularly interesting for the development of heterotic pools for oilseed rape hybrid breeding because it underlines the possibility of transgressive segregation for reduced glucosinolate content in crosses among genetically diverse genotypes with potentially improved yield heterosis. The development of genetic markers is of particular interest in efforts to select for complex quantitative traits that are strongly inﬂuenced by environment, for example seed color. Various groups have in recent years investigated the inheritance of yellow seed color in different segregating crosses involving yellow- and black-seeded genotypes of different origin. Somers et al. (2001) investigated seed-color inheritance in a DH population derived from the F1 generation of the cross Apollo (black-seeded) × YN90-1016 (yellow-seeded). The offspring were analysed via BSA to identify molecular markers associated with the yellow-seed trait, and a single major gene ﬂanked by eight RAPD markers was found to cosegregate with the yellow seed coat color trait in the population. This gene explained over 72% of the phenotypic variation in seed coat color. Further analysis of the yellow-seeded portion of this DH population revealed two additional genes favoring Apollo alleles, which respectively explained 11 and 8.5% of the variation in seed coat color. The data suggested a dominant, epistatic interaction between the major locus and the two additional genes, a hypothesis that was supported by segregation data presented by Liu et al. (2005) in crosses between a yellow-seeded DH line, derived from a resynthesised B. napus, with different black-seeded genotypes. However, the latter study also found that in one of the two populations only one of the two epistatic loci was segregating. We have also analysed QTL and segregation patterns for seed color in two independent populations segregating for the yellow-seed trait (Badani et al. in press). The ﬁrst was a DH population derived from a cross between a yellow-seeded B. napus winter-type 25629-3 with high erucic acid and high glucosinolate (++) quality and the black-seeded ++-quality DH line K26-96. For comparative QTL and segregation analyses, an F2 :F3

Chapter 2 Oilseed Rape

population, along with a large DH population, was also derived from a second cross involving a different source of yellow seed color. This cross involved an inbred line of the black-seeded 00-quality German winter oilseed rape cv. Express and the yellow-seeded ++-line 1012/98. Mapping was performed including reference AFLP and SSR markers mapped in the Mansholt’s × Samourai population used by Uzunova et al. (1995) and Ecke et al. (1995), meaning that the maps could be closely aligned for comparison of the respective QTLs. In concurrence with Somers et al. (2001) and Liu et al. (2005), it was found that the yellow-seed trait in both crosses was controlled by a major QTL controlling around 50% of the phenotypic variance for seed color in each cross. Furthermore, this locus was found to co-localise with a major QTL contributing to reduced raw ﬁber content in the seed meal, establishing a causative relationship between seed coat thickness and seed color. This observation suggests that the same major gene contributes to expression of the yellow-seed trait in yellow-seeded lines of different origin. Further contributing QTL were localised on different chromosomes in the two crosses, indicating that seed color in different B. napus material is inﬂuenced by different epistatic genes. As described by Somers et al. (2001) one cross had two epistatic loci. The other cross showed only two epistatic loci, corresponding to the suggestion of Liu et al. (2005) that epistatic genes for seed color may not segregate in all crosses between black and yellow genotypes. The hypothesised bigenic and trigenic inheritance of seed color in the respective crosses were conﬁrmed by segregation analyses, and markers which are closely linked to the seed-color loci are being used in ongoing work to attempt a map-based cloning of the gene corresponding to the main-effect QTL.

2.6.4 Mapping of Genes and QTL for Disease Resistance A signiﬁcant focus of mapping efforts in oilseed rape has been the genetic characterization of disease resistances. Pilet et al. (1998a,b, 2001) identiﬁed QTL for ﬁeld resistance to blackleg (L. maculans) in crosses of different genetic background, whereas other authors (e.g. Ferreira et al. 1995a; Mayerhofer et al. 1997) mapped major loci associated with race-speciﬁc L. maculans resistance genes. In the former work, genomic regions controlling blackleg resistance at the

91

adult plant stage were detected using 152 DH lines derived from the F1 of the cross Darmor-bzh × Yudal. Blackleg resistance of each DH line was evaluated in ﬁeld tests in 1995 and 1996 by measuring the mean disease index (I) and the percentage of lost plants (P). In the ﬁrst year of ﬁeld trials ten QTLs were detected. Seven QTLs for I and six QTLs for P explained 57 and 41% of the genotypic variation, respectively. Three of these QTLs were common to I and P. In a second year of trials seven QTLs were identiﬁed, including ﬁve for I and two different QTLs for P that accounted for 50 and 23% of the genotypic variation, respectively. In the second year, one QTL located close to a dwarf gene Bzh was detected with a very strong effect, masking more QTL detection. Four major genomic regions were revealed in both years with the same parental contribution, one of which carried a resistance allele from the susceptible parent. Genes involved in blackleg resistance were also mapped by Dion et al. (1995) with RFLP markers. One hundred seventy-ﬁve polymorphic RFLP loci were mapped in a segregating population composed of 98 DH lines from the cross Cresor (blackleg resistant) × Westar (susceptible). A single chromosomal region, accounting for a high proportion of the variation in blackleg reaction, was found to be responsible for resistance in each of four environments tested. A second QTL, with a smaller effect on blackleg reaction, was present in one of four year/site assays. A Mendelian approach, using blackleg disease ratings for classifying DH lines as resistant or susceptible, allowed a single resistance locus to be mapped in the region of the major QTL. This strongly suggested the presence of a single major gene controlling adult plant resistance to blackleg in the resistant spring canola cv. Cresor. Mayerhofer et al. (1997) used BSA of DH lines derived from a cross between the Australian spring canola cultivars Shiralee and Maluka to identify 13 RAPD and two RFLP markers linked to blackleg resistance in this cross. QTLs for blackleg resistance were localised in the map from the segregating DH population and suggested the presence of a single major locus controlling resistance in the cultivar Shiralee. This conﬁrmed previous results obtained from Mendelian genetic analyses. Furthermore, mapping data for the cultivar Maluka also supported a single locus model for resistance and indicated that the resistance genes from Shiralee and Maluka were either linked or possibly identical. In further work on blackleg resistance breeding and analysis, Plieske and Struss (2001) converted RFLP and RAPD markers

92

R. Snowdon, W. Lühs, W. Friedt

linked with B-genome blackleg resistance into STS markers for use in marker-assisted backcrossing of the introgressed trait in B. napus, and Wretblad et al. (2003) isolated the cDNA sequence Lm1 from B. nigra, which gave enhanced resistance to Leptosphaeria maculans when overexpressed in oilseed rape. Genes involved in resistance to turnip yellows virus (TuYV) were mapped by Dreyer et al. (2001) in a DH population derived from a cross between a resynthesised rapeseed line (as donor for TuYV resistance) and the elite winter oilseed rape cv. Express. After screening 17 AFLP primer combinations (PstI/MseI and EcoRI/MseI), 143 AFLP markers were mapped to 20 LGs. QTLs for TuYV resistance were localised by composite interval mapping. A major QTL was found that explained some 50% of the phenotypic variation for resistance in this cross. Because no other factors displaying a signiﬁcant effect on the expression of resistance could be identiﬁed, a simple mode of inheritance for TuYV resistance was suggested, and linked markers should hence be useful for MAS of resistant lines. Walsh et al. (1999) also mapped genes responsible for turnip mosaic virus (TuMV) resistance. In this case a dominant gene, designated TuRBO1, was localised using a set of DH lines from the population used by Sharpe et al. (1995) to develop a B. napus reference RFLP map. TuRBO1 was found to confer extreme resistance to some isolates of TuMV. The positioning of the gene on LG N6 of the B. napus A-genome indicated that the gene probably originated from B. rapa. The speciﬁcity of TnRBO1 was determined using a wide range of TuMV isolates. A second locus, TuRBO2, that appeared to control the degree of TuMV susceptibility in a quantitative manner was identiﬁed on the C-genome LG N14. By combining these resistance genes using marker-assisted breeding it should be possible to develop durable resistance to TuMV. A further genetic map was produced by Zhao and Meng (2003) speciﬁcally for QTL analysis of sclerotinia stem rot in partially resistant material from China. Quantitative loci involved in resistance to Sclerotinia sclerotiorum were detected in a population of 128 F2:3 families derived from a cross between a male sterility restorer line (H5200) and a partially resistant line (Ning RS-1). A total of 107 molecular markers including 72 RFLPs, 30 AFLPs, 3 SSRs and 2 RAPDs were used to construct a genetic linkage map with 23 LGs covering 1,625 cM. Resistance data from detached leaf inoculation at the seedling stage and in vivo stem inoculation at the mature plant stage were used to identify QTL involved in resistance. Of six de-

tected QTLs, three were associated with leaf resistance at the seedling stage and collectively accounted for 41% of the total phenotypic variation. Three further QTLs were found to correspond to the disease resistance at the mature plant stage, with additive epistatic interactions among the loci. The Major × Stellar cross described previously was used by Ferreira et al. (1995c) to localise a single dominant gene controlling resistance to white rust (Albugo candida) in B. napus, and the position of this locus relative to genes controlling A. candida resistance in oilseed B. rapa was later compared by Kole et al. (2002b). A minor resistance QTL was identiﬁed in B. rapa on the same homologous A-genome LG containing the dominant resistance gene in B. napus, however, at a different map position according to an alignment of the LGs using common loci. Furthermore, no resistance locus was identiﬁed in B. napus on the LG homologous to that carrying the gene controlling resistance in B. rapa. This indicates potential positions of additional resistance loci in both species that have not been mapped in the individual studies. Alignment of the respective maps to the physical map of the Arabidopsis genome was used to identify regions to target for comparative ﬁne mapping.

2.6.5 Mapping QTLs for Abiotic Stress One of the most important forms of biotic stress for oilseed rape is cold stress in winter forms sown in autumn, which in particularly cold climates or harsh winters must possess a certain degree of freezing tolerance to survive the winter. Besides acclimatised and non-acclimatised freezing tolerance, winter survival may also be affected by genetic variation for other cold-regulated traits like vernalisationresponsive ﬂowering time. Because biennial B. napus forms generally have a higher winter survival than annual forms, populations that segregate for resistance to cold stress can be generated by crosses between the two. Using the DH population produced by Ferreira et al. (1994) from the cross between the winter-hardy, freezing-tolerant winter rapeseed variety Major and the cold-sensitive spring canola cultivar Stellar, Teutonico et al. (1995) detected different QTLs for acclimatised and non-acclimatised freezing tolerance. Using the same immortal population, Kole et al. (2002a) localised QTLs for winter survival, freezing tolerance and ﬂowering time and compared the map positions

Chapter 2 Oilseed Rape

with corresponding loci in B. rapa. The B. napus population was evaluated in multiple winters, and 6 out of a total of 16 signiﬁcant QTLs for winter survival were detected in more than one winter. Some QTLs for the different traits were found to co-localise within both B. napus and B. rapa, suggesting that some alleles causing greater acclimatised freezing tolerance and later ﬂowering time also contributed to increased winter survival. Correspondence in the map positions of QTLs between species provided evidence for allelic variation at homologous loci in B. rapa and B. napus. Interestingly, many of the DH lines were found to exhibit better winter survival than the winter-hardy parent Major, suggesting that favorable alleles may also have been contributed by the cold-sensitive parent. This was supported by results from QTL mapping, in which alleles increasing winter survival were detected from both parents.

2.6.6 Towards an Integrated B. napus Genetic Map The most useful current technology for map integration is provided by SSR markers, which due to their highly polymorphic and robust nature and simple, relatively inexpensive analysis are a particularly valuable resource for map alignment among different crosses. The number of publicly available Brassica microsatellite primers is increasing as a result of publicly funded international initiatives (see www.brassica.info/ssr/SSRinfo.htm); however, in comparison to other important crop species relatively few markers are freely available to date, which has hindered the effective integration of genetic maps produced by different groups worldwide. In 2005 the primer sequences from a set of mapped B. napus SSR markers developed in the Celera AgGen Brassica Consortium involving 16 breeding companies from different countries were released to the public domain (Piquemal et al. 2005), and other groups are also planning the release of mapped SSR markers. The availablility in the public domain of robust, polymorphic, mapped SSR markers spanning the entire B. napus genome will without doubt assist the entire Brassica genetics community in consensus mapping and genome integration. Integration of consensus markers into existing and new genetic maps will considerably accelerate the progress of map and QTL alignment among diverse oilseed rape crosses and hence will ultimately play a pivotal role in

93

the correlation of candidate gene loci with important QTLs. During recent discussions at the Joint Meeting of the 14th Crucifer Genetics Workshop and the 4th ISHS Symposium on Brassicas in Daejeon, South Korea, it was agreed that a common international nomenclature must be adopted for the naming of B. napus LGs and chromosomes, respectively. The suggested standard is the convention already used by many authors that numbers the ten A-genome chromosomes N1– N10 and the nine C-genome chromosomes N11–N19, respectively (e.g. Parkin et al. 1995; Sharpe et al. 1995; our Fig. 2). By integrating mapped SSR markers from reference maps being developed from publicly available populations of B. rapa, B. oleracea and B. napus into existing maps, it should ultimately be possible to integrate and align all B. napus maps and establish corresponding associations to newly developed physical maps and physical karyotypes. This iniative, when achieved, will vastly increase the ability to exchange and compare information among different oilseed rape mapping populations. For example, the comparison of QTLs for agronomically important traits will be feasible among different crosses, giving a considerably broader overview of the genetic control of quantitative traits than was available to date in individual populations. To this end, an international collaboration has been initiated to publish for the ﬁrst time an integrated map that aligns the LGs of numerous genetic maps, developed from different crosses in different countries, and compares the locations and effects of previously localised QTLs in different genetic backgrounds (R. Delourme, INRA, France, pers. comm.). Of enormous beneﬁt to this task will be the increasing availablility of public SSR markers derived for example from bioinformatics approaches. Based on BAC-end sequence data from the ongoing B. rapa sequencing program, or using the already available B. oleracea shotgun sequences from TIGR (see below), the computer software Sputnik (originally by Chris Abajian at Washington University, now available at http://sputnik.btk.ﬁ/) enables the extraction of putative SSR sequences and design of primers for SSR analyses. Mapping of the resulting SSR loci will potentially provide a direct link between genetic and physical maps and a correspondence to annotated sequences in A. thaliana, and as the collection of Brassica genomic sequences continues to grow the availability of such markers will increase correspondingly.

94

R. Snowdon, W. Lühs, W. Friedt

Among the growing resources being developed and distributed by the Multinational Brassica Genome Project (MBGP, see below) are genetic and physical maps for a B. rapa cross between inbred lines of the Chinese cabbage (B. rapa L. ssp. pekinensis) varieties Chiifu and Kenshin. A group of international researchers has begun the construction of a highdensity genetic map of this cross using AFLP, PCRRFLP, EST and SSR markers. In October 2004 the map comprised around 900 markers with an average distance of around 2 cM, and the aim was to saturate this map with up to 5,000 markers during 2005. Because the Chiifu inbred line is the genotype being used for the complete sequencing of the B. rapa genome, this map provides the opportunity for a direct alignment with the B. rapa physical map and annotation to the genome sequence. Further progress in anchoring of ESTs to the B. rapa genetic map, along with the anchoring of a growing collection of B. rapa genomic sequence tags to the Arabidopsis physical map, will in the foreseeable future provide a powerful new set of integrated data linking genetic and physical map information between the model and crop genomes. This information will be of enormous relevance to B. napus genome analysis, particularly when the markers on the B. rapa reference map are used to create similar high-densiity annotated maps in B. napus. One example of such an initiative is the multinational collaboration IMSORB (Integrated Marker System for Oilseed Rape Breeding; http://brassica.bbsrc.ac.uk/IMSORB/). In this collaboration between European and Chinese scientists, a reference B. napus genetic map is being established by mapping EST sequences in a DH population derived from a cross between the European oilseed rape cv. Tapidor and the Chinese cv. Ningyou 7. Genespeciﬁc hybridization probes from A. thaliana are being used to identify clones from a B. napus Tapidor BAC library representing around 1,100 regions of the genome of B. napus. To allow alignment of the B. napus BAC contigs with A- and C-genome physical maps currently under construction, corresponding gene sequences or PCR products are being obtained from the appropriate clones from A- and C-genome BAC libraries. By sequencing alleles from Ningyou 7 corresponding to the respective gene copy in Tapidor, SNP markers are to be identiﬁed that allow the genetic mapping of up to 500 genes in the segregating DH population. The resulting map will be aligned with existing B. napus maps using public-domain RFLP and SSR markers. Ultimately the intention is

to construct a publicly available database containing details of gene sequences, markers and polymorphisms, along with detailled genetic, physical and comparative mapping data.

2.7 Comparative Genomic Studies Due to the close phylogenetic relationship of crop Brassicas with Arabidopsis, for which the entire genome sequence has been available since 2000, it was anticipated that knowledge transfer for Brassica crop improvement would be straightforward. However, although the physiology and developmental biology of Arabidopsis and Brassica are very similar, the genomes of Brassica species are much more complex than that of A. thaliana, as a result of multiple rounds of polyploidy during their ancestry. For example, B. napus may contain over 100,000 genes, compared to only around 28,000 in A. thaliana. This makes the identiﬁcation of orthology relationships of genes extremely difﬁcult, and the presence in Brassica of multiple homologues of each gene in A. thaliana provides ample opportunity for divergence of gene function. Genome colinearity, or conservation of marker order, has been widely investigated in comparative genomic studies between the model crucifer A thaliana, which possesses the most extensively studied higher plant genome, and the closely related Brassica crops. Comparative genetic mapping experiments have established colinearity of genomes for species of the Brassicaceae. A plethora of physical mapping and sequencing experiments have revealed considerable conservation of gene sequence and order between Arabidopsis and Brassica, although genome rearrangements are often considerably more complex than they appear at ﬁrst glance. Physical genome maps and sequence data from A. thaliana together with comparative analysis of its syntenic relationships to Brassica genomes provide potentially powerful tools for genome analysis and gene discovery in oilseed rape, the closest major crop relative to the model plant. Generally 80 to 90% homology is found between the exons of putative orthologous genes in Arabidopsis and Brassica (Schmidt 2002), meaning that knowledge from Arabidopsis is highly relevant for gene isolation and characterization in Brassica crops. In comparative studies of genome

Chapter 2 Oilseed Rape

regions ﬂanking known genes, extensive colinearity between Arabidopsis and Brassica genome segments has been observed on a microsyntenic level. However, minor deletions, insertions and translocations are relatively common in regions surrounding Brassica orthologues of Arabidopsis genes. For example, Quiros et al. (2001) described small-scale colinearity between the region of A. thaliana chromosome 4 containing the ABI1-Rps2-Ck1 gene complex and a homoeologous segment of B. oleracea chromosome 4. Although almost complete microsynteny was observed, the B. oleracea regions contained an extra gene with homology to genes located on Arabidopsis chromosomes 2 and 5. In other words, even regions with well-preserved colinearity on a microsyntenic scale can be interrupted by translocations. In fact, one or several homologues of Arabidopsis genes may be missing from any particular triplicated region in Brassica (Schmidt 2002). The different orthologous regions in Brassica are therefore often comprised of a different gene repertoire. Nevertheless, due to the large-scale synteny over long chromosome stretches, it is still often feasible to utilise sequence information from markers ﬂanking genes or QTLs of interest in Brassica crops, to identify possible candidate genes from the corresponding chromosome regions in Arabidopsis. For example, different homoeologous regions in B. rapa and B. napus, which contain various QTLs inﬂuencing ﬂowering time, each show signiﬁcant colinearity to Arabidopsis chromosome sections containing a number of genes relevant to ﬂowering time (Lagercrantz et al. 1996; Osborn et al. 1997; Kole et al. 2001). The use of Arabidopsis as a tool in marker development, map-based gene cloning and candidate gene identiﬁcation in Brassica crop species is complicated by the complex arrangement of the (ancestral) polyploid Brassica genomes. As the genome relationships between Arabidopsis and Brassica have been unravelled, however (e.g. Paterson et al. 2000; Schmidt et al. 2001, Parkin et al. 2005), the model plant has developed into the most important resource for gene isolation and characterization in Brassica crops. In recent years it has become increasingly feasible to integrate genetic mapping with a candidate gene approach (Pﬂieger et al. 2001) using Arabidopsis resources and genome tools to identify gene loci involved in both simple and complex traits. Fourmann et al. (2002) and Chalhoub et al. (2003) described an effective method to use functional PCR markers for physical mapping of A. thaliana gene loci in B. napus. This approach

95

is based on so-called physical functional markers (PFMs) for candidate genes and centers on the observation that most A. thaliana gene loci are present as multiple copies in polyploid Brassica genomes. The availability of detailed Arabidopsis sequence information enables the use of syntenic regions surrounding candidate genes for better characterization of orthologous locus copies. For example, BAC clones identiﬁed by gene-speciﬁc ﬁlter hybridization or PCR can be separated into locus-speciﬁc contigs through the presence or absence of PCR markers that are ampliﬁed using speciﬁc primers within or ﬂanking the gene sequence. These markers not only enable the variable intron-exon structure of the orthologous gene copies to be better characterized, but can also be used as a basis for development of locus-speciﬁc SNPs. The latter are a promising basis for allele-trait association studies of relevant candidate genes and simultaneously provide a basis for integration of the loci in physical functional genetic maps. Cavell et al. (1998) assessed genomic colinearity to B. napus chromosome regions over a 7.5-Mbp region of the long arm of A. thaliana chromosome 4, equivalent to 30 cM. Estimates of copy number indicated that sequences present in a single copy in the haploid genome of A. thaliana were present in two to eight copies in the haploid genome of B. napus, while sequences present in multiple copies in A. thaliana were present in over ten copies in B. napus. Genetic mapping in B. napus of DNA markers derived from a segment of A. thaliana chromosome 4 revealed duplicated homologous segments in the B. napus genome. Physical mapping in A. thaliana of Brassica homologues derived from these regions conﬁrmed the identity of six duplicated segments with substantial homology to the 7.5-Mbp region of chromosome 4 in A. thaliana. These six duplicated Brassica regions (on average 22 cM in length) were found to be colinear, except that two of the six copies contained the same large internal inversion. Ryder et al. (2001) investigated colinearity of marker order between two different genome regions of B. oleracea and homologous regions of A. thaliana. Although widespread replication of marker loci was observed in both A. thaliana and B. oleracea, a combination of RFLP, CAPS and SSR markers mapped in B. oleracea enabled analysis and identiﬁcation of mediumscale chromosomal organisation and rearrangements. Probes were hybridized onto BAC contigs representing the whole A. thaliana genome. A total of 20 marker loci were sampled from throughout the shortest B. ol-

96

R. Snowdon, W. Lühs, W. Friedt

eracea LG and 21 from a 30-cM section of the longest LG which contained locus duplications. Locus order was conserved between a putative duplicated region of 10.5 cM and a discrete region comprising 25 cM of A. thaliana chromosome 1. This was supported by evidence from seven paralogous loci, three of which were duplicated in a 30.6-cM region of the largest B. oleracea LG. The pattern of locus order for the remainder of this LG and the sampled section of the shortest LG was more complex when compared with the A. thaliana genome. Although there was some conservation of locus order in this case, this was superimposed upon a complex pattern of additional loci which were replicated in both A. thaliana and B. oleracea. Parkin et al. (2002) investigated chromosomal colinearity between chromosome 5 of A. thaliana and the genome of B. napus using in silico sequence homology to identify conserved loci between the two species. An 8-Mb region of chromosome 5 was found in six highly conserved copies in the B. napus genome. A single inversion appeared to be the predominant rearrangement that had separated the two lineages leading to the formation of Arabidopsis chromosome 5 and the homologous chromosome regions in B. napus. The observed results were explained by the putative fusion of three ancestral genomes with strong similarities to modern-day Arabidopsis, which subsequently led to the constituent diploid B. napus genomes. This result supports the hypothesis that the diploid Brassica genomes evolved from a common hexaploid ancestor. Alignment of the genetic linkage map of B. napus with the Arabidopsis genomic sequence indicated that for speciﬁc regions a genetic distance of 1 cM in B. napus was equivalent to only 285 kb of Arabidopsis DNA sequence. This underlines the high suitability of Arabidopsis for marker development, map-based gene cloning, and candidate gene identiﬁcation in oilseed rape and other Brassica crops. Sillito et al. (2000) mapped Arabidopsis ESTs with homology to cloned plant disease resistance genes in A. thaliana and B. napus in order to identify candidate resistance gene loci and investigate intergenomic colinearity. A total of 103 resistance gene EST loci were localised on a B. napus genetic map, 48 of which could also be locted on the Columbia × Landsberg A. thaliana map. The mapped loci identiﬁed colinear regions between Arabidopsis and Brassica that had been observed in previous comparative mapping studies, and the detection of syntenic genomic regions indicated

that there was no apparent rapid divergence of the identiﬁed genomic regions containing the resistance EST loci. Genetic maps of B. napus generally show almost complete colinearity (e.g. Fig. 2; also Lydiate et al. 1993; Lombard and Delourme 2001), and it has been found that the A- and C-genome LGs in B. napus have remained essentially unchanged in comparison to the diploid genomes (Parkin et al. 1995). Because of the high level of similarity between the A and C genomes this means that the diploid species provide an extremely useful tool for analyses of colinearity and duplication between Arabidopsis and the complex amphidiploid genome of B. napus. Comparison of B. rapa with B. oleracea and B. napus supports the close evolutionary relationship between the two diploids but indicates that deletions and insertions may have occurred after divergence of the two species (Hoenecke and Chyi 1991). The genome of synthetic B. napus is essentially unrearranged with respect to its B. oleracea and B. rapa progenitors (Lydiate et al. 1993; Parkin et al. 1995; Sharpe et al. 1995), although the evolution of wild B. napus may have been been accompanied by more complex rearrangements (Cheung and Landry 1996). Extensive segmental duplications are found in both diploid and amphidiploid maps, supporting the hypothesis that diploid Brassica species are derived from an ancestor with a lower original basic chromosome number. However, no duplications of whole LGs have been found. Homoeologues of B. oleracea are differentiated from one another by a minimum of 22 chromosomal rearrangements (Lan et al. 2000; Paterson et al. 2000). Figure 3 (from Paterson et al. 2000) gives a detailed and impressive view of homology and duplications between the genomes of Arabidopsis and B. oleracea revealed by comparative RFLP linkage mapping of B. oleracea and alignment of loci to the map of A. thaliana. Recently Parkin et al. (2005) published a comprehensive comparative map of the A. thaliana and B. napus genomes based on Brassica RFLP markers, whereby the ﬁve Arabidopsis chromosomes could be allocated via macrosyntenic comparisons to a minimum of 22 conserved blocks that are duplicated and rearranged throughout the B. napus genome. Although such representations emphasise the underlying complexity of the genome rearrangements between Brassica and Arabidopsis; on the other hand they also reveal the extensive colinearity and hence the great potential the model genome offers for comparative genetic analyses of oilseed rape.

Chapter 2 Oilseed Rape

97

Fig. 3. Composite RFLP linkage map of Brassica oleracea and its alignment to the map of Arabidopsis thaliana. Filled circles next to loci: homoeologous Brassica loci (chromosomes 1 to 9, near right) or homologous Arabidopsis loci (chromosomes 1 to 5, far right) detected by the same probe. When all the circles are open, no polymorphism was detected for homoeologous (Brassica) or homologous (Arabidopsis) loci. An R next to the probe name indicates that the probe hybridizes to a repetitive DNA sequence in Arabidopsis. Speciﬁc colors were assigned to each homoeologous (Brassica) and homologous (Arabidopsis) chromosome. Markers that appear to represent duplication of Brassica chromatin or orthology between Brassica and Arabidopsis (based on criteria described in Lan et al. 2000) were connected by colored columns. Open columns indicate possible triplicated (Brassica) or duplicated (Arabidopsis) regions. Vertical axis: centimorgans. This image, modiﬁed from Lan et al. (2000), was kindly provided by Andrew Paterson, University of Georgia, Athens, GA, USA and is used with permission from Cold Spring Harbor Laboratory Press

98

R. Snowdon, W. Lühs, W. Friedt

Fig. 3. (continued)

2.8 Physical Mapping and Genomics Tools 2.8.1 Physical Mapping The genomes of Brassica species, although four to ten times larger than that of A. thaliana, are still of a manageable size for application of genomic technologies, and in recent years increasing emphasis has been placed on phyiscal mapping of B. napus and particularly its diploid progenitor genomes. Physical maps are currently under construction for the Brassica A genome in Korea and for both the A and C genomes in the United Kingdom, and partial phys-

ical mapping of the genome of B. napus is being conducted by groups in Canada and in the EU. Although such physical maps will be of great value for the identiﬁcation of speciﬁc regions in the genomes of these important crops, they will not permit the detailed analysis of the entire Brassica genome, the preparation of microarrays to analyse the transcriptome, or the efﬁcient design of markers associated with the sequences of speciﬁc genes for use in breeding programs. To achieve these objectives, the complete sequence of at least one of the B. napus diploid progenitor genomes is required. Due to the extensive chromosomal and sequence colinearity of the A and C genomes, the sequence of one of these genomes will represent a powerful genomic resource for physical genome analysis in B. oleracea, B. rapa and B. napus alike.

Chapter 2 Oilseed Rape

Numerous countries have established national plant genome programs during the past decade with speciﬁc oilseed rape genome research projects. In Germany, for example, the program Genome Analysis in the Biological System Plant (GABI; http://www.gabi.de) has to date funded two consortia investigating different aspects of commercially relevant winter oilseed rape genomics. These efforts have included, for example, the development of genetic maps for analysis of different seed quality traits and a large-insert BAC library from the German winter rapeseed cv. Express, and are continuing with the development and testing of SNP markers for association studies. The French Génoplánte program (http://www.genoplante.fr) has been particularly active in the development of functional genomics tools for oilseed rape, placing particular emphasis on the use of Arabidopsis genome sequence data to maximally exploit a BAC library from the French dwarf rapeseed variety Darmor-Bzh. The approach taken is to develop physical functional markers (Fourmann et al. 2002) from a large set of A. thaliana coding sequences to physically map oilseed rape ESTs on the oilseed rape BAC library. All A. thaliana coding sequences or oilseed rape ESTs presumed to correspond to a biologically or agronomically important function in oilseed rape were selected as entry points for this approach. Based on the data gathered, functional physical and genetic databases are being developed which are linked at high resolution to the genome sequence of A. thaliana. These resources will facilitate rapid gene and QTL cloning in oilseed rape using both forward and reverse genetics approaches. In Canada, the project Enhancing Canola Through Genomics funded by Genome Prairie (http://www.genomeprairie.ca/canola) was founded in 2003 with the aim of developing integrated functional genomics tools for B. napus. At the time this chapter was prepared 27 embryo and seed-speciﬁc cDNA libraries had been constructed and used for the identiﬁcation of more than 10,000 Brassica unigenes from over 60,000 ESTs related to seed development. Particular emphasis is being placed on analyses of gene expression during seed development. As part of this work, Dong et al. (2004) identiﬁed a set of highly expressed genes from cDNAs isolated from canola seeds 15 d after pollination (DAP) and analysed their differential expression during seed development. From 104 differentially expressed sequence tags (ESTs) 54

99

unique genes could be identiﬁed, of which 33 had putative homologues in Arabidopsis or B. napus. These genes encoded diverse proteins ranging from proteins of unknown function to metabolic enzymes and proteins associated with cell structure and development. Twenty-ﬁve genes were seed-speciﬁc, and 11 of these started to express as early as 5 or 10 DAP. A large set of public EST sequences is also being generated in a joint initiative between the Agriculture and Agri-Food Canada (AAFC) Research Centre of Saskatoon and HRI Warwick, UK. The aim is to provide 30,000 EST sequences from the B. oleracea line A12DHd, for which high-quality genetic, physical and karyotype maps are already available. The ESTs resulting from this project will provide an excellent resource for annotation of the TIGR B. oleracea shotgun sequence (see below) and for improved navigation between B. napus and A. thaliana physical maps. Tools for annotation between the Arabidopsis and Brassica genomes have also been developed as part of the Brassica/Arabidopsis Genomics Initiative (BAGI) at AAFC Saskatoon. One interesting accessory provided by BAGI is the Brassica/Arabidopsis Comparative Genome Browser, with which B. napus ESTs developed by AAFC (3 and 5 sequences for 60,000 cDNAs) can be visualised relative to the Arabidopsis genome sequence as annotated by TIGR. The B. napus ESTs are not yet available in the public version of the browser; however, it is hoped that at least a subset will be made public in coming years. The browser allows visualisation of Brassica DNA sequences relative to homologous sequences in the Arabidopsis genome, allowing provisional physical mapping of ESTs through colinearity, access to the corresponding Arabidopsis gene annotation and clustering of related members of Brassica multigene families. The construction of the database allows users to view all ESTs with similarity to any part of the Arabidopsis genome and see how the different ESTs align with Arabidopsis genes. Furthermore it is possible to search for a speciﬁc gene or EST, to access gene annotations and to identify duplicate genes in Arabidopsis. At the time this chapter went to press negotiations were well advanced for the generation of a new Affymetrix gene chip comprising more than 100,000 Brassica ESTs from public sources and a large number of ESTs from private sources. Release of the Brassica chip was expected in September 2006.

100

R. Snowdon, W. Lühs, W. Friedt

2.8.2 Public Genome Resources: The Multinational Brassica Genome Project Recently the MBGP was established by international Brassica researchers to coordinate Brassica genomics activities and pool resources to achieve common goals. The primary aim of this initiative is the provision of freely available genetics resources for Brassica genome analysis, including mapping populations, markers, genomic libraries, ESTs and genomic sequences. The ﬁrst publicly available physical maps of the B. rapa and B. oleracea genomes were estabished by researchers afﬁliated with the BBSRC in the United Kingdom. BAC libraries were constructed from B. oleracea (JBo and BoB) and B. rapa (JBr) at the John Innes Centre in Norwich and Horticulture Research International in Wellesbourne, UK. Fingerprinting and contig assembly were achieved using computer software developed at the Sanger Institute in Cambridge, UK, and physical maps of the A and C genomes were generated by ﬁngerprinting around 35,000 genomic clones each from the two genomes and integrating these with the genome sequence of A. thaliana by hybridization with selected genomic sequence tags (GSTs). This enabled coverage of some 462 Mb of the B. rapa genome with 2041 BAC contigs, along with the construction of 2433 B. oleracea contigs. The physical map databases can be freely accessed via the Internet (http://brassica.bbsrc.ac.uk/IGF), including a browsing facility and an online order form for BAC clones. Query forms are provided to enable users to easily interrogate the database, obtain the hybridization results for each probe and link to any contigs that it might anchor. As a participant in the MBGP, the Plant Biotechnology Centre (PBC) at Latrobe University in Bundoora, Australia is involved in developing a set of bioinformatic tools for Brassica functional genomics (http://hornbill.cspp.latrobe.edu.au/brassica.html). Among other things applications have been developed for the rapid discovery of SSR and SNP markers in Brassica species using data analysis of accumulated sequence data for primer design (Barker et al. 2003), and the data have been incorporated within an integrated gene annotation database, Brassica ASTRA, that includes modules for the gene ontology (GO) annotation of Brassica sequences and comparative mapping with A. thaliana. Further ongoing developments include the integration of gene expression data from microarray analyses along with

molecular genetic and allele diversity data, which will be interrogated through an Ensembl database. Of particular interest in terms of the whole-genome sequencing of B. rapa is a comparative BAC viewer for the identiﬁcation of Brassica BACs which are syntenic to regions of the Arabidopsis genome. The application of this tool has the potential to enable rapid identiﬁcation of candidate Brassica genes based on microsynteny to corresponding regions between the Brassica and Arabidopsis genomes. A leading role in the MGBP is being played by scientists from the Korean National Institute of Agricultural Biotechnology (NIAB) and the Chungnam National University in Daejeon, South Korea. In particular this has involved the provision of BAC libraries from Chinese cabbage (B. rapa ssp. pekinensis), which together with high-density ﬁlters are freely available for use in the public domain, and an active participation in the ongoing sequencing and physical mapping activities. At the time of writing, a library of more than 10,000 cDNAs had already been developed. More than 1000 molecular markers have been localised on a newly established B. rapa reference map, with alignment to the A-genome B. napus chromosomes. Integration of this map with the physical and karyotype maps will provide a similarly powerful resource for the B. napus A genome to that being developed in the abovementioned activities for the C genome of B. oleracea. Together this rapid accumulation of functional and physical genome orientation data and tools will revolutionise the study of the B. napus genome and enable massive strides in the utilisation of genome and map information for oilseed rape breeding.

2.8.3 Genome Sequencing in B. oleracea and B. rapa In recent years considerable progress has also been made in the endeavour to sequence the complete genome of the diploid B. napus progrenitor species. A total of 454,274 B. oleracea genomic sequence reads, with an average length of around 650 bp, have been generated from a joint whole-genome shotgunsequencing initiative between The Institute for Genomic Research, Rockville, MD, USA (TIGR) and Cold Spring Harbor Laboratory, NY, USA, funded by the United States National Science Foundation. The reads (available at http://www.tigr.org) cover some 295 Mbp (about 0.45x) of the B. oleracea genome, and around a quarter have a match to known proteins. Interest-

Chapter 2 Oilseed Rape

ingly, however, only around 40% of the sequences appear to have a high-quality match to the genome sequence of Arabidopis, although some 90% of the Arabidopsis proteome was represented in the B. oleracea sequence reads. As expected, matches to Arabidopsis sequences are very good in exons and reduced in introns, whereby the conserved regions generally extend into the introns somewhat (i.e. intron-exon splice sites are also converved). According to Town et al. (2006), the B. oleracea sequence data not only provide an exciting new resouce for analysis of Brassica genomes, but they will also be extremely useful for improved gene annotation in Arabidopsis. First estimates using the B. oleracea sequence data to reprogram A. thaliana gene prediction models appeared to identify some 2,000 to 5,000 novel genes in the model genome. Very rough ﬁrst estimates of the gene number in B. oleracea predicted a minimum number of around 41,000 genes based on the sequences available. One important outcome of the MBGP initiative has been the establishment of a project aimed at sequencing the complete genome of B. rapa as a basic DNA sequence resource for Brassica A- and C-genome crops. The Brassica genome sequencing project aims initially to generate fully oriented and ordered Phase 2 sequence (meaning that it will contain some small sequence gaps and low-quality sequences) using BAC clones covering the 500-Mb genome of B. rapa ssp. pekinensis (Chinese cabbage). The genome sequence will be anchored to a reference genetic map containing some 1,000 molecular markers. Scientists requiring ﬁnished sequence from a speciﬁc region will be able to complete it themselves by accessing trace ﬁles that will be archived in an agreed format at TIGR (http://www.tigr.org), MIPS (Munich Information Center for Protein Sequences, Germany; http://mips.gsf.de) and NIAB (National Institute of Agricultural Biotechnology, Suweon, South Korea; http://www.niab.go.kr/homepage/english). The systematic annotation of the genome sequence will be performed with the help of the annotated A. thaliana genome sequence. The B. rapa sequencing is being conducted by an international consortium using common resources. Integration, annotation and public serving of the data will be coordinated by TIGR, MIPS and NIAB. The sequencing program has been divided into three stages. Initially, an online Brassica information resource was established (http://www.brassica.info) along with an online portal for the MBGP (http://brassica.bbsrc.ac.uk/

101

and http://www.niab.go.kr). Communal databases have been constructed for the deposition of genetic (http://ukcrop.net/perl/ace/search/BrassicaDB) and physical mapping data (http://brassica.bbsrc.ac.uk/ IGF/index.htm). Reference genome libraries have been produced for the B. rapa variety Chiifu (Chinese cabbage, B. rapa ssp. pekinensis). The KBrH and KBrB libraries, each consisting of 144 × 384 well plates, were generated using HindIII (KBrH) and BamHI (KBrB) digested genomic DNA, respectively. A total of 110,592 clones are available, providing 20-fold redundant genome coverage. International distribution centers for the two libraries have been set up at NIAB in South Korea and at the John Innes Centre, UK. Reference DH and RIL populations for low- and high-resolution genetic mapping of the Chinese cabbage variety Chiifu are under construction, and high-quality genetic maps for each population are being constructed using publicly available markers. International distribution centers for the reference populations are being established at Chungnam National University, Daejeon, South Korea, and at Horticulture Research International, Wellesbourne, UK. The full set of 110,592 BAC clones in the reference libraries are currently being end-sequenced, with data being deposited in searchable public databases at TIGR, MIPS and NIAB. The aim of the project is to deliver the ﬂanking sequences of all available BACs during 2005. Using the Brassica ASTRA computational tool developed by the Plant Biotechnology Centre of Latrobe University in Australia, the end sequences of the B. rapa BACs are being comparatively mapped onto the Arabidopsis genome (Love et al. 2005; our Fig. 4). Brassica BACs are electronically mapped onto A. thaliana by comparing the BAC end sequences with the Arabidopsis genome sequence, followed by ﬁltering for matches in an inverse orientation within a distance of less than 500,000 bp. The publicly available C-genome shotgun sequences have also been compared with the Arabidopsis genome sequence. These sequences were aligned to the Arabidopsis genome using only sequences with the highest BLAST hit in order to avoid duplicated genome regions (Love et al. 2005). As more A-genome sequence data become available the results of this analysis will give powerful new data regarding the comparative structure of the B. rapa, B. oleracea and A. thaliana genomes and will also give important data on the distribution of A-genome BACs throughout the genome, which is vital for the

102

R. Snowdon, W. Lühs, W. Friedt

Fig. 4. Physical comparison of B. rapa BAC-end sequences (red) and B. oleracea shotgun sequences (blue) by alignment to the ﬁve chromosomes of Arabidopsis (green). B. oleracea sequences, available at http://www.tigr.org, represent around 295 Mbp of B. oleracea genome and cover a considerable proportion of the A. thaliana genome. Brassica rapa BAC end sequences are being generated as part of the ﬁrst phase of a multinational whole-genome B. rapa sequencing initiative. This image was kindly provided by Christopher Love and Dave Edwards from the Plant Biotechnology Centre at Latrobe University, Australia. For further details on the Brassica ASTRA database tools used to create this alignment see Love et al. (2005)

Chapter 2 Oilseed Rape

selecton of seeding BACs for the B. rapa genome sequencing. The continuing accumulation of such data will lead to completion of the planned second phase for the B. rapa sequencing project, which foresees an unambiguous genetic anchoring of around 1,000 seed BACs to the Arabidopsis genome sequence using both end sequences, accompanied by the anchoring to one of the the B. rapa reference genetic maps using single-locus SSR, SNP or InDel markers. In the third and ﬁnal stage, the actual genome sequencing will be performed, with division on a chromosome-bychromosome basis as in other multinational genomesequencing programs. Participants will begin by sequencing all seed BACs on their chromosome or deﬁned subregion of a chromosome. All sequence data will be submitted to TIGR, MIPS and NIAB as soon as Phase 2 quality is achieved.

103

dardised nomenclature. As these deﬁcits are rectiﬁed it can be expected that the coming decade will see enormous advances in genome mapping and molecular breeding in oilseed rape. Coupled with technological developments for high-throughput genotyping and whole-genome sequencing, the focus of genetic research will ultimately advance from analysis of gene functions underlying traits of interest to a broader investigation of complete biosynthesis pathways underlying complex metabolic expression patterns. The increasing availability and accessibility of Brassica genomics tools and data, along with the close relationship between B. napus and the model plant Arabidopsis, make oilseed rape one of the major crop plants set to beneﬁt most from the developing technological platforms for functional genomics, proteomics and metabolomics.

References

2.9 Outlook In recent years enormous progress has been achieved in the international Brassica research community towards the accumulation of public genetic markers, mapping populations, genetic and physical maps, EST collections and genomic sequence data for B. rapa, B. oleracea and B. napus. Together these resources, combined with the available information from the model crucifer Arabidopsis and the increasing data from comparative mapping studies, are paving the way for increasingly detailled annotation and navigation between the Arabidopsis and Brassica genomes. This facility, based on knowledge from the model plant and from the Brassica diploid genomes, will in the coming decade undoubtedly continue to accelerate our ever-increasing understanding of the genetic functionality underlying complex genetic traits in oilseed rape. Furthermore, large-scale expression analyses, for example based on chip technologies, will also contribute to a better understanding of the genes involved in agronomically relevant traits and lead to new marker technologies for exploitation of allelic variation in oilseed rape breeding. One area where international B. napus genome research has been somewhat slow in comparison to other major crop species, for example barley, is in the availability of public marker and EST collections and the integration of genetic and physical maps via stan-

Abbadi A, Brummel M, Spener F (2000) Knockout of the regulatory site of 3-ketoacyl-ACP synthase III enhances shortand medium-chain acyl-ACP synthesis. Plant J 24:1–9 Ackman RG (1990) Canola fatty acids – an ideal mixture for health, nutrition, and food use. In: Shahidi F (ed) Canola and Rapeseed – Production, Chemistry, Nutrition and Processing Technology. Van Nostrand Reinhold, New York, pp 81–98 Anjou K, Lönnerdal B, Uppström B, Åman P (1977) Composition of seeds from some Brassica cultivars. Swedish J Agric Res 7:169–178 Appelqvist L-Å, Ohlson R (1972) Rapeseed – Cultivation, Composition, Processing and Utilization. Elsevier Publ Co, Amsterdam Armstrong SJ, Fransz P, Marshall DF, Jones GH (1998) Physical mapping of DNA repetitive sequences to mitotic and meiotic chromosomes of Brassica oleracea var. alboglabra by ﬂuorescence in situ hybridization. Heredity 81:666–673 Arnholdt B, Schuster W (1981) Durch Umwelt und Gentyp bedingte Variabilität des Rohprotein- und Rohfettgehaltes in Rapssamen. Fette Seifen Anstrichm 83:49–54 Auld DL, Heikkinen MK, Erickson DA, Sernyk JL, Romero JE (1992) Rapeseed mutants with reduced levels of polyunsaturated fatty acids and increased levels of oleic acid. Crop Sci 32:657–662 Babula D, Kaczmarek M, Barakat A, Delseny M, Quiros CF, Sadowski J (2003) Chromosomal mapping of Brassica oleracea based on ESTs from Arabidopsis thaliana: complexity of the comparative map. Mol Gen Genom 268:656–665 Badani AG, Snowdon RJ, Baetzel R, Lipsa FD, Wittkop B, Horn R, De Haro A, Font R, Lühs W, Friedt W (2006) Co-localisation of a partially dominant gene for yellow seed color with a

104

R. Snowdon, W. Lühs, W. Friedt

major QTL inﬂuencing acid detergent ﬁber (ADF) content in different crosses of oilseed rape (Brassica napus). Genome (in press) Banga SS, Banga SK, Sandha GS (1995) Development and characterization of tournefortii CMS system in Brassica napus L. In: Proc 9th Int Rapeseed Congress, Cambridge, UK, 1:55–57 Bannerot T, Boulidard L, Cauderon Y, Tempe J (1974) Transfer of cytoplasmic male sterility from Raphanus sativus to Brassica oleracea. Proc Eucarpia Meeting on Cruciferae, Dundee, UK, pp 52–54 Barker G, Batley J, O’Sullivan H, Edwards KJ, Edwards D (2003) Redundancy based detection of sequence polymorphisms in expressed sequence tag data using AutoSNP. Bioinformatics 19:421–422 Barret P, Guerif J, Reynoird JP, Delourme R, Eber F, Renard M, Chèvre AM (1998) Selection of stable Brassica napus – Brassica juncea recombinant lines resistant to blackleg (Leptosphaeria maculans). 2. A ‘to and fro’ strategy to localise and characterise interspeciﬁc introgressions on the B. napus genome. Theor Appl Genet 96:1097–1103 Bartkowiak-Broda I, Poplawska W, Gorska-Paukszt M (1991) Transfer of CMS juncea to double low winter rape (Brassica napus L.). In: Proc 8th Int Rapeseed Congress, Saskatoon, Saskatchewan, Canada, 5:1502–1505 Bauer-Weston WB, Keller W, Webb J, Gleddie S (1993) Production and characterization of asymmetric somatic hybrids between Arabidopsis thaliana and Brassica napus. Theor Appl Genet 86:2–3 Becker HC, Dambaard C, Kahrsson B (1992) Environmental variation for outcrossing rate in rapeseed (Brassica napus). Theor Appl Genet 84:303–306 Becker HC, Engqvist GM, Karlsson B (1995) Comparison of rapeseed cultivars and resynthesized lines based on allozyme and RFLP markers. Theor Appl Genet 91:62–67 Bellaoui M, Grelon M, Pelletier G, Budar F (1999) The restorer Rfo gene acts post-translationally on the stability of the ORF138 Ogura CMS-associated protein in reproductive tissues of rapeseed cybrids. Plant Mol Biol 40:893–902 Bett KE, Lydiate DJ (2004) Mapping and genetic characterization of loci controlling the restoration of male fertility in Ogura CMS radish. Mol Breed 13:125–133 Biermann U, Friedt W, Lang S, Lühs W, Machmüller G, Metzger JO, Rüsch M gen. Klaas, Schäfer HJ, Schneider MP (2000) New syntheses with oils and fats as renewable raw materials for the chemical industry. Angew Chem Int Ed 39:2206–2224 Bijral JS, Sharma TR (1996) Cytogenetics of intergeneric hybrids between Brassica napus L. and Eruca sativa Lam. Cruciferae Newslett 18:12–13 Botterman J, Leemans J (1988) Engineering herbicide resistance in plants. Trends Genet 4:219–222 Brandle JE, McVetty PBE (1989) Heterosis and combining ability in hybrids derived from oilseed rape cultivars and inbred lines. Crop Sci 29:1191–1195

Brandle JE, McVetty PBE (1990) Geographical diversity, parental selection and heterosis in oilseed rape. Can J Plant Sci 70:935–940 Brown GG (1999) Unique aspects of cytoplasmic male sterility and fertility restoration in Brassica napus. Heredity 90:351–356 Brown GG, Formanova N, Jin H, Wargachuk R, Dendy C, Patil P, Laforest M, Zhang JF, Cheung WY, Landry BS (2003) The radish Rfo restorer gene of Ogura cytoplasmic male sterility encodes a protein with multiple pentatricopeptide repeats. Plant J 35:262–272 Budar F, Thia-Toong L, Van Montagu M, Hernalsteens JP (1986) Agrobacterium-mediated gene transfer results mainly in transgenic plants transmitting T-DNA as a single Mendelian factor. Genetics 114:303–313 Budewig S, Léon J (2003) Higher yield stability for oilseed rape hybrids? In: Proc 11th Int Rapeseed Congress, 6–10 July 2003, Copenhagen, 1:347–349 Bundessortenamt (2004) Beschreibende Sortenliste 2004: Getreide, Mais, Ölfrüchte, Leguminosen (großkörnig), Hackfrüchte (außer Kartoffeln). Deutscher Landwirtschaftsverlag, Hannover Burns MJ, Barnes SR, Bowman JG, Clarke MHE, Werner CP, Kearsey MJ (2003) QTL analysis of an intervarietal set of substitution lines in Brassica napus: (i) Seed oil content and fatty acid composition. Heredity 90:39–48 Butruille DV, Guries RP, Osborn TC (1999) Increasing yield of spring oilseed rape hybrids through introgression of winter germplasm. Crop Sci 39:1491–1496 Canvin DT (1965) The effect of temperature on the oil content and fatty acid composition of the oils from several oil seed crops. Can J Bot 43:63–69 Cargeeg LA, Thurling N (1980) Contribution of host-pathogen interactions to the expression of the blackleg disease of spring rape (Brassica napus L.) caused by Leptosphaeria maculans (Desm.) Ces. et De Not. Euphytica 29:465–476 Carlson SE (1995) The role of PUFA in infant nutrition. INFORM 6:940–946 Cavell AC, Lydiate DJ, Parkin IAP, Dean C, Trick M (1998) Colinearity between a 30-centimorgan segment of Arabidopsis thaliana chromosome 4 and duplicated regions within the Brassica napus genome. Genome 41:62–69 Chalhoub B, Budin K, Aubourg S, Belcram H, Laffont P, Delourme R, Lecharny A, Renard M, Caboche M (2003) Comparative genomic approaches for high-throughput physical and genetic mapping and gene cloning in oilseed rape (Brassica napus) and application to breeding. Proc 11th Intl Rapeseed Congress, 6–10 July 2003, Copenhagen, 1:78 Chen BY, Heneen WK (1989) Fatty acid composition of resynthesized Brassica napus L., B. campestris L. and B. alboglabra Bailey with special reference to the inheritance of erucic acid content. Heredity 63:309–314 Chen JL, Beversdorf WD (1990) Fatty acid inheritance in microspore-derived populations of spring rapeseed (Brassica napus L.). Theor Appl Genet 80:465–469

Chapter 2 Oilseed Rape Chen BY, Haneen WK, Jonsson R (1988) Resynthesis of Brassica napus L. through interspeciﬁc hybridization between B. alboglabra Bailey and B. campestris L. with special emphasis on seed color. Plant Breed 101:52–59 Cheung WY, Landry BS (1996) Current status of genome mapping in the Cruciferae. In: Paterson AH (ed) Genome Mapping in Plants. Landes Bioscience, Austin, TX, pp 193–210 Cheung WY, Champagne G, Hubert N, Landry BS (1997) Comparison of the genetic maps of Brassica napus and Brassica oleracea. Theor Appl Genet 94: 569–582 Chèvre AM, Delourme R, Eber F, Powell AD (1991a) First results of rapeseed variety identiﬁcation by isozyme electrophoresis. Cruciferae Newslett 14/15:70–71 Chèvre AM, This P, Eber F, Deschamps M, Renard M, Delseny M, Quiros CF (1991b) Characterization of disomic addition lines Brassica napus–Brassica nigra by isozyme, fatty acid, and RFLP markers. Theor Appl Genet 81:43–49 Chèvre AM, Eber F, This P, Barret P, Tanguy X, Brun H, Delseny M, Renard M (1996) Characterization of Brassica nigra chromosomes and of blackleg resistance in B. napus – B. nigra addition lines. Plant Breed 115:113–118 Chèvre AM, Barret P, Eber F, Dupuy P, Brun H, Tanguy X, Renard M (1997) Selection of stable Brassica napus – B. juncea recombinant lines resistant to blackleg (Leptoshaeria maculans). 1. Identiﬁcation of molecular markers, chromosomal and genomic origin of the introgression. Theor Appl Genet 95:1104–1111 Chèvre AM, Ponce de Leon A, Jenczewski E, Eber F, Delourme R, Renard M, Brun H (2003) Introduction of blackleg resistance from Brassica rapa into Brassica napus. In: Proc 11th Int Rapeseed Congress, 6–10 July 2003, Copenhagen, 1:32–35 Chopra VL, Prakash S (1991) Taxonomy, distribution and cytogenetics. In: Chopra VL, Prakash S (eds) Oilseed Brassicas in Indian Agriculture. Vikas, New Delhi, pp 29–59 Cloutier S, Cappadocia M, Landry BS (1995) Study of microspore-culture responsiveness in oilseed rape (Brassica napus L.) by comparative mapping of a F2 population and two microspore-derived populations. Theor Appl Genet 91:841–847 Crane J, Miller AL, Van Roekel JW, Walters C (2003) Triacylglycerols determine the unusual storage physiology of Cuphea seed. Planta 217:699–708 Das S, Roscoe TJ, Delseny M, Srivastava PS, Lakshmikumaran M (2002) Cloning and molecular characterization of the Fatty Acid Elongase 1 (FAE1) gene from high and low erucic acid lines of Brassica campestris and Brassica oleracea. Plant Sci 162:245–250 De Block M, Botterman J, Vandewiele M, Dockx J, Thoen C, Gossele V, Movva N, Thompson C, Van Montagu M, Leemans J (1987) Engineering herbicide resistance in plants by expression of a detoxifying enzyme. EMBO J 6:2513–2518 De Block M, De Brouwer D, Tenning P (1989) Transformation of Brassica napus and Brassica oleracea using Agrobacterium

105

tumefaciens and the expression of the bar and neo genes in the transgenic plants. Plant Physiol 91:694–701 Dehesh K, Jones A, Knutzon DS, Voelker TA (1996) Production of high levels of 8:0 and 10:0 fatty acids in transgenic canola by overexpression of Ch FatB2, a thioesterase cDNA from Cuphea hookeriana. Plant J 9:167–172 Dehesh K, Edwards P, Fillatti J, Slabaugh M, Byrne J (1998) KAS IV: a 3-ketoacyl-ACP synthase from Cuphea sp. is a medium chain speciﬁc condensing enzyme. Plant J 15:383–390 Dehesh K, Tai H, Edwards P, Byrne J, Jaworski JG (2001) Overexpression of 3-Ketoacyl-Acyl-Carrier Protein Synthase IIIs in Plants Reduces the Rate of Lipid Synthesis. Plant Physiol 125:1103–1114 Dekker J, Duke SO (1995) Herbicide-resistant ﬁeld crops. Adv Agron 54:69–116 De Lorgeril M, Salen P, Laporte F, Foulon T, Payen N, de Leiris J (2001) Rapeseed oil and rapeseed oil-based margarine for the prevention and treatment of coronary heart disease. Eur J Lipid Sci Technol 103:490–495 Delourme R, Eber F (1992) Linkage between an isozyme marker and a restorer gene in radish cytoplasmic male sterility of rapeseed (Brassica napus L.). Theor Appl Genet 85:222–228 Delourme R, Bouchereau A, Hubert N, Renard M, Landry BS (1994) Identiﬁcation of RAPD markers linked to a fertility restorer gene for the ogura radish cytoplasmic malesterility of rapeseed (Brassica napus L.). Theor Appl Genet 88:741–748 Delourme R, Foisset N, Horvais R, Barret P, Champagne G, Cheung WY, Landry BS, Renard M (1998) Characterisation of the radish introgression carrying the Rfo restorer gene for the Ogu-INRA cytoplasmic male sterility in rapeseed (Brassica napus L.). Theor Appl Genet 97:129–134 Desloire S, Gherbi H, Laloui W, Marhadour S, Clouet V, Cattolico L, Falentin C, Giancola S, Renard M, Budar F, Small I, Caboche M, Delourme R, Bendahmane A (2003) Identiﬁcation of the fertility restoration locus, Rfo, in radish, as a member of the pentatricopeptide-repeat protein family. EMBO Rep 4:588–594 Diederichsen E, Sacristan MD (1996) Disease response of resynthesized Brassica napus L. lines carrying different contributions of resistance to Plasmodiophora brassicae Wor. Plant Breed 115:5–10 Diepenbrock W (2000) Yield analysis of winter oilseed rape (Brassica napus L.): a review. Field Crops Res 67:35–49 Diers BW, Osborn TC (1994) Genetic diversity of oilseed Brassica napus germplasm based on restriction fragment length polymorphisms. Theor Appl Genet 88:662–668 Diers BW, McVetty PBE, Osborn TC (1996) Relationship between heterosis and genetic distance based on restriction fragment length polymorphism markers in oilseed rape (Brassica napus L.). Crop Sci 36:79–83 Dion Y, Gugel RK, Rakow GFW, SeguinSwartz G, Landry BS (1995) RFLP mapping of resistance to the blackleg disease [causal agent, Leptosphaeria maculans (Desm) Ces, et

106

R. Snowdon, W. Lühs, W. Friedt

de Not] in canola (Brassica napus L). Theor Appl Genet 91:1190–1194 Dixelius C (1999) Inheritance of the resistance to Leptosphaeria maculans of Brassica nigra and B. juncea in near-isogenic lines of B. napus. Plant Breed 118:151–156 Dong J, Keller W, Yan W, Georges F (2004) Gene expression at early stages of Brassica napus seed development as revealed by transcript proﬁling of seed abundant cDNAs. Planta 218:483–491 Downey RK (1964) A selection of Brassica campestris L. containing no erucic acid in its seed oil. Can J Plant Sci 44:295–297 Downey RK (1983) The origin and description of Brassica oilseed crops. In: Kramer JKG, Sauer FD, Pigden WJ (eds) High and Low Erucic Acid Rapeseed Oils. Academic, New York, pp 61–83 Downey RK (1990) Brassica oilseed breeding: achievements and opportunities. Plant Breed Abstr 60:1165–1170 Downey RK, Bell JM (1990) New developments in canola research. In: Shahidi F (ed) Canola and Rapeseed – Production, Chemistry, Nutrition and Processing Technology. Van Nostrand Reinhold, New York, pp 37–46 Downey RK, Rakow GFW (1987) Rapeseed and mustard. In: Fehr WR (ed) Principles of Cultivar Development – Crop Species 2. Macmillan, New York, pp 437–486 Downey RK, Röbbelen G (1989) Brassica species. In: Röbbelen G, Downey R, Ashri A (eds) Oil Crops of the World. McGraw-Hill, New York, pp 339–382 Drexler H, Spiekermann P, Meyer A, Domergue F, Zank T, Sperling P, Abbadi A, Heinz E (2003) Metabolic engineering of fatty acids for breeding of new oilseed crops: strategies, problems and ﬁrst results. J Plant Physiol 160:779–802 Dreyer F, Graichen K, Jung C (2001) A major quantitative trait locus for resistance to Turnip Yellows Virus (TuYV, syn. beet western yellows virus, BWYV) in rapeseed. Plant Breed 120:457–462 Dupont J, White PJ, Johnston KM, Heggtveit HA, McDonald BE, Grundy SM, Bonanome A (1989) Food safety and health effects of canola oil. J Am College Nutr 8:360–375 Ecke W, Uzunova M, Weissleder K (1995) Mapping the genome of rapeseed (Brassica napus L.). 2. Localization of genes controlling erucic acid synthesis and seed oil content. Theor Appl Genet 91:972–977 Fahleson J, Eriksson I, Landgren M, Stymne S, Glimelius K (1994a) Intertribal somatic hybrids between Brassica napus and Thlaspi perfoliatum with high conten of the T. perfoliatum – speciﬁc nervonic acid. Theor Appl Genet 87:795–804 Fahleson J, Eriksson I, Glimelius K (1994b) Intertribal somatic hybrids between Brassica napus and Barbarea vulgaris – production of in vitro plantlets. Plant Cell Rep 13:411–416 Fahleson J, Lagercrantz U, Mouras A, Glimelius K (1997) Characterization of somatic hybrids between Brassica napus and Eruca sativa using species-speciﬁc repetitive sequences and genomic in situ hybridization. Plant Sci 123:133–142

Ferreira ME, Williams PH, Osborn TC (1994) RFLP mapping of Brassica napus using doubled haploid lines. Theor Appl Genet 89:615–621 Ferreira ME, Rimmer SR, Williams PH, Osborn TC (1995a) Mapping loci controlling Brassica napus resistance to Leptosphaeria maculans under different screening conditions. Phytopathology 85:213–217 Ferreira ME, Satagopan J, Yandell BS, Williams PH, Osborn TC (1995b) Mapping loci controlling vernalization requirement and ﬂowering time in Brassica napus. Theor Appl Genet 90:727–732 Ferreira ME, Williams PH, Osborn TC (1995c) Mapping of a locus controlling resistance to Albugo candida in Brassica napus using molecular markers. Phytopathology 85:218–220 Foisset N, Delourme R, Barret P, Renard M (1995) Molecular tagging of the dwarf BREIZH (BZH) Gene in Brassica napus. Theor Appl Genet 91:756–761 Foisset N, Delourme R, Barret P, Hubert N, Landry BS, Renard M (1996) Molecular-mapping analysis in Brassica napus using isozyme, RAPD and RFLP markers on a doubled haploid progeny. Theor Appl Genet 93:1017–1025 Forsberg J, Landgren M, Glimelius K (1994) Fertile somatic hybrids between Brassica napus and Arabidopsis thaliana. Plant Sci 95:213–223 Fourmann M, Barret P, Renard M, Pelletier G, Delourme R, Brunel D (1998) The two genes homologous to Arabidopsis FAE1 co-segregate with the two loci governing erucic acid content in Brassica napus. Theor Appl Genet 96:852–858 Fourmann M, Barret P, Froger N, Baron C, Charlot F, Delourme R, Brunel D (2002) From Arabidopsis thaliana to Brassica napus: development of ampliﬁed consensus genetic markers (ACGM) for construction of a gene map. Theor Appl Genet 105:1196–1206 Frankel EN (1991) Review: Recent advances in lipid oxidation. J Sci Food Agric 54:495–511 Friedt W, Lühs W (1998) Recent developments and perspectives of industrial rapeseed breeding. Fett/Lipid 100:219–226 Friedt W, Lühs W, Müller M, Ordon F (2003) Utility of winter oilseed rape (Brassica napus L.) cultivars and new breeding lines for low-input cropping systems. German J Agron 7:49–55 Friedt W, Leckband G, Frauen M (2004) NAPUS 2000 – Research for an overall improvement in rapeseed. INFORM 15:295–297 Fukui K, Nakayama S, Ohmido N, Yoshiaki H, Yambe M (1998) Quantitative karyotyping of three diploid Brassica species by imaging methods and localizaion of 45SrDNA loci on the identiﬁed chromosomes. Theor Appl Genet 96:325–330 Giancola S, Marhadour S, Desloire S, Clouet V, FalentinGuyomarc’h H, Laloui W, Falentin C, Pelletier G, Renard M, Bendahmane A, Delourme R, Budar F (2003) Characterization of a radish introgression carrying the Ogura fertility restorer gene Rfo in rapseed, using the Arabidopsis genome sequence and radish genetic mapping. Theor Appl Genet 107:1442–1451

Chapter 2 Oilseed Rape Graboski MS, McCormick RL (1998) Combustion of fat and vegetable oil derived fuels in diesel engines. Prog Energy Combust Sci 24:125–164 Graham IA, Li Y, Larson TR (2002) Acyl-CoA measurements in plants suggest a role in regulating various cellular processes. Biochem Soc Trans 30:1095–1099 Grami B, Baker RJ, Stefansson BR (1977) Genetics of protein and oil content in summer rape: heritability, number of effective factors, and correlations. Can J Plant Sci 57:937–943 Grant I, Beversdorf WE (1985) Heterosis and combining ability estimates in spring-planted oilseed rape (Brassica napus L.). Can J Gen Cytol 27:472–478 Grundy SM, Denke M (1990) Dietary inﬂuences on serum lipids and lipoproteins. J Lipid Res 31:1149–1172 Gupta SK, Röbbelen G (1986) Identiﬁcation of rapeseed (Brassica napus) cultivars by electrophoresis. Z Pﬂanzenzüchtg. 96:363–370 Gurr MI (1992) Dietary lipids and coronary heart disease: old evidence, new perspective. Prog Lipid Res 31:195–243 Hackenberg EM, Mündges-Christmann H, Köhler W (1990) Enzymmarker als Selektionshilfe in der Rapszüchtung. Vortr Pﬂanzenzüchtg 18:158–172 Halldén C, Nilsson NO, Rading I, Sall T (1994) Evaluation of RFLP and RAPD markers in a comparison of Brassica napus breeding lines. Theor Appl Genet 88:123–128 Han J, Lühs W, Sonntag K, Zähringer U, Borchardt DS, Wolter FP, Heinz E, Frentzen M (2001) Functional characterisation of β-ketoacyl-CoA synthase genes from B. napus L. Plant Mol Biol 46:229–239 Hansen M, Hallden C, Nilsson NO, Sall T (1997) Marker-assisted selection of restored male-fertile Brassica napus plants using a set of dominant RAPD markers. Mol Breed 3:449–456 Happstadius I, Ljungberg A, Kristiansson B, Dixelius C (2003) Identiﬁcation of Brassica oleracea germplasm with improved resistance to Verticillium wilt. Plant Breed 122:30–34 Harberd DJ (1972) A contribution to the cyto-taxonomy of Brassica (Cruciferae) and its allies. Bot J Linn Soc 65:1–23 Harberd DJ, McArthur ED (1980) Meiotic analysis of some species and genus hybrids in the Brassicaceae. In: Tsunoda S, Hinata K, Gómez-Campo C (eds) Brassica Crops and Wild Allies. Biology and Breeding. Japan Scientiﬁc Society Press, Tokyo, pp 65–87 Hasan M, Seyis F, Badani AG, Pons-Kuhnemann J, Lühs W, Friedt W, Snowdon RJ (2006) Surveying genetic diversity in the Brassica napus gene pool using SSR markers. Genet Res Crop Evol 53: 793–802 Hawkins DJ, Kridl JC (1998) Characterisation of acyl-ACP thioesterases of mangosteen (Garcinia manostana) seed and high levels of stearate production in transgenic canola. Plant J 13:743–752 Heslop-Harrison JS, Schwarzacher T (1996) Genomic southern and in situ hybridization for plant genome analysis. In: Jauhar PP (ed) Methods of Genome Analysis in Plants. CRC, Boca Raton, FL, pp 163–179

107

Hinata K, Nishio T (1978) S-Allele speciﬁcity of stigma proteins in Brassica oleracea and B. campestris. Heredity 41:93–100 Hitz WD, Yadav NS, Reiter RJ, Mauvis CJ, Kinney AJ (1995) Reducing polyunsaturation in oils of transgenic canola and soybean. In: Kader JC, Mazliak P (eds), Plant Lipid Metabolism. Kluwer, Dordrecht, pp 506–508 Hoenecke M, Chyi YS (1991) Comparison of Brassica napus and B. rapa genomes based on restriction fragment length polymorphism mapping. In: Proc 8th Int Rapeseed Congress, Saskatoon, Saskatchewan, Canada, 4:1102–1107 Horacek M, Acanova M (2003) Glucose-6-phosphate isomerase as a marker of a fertility restorer gene in rape. Czech J Genet Plant Breed 39:130–133 Horrobin DF (1995) Medical roles of metabolites of precursor EFA. INFORM 6:428–435 Howell PM, Marshall DF, Lydiate DJ (1996) Towards developing intervarietal substitution lines in Brassica napus using marker-assisted selection. Genome 39:348–358 Howell EC, Barker GC, Jones GH, Kearsey MJ, King GJ, Kop EP, Ryder CD, Teakle GR, Vicente JG, Armstrong SJ (2002) Integration of the cytogenetic and genetic linkage maps of Brassica oleracea. Genetics 161:1225–1234 Howell PM, Sharpe AG, Lydiate DJ (2003) Homoeologous loci control the accumulation of seed glucosinolates in oilseed rape (Brassica napus). Genome 46:454–460 Hu J, Quiros C, Arus P, Struss D, Röbbelen G (1995) Mapping of a gene determining linolenic acid concentration in rapeseed with DNA based markers. Theor Appl Genet 90:258–262 Hu FB, Stampfer MJ, Manson JE, Rimm E, Colditz GA, Rosner BA, Hennekens CH, Willett WC (1997) Dietary fat intake and risk of coronary heart disease in women. New Engl J Med 337:1491–1499 Hu J, Li G, Struss D, Quiros CF (1999) SCAR and RAPD markers associated with 18-carbon fatty acids in rapeseed, Brassica napus. Plant Breed 118:145–150 Imai R, Koizuka N, Fujimoto H, Hayakawa T, Sakai T, Imamura J (2003) Delimitation of the fertility restorer locus Rfk1 to a 43-kb contig in Kosena radish (Raphanus sativus L.). Mol Genet Genom 269:388–394 Inomata N (1994) Intergeneric hybrids between Brassica napus and Sinapis pubescens and the cytology and crossability of their progenies. Theor Appl Genet 89:540–544 Iwabuchi M, Koizuka N, Fujimoto H, Sakai T, Imamura J (1999) Identiﬁcation and expression of the Kosena radish (Raphanus sativus cv. Kosena) homologue of the Ogura radish CMS- associated gene, orf138. Plant Mol Biol 39:183–188 Jackson SA, Cheng ZK, Wang ML, Goodman HM, Jiang JM (2000) Comparative ﬂuorescence in situ hybridization mapping of a 431-kb Arabidopsis thaliana bacterial artiﬁcial chromosome contig reveals the role of chromosomal duplications in the expansion of the Brassica rapa genome. Genetics 156:833–838

108

R. Snowdon, W. Lühs, W. Friedt

Jain SK (1979) Estimation of outcrossing rates: some alternative procedures. Crop Sci 19:23–27 James C (2005) Global Status of Commercialized Biotech/GM Crops: 2005. International Service for the Acquisition of Agri-biotech Applications Executive Summary, Brief 34. Jones A, Davies HM, Voelker TA (1995) Palmitoyl-Acyl Carrier Protein (ACP) thioesterase and the evolutionary origin of plant acyl-ACP thioesterases. Plant Cell 7:359–371 Jönsson R (1977) Erucic-acid heredity in rapeseed (Brassica napus L. and Brassica campestris L.) Hereditas 86:159–170 Jourdan PS, Earle ED, Mutschler MA (1989) Synthesis of male sterile, triazine-resistant B. napus by somatic hybridization between cytoplasmic male sterile B. oleracea and atrazineresistant B. campestris. Theor Appl Genet 78:445–455 Jourdren C, Barret P, Horvais R, Foisset N, Delourme R, Renard M (1996a) Identiﬁcation of RAPD markers linked to the loci controlling erucic acid level in rapeseed. Mol Breed 2:61–71 Jourdren C, Barret P, Brunel P, Delourme R, Renard M (1996b) Speciﬁc molecular marker of the genes controlling linolenic acid content in rapeseed. Theor Appl Genet 93:512–518 Karpechenko GD (1927) Polyploid hybrids of Raphanus sativus X Brassica oleracea. Appl Bot Genet Plant Breed 17:305–410 Kelly AL, Sharpe AG, Nixon JH, Evans EJ, Lydiate DJ (1997) Indistinguishable patterns of recombination resulting from mate and female meioses in Brassica napus (oilseed rape). Genome 40:49–56 Kerlan MC, Chèvre AM, Eber F (1993) Interspeciﬁc hybrids between a transgenic rapeseed (Brassica napus) and related species: cytogenetical characterization and detection of the transgene. Genome 36:1099–1106 Kimber D, McGregor DI (1995) Brassica Oilseeds: Production & Utilisation. CAB, Wallingford, UK Kinsella JE (1991) α-linolenic acid: functions and effects on linoleic acid metabolism and eicosanoid-mediated reactions. Adv Food Nutr Res 35:1–184 Klewer A, Scheunemann R, Sacristan MD (2003) Incorporation of blackspot resistance from different origins into oilseed rape. In: Proc 11th Int Rapeseed Congress, Copenhagen, 1:65–67 Knutzon DS, Thompson GA, Radke SE, Johnson WB, Knauf VC, Kridl JC (1992) Modiﬁcation of Brassica seed oil by antisense expression of a stearoyl-acyl carrier protein desaturase gene. Proc Natl Acad Sci USA 89:2624–2628 Knutzon DS, Hayes TR, Wyrick A, Xiong H, Davies HM, Voelker TA (1999) Lysophosphatidic acid acyltransferase from coconut endosperm mediates the insertion of laurate at the sn-2 position of triacylglycerols in lauric rapessed oil and can increase total laurate levels. Plant Physiol 120:739–746 Kole C, Quijada P, Michaels SD, Amasino RM, Osborn TC (2001) Evidence for homology of ﬂowering-time genes VFR2 from Brassica rapa and FLC from Arabidopsis thaliana. Theor Appl Genet 102:425–430 Kole C, Thormann CE, Karlsson BH, Palta JP, Gaffney P, Yandell B, Osborn TC (2002a) Comparative mapping of loci con-

trolling winter survival and related traits in oilseed Brassica rapa and B. napus. Mol Breed 9:201–210 Kole C, Williams PH, Rimmer SR, Osborn TC (2002b) Linkage mapping of genes controlling resistance to white rust (Albugo candida) in Brassica rapa (syn. campestris) and comparative mapping to Brassica napus and Arabidopsis thaliana. Genome 45:22–27 Körbitz W (1995) Utilization of oil as a biodiesel fuel. In: Kimber D, McGregor DI (eds) Brassica Oilseeds. CAB, Wallingford, UK, pp 339–352 Kräling K, Röbbelen G (1991) Breeding strategies for the development of high performing winter rapeseed with improved polyenoic fatty acid composition. In: Proc 8th Int Rapeseed Congress, Saskatoon, Saskatchewan, Canada, 5:1536–1540 Kroll H (1994) Ein archäologischer Rapsfund des 16. Jahrhunderts, entdeckt in Heide in Holstein, Norddeutschland. J Agron Crop Sci 173:17–21 Kumar A, Rakow G, Downey RK (1998) Genetic characterization of glufosinate-ammonium tolerant summer rape lines. Crop Sci 38:1489–1494 Kuswinanti T, Koopmann B, Hoppe HH (1999) Virulence pattern of aggressive isolates of Leptosphaeria maculans on an extended set of Brassica differentials. J Plant Dis Protect 106:12–20 Lagercrantz U, Putterill J, Coupland G, Lydiate D (1996) Comparative mapping of Arabidopsis and Brassica, ﬁne scale colinearity and congruence of genes controlling ﬂowering time. Plant J 9:1903–1910 Lan TH, Delmonte TA, Reischmann KP, Hyman J, Kowalski S, McFerson J, Kresovich S, Paterson AH (2000) EST-enriched comparative map of Brassica oleracea and Arabidopsis thaliana. Genome Res 10:776–788 Landry BS, Hubert N, Etoh T, Harada JJ, Lincoln SE (1991) A genetic map for Brassica napus based on restriction fragment length polymorphisms detected with expressed DNA sequences. Genome 34:543–552 Lands WEM (1997) The two faces of essential fatty acids. INFORM 8:1141–1147 Larson TR, Edgell T, Byrne J, Dehesh K, Graham IA (2002) Acyl CoA proﬁles of transgenic plants that accumulate mediumchain fatty acids indicate inefﬁcient storage lipid synthesis in developing oilseeds. Plant J 32:519–527 Leckband G, Frauen M, Friedt W (2002) NAPUS 2000. Rapeseed (Brassica napus) breeding for improved human nutrition. Food Res Int 35:273–278 Lefort-Buson M, Guillot-Lemoine B, Dattée Y (1987) Heterosis and genetic distance in rapeseed (Brassica napus L.): crosses between European and Asiatic selfed lines. Genome 29:413–418 Lelivelt CLC, Krens FA (1992) Transfer of resistance to the beet cyst nematode (Heterodera schachtii Schm.) into the Brassica napus L. gene pool through intergeneric somatic hybridization with Raphanus sativus L. Theor Appl Genet 83:887–894

Chapter 2 Oilseed Rape Lelivelt CLC, Leunissen EHM, Frederiks HJ, Helsper JPFG, Krens FA (1993) Transfer of resistance to the beet cyst nematode (Heterodera schachtii Schm.) from Sinapis alba L. (white mustard) to the Brassica napus gene pool by means of sexual and somatic hybridization. Theor Appl Genet 85:688–696 Li G, Quiros CF (2001) Sequence-related ampliﬁed polymorphism (SRAP), a new marker system based on a simple PCR reaction: its application to mapping and gene tagging in Brassica. Theor Appl Genet 103:455–461 Li ZY, Luo P (1993) First intergeneric hybrids of Brassica napus × Orychophragmus violaceus. Oil Crops Newslett 10:27–29 Liu HL (1983) Studies on the breeding of yellow seeded Brassica napus L. In: Proc 6th Int Rapeseed Congress, Paris, 1:637–641 Liu XP, Tu JX, Chen BY, Fu TD (2005) Identiﬁcation and inheritance of a partially dominant gene for yellow seed color in Brassica napus. Plant Breed 124: 9–12 Löhden I, Frentzen M (1992) Triacylglycerol biosynthesis in developing seeds of Tropaeolum majus L. and Limnanthes douglasii R Br. Planta 188:215–224 Lombard V, Delourme R (2001) A consensus linkage map for rapeseed (Brassica napus L.): construction and integration of three individual maps from DH populations. Theor Appl Genet 103:491–507 Lombard V, Baril CP, Dubreuil P, Blouet F, Zhang D (2000) Genetic relationships and ﬁngerprinting of rapeseed cultivars by AFLP: Consequences for varietal registration. Crop Sci 40:1417–1425 Love CG, Robinson AJ, Lim GAC, Hopkins CJ, Batley J, Barker G, Spangenberg GC, Edwards D (2005) Brassica ASTRA: an integrated database for Brassica genomic research. Nucleic Acids Res 33:D656–D659 Lühs W, Friedt W (1994a) Major oil crops. In: Murphy DJ (ed) Designer Oil Crops. VCH, Weinheim, Germany, pp 5–71 Lühs W, Friedt W (1994b) Non-food uses of vegetable oils and fatty acids. In: Murphy DJ (ed) Designer Oil Crops. VCH, Weinheim, Germany, pp 73–130 Lühs W, Friedt W (1995) Breeding high-erucic acid rapeseed by means of Brassica napus resynthesis. In: Proc 9th Int Rapeseed Congress, Cambridge, UK, 2:449–451 Lühs W, Seyis F, Snowdon R, Baetzel R, Friedt W (2002) Genetic improvement of Brassica napus by wide hybridization. GCIRC Bull 18:227–234 Lühs W, Seyis F, Frauen M, Busch H, Frese L, Willner E, Friedt W, Gustafsson M, Poulsen G (2003a) Development and evaluation of a Brassica napus core collection. In: Knüpffer H, Ochsmann J (eds) Rudolf Mansfeld and Plant Genetic Resources. Proceedings of a symposium dedicated to the 100th birthday of Rudolf Mansfeld, Gatersleben, Germany, 8–9 October 2001. Schriften zu Genetischen Ressourcen. ZADI/IBV, Bonn, 19:284–289 (http://www.genres.de/infos/rei-bd22.htm)

109

Lühs W, Seyis F, Frauen M, Ulber B, Stemann G, Busch H, Friedt W, Gustafsson M, Poulsen G (2003b) Evaluation of a Brassica napus core collection and prospects of its exploitation through rapeseed breeding. In: Proc 11th Int Rapeseed Congress, Copenhagen, 2:371–374 Lydiate D, Sharpe A, Lagerkrantz U, Parkin I (1993) Mapping the Brassica genome. Outlook Agric 22:85–92 Mahler KA, Auld DL (1988) Fatty acid composition of 2100 accessions of Brassica. Winter rapeseed breeding program, University of Idaho, Moscow, ID Mailer RJ, Scarth R, Fristensky B (1994) Discrimination among cultivars of rapeseed (Brassica napus L.) DNA polymorphisms ampliﬁed from arbitrary primers. Theor Appl Genet 87:697–704 Manton I (1932) Introduction to the general cytology of the Cruciferae. Ann Bot 46:509–556 Marquard R, Schuster W (1981) Veränderungen von Sameninhaltsstoffen verschiedener Rapssorten unter kontrollierten Bedingungen. Fette Seifen Anstrichm 83:99–106 Marsic V, Yodice R, Orthoefer F (1992) The dietary role of monounsaturates. INFORM 3:681–686 Martínez de Ilárduya O, Mekhedov S, Ohlrogge J (1999) A survey of genes for plant glycerolipid biosynthesis. http://www.msu.edu/user/ohlrogge/test/index.html Marwede V, Gül MK, Becker HC, Ecke W (2005) Mapping of QTL controlling tocopherol contents in winter oilseed rape (Brassica napus L.). Plant Breed 124: 20–26 Marsic V, Yodice R, Orthoefer F (1992) The dietary role of monounsaturates. INFORM 3:681–686 Mattson FH, Grundy SM (1985) Comparison of effects of dietary saturated, monounsaturated, and polyunsaturated fatty acids on plasma lipids and lipoproteins in man. J Lipid Res 26:194–202 Mayerhofer R, Bansal VK, Thiagarajah MR, Stringam GR, Good AG (1997) Molecular mapping of resistance to Leptosphaeria maculans in Australian cultivars of Brassica napus. Genome 40:294–301 McDonald BE (1995) Oil properties of importance in human nutrition. In: Kimber DS, McGregor DI (Eds), Brassica Oilseeds – Production and Utilization. CAB, Wallingford, UK, pp 291–299 Mekhedov S, Martínez de Ilárduya O, Ohlrogge J (1999) Towards the functional catalogue of plant genes: a survey of genes for plant glycerolipid biosynthesis. In: Proc Symp Natl Plant Lipid Coop (NPLC). Fallen Leaf Lake, CA. http://www.msu.edu/user/ohlrogge/Abstracts99z.html Meng JL, Shi SW, Gan L, Li ZY, Qu XS (1998) The production of yellow-seeded Brassica napus (AACC) through crossing interspeciﬁc hybrids of B. campestris (AA) and B. carinata (BBCC) with B. napus. Euphytica 103:329–333 Mensink RP, Katan MB (1993) Trans monounsaturated fatty acids in nutrition and their impact on serum lipoprotein level in man. Prog Lipid Res 32:111–122

110

R. Snowdon, W. Lühs, W. Friedt

Mithen RF, Magrath R (1992) Glucosinolates and resistance to Leptosphaeria maculans in wild and cultivated Brassica species. Plant Breed 108:60–68 Mizushima U (1950) Karyogenetic studies of species and genus hybrids in the tribe Brassiceae of Cruciferae. Tohoku J Agr Res 1:1–14 Morinaga T (1934) Interspeciﬁc hybridization in Brassica. VI. The cytology of F1 hybrids of Brassica juncea and B. nigra. Cytologia 6:62–67 Mullis K, Faloona F (1987) Speciﬁc synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Meth Enzym 44:224–249 Mündges-Christmann H, Köhler W (1990) Enzymmarker zur Bestimmung der Fremdbefruchtungsrate und der genetischen Struktur bei Winterraps. Mitt Ges Pﬂanzenbauwiss 3:163–166 Mündges H, Diederichsen E, Köhler W (1989) Comparisons of isozyme patterns in resynthesized amphihaploid rapeseed (Brassica napus) and their parental species Brassica campestris and Brassica oleracea. Plant Breed 103:258–261 Murakami T, Anzai H, Imai S, Satoh A, Nagaoka K, Thompson CJ (1986) The bialaphos biosynthetic genes of Streptomyces hygroscopicus: Molecular cloning and characterization of the gene cluster. Mol Gen Genet 205:42–50 Murphy DJ (1999) Production of novel oils in plants. Curr Opin Biotechnol 10:175–180 Nanda-Kumar PBA, Prakash S, Shivanna KR (1989) Wide hybridization in Brassica: Studies on interspeciﬁc hybrids between cultivated species (B. napus, B. juncea) and a wild species (B. gravinae). In: Proc 6th Intl Congress SABRAO, pp 435–438 National Research Council Committee on Genetically Modiﬁed Pest-Protected Plants, Board on Agriculture and Natural Resources (2000) Genetically Modiﬁed Pest-Protected Plants - Science and Regulation. National Academy Press, Washington, DC Oelck MM, Phan CV, Eckes CP, Donn G, Rakow G, Keller WA (1991) Field resistance of canola transformants (Brassica napus L.) to ignite (phosphinothricin). In: Proc 8th Int Rapeseed Congress, Saskatoon, Sasketchewan, Canada, 1:292–297 Oelck MM, MacDonald R, Belyk M, Ripley V, Weston B, Bennett C, Dormann M, Eckes P, Schulz A, Schneider R, Gadsby M (1995) Registration, safety assessment and agronomic performance of transgenic canola cv. ‘Innovator’ in Canada. In: Proc 9th Int Rapeseed Congress, Cambridge, UK, 4:1430–1432 Ogura H (1968) Studies on the new male-sterility in Japanese radish with special reference to the utilization of this sterility towards the practical raising of hybrid seeds. Mem Fac Agric Kagoshima Univ 6:39–78 Ohlrogge JB, Browse J, Somerville CR (1991) The genetics of plant lipids. Biochem Biophys Acta 1082:1–26 Osborn TC, Kole C, Parkin IA, Sharpe AG, Kuiper M, Lydiate DJ, Trick M (1997) Comparison of ﬂowering time genes in

Brassica rapa, B. napus and Arabidopsis thaliana. Genetics 146:1123–1129 Padgette SR, Kolacz KH, Delannay X, Re DB, LaVallee BJ, Tinius CN, Rhodes WK, Otero YI, Barry GF, Eichholtz DA, Peschke VM, Nida DL, Taylor NB, Kishore GM (1995) Development, identiﬁcation, and characterization of a glyphosatetolerant soybean line. Crop Sci 35:1451–1461 Parkin IAP, Lydiate DJ (1997) Conserved patterns of chromosome pairing and recombination in Brassica napus crosses. Genome 40:496–504 Parkin IAP, Sharpe AG, Keith DJ, Lydiate DJ (1995) Identiﬁcation of the A and C genomes of amphidiploid Brassica napus (oilseed rape). Genome 38:1122–1131 Parkin IAP, Lydiate DJ, Trick M (2002) Assessing the level of colinearity between Arabidopsis thaliana and Brassica napus for A. thaliana chromosome 5. Genome 45:356–366 Parkin IA, Gulden SM, Sharpe AG, Lukens L, Trick M, Osborn TC, Lydiate DJ (2005) Segmental structure of the Brassica napus genome based on comparative analysis with Arabidopsis thaliana. Genetics 171:765–781 Paterson AH, Bowers JE, Burow MD, Draye X, Elsik CG, Jiang CX, Katsar CS, Lan TH, Lin YR, Ming R, Wright RJ (2000) Comparative genomics of plant chromosomes. Plant Cell 12:1523–1539 Peterka H, Budahn H, Schrader O, Ahne R, Schütze W (2004) Transfer of resistance against the beet cyst nematode from radish (Raphanus sativus) to rape (Brassica napus) by monosomic chromosome addition. Theor Appl Genet 109:30–41 Pﬂieger S, Lefebvre V, Causse M (2001) The candidate gene approach in plant genetics: a review. Mol Breed 7:275–291 Piazza GJ, Foglia TA (2001) Rapeseed oil for oleochemical usage. Eur J Lipid Sci Technol 103:450–454 Pilet ML, Delourme R, Foisset N, Renard R (1998a) Identiﬁcation of loci contributing to quantitative ﬁeld resistance to blackleg disease, causal agent Leptosphaeria maculans (Desm.) Ces. et de Not., in Winter rapeseed (Brassica napus L.). Theor Appl Genet 96:23–30 Pilet ML, Delourme R, Foisset N, Renard M (1998b) Identiﬁcation of QTL involved in ﬁeld resistance to light leaf spot (Pyrenopeziza brassicae) and blackleg resistance (Leptosphaeria maculans) in winter rapeseed (Brassica napus L.). Theor Appl Genet 97:398–406 Pilet ML, Duplan G, Archipiano H, Barret P, Baron C, Horvais R, Tanguy X, Lucas MO, Renard M, Delourme R (2001) Stability of QTL for ﬁeld resistance to blackleg across two genetic backgrounds in oilseed rape. Crop Sci 41:197–205 Piotrowska A, Krymanski J, Bartkowiak-Broda I, Krotka K (2003) Characteristic of yellow-seeded lines of winter oilseed rape. In: Proc 11th Int Rapeseed Congress, Copenhagen, 1:247–249 Piquemal J, Cinquin E, Couton F, Rondeau C, Seignoret E, Doucet I, Perret D, Villeger M-J, Vincourt P, Blanchard P (2005) Construction of an oilseed rape (Brassica napus

Chapter 2 Oilseed Rape L.) genetic map with SSR markers. Theor Appl Genet 111:1514–1523 Pleines S, Friedt W (1988) Breeding for improved C18-fatty acid composition in rapeseed (Brassica napus L.). Fat Sci Technol 90:167–171 Pleines S, Friedt W (1989) Genetic control of linolenic acid concentration in seed oil of rapeseed (Brassica napus L.). Theor Appl Genet 78:793–797 Plieske J, Struss D (2001) Microsatellite markers for genome analysis in Brassica. I. Development in Brassica napus and abundance in Brassicaceae species. Theor Appl Genet 102:689–694 Plieske J, Struss D, Röbbelen G (1998) Inheritance of resistance derived from the B-genome of Brassica against Phoma lingam in rapeseed and the development of molecular markers. Theor Appl Genet 97:929–936 Plümper B, Sacristán MD (1995) Asymmetric somatic hybrids Sinapsis alba (+) Brassica napus. In: Proc 9th Int Rapeseed Congress, Cambridge, UK, 4:1240–1242 Pollard MR, Anderson L, Fan C, Hawkins DJ, Davies HM (1991) A speciﬁc acyl-ACP thioesterase implicated in mediumchain fatty acid production in immature cotyledons of Umbellularia californica. Arch Biochem Biophys 284:306–312 Powell WG, Morgante M, Doyle JJ, McNicol JW, Tingey SV, Rafalski AJ (1995) Polymorphic simple sequence repeat regions in chloroplast genomes: applications to the population genetics of pines. Proc Natl Acad Sci USA 92:7759–7763 Prakash S, Chopra VL (1988) Introgression of resistance to shattering in Brassica napus from Brassica juncea through non-homologous recombination. Plant Breed 101:167–168 Purwantara A, Salisbury PA, Burton WA, Howlett BJ (1998) Reaction of Brassica juncea (Indian mustard) lines to Australian isolates of Leptosphaeria maculans under glasshouse and ﬁeld conditions. Eur J Plant Pathol 104:895–902 Quiros CF, Grellet F, Sadowski J, Suzuki T, Li G, Wroblewski T (2001) Arabidopsis and Brassica comparative genomics: sequence, structure and gene content in the ABI1-Rps2Ck1 chromosomal segment and related regions. Genetics 157:1321–1330 Rahman MH (2001) Production of yellow-seeded Brassica napus through interspeciﬁc crosses. Plant Breed 120:463–472 Rahman MH (2003) Yellow-seeded Brassica napus from interspeciﬁc crosses. In: Proc 11th Int Rapeseed Congress, Copenhagen, 1:199–201 Rakow G (1973) Selektion auf Linol- und Linolensäuregehalt in Rapssamen nach mutagener Behandlung. Z Pﬂanzenzüchtg 69:62–82 Rakow G, Stringam GR, McGregor DI (1987) Breeding Brassica napus L. canola with improved fatty acid composition, high oil content and high seed yield. In: Proc 7th Int Rapeseed Congress, Poznan, Poland, 2:27–32 Rao MVB, Babu VR, Radhika K (1993) Introgression of earliness in Brassica napus L. I. An interspeciﬁc B. juncea and B. napus cross. Int J Trop Agric 11:14–19

111

Rashid A, Rakow G, Downey RK (1994) Development of yellow seeded Brassica napus through interspeciﬁc crosses. Plant Breed 112:127–134 Riaz A, Li G, Quresh Z, Swati MS, Quiros CF (2001) Genetic diversity of oilseed Brassica napus inbred lines based on sequence-related ampliﬁed polymorphism and its relation to hybrid performance. Plant Breed 120:411–415 Rimmer SR, van den Berg CGJ (1992) Resistance of oilseed Brassica spp. to blackleg caused by Leptosphaeria maculans. Can J Plant Pathol 14:56–66 Rimmer SR, Buchwaldt L (1995) Diseases. In: Kimber D, McGregor DI (eds) Brassica Oilseeds Production and Utilisation. CAB, Wallingford, UK, pp 111–140 Röbbelen G (1960) Beiträge zur Analyse des Brassica-genomes. Chromosoma 11:205–228 Röbbelen G (1975) Totale Sortenumstellung bei Körnerraps. Bericht über die Arbeitstagung der Arbeitsgemeinschaft der Saatzuchtleiter, Gumpenstein 1975:119–146 Röbbelen G (1990) Mutation breeding for quality improvement – a case study for oilseed crops. Mutat Breed Rev 6:1–44 Röbbelen G, Nitsch A (1975) Genetical and physiological investigations on mutants for polynoic fatty acids in rapeseed, Brassica napus L. Z Pﬂanzenzüchtg 75:93–105 Röbbelen G, Thies W (1980) Biosynthesis of seed oil and breeding for improved oil quality of rapeseed. In: Tsunoda S, Hinata K, Gómez-Campo C (eds) Brassica Crops and Wild Allies. Japan Scientiﬁc Society Press, Tokyo, pp 253–283 Roy NN (1978) A study on disease variation in the populations of an interspeciﬁc cross of Brassica juncea L. × B. napus L. Euphytica 27:145–149 Roy NN (1984) Interspeciﬁc transfer of Brassica juncea-type high blackleg resistance to Brassica napus. Euphytica 33:295–303 Rücker B, Röbbelen G (1995) Development of high oleic acid rapeseed. In: Proc 9th Int Rapeseed Congress (GCIRC), Cambridge, UK, 2:389–391 Rücker B, Röbbelen G (1996) Impact of low linolenic acid content on seed yield of winter oilseed rape (Brassica napus L.). Plant Breed 115:226–230 Ryder CD, Smith LB, Teakle GR, King GJ (2001) Contrasting genome organisation: two regions of the Brassica oleracea genome compared with colinear regions of the Arabidopsis thaliana genome. Genome 44:808–817 Sabharwal PS, Dolezel J (1993) Interspeciﬁc hybridization in Brassica: application of ﬂow cytometry for analysis of ploidy and genome composition in hybrid plants. Biol Planta 35:169–177 Sacristán MD, Gerdemann M (1986) Different behavior of Brassica juncea and B. carinata as sources of Phoma lingam resistance in experiments of interspeciﬁc transfer to B. napus. Z Pﬂanzenzüchtg 97:304–314 Sacristan MD, Gerdemann-Knörck M, Schieder O (1989) Incorporation of hygromycin resistance in B. nigra and its transfer to B. napus through asymmetric protoplast fusions. Theor Appl Genet 78:194–200

112

R. Snowdon, W. Lühs, W. Friedt

Sakai T, Imamura J (1990) Intergeneric transfer of cytoplasmic male sterility between Raphanus sativus (CMS line) and Brassica napus through cytoplast-protoplast fusion. Theor Appl Genet 80:421–427 Salunkhe DK, Chavan JK, Adsule RN, Kadam SS (1992) World Oilseeds. Chemistry, Technology and Utilization, Van Nostrand Reinhold, New York Scarth R, McVetty PBE, Rimmer SR, Stefansson BR (1988) Stellar low linolenic-high linoleic acid summer rape. Can J Plant Sci 68:509–511 Scarth R, McVetty PBE, Rimmer SR (1997) Allons low linolenic acid summer rape. Can J Plant Sci 77:125–126 Schefﬂer JA, Sharpe AG, Schmidt H, Sperling P, Parkin IAP, Lühs W, Lydiate DJ, Heinz E (1997) Desaturase multigene families of Brassica napus arose through genome duplication. Theor Appl Genet 94:583–591 Schelfhout CJ, Snowdon RJ, Cowling WA, Wroth JM (2004) A PCR based B-genome speciﬁc marker in Brassica species. Theor Appl Genet 109: 917–921 Schelfhout CJ, Snowdon RJ, Cowling WA, Wroth JM (2006) Tracing B-genome introgressions in B. napus × B. juncea interspeciﬁc hybrids. Genome (in press) Schierholt A, Becker HC, Ecke W (2000) Mapping a high oleic acid mutation in winter oilseed rape (Brassica napus L.). Theor Appl Genet 101:897–901 Schmidt R (2002) Plant genome evolution: Lessons from comparative genomics at the DNA level. Plant Mol Biol 48:21–37 Schmidt R, Acarkan A, Boivin K (2001) Comparative structural genomics in the Brassicaceae family. Plant Physiol Biochem 39:253–262 Schuster W (1969) Vergleich von zwei Zuchtverfahren in der Erhaltungszüchtung bei Winterraps. Z Pﬂanzenzüchtg 62:47–62 Schuster W (1987) Die Entwicklung des Anbaues und der Züchtung von Ölpﬂanzen in Mitteleuropa. Fat Sci Technol 89:15–27 Schwetka A (1981) Samenfarbe bei Kohl und Rübsen und deren Einﬂuss auf die Samenfarbe synthetischer Rapsformen. Dissertation, Georg-August-Universität Göttingen, Germany, p 86 Seyis F, Snowdon RJ, Lühs W, Friedt W (2003a) Molecular characterisation of novel resynthesised rapeseed (Brassica napus L.) lines and analysis of their genetic diversity in comparison to spring rapeseed cultivars. Plant Breed 122:473–478 Seyis F, Friedt W, Lühs W (2003b) Resynthesised Brassica napus as a genetic resource in rapeseed improvement for quality and agronomic performance. In: Knüpffer H, Ochsmann J (eds) Rudolf Mansfeld and Plant Genetic Resources. Schriften zu Genetischen Ressources, ZADI/IBV, Bonn, 19:334–338 Shahidi F (1990) Canola and Rapeseed - Production, Chemistry, Nutrition and Processing Technology. Van Nostrand Reinhold, New York

Shaner DL (2000) The impact of glyphosate-tolerant crops on the use of other herbicides and on resistance management. Pest Manage Sci 56:320–326 Sharpe AG, Parkin IAP, Keith DJ, Lydiate DJ (1995) Frequent nonreciprocal translocations in the amphidiploid genome of oilseed rape (Brassica napus). Genome 38:1112–1121 Shiga T, Baba S (1973) Cytoplasmic male sterility in oilseed rape (Brassica napus L.), and its utilization to breeding. Jpn J Breed 23:187–197 Shirzagedan M, Röbbelen G (1985) Inﬂuence of seed color and hull proportions on quality properties of seeds in Brassica napus L. Fette Seifen Anstrichmittel 87:235–237 Sillito D, Parkin IAP, Mayerhofer R, Lydiate DJ, Good AG (2000) Arabidopsis thaliana: A source of candidate diseaseresistance genes for Brassica napus. Genome 43:452–460 Sjödin C, Glimelius K (1989) Transfer of resistance against Phoma lingam to Brassica napus by asymmetric somatic hybridization combined with toxin selection. Theor Appl Genet 78:513–520 Skarzhinskaya M, Landgren M, Glimelius K (1996) Production of intertribal somatic hybrids between Brassica napus L. and Lesquerella fendleri (Gray) Wats. Theor Appl Genet 93:1242–1250 Skarzhinskaya M, Fahleson J, Glimelius K, Mouras A (1998) Genome organization of Brassica napus and Lesquerella fendleri and analysis of their somatic hybrids using genomic in situ hybridization. Genome 41:691–701 Slocum MK, Figdore SS, Kennard WC, Suzuki JY, Osborn TC (1990) Linkage arrangement of restriction fragment length polymorphisms in Brassica oleracea. Theor Appl Genet 80:57–64 Slominski BA, Campbell LD, Guenter W (1994) Carbohydrates and dietary ﬁber components of yellow and brown-seeded canola. J Agric Food Chem 42:704–707 Slominski BA, Simbaya J, Campbell LD, Rakow G, Guenter W (1999) Nutritive value for broilers of meals derived from newly developed varieties of yellow-seeded canola. Anim Feed Sci Technol 78:249–262 Snowdon RJ, Köhler W, Friedt W, Köhler A (1997) Genomic in situ hybridization in Brassica amphidiploids and interspeciﬁc hybrids. Theor Appl Genet 95:1320–1324 Snowdon RJ, Winter H, Diestel A, Sacristan MD (2000) Development and characterisation of Brassica napus – Sinapis arvensis addition lines exhibiting resistance to Leptosphaeria maculans. Theor Appl Genet 101:1008–1014 Snowdon RJ, Friedrich T, Friedt W, Köhler W (2002) Identifying the chromosomes of the A and C genome diploid Brassica species B. rapa and B. oleracea in their amphidiploid B. napus. Theor Appl Genet 104:533–538 Somers DJ, Friesen KRD, Rakow G (1998) Identiﬁcation of molecular markers associated with linoleic acid desaturation in Brassica napus. Theor Appl Genet 96:897–903 Somers DJ, Rakow G, Prabhu VK, Friesen KRD (2001) Identiﬁcation of a major gene and RAPD markers for yellow seed coat color in Brassica napus. Genome 44:1077–1082

Chapter 2 Oilseed Rape Song KM, Osborn TC, Williams PH (1988) Brassica taxonomy based on nuclear restriction fragment length polymorphisms (RFLPs). 1. Genome evolution of diploid and amphidiploid species. Theor Appl Genet 75:784–794 Song KM, Suzuki JY, Slocum MK, Williams PH, Osborn TC (1991) A linkage map of Brassica rapa (syn. campestris) based on restriction fragment length polymorphisms. Theor Appl Genet 82:296–304 Song GK, Lu P, Tang K, Osborn TC (1995) Rapid genome change in synthetic polyploids of Brassica and its implication for polyploid evolution. Proc Natl Acad Sci USA 92:7719–7723 Stefansson BR (1983) In: Kramer JKG, Sauer FD, Pigden WJ (eds) High and Low Erucic Acid Rapeseed Oils. Academic, New York, pp 143–160 Stefansson BR, Hougen FW (1964) Selection of rape plants (Brassica napus) with seed oil practically free of erucic acid. Can J Plant Sci 44:359–364 Stiewe G, Sodhi YS, Röbbelen G (1995a) Establishment of a new CMS-system in Brassica napus. In: Proc 9thInt Rapeseed Congress, Cambridge, UK, 1:49–51 Stiewe G, Witt U, Hansen S, Theis R, Abel WO, Röbbelen G (1995b) Natural and experimental evolution of CMS for rapeseed breeding. Genetic mechanisms for hybrid breeding. Adv Plant Breed 18:59–76 Stoll C, Zarhloul MK, Syring-Ehemann A, Hausmann L, Spener F, Töpfer R, Friedt W, Lühs W (2003) Genetic modiﬁcation of medium-chain fatty acids in oilseed rape (Brassica napus L.) for industrial purposes. In: Proc 11th Int Rapeseed Congress, Copenhagen, 2:569–571 Strauch A, Wohlleben W, Pühler A (1988) Cloning of a phosphinothricin N-acetyltransferase gene from Streptomyces viridochromogenes Tü494 and its expression in Streptomyces lividans and Escherichia coli. Gene 63:65–74 Struss D, Quiros CF, Plieske J, Röbbelen G (1996) Construction of Brassica B genome synteny groups based on chromosomes extracted from three different sources by phenotypic, isozyme and molecular markers. Theor Appl Genet 93:1026–1032 Sundberg E, Glimelius K (1991) Effects of parental ploidy level and genetic divergence on chromosome elimination and chloroplast segregation in somatic hybrids within Brassicaceae. Theor Appl Genet 83:81–88 Sundberg E, Landgren M, Glimelius K (1987) Fertility and chromosome stability in B. napus resynthesized by protoplast fusion. Theor Appl Genet 75:96–104 Takamine N (1916) Über die ruhenden und die präsynaptischen Phasen der Reduktionsteilung. Bot Mag 30:293–303 Takeshita M, Kato M, Tokumasu S (1980) Application of ovule culture to the production of intergeneric or interspeciﬁc hybrids in Brassicaand Raphanus. Jpn J Genet 55:373–387 Tang J, Scarth R (2004) Effect of genetic background on the target fatty acids of acyl-ACP thioesterase transgenes in Brassica napus. Plant Breed 123:254–261 Tanhuanpaa P, Vilkki J, Vihinen M (1998) Mapping and cloning of FAD2 gene to develop allele-speciﬁc PCR for oleic acid in

113

spring turnip rape (Brassica rapa ssp. oleifera). Mol Breed 4:543–550 Teutonico RA, Osborn TC (1994) Mapping of RFLP and qualitative trait loci in Brassica rapa and comparson to the linkage maps of Brassica napus, Brassica oleracea, and Arabidopsis thaliana. Theor Appl Genet 89:885–894 Teutonico RA, Yandell B, Satagopan JM, Ferreira ME, Palta JP, Osborn TC (1995) Genetic analysis and mapping of genes controlling freezing tolerance in oilseed Brassica. Mol Breed 1:329–339 Theander O, Aman P, Miksche GE, Yasuda S (1977) Carbohydrates, polyphenols, and lignin in seed hulls of different colors from turnip rapeseed. J Agric Food Chem 25:270–273 Thierfelder A, Friedt W (1995) Development of novel rapeseed (Brassica napus) varieties resistant against beet cyst nematodes (Heterodera schachtii). In: Proc 9th Int Rapeseed Congress, Cambridge, UK, 4:1208–1210 Thies W (1991) Determination of the phytic acid and sinapic acid esters in seeds of rapeseed and selection of genotypes with reduced concentrations of these compounds. Fat Sci Technol 93:49–52 Thompson KF (1972) Cytoplasmic male sterility in oilseed rape. Heredity 29:253–257 Thompson KF (1983) Breeding winter oilseed rapeseed Brassica napus. Adv Appl Biol 7:1–104 Thormann CE, Ferreira ME, Camargo LEA, Tivang JG, Osborn TC (1994) Comparison of RFLP and RAPD markers to estimating genetic relationships within and among cruciferous species. Theor Appl Genet 88:973–980 Thormann CE, Romero J, Mantet J, Osborn TC (1996) Mapping loci controlling the concentrations of erucic and linolenic acids in seed oil of Brassica napus L. Theor Appl Genet 93:282–286 Tokumasu S (1951) Male sterility in Japanese radish (Raphanus sativus L.). Sci Bull Fac Agric Kyushu Univ 13:83–89 Töpfer R, Martini N (1994) Molecular cloning of cDNAs or genes encoding proteins involved in de novo fatty acid biosynthesis in plants. J Plant Physiol 143:416–425 Töpfer R, Martini M, Schell J (1995) Modiﬁcation of plant lipid synthesis. Science 268:681–686 Toroser D, Thormann CE, Osborn TC, Mithen R (1995) RFLP mapping of quantitative trait loci controlling seed aliphatic-glucosinolate content in oilseed rape (Brassica napus L.). Theor Appl Genet 91:802–808 Town CD, King G, Ayele M, Haas B, Cheung F, Van Aken S, Utterback T, Trick M (2006) An analysis of the Brassica oleracea genome based on whole genome shotgun sequencing. Acta Hort (in press) Trautwein EA (1997) Food quality of rapeseed oil – aspects from a nutritional point of view. GCIRC Bull 14:123–128 Trautwein EA (2001) n-3 Fatty acids – physiological and technical aspects for their use in food. Eur J Lipid Sci Technol 103:45–55 Udall JA, Quijada PA, Osborn TC (2005) Detection of chromosomal rearrangements derived from homoeologous recom-

114

R. Snowdon, W. Lühs, W. Friedt

bination in four mapping populations of Brassica napus L. Genetics 169: 969–979 U N (1935) Genomic analysis of Brassica with special reference to the experimental formation of B. napus and peculiar mode of fertilization. Jpn J Bot 7:389–452 Udupa SM, Baum M (2001) High mutation rate and mutational bias at (TAA)n microsatellite loci in chickpea (Cicer arietinum L.). Mol Gen Genom 265:1097–1103 Uzunova M, Ecke W (1999) Abundance, polymorphism and genetic mapping of microsatellites in oilseed rape (Brassica napus L.). Plant Breed 118:323–326 Uzunova M, Ecke W, Weissleder K, Robbelen G (1995) Mapping the genome of rapeseed (Brassica napus L.). 1. Construction of an RFLP linkage map and localization of QTLs for seed glucosinolate content. Theor Appl Genet 90:194–204 Valsta LM, Jauhiainen M, Aro A, Katan MB, Mutanen M (1992) Effects of monounsaturated rapeseed oil and polyunsaturated sunﬂower oil diet on lipoprotein in humans. Arteriosclerosis and Thrombosis 12:50–57 Voelker TA, Worrell AC, Anderson L, Bleibaum J, Fan C, Hawkins DJ, Radke SE, Davies HM (1992) Fatty acid biosynthesis redirected to medium chains in transgenic oilseed plants. Science 257:72–74 Voelker TA, Hayes TR, Cranmer AM, Turner JC, Davies HM (1996) Genetic engineering of a quantitative trait: metabolic and genetic parameters inﬂuencing the accumulation of laurate in rapeseed. Plant J 9:229–241 Voelker TA, Jones A, Cranmer AM, Davies HM, Knutzon DS (1997) Broad-range and binary-range suggest an alternative mechanism for medium-chain production in seeds. Plant Physiol 114:669–667 Vos P, Hogers R, Bleeker M, Reijans M, Lee T, Homes M, Freiters A, Pot J, Peleman J, Kuiper M, Zabeau M (1995) AFLP: a new concept for DNA ﬁngerprinting. Nucleic Acids Res 23:4407–4414 Voss A, Snowdon RJ, Lühs W, Friedt W (2000) Intergeneric transfer of nematode resistance from Raphanus sativus into the Brassica napus genome. Acta Hort 539:129–134 Walker KC, Booth EJ (2001) Agricultural aspects of rape and other Brassica products. Eur J Lipid Sci Technol 103:441–446 Walsh JA, Sharpe AG, Jenner CE, Lydiate DJ (1999) Characterisation of resistance to turnip mosaic virus in oilseed rape (Brassica napus) and genetic mapping of TuRB01. Theor Appl Genet 99:1149–1154 Wang YP, Snowdon RJ, Rudloff E, Wehling P, Friedt W, Sonntag K (2004) Cytogenetic and molecular characterization of progeny from asymmetric somatic hybrids between Brassica napus and Crambe abyssinica. Genome 47:666–673 Warner K, Mounts TL (1993) Frying stability of soybean and canola oils with modiﬁed fatty acid composition. JAOCS 70:983–988 Weber S, Zarhloul MK, Friedt W (2000) Modiﬁcation of oilseed quality by genetic transformation. Prog Bot 62:140–174

Weber S, Ünker F, Friedt W (2005) Improved doubled haploid production protocol for Brassica napus using microspore colchicine treatment in vitro and ploidy determination by ﬂow cytometry. Plant Breed 124:511–513 Westman AL, Kresovich S (1998) The potential for cross-taxa simple-sequence repeat (SSR) ampliﬁcation between Arabidopsis thaliana L. and crop brassicas. Theor Appl Genet 96:272–281 Whetten RW, Mackay JJ, Sederoff R (1998) Recent advances in understanding lignin biosynthesis. Annu. Rev. Plant Physiol. Plant Mol Biol 49:585–609 Wiberg E, Banas A, Stymne S (1997) Fatty acid distribution and lipid metabolism in developing seeds in laurate-producing rape (Brassica napus L.). Planta 203:341–348 Williams JGK, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV (1990) DNA polymorphisms ampliﬁed by arbitrary primers are useful as genetic markers. Nucleic Acids Res 18:6531–6535 Winter H, Snowdon RJ, Diestel A, Gärtig S, Sacristán MD (1999) Untersuchungen zum Transfer von Resistenzen gegen Leptosphaeria maculans aus Wildcruciferen in den Raps. Vortr Pﬂanzenzüchtg 46:340–342 Winter H, Diestel A, Gärtig S, Krone N, Sterenberg K, Sacristan MD (2003) Transfer of new blackleg resistances into oilseed rape. In: Proc 11th Int Rapeseed Congress, Copenhagen, 1:19–21 Wong R, Patel JD, Grant I, Parker J, Charne D, Elhalwagy M, Sys E (1991) Development of high oleic acid canola lines through seed mutagenesis. In: Proc 8th Int Rapeseed Congress, Saskatoon, Sasketchewan, Canada, 1:207–212 Wretblad S, Bohman S, Dixelius C (2003) Overexpression of a Brassica nigra cDNA gives enhanced resistance to Leptosphaeria maculans in B. napus. Mol Plant Microbe Interact 16:477–484 Xiao D, Liu HL (1982) Correlation analysis of seed color and seed oil in Brassica napus L. Acta Agron Sin 8:24–27 Yoder JI, Goldsbrough AP (1994) Transformation systems for generating marker-free transgenic plants. Biotechnology 12:263–267 Zarhloul MK, Lühs W, Ehemann AS, Hausmann L, Friedt W, Töpfer R (1999) Molecular Approaches to the biosynthesis of medium-chain triacylglycerols in Brassica napus. In: Proc 10th Int Rapeseed Congress, Canberra, Australia. http://www.regional.org.au/au/gcirc/4/445.htm Zhao JW, Meng JL (2003) Genetic analysis of loci associated with partial resistance to Sclerotinia sclerotiorum in rapeseed (Brassica napus L.). Theor Appl Genet 106:759–764 Zhou YM, Liu HL (1987) Studies on the inheritance of major fatty acid composition in the oil of rapeseed (Brassica napus L.). Acta Agron Sin 13:1–10 Zietkiewicz E, Rafalski A, Labuda D (1994) Genome ﬁngerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction ampliﬁcation. Genomics 20:176–183

CHAPTER 3

3 Peanut S.L. Dwivedi1 , D.J. Bertioli2 , J.H. Crouch1 , J.F. Valls3 , H.D. Upadhyaya1 , A. Fávero3 , M. Moretzsohn3 , and A.H. Paterson4 1

2 3

4

International Crops Research Institute for the Semi Arid Tropics (ICRISAT), ICRISAT Patancheru PO, 502324, AP, India e-mail: [email protected] Universidade Catolica de Brasilia, Pos Graduacao Campus II, SGAN 916, DF CEP 70.790-160, Brasilia, Brazil EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN), Parque Estação Biológica-pqEB, Final Av. W5 Norte, CEP: 70770-900, Brasília-DF, Brazil Distinguished Research Professor and Director, Plant Genome Mapping Laboratory, University of Georgia, Rm. 228, 111 Riverbend Road, GA 30602Athens, USA

3.1 Introduction 3.1.1 Origin The legume genus Arachis is of South American origin and contains about 80 known species with natural distributions restricted to Brazil, Bolivia, Paraguay, Argentina, and Uruguay (Valls and Simpson 1994). These wild Arachis species are divided into nine taxonomical sections, based upon morphology and sexual compatibilities. A. hypogaea, the cultivated tetraploid peanut (also known as groundnut), is found in the section Arachis, along with some 25 wild diploid species. This section also contains another tetraploid species, A. monticola, which readily hybridizes with A. hypogaea, is almost indistinguishable using DNA markers, and may best be considered as conspeciﬁc. It seems that the origin of A. hypogaea was through the hybridization of two diploid species with distinct genomes giving rise to a sterile hybrid. A spontaneous duplication of chromosomes restored fertility, but left the plant reproductively isolated from its wild relatives (Kochert et al. 1991; Jung et al. 2003; Seijo et al. 2004). It is most likely that these events occurred once or only a few times. There is doubt about exactly which diploid species were involved and as to where these events occurred. However, it seems logical that the diploid species involved were probably brought together by human action through the cultivation of species that have distinct and separate natural distributions. In support of this, archaeological ﬁnds of shells of fruits closely resembling A. duranensis Krapov. and W.C. Gregory; A. magna Krapov., W.C. Gregory and C.E.

Simpson; A. ipaensis Krapov. and W.C. Gregory; and A. monticolaKrapov. and Rigoni were excavated near Casma in coastal Peru. These species today are considered entirely wild but were apparently cultivated in the remote past. It is not necessarily the case that the origin of A. hypogaea was in this region; indeed, it seems that this would be more likely to have happened in the Eastern slopes of the Andes where A. monticola exists today in a wild state. The climate in this region is moister, not as good for the preservation of archaeological remains, but more favorable for plant growth, and a better environment for the wild bees that would have done the necessary initial hybridization (Simpson et al. 2001). 3.1.2 Botanical Types and Distribution A. hypogaea is divided into two subspecies, hypogaea and fastigiata, and six botanical varieties (Krapovickas and Gregory 1994). The subsp. hypogaea var. hypogaea has a long cycle, no ﬂowers on the central stem, and regularly alternating vegetative and reproductive side branches. It is exempliﬁed by the Virginia types that are widely present along the tributaries of the right margin of the Amazon Basin in Brazil and Bolivia. Also classiﬁed within subsp. hypogaea, but with more hirsute leaﬂets and even longer cycle, is the variety hirsuta Kohlër. Nowadays this variety is concentrated in the coastal regions of Peru, from where it extends to Central America and Mexico, Asia, and Madagascar. The variability of this variety found in the Old World even suggests the possibility of pre-Colombian contacts (Simpson et al. 2001).

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

116

S.L. Dwivedi et al.

The subspecies fastigiata Waldron has a shorter cycle, ﬂowers on the central stem, and reproductive and vegetative shoots distributed in a disorganized way. This subspecies includes four varieties. The variety vulgaris C. Harz has its distribution/spread along the basin of the river Uruguay, usually the fruits are two seeded, and correspond to the agricultural type Spanish. The variety fastigiata has fruits with more than two seeds and a smooth pericarp. This variety corresponds to the agricultural type Valencia; centers of diversity are in Paraguay and in central and northeastern Brazil extending to Peru. The other two varieties, aequatoriana Krapov. and W. C. Gregory (Equador and North of Peru) and peruviana Krapov. and W.C. Gregory (Peru, northeast Bolivia, and the Brazilian state of Acre), have fruits with more than two seeds, heavy reticulation of the pericarp, and very restricted distributions (Krapovickas and Gregory 1994). In addition, Brazilian germplasm includes material that is difﬁcult to ﬁt into the above classiﬁcation. Particularly notable in this respect is material from the Xingu river basin (Freitas and Valls 2001). Modern cultivars generally have a narrow genetic base (Isleib et al. 2001). Nevertheless they are generally classiﬁed into the agricultural types Spanish, Valencia, or Virginia. These cultivars have been widely used as representatives of the botanical varieties in analyses of genetic variability. These cultivars often have their origin in more than one variety or subspecies and are not correct taxonomic representatives; their use as such may lead to incorrect conclusions. 3.1.3 Crop Production and Uses Peanut is the major oilseed crop in the world, grown on 26 million ha producing nearly 36 million tons annually. Although the global average productivity is low (1.35 t ha−1 ), many countries achieve much higher levels of productivity, including the USA (3.54 t ha−1 ) and China (2.62 t ha−1 ) (FAO 2003). Developing countries contribute about 94% of the world peanut production, grown mostly under rainfed conditions (predominantly in Asia and Africa) (Table 1). Analysis of peanut productivity from the 1960s to the 1990s reveals interesting proﬁles across regions and in speciﬁc countries. While the average global

peanut productivity has steadily increased during that period, from 8% in the 1970s (average yield 0.85 t ha−1 in the 1960s) to 18% each in the 1980s (average yield 0.92 t ha−1 in the 1970s) and 1990s (average yield 1.09 t ha−1 in the 1980s), changes in productivity have been highly variable across different regions. For example, peanut yield in Asia increased by 14% in the 1970s (average yield 0.82 t ha−1 in the 1960s), 20%in the 1980s (average yield 0.93 t ha−1 in the 1970s), and 32% in the 1990s (average yield 1.12 t ha−1 in the 1980s), while in Africa productivity has largely stagnated or even declined during certain periods. In north Central America, tremendous yield increases (45%) were achieved during the 1970s (average yield 1.62 t ha−1 in the 1960s) but thereafter only marginal increases were seen as the importance of the crop in this region began to decline. A similar trend was seen in the USA with 54% increase in peanut productivity during the 1970s (average yield 1.73 t ha−1 in the 1960s) but little increase thereafter due to emphasis on stabilizing the yield by incorporating resistance/tolerance to pests and diseases and improving seed quality. In contrast, peanut yield remained stagnated during the 1960s and 1970s in South America but then increased by 28% in the 1980s (average yield 1.24 t ha−1 in the 1970s) and 16% in 1990s (average yield 1.58 t ha−1 in the 1980s). The peanut yield in Argentina and Brazil remained stagnated in the 1960s and 1970s but registered a 89% increase in Argentina (average yield 1.16 t ha−1 in the 1970s) and a 31%increase in Brazil (average yield 1.32 t ha−1 in the 1970s) in the 1980s and remained stagnated at this level in the 1990s. The greatest sustained improvement has been seen in China with a 19%increase during the 1970s (average yield 1.03 t ha−1 in the 1960s), 47% in the 1980s (average yield 1.23 t ha−1 in the 1970s), and 41% in the 1990s (average yield 1.81 t ha−1 in the 1980s). These statistics seem to indicate a direct correlation of perceived national economic importance (and presumably in turn investment in research and breeding) with increases in productivity. The prevalence of biotic and abiotic stresses and the level of technological innovation at the farm level are probably a major source of yield variation observed across regions and between countries within regions. However, it is equally inevitable that a major portion can be attributed to progress in genetics and breeding research that have been captured in new varieties. This is surely a strong justiﬁcation for increased investment in plant research for genetics

Chapter 3 Peanut

117

Table 1. Area, production and productivity of the peanut across globe, regions, and major peanut producing countries within the region (FAO 2003) Region

Country within region

Asia India China Indonesia Myanmar Vietnam Africa Nigeria Sudan Senegal Chad Congo (DR) Burkina Fasso Mozambique Cameroon Zimbabwe Niger North Central America

USA Mexico

South America Argentina Brazil Oceanea Australia World +

Area (000 ha)

Production (000 t)

Average yield (t ha−1 )

14988.60 (56.64)+ 8000.00 (53.37)++ 5125.40 (34.19) 682.94 (4.56) 575.00 (3.84) 240.30 (1.60) 10472.57 (39.57) 2800.00 (26.74) 1900.00 (18.14) 900.00 (8.59) 480.00 (4.58) 456.59 (4.36) 331.00 (3.16) 292.54 (2.79) 283.00 (2.70) 260.00 (2.48) 230.00 (2.19) 657.10 (2.48) 530.95 (80.80) 62.01 (9.44) 307.08 (1.16) 156.40 (50.93) 85.02 (27.68) 25.76 (0.097) 22.00 (85.40) 26462.86

24000.55 (67.31)+ 7500.00 (31.25)++ 13447.45 (56.03) 1377.00 (5.74) 730.00 (3.04) 400.00 (1.67) 8969.19 (25.15) 2700.00 (30.10) 1200.00 (13.38) 900.00 (10.03) 450.00 (5.02) 355.18 (3.96) 301.00 (3.55) 109.92 (1.22) 294.89 (3.29) 125.00 (1.39) 100.00 (1.11) 2077.77 (5.83) 1879.75 (90.47) 74.64 (3.57) 559.92 (1.57) 315.60 (56.36) 177.06 (31.62) 40.88 (0.12) 37.00 (90.51) 35658.43

1.60 0.94 2.62 2.02 1.27 1.66 0.86 0.96 0.63 1.00 0.94 0.78 0.91 0.38 1.04 0.48 0.43 3.16 3.54 1.20 1.82 2.02 2.08 1.59 1.68 1.35

% of global area and production % of region area and production

++

and genomics-enhanced peanut-breeding programs. China, the USA, Argentina, and Vietnam together contribute 75.5% of the world shelled peanut trade (1.13 million tons), with a total export value of US$ 593.6 million (FAO 2002). The US shelled peanut seeds command a premium price of US$ 786 per metric ton in the international market. Abiotic and biotic stresses are the major constraints on world peanut production and are extensively reviewed in Dwivedi et al. (2003a). In addition, devastating new diseases are also emerging. For example, a new disease diagnosed as peanut stem necrosis disease (PSND) caused by tobacco streak virus (TSV) affected nearly 225,000 hapeanut crops that resulted in yield losses of over US$65 million in India during 2000 (Rao et al. 2003a).

Peanut is a rich source of oil, protein, minerals (Ca, Mg, P, and K), and vitamins (E, K, and B1 ) (Savage and Keenan 1994). Freeman et al. (1999) predicted a continued increase in peanut production in Asia, a slow increase in sub-Saharan Africa, and decline in Latin America. There will be a gradual shift away from peanut oil and meals to peanut confectionary products in Asia, Latin America, and the Caribbean. The cake remaining after oil extraction is used in human food or incorporated into animal feeds (Savage and Keenan 1994). Peanut haulm is excellent forage for cattle as it is rich in protein and more palatable than many other fodders (Cook and Crosthwaite 1994). Wild Arachis species are used in pasture improvement in the Americas and Australia (Kerridge and Hardy 1994).

118

S.L. Dwivedi et al.

3.1.4 Improved Quality Requirements: Reduced Allergenicity and Toxicity

terson et al. 2004). Thus in this section we will only provide a brief overview of the current status of genetic resources being maintained in national and international gene banks and the range of wild and culPeanut is widely used in the food and confectionery tivated accessions with beneﬁcial traits. industry due to its high nutritive value. However, it is well known for its allergenic properties affecting both children and adults. A study in the USA revealed 3.2.1 that about 3 million American children and adults Wild Arachis Species and Interspecific Gene suffer from allergy to peanut or tree nuts (Sicherer Introgression into Cultivated Peanut et al. 1999). Most importantly, just a trace of peanut can provoke an abnormal IgE-mediated immunolog- ICRISAT gene bank maintains 453 accessions, repical reaction ranging from nausea or drowsiness and resenting 9 sections and 44 wild Arachis species. vomiting to anaphylactic shock and death. Peanut has Of these, 352 accessions belonging to 41 species seven distinct allergenic proteins, Ara h1 to Ara h7 are seed producing, 100 accessions of 2 species are (Burks et al. 1991, 1992; De Jong et al. 1998; Kleber- vegetatively propagated, and 1 accession (A. batiJanke et al. 1999; Herman 2004), that together include zogaea) is a natural hybrid (seed producing). One the vast majority of proteins in seeds and include both hundred ninety-ﬁve accessions of 16 species are storage protein families (Koppelman et al. 2001). Ara annual and 232 accessions of 17 species are perennial. h1 and Ara h2 are major allergens (Burks et al. 1995; Information on 26 accessions of 11 species is not Stanley et al. 1997; Kleber-Janke et al. 1999) and the known. The gene bank acquired 418 accessions remaining ﬁve are minor allergens (Kleber-Janke et al. as donations from 8 cooperators in 4 countries 1999). North American populations are more prone (Brazil 22, United Kingdom 3, India 8, USA 285) to Ara h1 than the European populations (Koppel- and 31 accessions through 4 collecting missions man et al. 2001). A vaccine that can protect people in Brazil (Rao et al. 2003b; http://www.icrisat.org/ from peanut allergies has been developed, and tests GroundNut/Arachis/Start.htm). The whereabouts revealed that the vaccine effectively protected mice of the remaining four accessions maintained from peanut allergies, which provides some hope that in the ICRISAT gene bank are not known. we should be able to protect humans from peanut The Southern Regional Plant Introduction Station, USDA-ARS at Grifﬁn, GA, USA, maintains allergy (http://www.nature.com/reviews/immunol). Quality characters are also of great importance over 700 accessions of 60 wild Arachis species in peanut production. For example, Aspergillus ﬂavus (USDA-ARS 2002; http://www.ars-grin.gov/cgi-bin/ Link ex Fries infection does not signiﬁcantly affect npgs/html/site_holding.pl?S9). Large collections peanut yields but its production of aﬂatoxins makes of wild Arachis species are also maintained at contaminated grain dangerous for animal and human Texas A&M and North Carolina State University, consumption. The presence of aﬂatoxins also dramat- Raleigh, NC, USA. The National Center of Genetic ically inﬂuences the marketing of peanut kernels and Resources (CENARGEN) in Brazil maintains over cake because of stringent international standards for 1200 accessions of 81 species belonging to 9 sections. Unlike cultivated peanut germplasm, wild permissible levels of aﬂatoxin contamination set by importing countries in Europe, North America, and Arachis species are reported to possess high levels of resistance to rust, leaf spots, nematodes, peanut Australia. bud necrosis virus (PBNV), tomato spotted wilt virus (TSWV), groundnut rosette virus (GRV) and groundnut rosette assistor virus (GRAV), leaf miner, 3.2 Spodoptera, aphids, thrips, and jassids (Stalker Genetic Resources in Peanut and Simpson 1995; Dwivedi et al. 2003a; Rao et al. 2003b). Wild Arachis species are also reported to There are a number of review articles covering the ge- show wide variation for most of the morphological netic resources of cultivated and wild Arachis species traits (Singh et al. 1996; Chandran and Pandya 2000). (Singh and Simpson 1994; Stalker and Simpson 1995; ICRISAT scientists characterized/evaluated 267 wild Dwivedi et al. 2003a; Holbrook and Stalker 2003; Pa- Arachis accessions from 37 species under greenhouse

Chapter 3 Peanut

conditions for 33 qualitative and 15 quantitative traits. The frequency distribution of 17 qualitative descriptors was uniformly distributed whereas for the other 16 traits it was skewed (70% or more) toward one class. These species, except for the height of the main stem, stem thickness, and basal leaﬂet on the main stem, showed large variation for lateral branches, plant width, stipule length, adnation of stipule on the main stem, petiole length on the main stem, apical leaﬂet length and width on the main stem, apical length and width on the primary lateral, hypanthium length, standard petal length, and peg length as revealed by the Shanon-Weaver diversity index that ranged from 0.022 for hairiness on the margin of the stipule of the main stem to 0.836 for basal leaﬂet shape on the primary lateral (H.D. Upadhyaya, ICRISAT unpubl. data). The primary gene pool in peanut consists of accessions that belong to cultivated peanut (Arachis hypogaea) and the wild tetraploid species A. monticola. The secondary gene pool is represented by diploid species of the section Arachis that are cross compatible with cultivated peanut, while the tertiary gene pool includes species of the other sections that cannot be hybridized with A. hypogaea by conventional means. Both pre- and postzygotic hybridization barriers have been shown to restrict crossing between Arachisspecies. Despite the crossing barriers, several interspeciﬁc tetraploid derivatives have been developed that possess high levels of resistance to rust, early leaf spot (ELS), late leaf spot (LLS), nematodes, southern corn rootworm, corn earworm, Spodoptera, and jassids (see Dwivedi et al. 2003a and references therein) that are semi-improved genetic resources that researchers may use for mapping and genetic enhancement in peanut. From the interspeciﬁc crossing, two root-knot nematode resistant peanut lines, Coan and Nema TAM, have been released for cultivation in areas heavily infested with nematodes in USA (Simpson and Starr 2001; Simpson et al. 2003).

3.2.2 Cultivated Germplasm The world’s largest peanut collection of 14,966 accessions from 93 countries is housed at the RS Paroda Gene Bank in ICRISAT, Patancheru, India. This collection represents six botanical varieties: 45.8% var. hypogaea (6,838 accessions), 36.6% var. vulgaris (5,493 accessions), 15.7% var.

119

fastigiata (2,351 accessions), 0.1% var. aequitorania (14 accessions), 0.13% var. hirsuta (19 accessions), and 1.7% var. peruviana(251 accessions). Approximately 43% of the collection consists of landrace germplasm, 7% cultivars, 31% breeding lines, and 19% other genetic stocks (mutants and experimental germplasm) (Upadhyaya et al. 2001a). Passport and characterization data are accessible through the internet (http://www.icrisat.org/ GroundNut/Project1/pﬁrst.asp?gname=entire) and the germplasm is freely available for distribution providing the requisitioned signs a material transfer agreement with ICRISAT. Other gene banks holding sizable numbers of cultivated peanut accessions are 9,027 accessions at USDA Southern Plant Introduction Center, Grifﬁn, GA, USA; about half of these collections are unimproved landraces collected in the crop’s centers of diversity in South America (Holbrook 2001). A series of descriptors has been developed for standardizing the characterization of peanut genetic resources using various morphophysiological, reproductive, and biochemical traits (IBPGR and ICRISAT 1992). The majority of the ICRISAT peanut germplasm showed a large variation for qualitative and quantitative traits, seed quality traits, and resistance to biotic and abiotic stresses (Upadhyaya et al. 2001a). Field evaluation of these germplasms identiﬁed a large number of accessions possessing tolerance to drought and resistance to biotic stresses (Table 2). Some of these genetic resources have been used in breeding programs to develop improved breeding lines/cultivars resulting in signiﬁcant economic gains to peanut farmers. For example, the largest impacts have been from the development of cultivars with resistance to sclerotinia blight (Sclerotinia minor Jagger), the peanut root-knot nematode [Meliodogyne arenaria (Neal) Chitwood race 1], and TSWV, which had an estimated economic impact of more than US$ 200 million annually for US peanut farmers (Holbrook 2001).

3.2.3 Core Collections The development of a core collection could facilitate easier access to peanut genetic resources, improve the efﬁciency of germplasm evaluations by reducing the number of accessions to be evaluated while increasing the probability of locating genes of interest thus

120

S.L. Dwivedi et al.

Table 2. Sources of resistance to rust, leaf spots, sclerotinia blight, groundnut rosette virus, aﬂatoxin, nematode, defoliator, aphid, and drought reported in cultivated and wild Arachis species Trait

Peanut accessions with beneﬁcial traits reported Cultivated Reference Wild species Arachis species

Rust Late leaf spot Early leaf spot Groundnut rosette virus Nematode Seed infection and/or aﬂatoxin production by Aspergillus ﬂavus Sclerotinia blight Defoliator (Leaf miner and Spodoptera)

169 69 37 116

Aphid

EC 36892 and ICG 12991 40

Drought

Reference

Singh et al. 1997 Singh et al. 1997 Singh et al. 1997 Subrahmanyam et al. 1998

29 27 11 12

Subrahmanyam et al. 1995 Upadhyaya et al. 2001a Upadhyaya et al. 2001a Subrahmanyam et al. 2001

21 21

Holbrook et al. 2000 Singh et al. 1997

– 4

– Thakur et al. 2000

50 9

Damicone et al. 2003 Dwivedi et al. 1993; Wightman and Rao 1994; Rao and Wightman 1999; Stalker and Lynch 2002 Padagham et al. 1990; Minja et al. 1999

– – 38 Wightman and Rao 1994; (leaf miner) Lynch and Mack 1995 and 67 (Spodoptera) Wild species not evaluated

Nigam et al. 2003b; Seetharama et al. 2003

Wild species not evaluated

enhancing their use in crop improvement programs, and simplify the gene bank management. A very small proportion of the germplasm accessions are being used in peanut-breeding programs (Upadhyaya et al. 2002a). At ICRISAT, where about 14,966 accessions of cultivated peanut and 453 accessions of wild Arachis are available for use, only 132 cultivated germplasm and 10 wild accessions have been used in developing 8,279 breeding lines in 17 years from 1986 to 2002 (H.D. Upadhyaya, ICRISAT, unpubl. data). Few accessions have been extensively used in breeding programs: Chico (ICG 476) 1180 times and Robut 33-1 also known as Kadiri 3 (ICG 799) 3,096 times. Holbrook et al. (1993) were the ﬁrst to develop a core collection of 831 accessions from a set of 7432 USA peanut germplasms based on 6 morphological variables. Subsequently, a global core consisting of 1,704 accessions (14 morphological descriptors on 14,310 accessions) (Upadhyaya et al. 2003) and an Asia region core of 504 accessions (15 morphological descriptors on 4,738 accessions) (Upadhyaya et al. 2001b) were developed. However, when the size of col-

lection is too large and a core collection (10% of entire collection) becomes unmanageable, Upadhyaya and Ortiz (2001) suggested a strategy to select a minicore collection (10% of core or 1% of entire collection). Using this strategy, Upadhyaya et al. (2002a) developed a minicore consisting of 184 accessions (based on 16 to 18 agronomic and quality traits scored on 1704 core collection accessions in 2 constrasting seasons) that captured variability present in the core collection (1,704 accessions) and also in the entire collection (14,310 accessions). Holbrook and Dong (2003) evaluated the USDA peanut core collection (831 accessions) for 16 morphological traits to develop a core of the core consisting of 111 accessionsdemonstrating that the genetic variation expressed in the core had been preserved in this core of the core collection. The accessions included in the core potentially possess new sources of variation for economically important traits. When the peanut global core, Asia region core and/or minicore collections, were evaluated for various traits in multienvironment trials, ICRISAT scientists identiﬁed new sources

Chapter 3 Peanut

of early maturity (21 accessions) (Upadhyaya et al. 2006) and tolerance to low temperature (12°C) at germination (158 accessions) from the global core (Upadhaya et al 2001a), for drought-tolerance traits (18 accessions) from the minicore (Upadhaya 2005), and 60 accessions (15 fastigiata, 20 vulgaris, and 25 hypogaea) from the Asia region core that showed high yield potential, greater meat content (also known as shelling percentage), and 100-seed weight (Upadhyaya et al. 2005). These new accessions have trait-speciﬁc characteristics similar to the best control, for example Chico, for early maturity but were agronomically similar or superior but diverse. The use of these diverse sources would help in bringing in much needed diversity and broaden the genetic base of cultivars (Upadhyaya 2005). Similarly, when the USDA peanut core collection was evaluated, 21 accessions were resistant to peanut root-knot nematode (Meloidogyne arenaria(Neal) Chitwood race 1 (Holbrook et al. 2000); 55 accessions were resistant to TSWV (Anderson et al. 1996); 11 and 12 accessions were resistant to cylindrocladium black rot [Cylindrocladeum crotalariae (Loos) Bell and Sobers] and ELS, respectively (Isleib et al. 1995); 6 accessionsexhibited a 90% reduction in preharvest aﬂatoxin contamination (Holbrook et al. 1998); 6 accessions were resistant to rhizoctomia limb rot (Rhizoctonia solanii Kuhn) (Franke et al. 1999); and 20 and 30 accessions were classiﬁed as highly resistant (no disease) and resistant (<10% disease incidence) to sclerotinia blight [Sclerotinia minor Jagger and Sclerotinia sclerotiorum(Lib) de Bary], whereas 10 accessions were resistant to pepper spot [Leptosphaerulina crassiasca (Sechet) Jackson and Bell] (Damicone et al. 2003). These new sources performed better than or similar to the best cultivars for the target traits but were known to be diverse from all previous sources of those traits. Thus, these accessions are a good resource for analyzing genetic relationships and detecting allelic variation associated with beneﬁcial traits through association analysis. Moreover, where allelic tests indicate an independent genetic basis for a given trait, there is an opportunity for pyramiding genes to enhance the level of expression of that trait, provided the genetic basis is highly additive and epistatically positive. It is noteworthy that the core and minicore collections discussed in this section do not include accessions from wild Arachisspecies. These genotypes tend to have a much longer cropping duration than the cultivated germplasm and need special care when

121

grown under ﬁeld conditions outside their natural habitat. Thus, it is very difﬁcult to conduct routine large-scale evaluation trials that include both wild and cultivated material. However, there are considerable ongoing efforts focused on the characterization of wild accessions for various morphophysiological, reproductive, and quality traits (Sect. 2.1) that should lead to increased exploitation of wild germplasm in peanut-breeding programs.

3.3 Appropriate Germplasm and Evaluation Systems for Mapping Economically Important Traits in Peanut The efﬁciency of marker-assisted selection (MAS) is highly dependent on the quality of the prior mapping process, which requires parental genotypes with substantially contrasting phenotypes (for the target trait), highly accurate and precise evaluation techniques, and large mapping populations. In addition, the cost-effective application of MAS requires one marker very close to (or preferably within) the gene of interest and two markers closely ﬂanking either side and that these markers be based on simple robust PCR-based marker assays. In this section, we provide an overview of genetically diverse sources of economically important traits that may be useful in genome mapping and marker-assisted genetic enhancement in peanut. Then we brieﬂy discuss the mechanisms and genetics of resistance/tolerance to important biotic and abiotic stresses and the key steps involved in generating accurate phenotyping data for those target traits.

3.3.1 Phenotypic Screens, Resistance/Tolerance Mechanism, and Genetics

Rust and Leaf Spots It is essential that a pure disease inoculum be used for inoculating the test materials. This should be generated and maintained on incubated, inoculated, detached leaves of a highly susceptible cultivar such as TMV 2 in a plant growth chamber using a temperature of 23 ◦ Cand 12-h photoperiod. The early leaf spot

122

S.L. Dwivedi et al.

(ELS) and late leaf spot (LLS) conidia and rust urediniospores should be harvested with a cyclone spore collector and used for inoculation of the test materials separately. A two-step evaluation is suggested for phenotyping: the ﬁeld and glasshouse evaluation. The former is useful as a ﬁrst step wherein a large number of genotypes/mapping populations can be evaluated for overall disease score and related to yield loss, whereas the latter is recommended for dissecting the components of resistance under more controlled conditions. For ﬁeld evaluation, the infector row technique is adapted wherein the resistant and susceptible controls are sown alternately every ﬁve rows of test materials. Sunﬂower is sown in a 4-m wide strip around the experimental plot to avoid aerial contamination by other foliar fungal pathogens. At 35 d after sowing (DAS) the infector row and the test materials are inoculated with disease inoculum containing either 20,000 ml−1 rust urediniospores or 20,000 ELS or LLS conidia, late in the evening. Prior to fungal inoculation, the whole experimental ﬁeld is given light overhead sprinkler irrigation for 30 min to create leaf wetness on the foliage throughout the night. Thereafter, overhead irrigation is given every day for 30 min in the late evening for 10 d to maintain alternate wet (night) and dry (day) periods to obtain maximum disease development (Butler et al. 1994). The experimental plot should be protected against leaf spots when screening for resistance to rust [using weekly application of Chlorothalonil (Kavach) at 2 g l−1 water (500 lha−1 )] while rust should be controlled when screening for resistance to leaf spots [using weekly application of calixin at 0.5 ml l−1 water (500 l ha−1 )]. There is no fungicide that gives differential protection (i.e., protection against ELS in LLS screening trial and protection against LLS in ELS screening trial), so the phenotyping should be conducted in hot spots prone only either to ELS or LLS. A nine-point modiﬁed scale is used, where 1 = no disease and 9 = 81 to 100% disease (Subrahmanyam et al. 1995), to record % leaf area damaged, % leaf defoliation (in case of leaf spots), and disease scored at 15-d intervals from 35 d after inoculation until 1 week before harvest. For greenhouse evaluation, the test materials along with susceptible and resistant controls are grown in 15-cm-diameter plastic pots containing autoclaved soil and farmyard manure (v/v 4:1 ratio). The inoculum procedure is the same as described in the case of ﬁeld screening. Immediately after

inoculation, the pots are shifted into dew chambers (Clifford 1973) at 23 ◦ C to ensure wetness of the leaf surface during the night. The pots are removed from the dew chambers on the morning of the following day and returned to the greenhouse to maintain a dry period during the day. This alternation of wet (16 h) and dry (8 h) periods is repeated for 10 d. The pots are then kept permanently in the glasshouse until completion of the experiment. Two undamaged fully expanded quadrifoliate leaves of the main axis of a plant per pot, ﬁve plants per replication for each genotype, are tagged to study the components of resistance (Dwivedi et al. 2002a). The experiment should be terminated at 45 to 50 d after inoculation or when the disease is fully developed and most of the leaves are either completely weathered due to rust or defoliated due to leaf spots. Reports on the genetic basis of resistance to rust and leaf spots vary from a few genes to quantitative inheritance (reviewed by Dwivedi et al. 2003a). Resistance to rust is due to longer incubation and latent periods, fewer pustules per leaf, smaller pustule diameter, lower sporulation index, less leaf area damage, and lower disease score. Since these components interact in an additive manner, selection is based on evaluation of individual components of resistance together with the determination of green leaf area remaining on the plant. Resistance to LLS is due to longer incubation and latent periods, fewer lesions per leaf, smaller lesion diameter, lower sporulation index, less leaf area damage, and lower disease score. Selection based on components of resistance to LLS may not lead to plants with higher retained green leaf area. The remaining green leaf area on the plant and disease score should be the basis of selecting for resistance to LLS (Dwivedi et al. 2002a). The ELS-resistant germplasm accessions have longer incubation periods, reduced sporulation rates, lesion diameter, and infection frequencies, and less defoliation (Nevil 1981; Waliyar et al. 1993).

Aflatoxin Aﬂatoxins are produced by Aspergillus ﬂavusLink ex Fries, which produces only aﬂatoxin B1 . Resistance to A. ﬂavus in peanut operates independently in at least three tissues: pod, seed coat, and cotyledons (Mixon 1986). Resistance to pod infection is attributed to pod-shell structure, while resistance to seed invasion and colonization has been correlated with thickness, density of palisade cell layers, absence of ﬁssures and cavities, and presence of wax layers

Chapter 3 Peanut

on the seed coat. A number of genes are involved in seed response to colonization by A. ﬂavus, aﬂatoxin production, and preharvest infection (Utomo et al. 1990). Estimates of broad sense heritability vary from low to high for natural infection and seed colonization by A. ﬂavus and from low to moderate for aﬂatoxin production (http://www.aﬂatoxin.info/ groundnut_breeding.asp). Three-phase evaluation is recommended for phenotyping of the parental and mapping populations: preharvest infection (Mehan 1989), in vitro seed colonization (Mixon and Rogers 1973; Mehan and McDonald 1980), and aﬂatoxin production (Mehan and McDonald 1980). Drought during pod formation substantially increases the level of aﬂatoxin contamination. Preharvest infection requires a drought period of 30 to 35 d and a mean soil temperature of 29 to 31 ◦ C in the podding zone (Cole et al. 1989, 1995). The susceptibility of peanut to aﬂatoxin contamination is related to lower water activity (0.80 to 0.95) in the kernel and favorable temperature (25 to 32 ◦ C) for growth of A. ﬂavus (Sicherer et al. 1999). As the kernel moisture content decreases under end-of-season drought, protection from the natural defense mechanisms is lost and the kernels become vulnerable to colonization by A. ﬂavus and aﬂatoxin contamination. A line-source sprinkler irrigation system (Hanks et al. 1976) that imposes a water deﬁcit gradient is suggested to evaluate populations for their reaction to A. ﬂavus and aﬂatoxin production. The most common immunoassay used for mycotoxin detection are enzyme-linked immunosorbent assay (ELISA); analyzing a single analyte by ELISA costs ca. 5.9%and 2.5% of that by gas chromatography or gas chromatography-mass spectroscopy, respectively (Vogt 1984). Chu et al. (1987) developed an improved ELISA technique that is more sensitive than previously reported methods (El-Nakib et al. 1981; Ram et al. 1986a,b) and analytical time shortened to less than 1 h for quantitative analysis and less than 30 min for screening tests, with a detection power as little as 5 pg B1 . A few commercial kits available in the market are Aﬂatest, Agriscreen, AﬂaCup 10, AﬂaCup 20, and EZ-Screen, with 1 to 20 ppb detection power; approximate cost per sample ranges from US$ 8.5 to 65.0 for quantitative estimates and US$ 6.0 to 7.5 for visual tests (www.oznet.ksu.edu/grsiest).

123

food products (Holzhauser and Vieths 1999), whereas peanut allergens are measured in foods by immunoassays with human IgE antibodies (Keating et al. 1990). However, these assays do not provide quantitative measurements of exposure to food allergens. Hird et al. (2003) developed a sensitive and robust assay for the identiﬁcation of peanut in commercial products using real-time PCR technology, while Pomes et al. (2003) developed a new sensitive and speciﬁc monoclonal antibody-based ELISA to monitor Ara h1 content in food products and in developing thresholds for sensitization or allergic reaction in persons with peanut allergy. Dodo et al. (2002) provided a detailed procedure to detect peanut allergens that consists of defatting peanut seeds, extracting peanut proteins, and conducting an ELISA test.

Nematodes The peanut root-knot nematode Meloidogyne arenaria (Neal) Chitwood race 1 is widely distributed across peanut-growing areas. Holbrook et al. (1983) described the screening technique wherein greenhouse-grown plants are inoculated with 3000 eggs of M. arenaria race 1 cultured on tomato (Lycopersicon esculentum Mill. cv. Rutgers) or eggplant (Solanum melongena L. cv. Black Beauty). The nematode inoculum should be prepared using the NaOCl method (Hussey and Barker 1973) and applied 14 d after planting. Approximately 70 dafter inoculation, plants are uprooted and washed clean of soil. The roots are then placed in 1000-ml beakers containing 300 ml of 0.05% ploxine B solution for 3 to 5 min (Daykin and Hussey 1985) and scored for root galls and egg masses [using dissecting microscope (×20) to determine the number of eggs per gram of fresh root weight] based on a 0 to 5 rating where 0 = no galls or no egg masses, 1 = 1 to 2, 2 = 3 to 10, 3 = 11 to 30, 4 = 31 to 100, and 5 = more than 100 galls or egg masses per root system (Taylor and Sasser 1978). Some variation in screening procedure may be adapted as reported in other publications (Nelson et al. 1989; Starr et al. 1995; Choi et al. 1999). Resistance to M. arenaria is moderate in cultivated peanut and is due to reduction in the percentage of second-stage juveniles (J2) that establish a functional Allergens feeding site (Timper et al. 2000). In contrast to culELISA is the most commonly used method to de- tivated peanut, very high levels of resistance to M. tect peanut in the food manufacturing process and in arenariaexist in wild Arachis species conditioned by

124

S.L. Dwivedi et al.

two dominant genes: Mag inhibits root galling and and length, leaf hairiness, standard petal length and Mae inhibits egg production by M. arenaria (Garcia petal markings, basal leaﬂet width, and main stem thickness and hairiness, and stipule adnation length et al. 1996). and width showed signiﬁcant correlation and/or regression coefﬁcients with damage by defoliators Defoliating Insect Pests including S. litura(Sharma et al. 2003). The major defoliators in peanut are leaf miner [Aproaerema modicella (Deventer)] and tobacco armyworm (Spodoptera litura F.). It is difﬁcult to Groundnut Rosette Virus screen for resistance to defoliators under natural ﬁeld The rosette (chlorotic and green) is the most deinfestation because of variation in infestation in space structive disease of peanut in sub-Saharan Africa. and time. Sharma et al. (2002) developed a no-choice Three agents interact to produce rosette disease syncage technique to screen for resistance to S. litura. drome: groundnut rosette virus (GRV), groundnut Essentially the technique consists of raising the rosette assistor virus (GRAV), and satellite RNA (sat nuclear insect cultures and maintaining these on an RNA) (Bock et al. 1990). GRV is transmitted by aphid artiﬁcial diet (Taneja and Leuschner 1985), releasing (Aphis craccivora) but only from the plants that also a known number of ﬁrst- or third-instar larvae on contain GRAV. GRAV is not mechanically transmis15-d-old greenhouse plants (temperature 28 ± 5 ◦ C sible and causes no apparent symptoms in peanut. and RH > 65%) grown in pots (30 cm diameter, 30 cm The sat RNA, which is dependent on GRV for muldeep) under a plastic jar cage (11 cm diameter and tiplication and on GRAV for aphid transmission, is 26 cm height) with two wire-mesh-screened windows largely responsible for rosette symptoms (Murant (4 cm diameter) for varying periods of time and et al. 1988). Variation in sat RNA has been corthen recording observations on insect survival and related with the different forms of rosette disease leaf area damage. Two plants are grown in each pot, (Murant and Kumar 1990). All three agents must where one plant is infested with the larvae inside the be present together in the host plant for successcage and the other plant remains outside the cage ful transmission of the disease by the aphid vecand is left as an uninfested control. Observations are tor. Bock and Nigam (1988) developed the infectorthen recorded on the number of surviving larvae, the larval weight (4 hfollowing termination of the row technique where a highly susceptible peanut culexperiment), and percentage leaf area damage on a 1 tivar is grown in every two rows of the test materito 9 scale, where 1 ≤ 10% leaf area damage, 2 = 11 to als under ﬁeld conditions. Potted spreader plants of 20%, 3 = 21 to 30%, 4 = 31 to 40%, 5 = 41 to 50%, 6 = the highly susceptible cultivar showing severe rosette 51 to 60%, 7 = 61 to 70%, 8 = 71 to 80%, and 9 > 80% symptoms and heavily infested with aphids are raised in a glasshouse and transplanted to the infector rows leaf area damaged. At present screening for resistance to leaf miner (1 plant per 3 m rows) 10 d after sowing the test mais dependent on natural infestation under ﬁeld con- terials. The disease development in the infector rows ditions as it is a difﬁcult pest to devise a cage-based is monitored ca. 2 weeks later, and viruliferous aphids greenhouse screening procedure for. However, pro- are released onto the plants that are free from the longed drought and planting soybean as infester crop disease. The disease incidence is then assessed at the favor high intensity of leaf miner infestation under pod-ﬁlling stage. The percentage of disease incidence ﬁeld conditions. A 1 to 9 scale, similar to that used is determined based on the total number of plants per for screening resistance to Spodoptera, is suggested to plot and the number of plants showing rosette symptoms with severe stunting; using a 1 to 3 scale for clasrecord percentage leaf area damage by leaf miner. A high level of tolerance to leaf miner and sifying the disease reaction where 1 = plants with no Spodoptera has been observed in breeding line ICGV symptoms on foliage and no stunting, 2 = plants with 86031 and is manifested as the enhanced ability of obvious rosette leaf symptoms and stunted to about the vegetative tissue to regrow subsequent defoliation 50% of the size of the normal plants, 3 = plants with se(Wightman and Rao 1994). Several wild Arachis vere rosette leaf symptoms and stunting greater than species have also shown resistance to leaf miner and 50% (Olorunju et al. 1991) with some modiﬁcation Spodoptera with morphological traits such as main (Subrahmanyam et al. 1998). The disease index is then stem thickness, hypanthium length, leaﬂet shape calculated based on this rating as (A + 2B +3C)/total

Chapter 3 Peanut

number of plants assessed per plant, where A, B, and C equal the number of plants with ratings of 1, 2, and 3, respectively. The selected lines from ﬁeld trials should be evaluated (50 to 60 d after aphid inoculations) for the presence of GRAV using TAS-ELISA (Rajeshwari et al. 1987). GRV and sat RNA are detected by RT-PCR as described by Naidu et al. (1998), and a ﬁeld and greenhouse evaluation is recommended to screen for resistance to aphid (Padagham et al. 1990). Resistance to GRV virus has been reported to vary from monogenic dominance to two independent recessive genes (Nigam and Bock 1990; Olorunju et al. 1992), while aphid resistance may be controlled by a single recessive gene (Herselman et al. 2004). Peanut Clump Virus Viruses of the genus Pecluvirus cause peanut clump disease (Torrance and Mayo 1997), referred to as peanut clump virus (PCV) in West Africa and Indian peanut clump virus (IPCV) in South Asia. The fungus Polymyxa ssps transmits IPCV (Ratna et al. 1991) and PCV is suspected to have the same vector. Both IPCV and PCV have an extremely wide host range including many monocots (Ratna et al. 1991; Delfosse et al. 1996), are soilborne, and produce similar symptoms on peanut. However, IPCV is not serologically related to two West African isolates (Reddy et al. 1983). Severely stunted plants with dark green leaves and mosaic mottling with chlorotic rings on new quadrifoliates characterize the peanut clump disease in peanut (Reddy et al. 1983). The disease occurs in patches in the ﬁeld, which reappears in the same positions in the following season, indicating high resilience but slow movement of the vector. A ﬁeld with a known history of high disease incidence should be selected for screening for resistance to peanut clump disease. An ELISA and nucleic acid hybridization test is recommended to conﬁrm the presence of the virus in the infected plants from the ﬁeld (Delfosse et al. 1999). The intensity of disease incidence is measured by visual symptoms and ELISA test. Drought Genotypes are usually evaluated for drought tolerance under ﬁeld conditions during the dry season with controlled supplementary irrigation. However, it is difﬁcult to fully represent the natural drought environment where moisture stress occurs during the

125

rainy season in which the crop is largely grown by the farmers. Thus, it is recommended that rainout shelters be created for drought stress screening during the rainy season, but this does require considerable investment. Crop phenology is the single most important trait for enhancing performance under drought stress. Fortunately, peanut is temperature sensitive but not daylight sensitive like many crops, so its phenology is not especially driven by the environment. Specht et al. (1986) deﬁned drought tolerance in three general categories: (a) escape for drought by tailoring plants with appropriate phonologies to ﬁt the most appropriate growing period, (b) dehydration avoidance through identiﬁcation and incorporation of traits that lessen evaporatory water loss or increase water uptake through deeper and more extensive root system, and (c) dehydration tolerance by selecting traits that maintain cell turgor (a driving force for plant growth) or enhancing cellular constituents that protect cytoplasmic proteins and membranes from desiccation. In contrast, empirical or yield-based deﬁnitions of drought tolerance fall into two categories: (i) absolute, where breeders select for the highest yielding genotypes in environments where seasonal drought is predictably recurrent, and (ii) relative, where breeders select for genotypes with the smallest yield decline per unit of reduced seasonal rainfall (Specht et al. 1986). Three proﬁles of drought stress can be differentiated for all crops including peanut – (i) early-season drought, (ii) midseason drought, and (iii) end-ofseason (terminal) drought – and the effect of these on yield depends on the severity and duration of water deﬁcit stress and the drought management practices applied. Moderate early drought stress is actually beneﬁcial for the establishment of the peanut crop, while midseason and terminal drought can cause substantial reduction in peanut yield (Nageswara Rao et al. 1989). To impose midseason drought stress in peanut, the irrigation is withheld from 40 d after sowing (DAS) to 80 DAS, and then the stress is released (by irrigating the crop on a regular basis until maturity) to measure the recovery response of the stressed genotypes. These 40 d of stress concide with ﬂowering and early pod development in peanut. For end-of-season drought, the irrigation is withheld from 80 DAS until the crop is harvested. The line-source sprinkler technique (Hanks et al. 1976) is used to create a gradient of irrigation across contiguous plots. The plot nearest to the sprinkler head receives ca. 50 mm of water, thus providing an

126

S.L. Dwivedi et al.

irrigated control. The amount of water then decreases in a linear fashion as the distance of the plot from the sprinkler head increases. Several sources of tolerance to midseason and/or terminal drought have been reported in peanut (Nageswara Rao et al. 1989; Nigam et al. 2003a,b) that showed variation for physiological traits such as speciﬁc leaf area (SLA), water use efﬁciency (WUE), amount of water transpired (T), transpiration efﬁciency (TE), and harvest index (HI) under drought stress conditions (Nageswara Rao et al. 1993; Nageswara Rao and Wright 1994; Wright et al. 1994, 1996; Craufurd et al. 1999; Nageswara Rao and Nigam 2001). The component trait-based approach based on assessing variation in SLA, WUE, T, TE, and HI under drought stress conditions is recommended for accurate and precise dissection of drought tolerance in peanut. Variation in WUE arises mainly from genotypic differences in water use. Carbon isotope discrimination (Δ) can be used to select genotypes with improved WUE under drought conditions in the ﬁeld. A strong relationship between WUE and SLA and between Δ and SLA revealed that genotypes with thick leaves had greater WUE (Wright et al. 1994). SLA could therefore be used as a rapid and inexpensive indirect selection criterion for WUE to facilitate selection for terminal drought tolerant genotypes (Nageswara Rao and Wright 1994). However, there appears to be a negative relationship between WUE and HI under terminal drought stress conditions, suggesting that selection for high WUE might enhance dry matter production under stress but not necessarily improve pod yield (Nageswara Rao and Wright 1994; Wright et al. 1994). SLA is also highly inﬂuenced by G × E interaction. Nageswara Rao et al. (2001) demonstrated the use of a portable SPAD chlorophyll meter for rapid assessment of SLA and speciﬁc leaf nitrogen (SLN) as surrogate traits to measure TE in peanut. Both additive and additive × additive epistasis for SLA and HI and additive genetic effect for Δ are reported (Jayalakshmi et al. 1999; Nigam et al. 2001).

(NMR) spectrometer (Jambunathan et al. 1985) is preferred over the conventional Soxhlet (Kuck and St Angelo 1980) method for determining the oil content as these tests are rapid (2 to 3 min), do not require any extraction reagents or supporting equipment, and the seeds can be used after the test. However, NIR requires a larger sample (150 g) over NMR (3 to 5 g) but can also provide additional information on protein content and other quality parameters. A Technicon autoanalyzer may also be used to determine nitrogen concentration (Singh and Jambunathan 1980) and then multiply the value by a factor of 5.46to convert nitrogen into crude protein content (United Nations University 1980). Fatty acid composition is determined following Hovis et al. (1979), and from it the following quality parameters can be determined (Mozingo et al. 1988). 1. Iodine value (IV): (% oleic acid) (0.8601) + (% linoleic acid) (1.7321) + (% eicosenoic acid) (0.7854) 2. Oleic (O)/linoleic (L) acid ratio = % oleic acid/% linoleic acid 3. Total saturated fatty acids (%) (TSF) = % palmitic acid + %stearic acid + % arachidic acid + % behenic acid + % lignoceric acid 4. Polyunsaturated (P)/saturated (S) ratio = % linoleic acid/ % TSF 5. Total long-chain saturated fatty acids (%) (TLCSF) = % arachidic acid + % behenic acid + % lignoceric acid

3.3.2 Germplasm with Beneficial Traits for Mapping and Genetic Enhancement

A large number of accessions possessing resistance/tolerance to abiotic and biotic stresses have been identiﬁed both in cultivated and wild Arachis species (Table 2). Of these, a number of promising accessions/breeding lines, mostly based on genetic diversity, differing in resisSeed Quality Total oil and protein content and fatty acid proﬁle are tance/tolerance mechanism to biotic and abiotic the important seed quality traits that substantially stresses, or varying in seed quality, including inﬂuence the edible uses of peanut. Near-infrared peanut allergens have been recommended for use (NIR) transmittance spectroscopy (Panford 1990; in mapping and genetic enhancement in peanut Misra et al. 2000) and nuclear magnetic resonance (Table 3).

Chapter 3 Peanut

127

Table 3. List of potential germplasm/breeding lines resistant to rust, leaf spots, aﬂatoxin, rosette, nematode, defoliators, aphid, drought, low temperature, and those with improved agronomic traits, oil chemistry and low allergens for mapping and genetic enhancement in cultivated peanut Trait

Identity

Reference

Rust

ICGV# 99003 and ICGV 99005

Dwivedi et al. 2002a; ICRISAT 2004

Leaf spots

ICG # 405, 1705, 6284, 9987, 9991, 9989, 10000, and 10914; ICGV# 99001 and 99104; PI 565287 (TxAG-6) and PI 565288 (TxAG-7)

Dwivedi et al. 2002a; Dwivedi and Gurtu 2002; Simpson et al. 1993; ICRISAT 2004

Groundnut rosette virus Nematode

ICG#3436, 6323, 7827, 9558, 11044, and 11968

Dwivedi et al. 2003a

PI 565287 (TxAG-6) and PI 565288(TxAG-7)

Simpson et al. 1993

Seed infection and/or aﬂatoxin production by Aspergillus ﬂavus

ICG# 1326, 1448, 1471, 4681, 4749, and 7101 ICGV 88145 and ICGV 89104 ICGV 91278, ICGV 91279, and ICGV 91283

Dwivedi et al. 2001; Mehan et al. 1985 Rao et al. 1995 Upadhyaya et al. 2001c

Defoliators (Leaf miner and Spodoptera)

ICGV# 86031, 87154 and 87160; ICG# 2271 and 1697 + from GP-NCWS7, GP-NCWS8, GP-NCWS9, and GP-NCWS10

Dwivedi et al. 1993; Wightman and Rao 1994 Stalker and Lynch 2002

Aphid

ICG 12991

Herselman et al. 2004

Drought

ICG 1471; ICGV# 94106, 94113, 96294, 97068, 97093, 98381, 98382, 99231; 99233, 99235, 99236; 99237, 99238, 99241, 99243, 99247; 99249, and 99255; and CSMG 84-1 ICG# 766 and 14523; ICGV 86031 and TAG 24 ICG# 118, 532, 862, 2106, 2511, 2773, 4527, 5236, 5827, 6654, 6766, 7243, 8285, 11219, 11855, 14475, 14523, 14985 UF 435-2-1, and UF 435-2-2

Dwivedi and Varma 2002

Norden et al. 1987

PI 261924 and PI 338386

Dodo et al. 2002

ICG # 3200, 3540, 3631, 4558, 4729, 4890, 5512, 5560, 5881, 9427, 9930, 9968, 11605, 11914, 13585, 13606, 13647, 14390, 14788, 14814, 14815 158 accessions ICGV 92267

Upadhyaya et al. 2006

60 accessions

Upadhyaya et al. 2005

Oleic/linoleic fatty acid ratio Allergen Early maturity

Low temperature Yield and agronomic traits

ICRISAT 2004 Upadhyaya 2005

ICRISAT unpubl. data Upadhyaya et al. 2002b

128

S.L. Dwivedi et al.

3.4 Genomic Resources in Peanut 3.4.1 DNA Markers (RFLPs, RAPDs, AFLPs, SSRs) Early studies on peanut genomics focused on screening cultivated peanut germplasm and/or tetraploid interspeciﬁc breeding lines with 67 polymorphic RAPD markers (Burow et al. 1996; Bhagwat et al. 1997; Choi et al. 1999; Subramanian et al. 2000; Raina et al. 2001; Dwivedi et al. 2001, 2002b) and 404 polymorphic RFLP markers (Garcia et al. 1995; Burow et al. 2001). However, these marker assays are not ideal for use in MAS. RFLP, although providing high-quality codominant information, is labor intensive and time consuming, requires large amount of DNA, and is dependent on radioisotope-based protocols, while RAPD analysis provides only dominant information and frequently suffers from reproducibility problems. However, it is possible to convert tightly linked RFLP markers into PCR-based sequence-tagged site (STS) markers (Olson et al. 1989) and similarly to convert RAPD bands into sequence-characterized ampliﬁed region (SCAR) markers (Paran and Michelmore 1993). STS and SCAR assays provide substantially more reliable markers with a relative high-throughput potential. Most of the genetically mapped RFLPs have also been sequenced. Like RAPDs, AFLPs are also a dominant marker class but can be converted into codominant marker such as SCAR (Paran and Michelmore 1993; Negi et al. 2000; Huaracha et al. 2004) and CAPS (Konieczny and Ausubel 1993). Forty-ﬁve of the 64 EcoRI/MseI primer pairs were polymorphic in cultivated peanut germplasm/cultivars and interspeciﬁc derivatives (Table 4). Herselman (2003) used two different rare cutters enzymes, EcoRI and MluI, in combination with the frequent cutter MseI, and found that both EcoRI/MseI and MIul/MseI AFLP enzyme combinations efﬁciently detected polymorphisms within closely related cultivated peanut accessions, although the EcoRI/MseI enzyme combination detected more fragments per primer combination (on average 67.8) as opposed to 29.1 by the MIul/MseI enzyme combination on the similar peanut accessions. Simple sequence repeat (SSR, also known as microsatellites) markers have become the assay of choice

for molecular breeding of most crops. SSR markers are valuable for a multitude of applications due to their abundance and uniformity of distribution throughout most genomes, their multiallelic, codominance inheritance, and their highly polymorphic and reproducible nature where analysis is simple and readily transferable (Weber 1990). Hopkins et al. (1999) were the ﬁrst to report six polymorphic SSR markers in cultivated peanut. Further search for SSRs in peanut led to the development of 553 SSRs of which 192 SSRs were polymorphic in a diverse range of cultivated peanut accessions (Table 5). Moretzsohn et al. (2004) reported high marker transferability for markers from species related to peanut: up to 76% from species of the section Arachis and up to 45% from species of the other eight Arachis sections. Similarly, efforts at ICRISAT have supported this ﬁnding (Mace et al. unpubl. data). However, there appears to be a high level of redundancy in this approach, such that, although SSR markers from related species and genera do amplify in Arachis germplasm, few are found to be polymorphic in groundnut breeding populations. Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) and Universidade Catolica de Brasilia (UCB), Brazil, have recently put considerable effort into large-scale development of additional SSR markers. Genomic libraries enriched for TC and AC repeats (Rafalski et al. 1996) have yielded 126 new SSR markers. To facilitate the selection of SSR-containing sequences, and the assembly of forward and reverse sequencing runs, a dedicated module (available from David Bertioli on request) has been developed for the Staden sequence assembly software (Staden et al. 2003a,b). In addition to the enriched genomic libraries, the module has also been used to “data-mine” 31 EST-SSR markers from data available in Genbank and 117 EST-SSR markers from A. stenosperma EST data. Similar efforts have also been made by ICRISAT through the library enrichment process. Based on these recent marker development projects, groundnut is now one of the best-served tropical legumes in terms of SSR markers for linkage mapping and molecular breeding (Table 5). However, it is notable that these recent advances have been driven largely by labs based in developing countries rather than by advanced labs in Western countries that have traditionally fulﬁlled this role. The availability of such a large number of AFLPs and SSRs markers holds great promise for diversity studies,

Chapter 3 Peanut Table 4. Polymorphic AFLP primer pairs reported in cultivated peanut accessions AFLP primer

E-AAC/M-CAT E-AAC/M-CTA E-AAC/M-CTC E-AAC/M-CTG E-AAC/M-CTT E-AAC/M-CAA E-AAC/M-CAC E-AAC/M-CAG E-AAG/M-CAC E-AAG/M-CAG E-AAG/M-CAT E-AAG/M-CTT E-ACC/M-CAA E-ACC/M-CTG E-ACC/M-CTC E -ACC/M-CAT E-ACC/M-CAC E-ACC/M-CAG E-ACC/M-CTA E-ACG/M-CAC E-ACG/M-CTA E-ACG/M-CAT E-ACG/M-CTG E-ACG/M-CTT E-ACG/M-CAG E-ACG/M-CAA E-ACG/M-CTC E-ACT/M-CAA E-ACT/M-CAT E-ACT/M-CTA E-ACT/M-CTC E-ACT/M-CTG E-ACT/M-CTT E-ACT/M-CAG E-AGC/M-CAA E-AGC/M-CTG E-AGC/M-CTT E-AGG/M-CAA E-AGG/M-CAC E-AGG/M-CAT E-AGG/M-CTA E-AGG/M-CTT E-AGG/M-CAG E-ACA/M-CAG E-ACA/M-CTC E-ACA/M-CTG E-ACA/M-CAA

Herselman 2003

Gimenes et al. 2002

∗ ∗ ∗ ∗

Dwivedi et al. 2002b

He and Prakash 2001

He and Prakash 1997

∗

∗

∗ ∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗

∗ ∗ ∗ ∗

∗

∗ ∗ ∗ ∗ ∗ ∗ ∗

∗

∗

∗ ∗

∗ ∗ ∗ ∗ ∗

∗

∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗ ∗

∗ ∗

129

Total number

26

56 226 67 122 112 38 125

Source and method of isolation

cDNA libraries from peanut cultivar Florunner and New Mexico Valencia

SSR enrichment procedure (Edwards et al. 1996)

cDNA libraries from peanut cultivar Florunner

Rafalski et al. (1996)

SSR enrichment procedure (Rafalski et al. 1996)

EMBRAPA ESTs and Genbank deposited sequences

Data-mining other sequences

Edwards enrichment process (Edwards et al. 1996)

GT CA

CT AG

AAG AAT CT

TC AC

TTG AAC

ATT GA

GA CT

GA

Most frequently repeat

32 diverse genotypes include 24 from Ferguson et al. 2004b

16 cultivated peanut accessions

16 cultivated peanut accessions

16 cultivated peanut accessions

60 cultivated peanut accessions

24

24

19

55

5

9

53

3

110

19

5

2.1 (1–4)

4.75 (4–6)

4.50 (2–8)

5.96 (2–12)

9.8 (2–27)

3.4 (2–5.7)

4.3 (2–8)

6.3 (2–14)

Results from screening cultivated germplasm Germplasm Number detecting Number of alleles represented polymorphism detected

Table 5. List of total number of SSRs and those polymorphic in cultivated peanut accessions

Tho et al. unpub data (ICRISAT)

EMBRAPA (unpub)

EMBRAPA (unpub)

EMBRAPA (unpub)

Moretzsohn et al. 2004

Ferguson et al. 2004a

He et al. 2003

Hopkins et al. 1999

Reference

130 S.L. Dwivedi et al.

Chapter 3 Peanut

131

genetic mapping and MAS, and gene discovery in bacterial wilt (Mace et al. unpubl. data). In these studies, SSR markers were able to detect a surprisingly peanut. high level of polymorphism. In some cases, more than half of the markers detected polymorphism with PIC values of over 0.5. This has also opened the possi3.4.2 bility of following association mapping in cultivated Molecular Diversity peanut germplasm for the identiﬁcation of markers Diversity assessment and construction of genetic link- for disease resistance. Although recent studies on genetic diversity reage maps are the two important steps in the development of molecular breeding programs. The Inter- vealed polymorphisms in cultivated peanut, the levels national Crops Research Institute for the Semi-Arid of polymorphism detected are still low for easy conTropics (ICRISAT), Patancheru, India, hosts the world struction of saturated maps. In contrast, much higher collection of 14,966 accessions of the cultivated and polymorphism is reported in wild Arachis species. 453 of wild Arachis species. These accessions differ in For example, only three of the 67 SSRs were polymany morphophysiological, reproductive, and qual- morphic on 60 cultivated accessions belonging to 6 ity traits, and in response to biotic and abiotic stresses botanical varieties, whereas 28 were polymorphic on (Rajgopal et al. 1997; Singh and Nigam 1997; Upad- two wild Arachisaccessions (A. duranensis K7988 and hyaya et al. 2001a, 2003). For enhancing the use of A. stenospermaV10309) (Moretzsohn et al. 2004) used peanut germplasm in breeding, two-core and mini- to make the diploid AA genome mapping populacore collections (ICRISAT core of 1,704 and minicore tion (see Sect. 4.3. for further details). This higher of 184 accessions and USDA core of 831 accessions and level of polymorphism greatly facilitates genetic mapcore of core 111 accessions) are reported in peanut. ping. Conventionally, the use of interspeciﬁc mapThe accessions included in the core and minicores ping populations has not been encouraged by plant have the potential to identify new sources of variation breeders because of the divergent recombination patfor use in peanut genomics and breeding (see Sect. 2.3. terns evident in such populations as compared with for further details). The minicore is good starting ma- the intraspeciﬁc breeding populations where resulterial for association mapping and for the detection of tant markers would be applied. This divergence often rare allelic variation associated with beneﬁcial traits. leads to a loss of selective power of the marker. HowIn contrast with the historical generalization that ever, peanut is an amphiploid, viz. an allotetraploid cultivated peanut lacks genetic variation (Griesham- with two different genomes, that behaves genetically mer and Wynne 1990; Kochert et al. 1991; Bhag- as if two separate diploids are in the same cell. Thus, wat et al. 1997; He and Prakash 1997; Subramanian the application of diploid maps may be much more et al. 2000), genetic diversity studies in the last few directly applicable and effective than conventional inyears have revealed sufﬁcient polymorphic variations terspeciﬁc mapping populations. among cultivated peanut germplasms that could be tapped to identify markers associated with beneﬁcial traits and possibly effect marker-assisted ge- 3.4.3 netic enhancement in peanut (Hopkins et al. 1999; Mapping Population Dwivedi et al. 2001; He and Prakash 2001; Raina et al. 2001; Dwivedi et al. 2002b; Dwivedi and Gurtu Near-isogenic lines (NILs) (Muehlbauer et al. 1988), 2002; Dwivedi and Varma 2002; Gimenes et al. 2002; recombinant inbred lines (RILs) (Burr et al. 1988), Dwivedi et al. 2003a,b; Herselman 2003; Krishna et al. doubled-haploid (DH) populations (Heun 1992), and 2003; Ferguson et al. 2004b; Moretzsohn et al. 2004). advanced backcross-derived RILs are the preferred Both AFLP and SSR are useful for estimating diver- types of mapping populations in plant genomic studsity among the Arachis species and six botanical types ies as these are immortal genetic stocks (unlike F2 ) of cultivated peanut (He and Prakash 2001; Gimenes that can be recurrently tested in replicated trials across et al. 2002; Herselman 2003; Ferguson et al. 2004b; locations and seasons. Tanksley and Nelson (1996) Moretzsohn et al 2004). have proposed advanced backcross lines for the siMost recently, ICRISAT has been using SSR mark- multaneous discovery and transfer of valuable QTLs ers to analyze the genetic diversity among cultivated from unadapted and wild germplasm into elite breedgermplasm resistant to late leaf spot (LLS), rust, and ing lines. Similarly, Podlich et al. (2004) have proposed

132

S.L. Dwivedi et al.

a reﬁnement and expansion of this type of approach. This approach acknowledges that the size of mapping populations and the presence of a consistently adapted agronomic background across all members of that population are critically important factors for the precise and accurate mapping of economically important characters, particularly complex traits. A population of 250 to 300 F2 derived RILs should be sufﬁcient to detect the chromosomal region (more precisely the QTL location) associated with most beneﬁcial traits. The small population sizes all too often used for QTL detection lead to overestimation of QTL effect and underestimation of QTL number and interaction due to what is now commonly referred to as the “Beavis Effect” (Beavis 1998; Melchinger et al. 1998). Efforts are being made by ICRISAT and EMBRAPA to develop mapping populations involving A. hypogaea × A. hypogaea, A. hypogaea × wild Arachis, and wild Arachis × wild Arachis species crosses. RIL mapping populations are now available for rust, LLS, and drought for evaluation, and seeds from these populations are available upon request under the respective institutional material transfer agreement. Recently, EMBRAPA-CENARGEN (EMBRAPA Recursos Genéticos e Biotecnologia), UCB, and UNESP (Universidade Estadual de São Paulo)-Botucatu (Brazil) have developed diploid mapping populations from crosses of wild Arachis. F2 populations have been made that represent the AA and BB genomes of cultivated peanut, A. duranensis and A. ipaensis crossed with closely related species A. stenosperma and A. magna, respectively. The original hybrids are being maintained and the F2 plants multiplied by cuttings. Currently the mapping populations are of 93 plants that are being advanced to develop RILs. Through this strategy, concensus maps will be created by comparative analysis of diploid and tetraploid maps. These diploid maps will facilitate the marker-assisted introgression of a wide range of important agronomic traits into cultivated peanut. In addition, a tetraploid mapping population has been developed from the cross of a synthetic amphidiploid (A. duranensis × A. ipaensis)c with cultivated A. hypogaea.

terson et al. 1988; Lander and Botstein 1989), for cloning genes by chromosome walking (Wicking and Williamson 1991), and for developing MAS systems for desirable traits in breeding programs (Burr et al. 1983; Tanksley et al. 1989). Cultivated peanut is a disomic polyploid (2n = 4x = 40); thus in genetic maps we expect 20 linkage groups (LGs), each representing one haploid complement chromosome. Halward et al. (1993) developed the ﬁrst low-density RFLP-based genetic linkage map in peanut, derived from an interspeciﬁc F2 population involving A genome diploid species A. stenosperma and A. cardenasii. This map comprised 117 markers on 11 LGs with a total map distance of ca. 1,063 cM and average marker density of 9.08 cM. Subsequently, Burow et al. (2001) reported an RFLP-based tetraploid genetic linkage map derived from a BC1 population (n = 78) from the cross: synthetic amphidiploid {[A. batizocoi K9484 × (A. cardenasiiGK10017 × A. digoi GKP10602)]4x } crossed with cv. Florunner. A. cardenasii and A. digoi are A genome while A. batizocoi belongs to B genome species. This map consists of 370 RFLP loci distributed into 23 LGs with a total map distance of 2,210 cM and average marker density of 5.97 cM. These RFLP loci will detect alleles in populations involving crosses between wild species or between A. hypogaea× wild Arachis species. They are unlikely to detect alleles in A. hypogaea × A. hypogaea crosses. In the last few years, there has been substantial progress on identifying polymorphic AFLPs and SSR markers (see sections 4.1 and 4.2). A partial AFLPbased genetic linkage map, based on an intraspeciﬁc A. hypogaea cross, has been developed for mapping aphid resistance that mapped 12 markers to ﬁve LGs covering a map distance of 139.4 cM (Herselman et al. 2004). Signiﬁcant progress has also been made at ICRISAT in the mapping of disease resistances using AFLP, SSR, and RGA markers (Mace et al. unpubl. data). Preliminary maps comprise around 75 markers across 16 LGscovering a map length of 423 cM (rust resistance) and around 70 markers across 9 LGs covering a map length of 175 cM (LLS resistance). A skeleton map has also been generated for resistance to ELS, and mapping of bacterial wilt resistance is ongoing. As new SSR markers are becoming available, they are 3.4.4 being integrated into these maps in order to increase Genetic Linkage Map the total map length and marker density. The new SSR markers developed at EMHigh-density genetic linkage maps are a useful basis for identifying markers tightly linked to QTLs BRAPA/UCB and ICRISAT are being used for the that contribute to economically important traits (Pa- development of diploid maps of the A and B genomes.

Chapter 3 Peanut

133

the diploid AA genome Arachis mapping population (David Bertioli, unpubl. data). Comparing the map positions of ECS markers in different legumes should allow the development of a preliminary comparative map across legume crops and model systems. The inclusion of Arachis within this analysis is likely to be especially informative because Arachis together with lupin occupies a basal phylogenetic position within the Papilionoideae. This work will then enable model organisms with wellcharacterized genomes to serve as genic frameworks for the poorly characterized Arachis genome. The genomes of two model legumes are currently being sequenced, L. japonicus by the Kazusa DNA Research Institute (http://www.kazusa.or.jp/lotus/) and M. truncatula by the Medicago truncatulaConsortium 3.4.5 (http://www.medicago.org/genome/). These model Comparative Mapping with Model Genomes legumes have genomes of about 420 to 470 Mbp (Young et al. 2003), almost ten times smaller An ongoing project between Aarhus University, Den- than that of the A. hypogaea genome (3,479 Mbp, mark, EMBRAPA, and UCB Brazil aims to integrate http://www.rbgkew.org.uk/cval/database1.html). Arachis into a single uniﬁed legume genetic frame- Thus, these conserved gene-based markers will work using “legume family anchor markers” for greatly assist researchers to quickly identify orthololegumes. These are gene-based markers with a single gous genic markers in peanut. These will signiﬁcantly homolog representation in the Arabidopsis proteome speed up the identiﬁcation of candidate genes for that are being mapped in Lotus japonicus, lupin, bean, MAS and the positional cloning of genes for the development of transgenic varieties. and the diploid AA genome of Arachis. To efﬁciently identify potential anchor marker sequences, a computer “pipeline” that uses multispecies EST- and genome-sequence data has been devel- 3.4.6 oped. Comparison of ESTs from Medicago truncatula, BAC Libraries and New Generation Markers Glycine max, and L. japonicus identiﬁes evolutionary conserved sequences (ECSs) that have a high probability of being conserved in less well-characterized BAC Libraries legumes. Alignment of ECSs and a corresponding ge- Bacterial artiﬁcial chromosome (BAC) libraries, pronomic sequence deﬁnes sets of PCR primer sites ﬂank- viding whole-genome coverage in segments of about ing introns. Introns are targeted because purifying se- 100 kb, have become central to a wide range of goals in lection is less stringent for coding regions, and they biology and genomics. Recently, the ﬁrst large-insert are more likely to be polymorphic. The length of in- DNA library for A. hypogaea was constructed (B. Yuktrons is important because short introns are less likely sel and A.H. Paterson, manuscript in preparation). to be polymorphic than longer ones and because the The library contains 182,784 clones; only 5,484 (3%) ﬁnal PCR reaction is limited to a maximum ampli- of them had no inserts; and average insert size is con size of ∼2.5 kbusing standard polymerases. Fi- 104 kb. About 1,208 (0.66% of) clones appear to cornally, only marker sequences with single homologs in respond to the 45S ribosomal DNA, and only 9 clones the Arabidopsis proteome are selected for further de- hybridize to chloroplast probes. The depth of covervelopment. Using this approach, 867 ECSs have been age is estimated to be 6.5 times, allowing the isolation identiﬁed, and these are being used for marker devel- of virtually any single-copy locus. The identiﬁcation opment. Polymorphisms are identiﬁed by size or se- of multiple loci by most probes in polyploids comquence differences in PCR products, ampliﬁed from plicates anchoring of physical and genetic maps. The mapping parents, and CAPs or dCAPs markers de- research group at the University of Georgia, Athens, veloped. So far, 40 markers have been developed for GA, USA, explored the practicality of a hybridizationTo date, an SSR-based linkage map of the AA genome of Arachis contains 153 SSR markers mapped on 11 LGs with a total map distance of 1,138.39 cM and 7.43 cM average marker density. In addition, a BBgenome linkage map is being made in collaboration with UNESP-Botucatu. It is anticipated that these diploid linkage maps will enable mapping of some markers and traits that would be difﬁcult to deal with in a tetraploid background. Diploid maps will then be veriﬁed using a tetraploid mapping population based on an F2 population derived from a cross between a synthetic amphidiploid (A. duranensis × A. ipaensis)c and A. hypogaea.

134

S.L. Dwivedi et al.

based approach for determination of map locations of BAC clones in peanut by analyzing 94 clones detected by seven different overgos. The banding patterns on Southern blots were good predictors of contig compositions. This BAC library has great potential in terms of advancing the future research about the peanut genome.

Expressed Sequenced Tags (ESTs) Hundreds of thousands of ESTs are available for soybean (Shoemaker et al. 2002; Tian et al. 2004), M. truncatula (Fedorova et al. 2002; Journet et al. 2002; htpp://www.medicago.org), and Phaseolus (Hernandez et al. 2004). In constrast, there are only 1,825 ESTs available for peanut derived from two cDNA libraries constructed using mRNA from immature pods of a drought-tolerant line (A13) and from leaves of tomato spotted wilt virus (TSWV) and leaf-spotresistant line (C33-24): NCBI GeneBank accession numbers CD037499 to CD038843. These have been successfully used to develop 44 EST-derived SSR markers of which over 20% were polymorphic among 24 cultivated peanut accessions (Luo et al. 2003). This group has also arrayed about 400 unigenes of adversity resistance on glass slides. This macroarray is being used with mRNA probes from different lines that have been exposed to various proﬁles of drought stress or fungal infection to identify genes related to biotic or abiotic stresses. Another group at the University of Florida, Gainesville, USA, has constructed leaf, seed, and peg/pod cDNA libraries from developmentally pooled tissues of “SunOleic 97R” (peanut cultivar with high O/L ratio). These libraries are currently being sequenced to develop gene expression proﬁles that will lead to greater understanding of peanut’s responses to various abiotic and biotic stresses. It is hoped that this will provide the necessary knowledge and tools to alter peanut to achieve maximum performance under given growth conditions (Chengalrayan and Gallo-Meagher 2003). Aﬂatoxin is a serious quality problem in peanut. Drought and hightemperature stresses are conducive to Aspergillus ﬂavus infection and aﬂatoxin contamination. Differential display reverse transcription PCR (DD-RTPCR) (Liang and Pardee 1992) and EST/microarray are now used to locate multiple genes that enable plants to withstand biotic and abiotic stresses. Using DD-RT-PCR, Guo et al. (2003) revealed that some cDNA fragments are up- or down-regulated by induced drought stress and identiﬁed a novel PLD gene

that encodes a putative phospholipase D, a primary enzyme responsible for the drought-induced degradation of membrane phospholipids in plants. They studied the PLD gene expression under drought stress in the greenhouse using two peanut lines, Tifton 8 (drought tolerant) and Georgia Green (drought sensitive). Northern analyses showed that the PLD gene expression is induced sooner by drought stress in Georgia Green than in Tifton 8. After the PLD gene in peanut is characterized, the researchers plan to attempt gene silencing using genetic transformation to suppress PLD gene expression and induce drought tolerance. An A. ﬂavus ESTs program at USDA/ARS Southern Regional Research Center in New Orleans, LA (Yu et al. 2002) and USDA-ARS Labs at Tifton, GA has resulted in about 8000 expressed unique genes that will help to identify genes that could be used to inhibit fungal growth or aﬂatoxin formation by the fungi. Finally, a group at the University of Agricultural Sciences, Bangalore, India, has developed subtractive libraries for water use efﬁciency. ESTs from this project are being sequenced, and the most promising candidate gene markers will be mapped using an RIL population speciﬁcally designed for this purpose (Udaya Kumar et al. unpubl. data).

Transcriptional Profiling Jain et al. (2001) used an RT-PCR-based procedure (differential display) to identify cDNA corresponding to transcripts affected by water stress in peanut and identiﬁed several mRNA transcripts that are upregulated or down-regulated following water stress. With 21 primer combinations, they observed 1235 and 950 differential-display products in irrigated and drought-stressed samples, respectively. Forty-three peanut transcripts responsive to drought (PTRDs) were signiﬁcantly altered due to water stress. Slot blot analysis of 16 PTRDs revealed that 12 were completely suppressed by prolonged drought while 2 were down-regulated, and 2 were up-regulated under drought-stress conditions. RNA dot-blot analysis of the 12 completely suppressed transcripts revealed that PTRD-1, PTRD-10, and PTRD-16 were expressed for a longer period in the tolerant line compared to the susceptible line. All these sequences may be useful candidate gene markers for mapping components of drought tolerance in peanut.

Chapter 3 Peanut

Variation in fatty acid proﬁle is the major determinant of oil quality in peanut. Oils high in monounsaturated (oleic) and low in polyunsaturated fatty acids (linoleic and linolenic) are commercially and nutritionally desirable. Polyunsaturated fatty acyl residues are susceptible to oxidation, the products of which cause unpleasant odors and tastes commonly associated with rancidity. These oxidized products have potential atherogenic effects, while oils high in monosaturates have been reported to be effective in lowering cholesterol levels (St Angelo and Ory 1973; Broun et al. 1999). Mapping or isolation of genes associated with increased oleic acid accumulation would provide opportunities to alter fatty acid composition in peanut by MAS as has been achieved in soybean (Kinney and Knowlton 1998) and rapeseed (Friedt and Luhs 1999; Tanhuanpaa and Vilkki 1999). Jung et al. (2000a) isolated two cDNA sequences coding for microsomal oleoyl-PC desaturases (ahFAD2A and ahFAD2B) from the developing peanut seed with a normal oleate phenotype; these desaturases are nonallelic but homeologous genes originating from two different diploid species. The gene ahFAD2Ais expressed in both normal and high oleate peanut seeds, but the ahFAD2B transcript is severely reduced in the high oleate peanut, suggesting that the reduction in ahFAD2B transcript level in the developing seeds is correlated with a high oleate trait. Further studies revealed that a mutation in ahFAD2A and a signiﬁcant reduction in levels of the ahFAD2B transcript together cause the high oleate phenotype in peanut, and that of expressed gene encoding a functional enzyme appears to be sufﬁcient for the normal oleate phenotype (Jung et al. 2000b).

Single Sequence Polymorphism (SNPs) SNPs are the most elemental difference between genotypes, a difference in DNA sequence; therefore they are the most direct means of DNA ﬁngerprinting that can ever exist. SNPs have replaced SSRs as the preferred marker in mammalian genomics. A wide range of emerging, high-efﬁciency techniques for ﬁnding SNPs, even when polymorphism is rare (such as in peanut), sets the stage for use of genomic tools on a scale not previously possible. SNPs provide enabling biotechnologies in the form of low-cost molecular markers and genetic ﬁngerprinting tools suitable not only for plant variety protection but broadly applicable to the implementation of environmentally friendly

135

genetic solutions to challenges that increase the economic and environmental costs of peanut production. SNP discovery in polyploids such as peanut poses a problem not faced in diploids, i.e., that most PCR ampliﬁcation products are likely to be mixtures of sequences from two or more divergent loci. This precludes many otherwise attractive SNP discovery strategies based on direct resequencing of PCR products. It remains to be determined exactly the degree to which this will be a problem in peanut as there is very little comparative sequence data for both cultivated peanut and its diploid progenitors. Further, the antiquity of polyploid formation in peanut will also bear on this problem – if polyploid formation were ancient, there might be an appreciable degree of “diploidization” or loss of some duplicated gene copies, suggesting that reasonable populations of truly single-copy loci might be found. However, polyploid formation is thought to be relatively recent in view of the generally low polymorphism rate. Further investigation of the structure and evolutionary history of the peanut genome will be needed to evaluate various SNP discovery strategies and implement optimal strategies across the genome and the gene pool. A few SNPs for particular high-priority genes have already been discovered. Lopez et al. (2000) used peanut lines with a low (T-90) or high (F435) oleic (O) to linoleic (L) fatty acid ratio to isolate and characterize the Δ12 -fatty acid desaturase (FAD) gene. The Δ12 -FAD contains a putative intron, the coding region at the 3 end, and an open reading frame (ORF) of 1,140 bp encoding 379 amino acids. A comparison of coding sequences from the high and low oleic acid genotypes revealed several SNPs: one SNP in the ﬂanking region at 229 bpupstream of the start codon and a cluster of four SNPs in the coding region. Two polymorphisms appear to be associated with the high O/L trait. The ﬁrst is an “A” insertion 442 bp after the start codon that shifts the amino acid reading frame, probably resulting in a truncated, inactive protein and the loss of one of three histidine boxes believed to be involved in metal ion complexation required for the reduction of oxygen and another polymorphism at 448 bp from the start codon that results in an amino acid change. Several independently derived backcross lines with high O/L ratio had either the “A” insertion or the amino acid substitution. This association of the molecular polymorphisms with the low and high oleate trait in peanut should allow peanut breeders to develop an effec-

136

S.L. Dwivedi et al.

tive and low-cost molecular assay for the high O/L a good resource for the peanut research and breeding community. trait.

Resistant Gene Analog (RGA) Plants have distinct mechanisms for defending themselves against diseases. One of these involves the speciﬁc recognition of, and response to, pathogens. Many of the genes that control this type of resistance encode proteins with an NBS (nucleotide-binding site) domain (Meyers et al. 1999). The only function so far associated with the NBS in plants is disease resistance. Amino acid motifs within the NBS can be used to design degenerate PCR primers that amplify diverse NBS encoding regions from plant genomic or total cDNA. These NBS encoding regions, isolated by cloning and converted into genetic markers, have, in some studies, been shown to be genetically linked to known R-genes, or indeed to be fragments of the known R-genes themselves (Kanazin et al. 1996; Aarts et al. 1998; Collins et al. 1998, 1999, 2001; Shen et al. 1998; Hayes and Saghai-Maroof 2000; Donald et al. 2002). NBS encoding regions are therefore ideal candidate gene markers for disease resistances in peanut. There is a very large number of NBS encoding regions in plant genomes (about 150 in Arabidopsis and many more in larger genomes) potentially creating a high level of redundancy for this process. However, since many resistance genes occur in clusters it may not be necessary to speciﬁcally detect the correct candidate gene but instead effective MAS systems may be derived from any resistance gene analog in the correct genomic region. Thus, Bertioli et al. (2003) have used degenerate primers to isolate 78 complete NBS encoding regions from genomic DNA of a number of Arachis species. EMBRAPA and UCB have been working to convert these NBS sequences to molecular markers for the diploid AA genome mapping population (see Sect. 4.3 for further details). Southern blots using NBS-based probes show high polymorphism, in many cases cosegregation of homologs, and often differences in the numbers of homologs between the mapping parents. In our opinion it certainly is worthwhile to place the major resistance gene clusters on the Arachis genetic map. Incorporated within a framework of transferable PCR-based markers (SSRs), these markers should serve as

3.5 Successes and Limitations of Conventional Breeding in Peanut Progress in conventional peanut breeding has recently been reviewed elsewhere (Dwivedi et al. 2003a). Peanut-breeding programs, in developed and developing countries, have made signiﬁcant progress toward developing cultivars with crop durations ranging from 90 to 150 d and pod yield potentials from 3.0 t ha−1 to 9.0 t ha−1 . However, farmers in most countries do not come close to realizing these types of yields (i.e., world average yield of 1.35 t ha−1 ). The highest average national yields are 2.6 t ha−1 in China and and 3.5 t ha−1 in the USA) (FAO 2003), although even higher yields have been reported in isolated farmers’ ﬁelds in China and Zimbabwe (Smartt 1978; Yanhao and Caibin 1990). Resistance to rust, bacterial wilt, and groundnut rosette virus (GRV) has been successfully incorporated into improved genetic background. Bacterialwilt-resistant cultivars in Southeast Asia and the Far East and GRV-resistant cultivars in sub-Saharan Africa are now grown on large areas. A few cultivars with moderate resistance to rust and leaf spots have also been developed; however, these have not become popular among farming communities in the semiarid tropics because of their relatively long duration, low shelling out-turn, and inferior pod/seed characteristics compared to preferred cultivars. More recently, several peanut-breeding programs have been successful in diluting this undesirable linkage, facilitating the development of breeding lines with a shorter duration and moderate resistance to rust and/or LLS plus excellent pod/seed characteristics (Upadhyaya et al. 2002b). In contrast, peanut breeders have not been successful in developing cultivars that show complete resistance to Aspergillus ﬂavus in order to eliminate aﬂatoxin contamination. Several germplasm accessions and breeding lines are available that offer various components of genetic resistance (such as resistance to pod infection, to seed invasion, and to aﬂatoxin production). But again, when these traits are introgressed into breeding programs, the genetic resistance has not

Chapter 3 Peanut

improved over what is already available in germplasm lines. Interspeciﬁc crossing and selection has resulted in the release of two nematode resistant varieties, Coan and NemaTAM, in the USA (Simpson and Starr 2001; Simpson et al. 2003). Conventional breeding has had some success in selecting for drought tolerance in peanut. However, trait-based (speciﬁc leaf area, wateruse efﬁciency, amount of water transpired, transpiration efﬁciency, and harvest index) selection is likely to be a more rewarding strategy to substantially enhance drought tolerance. ICRISAT has developed breeding lines originating from trait-based selection that are being compared with breeding lines originating from conventional selection for their response to drought and yield potential (Nigam et al. 2003a). Peanut oil quality is determined by the ratio of oleic (O) fatty acid/linoleic (L) fatty acid: a higher ratio results in a better storage quality of the oil and longer shelf life of peanut products. With the availability of peanut germplasm with an exceptionally high O/L fatty acid ratio (Norden et al. 1987), US peanut breeders have been successful in transferring this trait into improved genetic backgrounds, and several newly developed cultivars with improved oil chemistry are now commercially grown in the USA.

137

Mag gall formation. RAPD and RFLP markers closely linked to the resistance loci have been identiﬁed (Table 6). The RFLP loci R2430E and R2545E are easy to score and sufﬁciently close to the resistance allele for an acceptable selective power. Similarly, a RAPD marker, Z3/265, linked at 10 cM and 14 cM from Mag and Mae, respectively, has been converted into a SCAR marker and RFLP probe that conﬁrmed linkage with nematode resistance. Association of an RFLP probe R2430E linked to a locus for resistance to root-knot nematode race 1 in four breeding populations has further validated these markers. US peanut breeders now routinely use these markers to select for nematode resistance. RAPD markers associated with resistance to southern corn rootworm, ELS, and cylindrocladium black rot have also been reported (Table 6). Aphid is a vector of GRV that causes substantial yield losses in peanut production in sub-Saharan Africa, and identiﬁcation of markers linked with aphid resistance should help peanut breeders select for combined resistance to vector and the GRV. A single recessive gene that confers resistance to aphid has been mapped on LG 1, 3.9 cM from a marker originating from the susceptible parent explaining 76% of the phenotypic variation for aphid resistance (Herselman et al. 2004). Resistance to a number of diseases (ELS and LLS, rust, and bacterial wilt) is being mapped using SSR and AFLP markers at ICRISAT. Preliminary results have identiﬁed a number of markers closely linked 3.6 to QTL for each resistance trait (Mace et al. unpubl. Biotechnological Applications data). to Genetic Enhancement in Peanut Urgent needs include the development of large numbers of user-friendly genetic mapping tools; sequencing of substantial populations of expressed se3.6.1 quences from diverse tissues, genotypes, and stress Marker/Trait Associations proﬁles; assembly of a genetically anchored physical map and its alignment to the emerging model legume Unlike with other oilseed crops such as soybean, genomes such as Lotus and Medicago; and sampling oilseed rape, and sunﬂower, the genomics and molec- the gene-rich regions to quantify the additional inforular breeding of peanut is still in its infancy. Although mation that may be gained by further sequencing of peanut is a complex polyploid (like oilseed rape), the peanut genome (Paterson et al. 2004). the primary reason for slow progress is the lack of detectable molecular variation in cultivated peanut. This problem has been somewhat resolved by the 3.6.2 large-scale development of SSR markers. A few eco- Unlocking the Genetic Variation nomically important traits have now been mapped in from Wild Genetic Resources peanut. Resistance to root-knot nematode has been mapped using RFLP and/or RAPD assays in an inter- Although there is high morphological diversity speciﬁc cross A. hypogaea Florunner × wild Arachis among varieties and landraces of A. hypogaea, species. Two dominant genes confer resistance to molecular genetic diversity and variability for some root-knot nematode: Maerestricts egg number and important traits of agronomic interest are low.

138

S.L. Dwivedi et al.

Table 6. Summary of marker-trait relationships reported in mapping populations derived from interspeciﬁc crosses in peanut Trait

Root-knot nematode (Meloidogyne arenaria (Neal) Chitwood)

Early leaf spot and southern corn rootworm

Summary of DNA markers linked with beneﬁcial traits

Arachis hypogaea × Wild Arachis species crosses BSA identiﬁed RAPD markers, RKN410, RKN440, and RKN229, linked with nematode resistance in BC4 F2 population of the cross Florunner × TxAG7 and further validated by screening 21 segregating BC4 F2 and 63 BC5 F2 single plants. Recombination fraction between RKN410 and resistance and between RKN440 and resistance was 5.4 cM and 5.8 cM, respectively. These two markers identiﬁed a resistance gene derived from either A. cardenasii or A. digoi and were closely linked to each other. Marker RKN229, that inherited from A. cardenasii or A. digoi, was 9 cM away from resistance locus. Two dominant genes conditioning resistance to the root-knot nematode reported in segregating F2 populations involving a root-knot nematode introgression line GA 6 (A. hypogaea × A. cardenasii) and a highly susceptible recurrent parent PI 261942. The gene Mae restricts egg number and Mag gall formation. A RAPD marker Z3/265 was linked at 10 cM and 14 cM from Mag and Mae, respectively. They cloned this marker to make SCAR and RFLP probes, and these markers conﬁrmed the linkages with nematode resistance. An RFLP probe R2430E linked to a locus for resistance to Meloidogyne arenaria race 1 in four breeding populations and three peanut lines, demonstrating that RFLP probe R2430E linked to nematode resistance provide a useful selection method for identifying resistance to the peanut root-knot nematode. Three RFLP loci (R2430E, R2545E, and S1137E) linked with resistance to nematode at distances of 4.2 to 11.0 cM in BC2 F2:4 population of the cross Florunner × TxAG7. R2430E and R2545E are easy to score and sufﬁciently close to the resistance allele that can be used with a high level of conﬁdence to select resistant progenies based on marker information. Evaluated six polymorphic RAPD markers (AD 1, AI 11, AI 19, AJ 19, AK 20, and AN 15) for components of resistance to early leaf spot (ELS) and southern corn rootworm resistance in F2 population involving A. hypogaeacultivar (NC 7) and ELS resistant tetraploid interspeciﬁc derivative NC GP WS 1, and established association between RAPD markers and sporulation, lesion diameter, and defoliation and for southern corn rootworm resistance.

Reference

Burow et al. 1996

Garcia et al. 1996

Seib et al. 2003

Choi et al. 1999; Church et al. 2000

Stalker and Mozingo 2001

Chapter 3 Peanut

139

Table 6. (continued) Trait

Summary of DNA markers linked with beneﬁcial traits

Reference

Arachis hypogaea × Arachis hypogaea crosses Early leaf spot and cylindrocladium black rot Aphid

Cylindrocladium black rot resistance was associated with RAPD marker AM 1101 and ELS sporulation was associated with AM 1102 in F2 population of the cross NC 7 × PI 109839. Aphid resistance in ICG 12991 was mapped on linkage group 1, 3.9 cMfrom a AFLP marker originating from the susceptible parent, that explained 76.1% of the phenotypic variation for aphid resistance.

This is because of an extreme genetic bottleneck at the origin of this species. Peanut evolved through the hybridization of two wild diploid species followed by spontaneous duplication of chromosomes. The resultant allotetraploid (or amphidiploid) plant would have captured good hybrid vigour but been reproductively isolated from its wild relatives. There are also good reasons to believe that the lack of allelic diversity in A. hypogaea has also led to genetic restrictions to increasing productivity. The best sources of disease resistance genes are found in wild species (Dwivedi et al. 2003a). A. monticola is the only wild relative that is sexually compatible with cultivated A. hypogaea. A few cultivars including Spancross and Tamnut 74 have been developed that include A. monticola within their ancestry (Isleib et al. 2001). However, the bulk of agronomically useful genetic diversity lies in the diploid species. There are three main pathways that have been proposed for the incorporation of this diversity into breeding programs (Simpson 2001): (1) A diploid wild species is crossed with A. hypogaea to generate a sterile triploid hybrid. This hybrid is treated with colchicine to double the chromosomes and produce a hexaploid plant with 60 chromosomes, which is crossed and backcrossed with A. hypogaea until the progeny regains the normal chromosome number of 40. (2) Two wild species, one with genome type AA and the other BB, are treated with colchicine to create tetraploids. These are then crossed to give a plant with a genome type AABB that is then crossed and backcrossed with A. hypogaea to regain the

Stalker and Mozingo 2001

Herselman et al. 2004

cultivated agronomic background while selecting for the exotic trait of interest. (3) Two wild diploid plants are crossed; the primary hybrid is treated with colchicine to double the chromosomes and produce a synthetic amphidiploid (allotetraploid). This amphidiploid is then crossed and backcrossed with A. hypogaea. This pathway is likely to be most successful when species with AA and BB genomes are used to make this primary cross, as synthetics with other genomes may not be readily cross fertile with cultivated peanut. The ﬁrst pathway has been successfully used for the development of new varieties (reviewed by Dwivedi et al. 2003a and Holbrook and Stalker 2003). The second pathway is reported to have had limited success because of sterility problems. The third (resynthesis) pathway essentially attempts to artiﬁcially recreate events similar to those that gave rise to the evolutionary speciation of A. hypogaea. A variant of this pathway in a cross has led to the development of cultivars that incorporate wild resistance genes (Simpson and Starr 2001; Simpson et al. 2003). The crossing used a hybrid between two AA-genome species (A. cardenasii and A. diogoi) as the A donor crossed with A. batizocoi as the B donor. However, it is now known that A. batizocoi has a very different genome to the B genome of A. hypogaea. For instance, the chromosomes of A. batizocoi have centromeric heterochromatic bands absent in the B genome of A. hypogaea. Nevertheless, these contrasting differentiations did not lead to the sterility problems that might have been expected.

140

S.L. Dwivedi et al.

Nowadays our knowledge of the afﬁnities of the genomes of species within the taxonomical section Arachis are much better deﬁned. In particular, the wild species with genomes most similar to the ancestral genomes of A. hypogaea have been identiﬁed: A. duranensis as the contributor of the A genome and A. ipaensis as the contributor of the B genome (Seijo et al. 2004). With this in mind EMBRAPA scientists, in collaboration with Charles Simpson of Texas A&M University, USA, has recently undertaken work to “resynthesize” A. hypogaea using the ancestral and related species (A. Fávero et al. unpubl. data). This approach of resynthesis is attractive because it may minimize both sterility barriers and suppression of recombination, both major barriers in the utilization of wild species in breeding. The resynthesis of allopolyploid crops has been successfully used for introgressing exotic traits in both oilseed rape (Akbar 1989; Chen and Heneen 1989; Lu et al. 2001) and wheat (Fernandes et al. 2000). So far, ﬁve synthetic Arachis amphidiploids have been generated: [A. hypogaea × (A. ipaensis × A. duranensis)c , A. hypogaea × (A. hoehnei × A.cardenasii)c , A. hypogaea × (A.aff.magna × A. villosa)c , (A. aff.magna × A.aff.diogoi)c , and (A. hoehnei × A. helodes)c ]. Fertile hybrids from crosses between three of these with A. hypogaea × (A. hoehnei KG30006 × A. cardenasii GKP10017)c , A. hypogaea× (A. aff.magna V6389 × A. villosaV12812)c , and A. hypogaea × (A. ipaensisKG30076 × A. duranensis V14167)c have been obtained. The c in the description of the amphidiploid crosses indicates that the plants have been treated with colchicine and have chromosome number 2n = 40. The accessions used for these crosses were chosen based on the results of bioassays with late leaf spot (LLS) [Phaeoisariopsis personata(Berk. and Curtis) Deighton] and rust (Puccinia arachidis Speg) isolates from Brazil. It was noteworthy that all of the 97 wild accessions tested had higher levels of resistance than the 10 control cultivars of A. hypogaea, and that there was great heterogeneity within species as regards disease resistance. Therefore, it is invalid to regard a particular species as being uniformly resistant against any of the fungi tested. These synthetic amphidiploids incorporate new disease resistance genes from both the A and B genomes. The usefulness of resistances of these wild sources has been further conﬁrmed in greenhouse screens using a severe combined challenge with LLS and rust. All A. hypogaeacultivars were severely affected, many losing

almost all of their photosynthetic leaf area, while all synthetic amphidiploids and their F1 hybrids with A. hypogaea showed high levels of resistance (A. Fávero et al. unpubl. data). In order to efﬁciently use the synthetic amphidiploids in prebreeding, it will be necessary to apply foreground and background marker-assisted introgression and backcross breeding. Genetic maps, constructed with markers that have good transferability across species and that are reasonably easy to use (SSRs would seem to be the best choice), will facilitate the effective introgression of target traits while simultaneously efﬁciently eliminating the remainder of the wild species genome. This approach will be heavily dependent on the microsatellite-based genetic maps that have already been developed for the A genome and that are ongoing for the B genome. These diploid maps will be validated in a tetraploid population derived from a cross of an amphidiploid with A. hypogaea. The combination of SSR-based genetic maps of diploid species and synthetic amphidiploids incorporating various exotic genomes are ﬁrst steps toward a new molecular-enhanced paradigm in peanut breeding that will unlock the value of wild Arachis germplasm that has been hitherto largely beyond the reach of most breeders. New peanut varieties incorporating wild Arachis genes will have improved resistance to biotic stresses and tolerance to abiotic stress together with allelic combinations for enhanced yield potential and increased quality proﬁles that would never have been possible through conventional approaches.

3.6.3 Transgenics An efﬁcient tissue culture and transformation system to introduce foreign DNA into peanut has been reported (Sharma and Anjaiah 2000), and transgenic peanuts carrying genes for resistance to TSWV, lesser cornstalk borer, and sclerotinia blight in the USA; a gene carrying resistance to peanut stripe virus in China; and genes carrying IPCV cp or IPCV replicase for resistance to peanut clump virus, GRAV cp , for resistance to groundnut rosette assistor virus (GRAV), rice chitinase for resistance to fungal diseases, and drought responsive elements of Arabidopsis(rd29A:DREB1A) for improving drought tolerance have been produced at ICRISAT (Table 7). These transgenics are in var-

Chapter 3 Peanut

141

Table 7. List of transgenics carrying genes for resistance to peanut clump virus, groundnut rosette assistor virus, tomato spotted wilt virus, peanut stripe virus, fungal and soil born diseases, drought and allergens in peanut Trait

Gene and host cultivar Current status of transgenics

Reference

Peanut clump virus (PCV)

IPCV cp or IPCV replicase gene

ICRISAT 2004

Groundnut rosette assistor virus (GRAV) Tomato Spotted Wilt Virus (TSWV) Peanut Stripe virus (PStV) Fungal diseases (rust and leaf spots) Sclerotinia blight (Sclerotinia minor)

GRAV cp gene

Drought

Allergens

2-year ﬁeld trials of transgenic peanuts having coat protein-mediated resistance (IPCV cp12, IPCV cp12, and IPCV cp51) or replicase-mediated resistance (IPCV rep3) have consistently shown resistance to PCV. Over 50 T2 -T3 generation transgenic lines containing GRAV cp gene characterized for gene integration and expression using RT-PCR and Southern hybridization.

ICRISAT 2004

Nucleocap-sid protein gene

The transgenic peanut showed lower TSWV incidence in comparison to nontransgenic control, both in the ﬁeld and in controlled environment conditions.

Yang et al. 2004

Coat protein gene

Transgenic plant carrying copies of viral coat protein gene exhibited high levels of resistance to PStV.

Dietzgen et al. 2004

Rice chitinase gene

Thirty-six transgenic plants were evaluated for resistance to rust using a detached leaf technique and at 25 d after inoculation, 7 and 20 transgenic plants showed over 85% and 50% reduction in rust pustules, respectively. Antifungal gene A 3-year study of evaluation of several transgenic lines containing antifungal gene under high disease pressure revealed an average reduction of 32%S. minor infection compared to susceptible control Okrun and were comparable for shelling percentage and 100-seed weight to that of nontransgenic control Okrun. Two lines consistently averaged S. minorincidence similar to resistant control Southwest Runner. Oxalate oxidase gene Transgenic peanuts containing oxalate oxidase gene, evaluated in greenhouse, expressed higher levels of oxalate oxidase activity than untransformed controls. 79% of Perry, 78% of Wilson, and 35% of NC7 transgenics showed signiﬁcantly elevated expression, and few selected lines showed 3 to 4 times as much activity as untransformed controls. rd29A:DREB1A T2 transgenic lines revealed positive gene integration and expression. DDRT-PCR test performed on these lines, subjected to various abiotic stresses (salinity, high and low temperature) under in vitro conditions, revealed the up and down regulation of several mRNAs besides identifying some new mRNA clones. Stable transgenic peanut with knock down expression p DK2 construct and a plasmid p CB13 of Ara h2 gene produced and Northern hybridization revealed that Ara h2gene is expressed only in peanut seeds containing hygromycine marker and not in vegetative tissues.

ICRISAT 2004

Chenault and Melouk 2003

Livingstone et al. 2003

ICRISAT 2004

Konan et al. 2002

142

S.L. Dwivedi et al.

ious stages of characterization under containment glasshouse and/or controlled ﬁeld evaluations. In addition, Peggy Ozias-Akins’s group in Tifton/Georgia, USA, has adopted a three-tier approach to develop transgenic peanut by incorporating resistance to insect damage using a Bt gene, resistance to fungal growth using the tomato anionic peroxidase gene (tap1), or antifungal peptide D4E1, and inhibition of aﬂatoxin biosynthetic pathway using the lipoxygenase gene lox1(Ozias-Akins et al. 2002). Transgenics clearly offers tremendous potential for introgressing genes not available in the Arachisgenus or that have pleiotropic effects on yield potential or quality proﬁles. Once favorable genes have been introduced into a cultivated peanut genotype, they can quickly be introgressed in a wide range of locally adapted and preferred backgrounds through marker-assisted breeding.

3.7 Conclusions and Future Outlook The genus Arachis probably arose during the tertiary period, and the genus now contains genetically diverse accessions and species. Most of this variation is not directly available to plant breeders because genes of interest are within genomes that are too diverged from the genome of A. hypogaea for sexual compatibility and too large and uncharacterized to allow positional cloning. The genomes of accessions within the taxonomical section Arachis are, however, available through the routes detailed in Sect. 6.2. To date, however, there are very few examples of released cultivars that contain genes from wild species. The events that gave rise to A. hypogaea imposed a severe genetic bottleneck at the origin of the crop, and the genetic diversity in cultivated germplasm today results from only some 4,000 years of mutation and selection. In addition, a second genetic bottleneck has been imposed by modern breeding programs, which so far have only used a tiny fraction of the variation within A. hypogaea. Therefore, commercial cultivars grown today have a very narrow genetic base, and the allelic combinations available from working with elite germplasm are limited. Therefore, there is an urgent need to broaden the genetic base of cultivated peanut germplasm.

Genetic resources of peanut currently available in germplasm banks consist of ca. 15,000 cultivated and 400 wild Arachis species accessions maintained at ICRISAT, USDA, and CENARGEN. These genetic resources harbor genes for resistance/tolerance to biotic and abiotic stresses in addition to showing variability for a range of morphophysiological, reproductive, and seed quality traits. There are also two well-deﬁned core and minicore collections representing the majority of variation present in the cultivated peanut germplasm. These are good resources to analyze genetic relationships and detect allelic variation linked with beneﬁcial traits through association mapping, and they provide an effective entry point to the entire collection. Peanut genomics has progressed rapidly during the past decade such that the peanut genomic resources now include availability of a large number of RAPD, AFLP, RFLP, and SSR markers with EST and SNP markers just beginning to emerge. These markers are being used in genetic diversity and marker-trait associations and for the development of genetic linkage maps. An RFLP-based map of tetraploid Arachis, derived from an interspeciﬁc backcross population, is already available to peanut researchers. However, it has limited value to peanut breeders as the RFLP loci placed on this map are unlikely to detect polymorphic alleles in intraspeciﬁc cultivated A. hypogaea crosses. There is an urgent need to develop more PCRbased genetic linkage maps as only a few sparsely spaced AFLP- and SSR-based genetic maps have been reported for intraspeciﬁc A. hypogaea crosses. Similarly, there is need to saturate the preexisting maps with more PCR-based markers. Considerable effort has been directed toward generating new SSR markers. EST and SNP markers are already available for oil quality and drought tolerance, but large-scale development of EST, RGA, and SNP markers will now be of substantial importance. Efforts are also being directed toward developing an SSR-based linkage map of the A and B diploid genomes that should enable mapping of exotic and/or complex traits (especially polygenic ones) that would have been difﬁcult or impossible to deal with in a conventional tetraploid background. Peanut is also being included in the development of a consensus legume genetic linkage map using legume family anchor markers. Eight hundred sixtyseven evolutionary conserved sequences (ECSs) that are likely to be well conserved within the legumes have been identiﬁed, and these are being used for marker development. Comparison of the map positions of these markers in different legumes should

Chapter 3 Peanut

allow the development of a single genetic framework map for legumes that in turn should assist peanut researchers to use genomic information from the model plants and facilitate the generation of genic markers, the identiﬁcation of candidate genes, and positional cloning in Arachis. Transgenic peanut carrying genes for resistance to several fungal and virus diseases and for some insect pests, which are in various stages of evaluation, will be available to peanut researchers for introgression into their target peanut cultivars. It is proposed to adopt an approach that combines transgenic techniques, MAS, and conventional breeding to provide intrinsic, low-cost, and environmentally benign solutions to the many challenges that increase the cost and risk of peanut production and cause peanut to fall short of consumer needs and desires.

Acknowledgement. The senior author wishes to thank the staff of ICRISAT library for their tireless efforts to conduct literature searches and arrange reprints; to KDV Prasad for text editing, references, and tables; and to ICRISAT management for providing the opportunity to contribute to the writing of this invited book chapter.

References Aarts MG, te Lintel HB, Holub EB, Beynon JL, Stiekema WJ, Pereira A (1998) Identiﬁcation of R-gene homologous DNA fragments genetically linked to disease resistance loci in Arabidopsis thaliana. Mol Plant-Micr Interact 11:251–258 Akbar MA (1989) Resynthesis of Brassica napus aiming for improved earliness and carried out by different approaches. Hereditas 111:239–246 Anderson WF, Holbrook CC, Culbreath AK (1996) Screening the core collection for resistance to tomato spotted wilt virus. Peanut Sci 23:57–61 Beavis WD (1998) QTL analysis: power, precision and accuracy. In: Paterson AH (ed) Molecular Analysis of Complex Traits. CRC, Boca Raton, FL, pp 145–161 Bertioli DJ, Leal-Bertioli SCM, Lion MB, Santos V, Pappas G, Cannon S, Guimarães P (2003) A large scale analysis of resistance gene homologues in Arachis. Mol Genet Genom 270:34–45 Bhagwat A, Krishna TG, Bhatia CR (1997) RAPD analysis of induced mutants of groundnut (Arachis hypogaea L.). J Genet 76:201–208 Bock KR, Murant AF, Rajeshawri R (1990) The nature of resistance in groundnut to rosette disease. Ann Appl Biol 117:379–384

143

Bock KR, Nigam SN (1988) Methodology of groundnut rosette screening and vector-ecology studies in Malawi. In: Coordinated Research on Groundnut Rosette Virus Disease, ICRISAT. Patancheru, AP, India, pp 6–10 Broun P, Gettner S, Somerville C (1999) Genetic engineering of plant lipids. Annu Rev Nutr 19:197–216 Burks AW, Williams LW, Helm RM, Connaughton C, Cockrell G, O’Brien TJ (1991) Identiﬁcation of a major peanut allergen, Ara h I, in patients with atopicdermatitis19 and positive peanut challenges. J Allergy Clin Immunol 88:172–179 Burks AW, Williams LW, Connaughton C, O’Brien TJ, Helm RM (1992) Identiﬁcation and characterization of a second major peanut allergen Ara h II, with the use of the sera of patients with atopic dermatitis and positive peanut challenge. J Allergy Clin Immunol 90:962–969 Burks AW, Cockrell G, Stanley JS, Helm RM, Bannon GA (1995) Recombinant peanut allergen Ara h1 expression and IgE binding in patients with peanut hypersensitivity. J Clin Invest 96:1715–1721 Burow MD, Simpson CE, Paterson AH, Starr JL (1996) Identiﬁcation of peanut (Arachis hypogaea L.) RAPD markers diagnostic of root-knot nematode (Meloidogyne arenaria (Neal) Chitwood) resistance. Mol Breed 2:369–379 Burow MD, Simpson CE, Starr JL, Paterson AH (2001) Transmission genetics of chromatin from a synthetic amphiploid in cultivated peanut (Arachis hypogaea L.): Broadening the gene pool of a monophyletic polyploidy species. Genetics 159:823–837 Burr B, Evola SV, Burr FA, Beckmann JS (1983) Application of restriction fragment length polymorphism to plant breeding. In: Setlow JK, Hollaender A (eds) Genetic Engineering, Vol 5. Plenum, New York, pp 45–49 Burr B, Burr FA, Thompson KH, Albertson MC, Stubber CW (1988) Gene mapping with recombinant inbred in maize. Genetics 118:519–526 Butler DR, Wadia KD, Jadhav DR (1994) Effect of leaf wetness and temperature on late leaf spot infection of groundnut. Plant Pathol 43:112–120 Chandran K, Pandya SM (2000) Morphological characterization of Arachis species of section Arachis. Plant Genet Resource Newslett 121:38–41 Chen B-Y, Heneen WK (1989) Resynthesized Brassica napus L: a review of its potential in breeding and genetic analysis. Hereditas 111:255–263 Chenault KD, Melouk HA (2003) Resistance to Sclerotinia minor infection in transgenic peanut – a three year study. Proc Am Peanut Res Edu Soc 35. http://www.apres.okstate.edu/ old%20proceedings/APRES%202003%20Proceedings%20 vol%2035.pdf#search=‘APRES%202003’ Chengalrayan K, Gallo-Meagher M (2003) Developing peanut expressed sequence tag (EST) libraries. ASGSB 2003 Annual Meeting (Abstr) Choi K, Burow MD, Church G, Burow G, Paterson AH, Simpson CE, Starr JL (1999) Genetics and mechanism of resistance

144

S.L. Dwivedi et al.

to Meloidogyne arenaria in peanut germplasm. J Nematol 31:283–290 Chu FS, Fan TSL, Zhang G-S, Xu Y-C, Faust S, McMahon PL (1987) Improved enzyme-linked immunosorbent assay for aﬂatoxin B1 in agricultural commodities. J Assoc Off Anal Chem 70:854–857 Church GT, Simpson CE, Burow MD, Paterson AH, Starr JM (2000) Use of RFLP markers for identiﬁcation of individuals homozygous for resistance to Meloidogyne arenaria in peanut. Nematology 2:575–580 Clifford BC (1973) The construction and operation of a dewsimulation chamber. New Phytol 77:619–623 Cole RJ, Sanders TH, Doener JW, Blankenship PD (1989) Environmental conditions required to induce preharvest aﬂatoxin contamination of groundnuts: Summary of six years research. In: McDonald D, Mehan VK, Halls SD (eds) Proc Int Workshop on Afaltoxin Contamination of Groundnut. ICRISAT, Patancheru, AP, India, pp 279–287 Cole RJ, Dorner JW, Holbrook CC (1995) Advances in mycotoxin elimination and resistance. In: Pattee HE, Stalker HT (eds) Advances in Peanut Science. American Peanut Research and Education Society, Inc., Stillwater, OK 74078, USA, pp 456–474 Collins NC, Webb CA, Seah S, Ellis JG, Hulbert SH, Pryor A (1998) The isolation and mapping of disease resistance gene analogs in maize. Mol Plant-Micr Interact 11:968–978 Collins N, Drake J, Ayliffe M, Sun Q, Ellis J, Hulbert S, Pryor T (1999) Molecular characterization of the maize Rp1-D rust resistance haplotype and its mutants. Plant Cell 11:1365–1376 Collins N, Park R, Spielmeyer W, Ellis J, Pryor AJ (2001) Resistance gene analogs in barley and their relationship to rust resistance genes. Genome 44:375–381 Cook BG, Crosthwaite IC (1994) Utilization of Arachis species as forage. In: Smart J (ed) The Groundnut Crop: A Scientiﬁc Basis for Improvement. Chapman and Hall, London, pp 624–663 Craufurd PQ, Wheeler TR, Ellis RH, Summerﬁeld RJ, Williams JH (1999) Effect of temperature and water deﬁcit on wateruse efﬁciency, carbon isotope discrimination, and speciﬁc leaf area in peanut. Crop Sci 39:136–142 Damicone JP, Jackson KE, Dashiell KE, Melouk HA, Holbrook CC (2003) Reaction of the peanut core to sclerotinia blight and pepperspot. http://www.apres.okstate.edu/ old%20proceedings/APRES%202003%20Proceedings%20 vol%2035.pdf#search=‘APRES%202003’ Daykin ME, Hussey RS (1985) Staining and histopathological techniques in nematolody. In: Barker KR, Carter CC, Sasse JN (eds) An Advanced Treatise on Meloidogyne, Vol. II. Methodology. North Carolina State Univ Graphics, Raleigh, NC, pp 39–48 De Jong EC, van Zijverden M, Spanhaak S, Koppelman SJ, Pellegrom H, Penninks AH (1998) Identiﬁcation and partial characterization of multiple major allergens in peanut proteins. Clin Exp Allergy 28:743–751

Delfosse P, Devi PS, Reddy AS, Risopoulos J, Doucet D, Legreve A, Maraite H, Reddy DVR (1996) Epidemiology of the Indian peanut clump virus transmitted by Polymyxa sp. In: Sherwood JL, Rush CM (eds) Proc 3rd Symp Int Working Group on Plant Viruses with Fungal Vectors, Dundee, UK, 1996. Am Soc Sugar Beet Technologists, Denver, CO, pp 141–144 Delfosse P, Reddy AS, Legreve A, Devi PS, Devi KT, Maraite H, Reddy DVR (1999) Indian peanut clump virus (IPCV) infection on wheat and barley: symptoms, yield loss and transmission through seed. Plant Pathol 48:273–282 Dietzgen RG, Mitter N, Higgins CM, Hall R, Teycheney P-Y, Cruickshank A, Hapsoro D, Sudarsono (2004) Harnessing RNA silencing to protect peanuts from stripe disease. 4th Int Crop Sci Congr 2004. http://www.cropscience.org.au/ icsc2004/3/8/324_dietzgenrg.htm. Dodo H, Marsic D, Callender M, Cebert E, Viquez O (2002) Screening 34 peanut introductions for allergen content using ELISA. Food Agric Immunol 14:147–154 Donald TM, Pellerone F, Adam-Blondon AF, Bouquet A (2002) Identiﬁcation of resistance gene analogs linked to a powdery mildew resistance locus in grapevine. Theor Appl Genet 104:610–618 Dwivedi SL, Gurtu S (2002) Molecular diversity among accessions possessing varying levels of resistance to early leaf spot in groundnut. Int Arachis Newslett 22:36–37 Dwivedi SL, Varma TS (2002) Molecular diversity among groundnut varieties differing in drought tolerance traits. Int Arachis Newslett 22:34–36 Dwivedi SL, Reddy DVR, Nigam SN, Rao GVR, Wightman JA, Amin PW, Nagabhushanam GVS, Reddy AS, Scholberg E, Ramraj VM (1993) Registration of ICGV 86031 peanut germplasm. Crop Sci 33:220 Dwivedi SL, Gurtu S, Chandra S, Yuejin W, Nigam SN (2001) Assessment of genetic diversity among selected groundnut germplasm. 1: RAPD analysis. Plant Breed 120:345–349 Dwivedi SL, Pande S, Rao JN, Nigam SN (2002a) Components of resistance to late leaf spot and rust among interspeciﬁc derivatives and their signiﬁcance in a foliar disease resistance breeding in groundnut (Arachis hypogaea L.). Euphytica 125:81–88 Dwivedi SL, Gurtu S, Nigam SN (2002b) AFLP diversity among selected foliar diseases resistant groundnut (Arachis hypogaea L.) germplasm. Ind J Plant Genet Resource 15:46–50 Dwivedi SL, Crouch JH, Nigam SN, Ferguson ME, Paterson AH (2003a) Molecular breeding of groundnut for enhanced productivity and food security in the semi-arid tropics: Opportunities and challenges. Adv Agron 80:153–221 Dwivedi SL, Gurtu S, Chandra S, Upadhyaya HD, Nigam SN (2003b) AFLP diversity among selected rosette resistant groundnut germplasm. Int Arachis Newslett 23:21–23 Edwards KJ, Barker JHA, Daly A, Jones C, Karp A (1996) Microsatellite libraries enriched for several microsatellite sequences in plants. Biotechniques 20:758–760

Chapter 3 Peanut El-Nakib O, Pestka JJ, Chu FS (1981) Determination of aﬂatoxin B1 in corn, wheat, and peanut butter by enzyme-linked immunosorbent assay and solidphase radioimmunoassay. J Assoc Off Anal Chem 64:1077–1082 FAO (2002) http://apps.fao.org/page/collections?subset=agriculture FAO (2003) http://apps.fao.org/page/collections?subset=agriculture Fedorova M, van del Mortel J, Matsumoto PA, Cho J, Twon CD, VandenBosch KA, Gantt JS, Vance CP (2002) Genome-wide identiﬁcation of nodule-speciﬁc transcripts in the model legume Medicago truncatula. Plant Physiol 130:519–537 Ferguson ME, Burow M, Schultze SR, Bramel PJ, Paterson A, Kresovich S, Mitchell S (2004a) Microsatellite identiﬁcation and characterization in peanut (Arachis hypogaea L.). Theor Appl Genet 108:1064–1070 Ferguson ME, Bramel PJ, Chandra S (2004b) Gene diversity among botanical varieties in peanut (Arachis hypogaea L). Crop Sci 44:1847–1854 Fernandes MIB-de-M, Zanatta ACA, Prestes AM, Caetano V-daR, Barcellos AL, Angra DC, Pandolﬁ V (2000) Cytogenetics and immature embryo culture at Embrapa Trigo breeding program: transfer of disease resistance from related species by artiﬁcial resynthesis of hexaploid wheat (Triticum aestivum L. em. Thell). Genet Mol Biol 23:1051–1062 Franke MD, Brenneman TB, Holbrook CC (1999) Identiﬁcation of resistance to Rhizoctonia limb rot in a core collection of peanut germplasm. Plant Dis 83:944–948 Freitas FO, Valls JFM (2001) Nota sobre a ocorrência de um tipo distinto de amendoim no Parque Indigena do Xingú e arredores e suas implicações etnobotânicas. In: SIRGEALC Simpósio de Recursos Genéticos para a América Latina e o Caribe 3, Londrina Anais Londrina IAPAR, pp 303–304 Freeman HA, Nigam SN, Kelley TG, Ntare BR, Subrahmanyam P, Boughton D (1999) The world groundnut economy: facts, trends, and outlook. ICRISAT, Patancheru, AP, India, pp 52 Friedt W, Luhs WW (1999) Breeding of rapeseed (Brassica napus) for modiﬁed seed quality-synergy of conventional and modern approaches. New Horizons for an Old Crop. Proc 10th Int Rapeseed Congress, Canberra, Australia. http://www.regional.org.au/au/gcirc/4/440.htm Garcia GM, Stalker HT, Kochert G (1995) Introgression analysis of an interspeciﬁc hybrid population in peanut (Arachis hypogaea L.) using RFLP and RAPD markers. Genome 38:166–176 Garcia GM, Stalker HT, Shroeder E, Kochert G (1996) Identiﬁcation of RAPD, SCAR, and RFLP markers tightly linked to nematode resistance genes introgressed from Arachis cardenasii into Arachis hypogaea. Genome 39:836–845 Gimenes MA, Loped CR, Vall JFM (2002) Genetic relationships among Arachis species based on AFLP. Genet Mol Biol 25:349–353 Grieshammer U, Wynne JC (1990) Isozyme variability in mature seeds of U.S. peanut cultivars and collections. Peanut Sci 17:72–75

145

Guo BZ, Yu J, Holbrook CC, Lee RD, Lynch RE (2003) Application of differential display RT-PCR and EST/Microarray technologies to the analysis of gene expression in response to drought stress and elimination of aﬂatoxin contamination in corn and peanut. J Toxicol 22:287–312 Halward T, Stalker HT, Kochert G (1993) Development of an RFLP linkage map in diploid peanut species. Theor Appl Genet 87:379–384 Hanks RJ, Keller J, Rasmussen VP, Wilson GD (1976) Linesource sprinkler for continuous variable irrigation crop production studies. J Am Soil Sci Soc 40:426–429 Hayes AJ, Saghai-Maroof MA (2000) Targeted resistance gene mapping in soybean using modiﬁed AFLPs. Theor Appl Genet 100:1279–1283 He G, Prakash C (1997) Identiﬁcation of polymorphic DNA markers in cultivated peanut (Arachis hypogaea L.). Euphytica 97:143–149 He G, Prakash C (2001) Evaluation of genetic relationships among botanical varieties of cultivated peanut (Arachis hypogaea L.) using AFLP markers. Genet Resource Crop Evol 48:347–352 He G, Meng R, Newman M, Gao G, Pittman RN, Prakash CS (2003) Microsatellite as DNA markers in cultivated peanut (Arachis hypogaea L.). BMC Plant Biol 3:3. http://www.biomedcentral/com/1471-2229/3/3 Herman EM (2004) Allergenic responses to legume proteins. In: Wilson RF, Stalker HT, Brummer EC (eds) Legume Crop Genomics. AOCS, Champaign, IL Hernandez G, Ramirez M, Blair MW, Lara M, Blanco L, Muno M, Barazesh S, Verdoom E, Graham M, Vance CP (2004) Comparative analysis of common bean (Phaseolus vulgaris) nodule, root, pod and leaf expressed sequence tag (ESTs) libraries: a platform for “Phaseomics” research. AEP/ICLGG meeting presented paper, Dijon, France Herselman L (2003) Genetic variation among Southern African cultivated peanut (Arachis hypogaea L.) genotypes as revealed by AFLP analysis. Euphytica 133:319–327 Herselman L, Thwaites R, Kimmins FM, Courtois B, van de Merwe PJA, Seal SE (2004) Identiﬁcation and mapping of AFLP markers linked to peanut (Arachis hypogaea L.) resistance to the aphid vector of groundnut rosette disease. Theor Appl Genet DOI:10.1007/S00122-004-1756-Z Heun M (1992) Mapping quantitative powdery mildew resistance of barley using a restriction fragment length polymorphism map. Genome 35:1019–1025 Hird H, Lloyd J, Goodier R, Brown J, Reece P (2003) Detection of peanut using real-time polymerase chain reaction. Eur Food Res Technol 217:265–268 Holbrook CC (2001) Status of the Arachis germplasm collection in the United States. Peanut Sci 28:84–89 Holbrook CC, Dong WB (2003) Selection of a core of the core collection for peanut. http://www.apres.okstate.edu/ old%20proceedings/APRES%202003%20Proceedings%20 vol%2035.pdf#search=‘APRES%202003’

146

S.L. Dwivedi et al.

Holbrook CC, Stalker HT (2003) Peanut breeding and genetic resources. Plant Breed Rev 22:297–356 Holbrook CC, Knauft DA, Dickson DW (1983) A technique for screening peanut for resistance to Melodoigyne arenaria. Plant Dis 67:957–958 Holbrook CC, Anderson WF, Pitmann RN (1993) Selection of core collection from US germplasm collection of peanut. Crop Sci 33:859–861 Holbrook CC, Wilson DM, Matheron ME (1998) Sources of resistance to pre-harvest aﬂatoxin contamination in peanut. Proc Am Peanut Res Edu Soc 30:54 (Abstr) Holbrook CC, Stephenson MG, Johnson AW (2000) Level and geographical distribution of resistance to Meloidogyne arenaria in the U.S. peanut germplasm collection. Crop Sci 40:1168–1171 Holzhauser T, Vieths S (1999) Indirect competitive ELISA for determination of traces of peanut (Arachis hypogaea L.) proteins in complex food matrices. J Agric Food Chem 47:603–611 Hopkins MS, Cassa AM, Wang T, Michell SE, Dean RE, Kochert GD, Kresovich S (1999) Discovery and characterization of polymorphic simple sequence repeats (SSRs) in peanut. Crop Sci 39:1243–1247 Hovis AR, Young CT, Kuhn CW (1979) Effect of two strains of peanut mottle virus on fatty acids, amino acids, and protein of six peanut lines. Peanut Sci 6:88–92 Huaracha E, Xu M, Korban SS (2004) Narrowing down the region of the Vf locus for scab resistance in apple using AFLP-derived SCARs. Theor Appl Genet 108:274–279 Hussey RS, Barker KR (1973) A comparison of methods of collection inocula for Meloidogyne spp., including a new technique. Plant Dis Rep 57:1025–1028 IBPGR, ICRISAT (1992) Descriptors for groundnut (Arachis hypogaea L.). IBPGR Rome. ICRISAT, Patancheru, AP, India ICRISAT (2004) Harnessing Biotechnology for the Poor. Archival Report 2003. ICRISAT, Patancheru, AP, India, p 124 Isleib TG, Beute MK, Rice PW, Hollowell JE (1995) Screening the peanut core collection for resistance to Cylindrocladium black rot and early leaf spot. Proc Am Peanut Res Edu Soc 27:25 (abstr) Isleib TG, Holbrook CC, Gorbet DW (2001) Use of plant introductions in peanut cultivar development. Peanut Sci 28:96–113 Jain AK, Basha SM, Holbrook CC (2001) Identiﬁcation of drought-responsive transcripts in peanut (Arachis hypogaea L). Electronic J Biotechnol 4 (2). (http://www.ejbiotechnology.info/content/vol4/issue2/full/2/reprint.html) Jambunathan R, Raju MS, Bedre SP (1985) Analysis of oil content of groundnuts by nuclear magnetic resonance spectrometry. J Sci Food Agri 36:162–166 Jayalakshmi V, Rajareddy C, Reddy PV, Nageswara Rao RC (1999) Genetic analysis of carbon isotope discrimination and speciﬁc leaf area in groundnut (Arachis hypogaea L). J Oilseeds Res 16:1–5

Journet EP, Tuinen D, Gouzy J, Crespeau H, Carreau V, Farmer MJ, Niebel A, Schiex T, Jaillon O, Chatagnier O, Godiard L, Micheli F, Kahn D, Gianinazzi-PearsonV, Gamas P (2002) Exploring root symbiotic programs in the model legume Medicago truncatula using EST analysis. Nucleic Acids Res 30:5579–5592 Jung S, Swift D, Senfoku E, Patel M, Teule F, Powell G, Moore K, Abbott A (2000a) The high oleate trait in the cultivated peanut (Arachis hypogaea L.). I. Isolation and characterization of two genes encoding microsomal oleoyl-PC desaturases. Mol Gen Genet 263:796–805 Jung S, Powell G, Moore K, Abbott A (2000b) The high oleate trait in the cultivated peanut (Arachis hypogaea L.). II. Molecular basis and genetics of the trait. Mol Gen Genet 263:806–811 Jung S, Tate PL, Horn R, Kochert G, Moore K, Abbott AG (2003) The phylogenetic relationship of possible progenitors of the cultivated peanut. J Hered 94:334–340 Kanazin V, Marek LF, Shoemaker RC (1996) Resistance gene analogs are conserved and clustered in soybean. Proc Natl Acad Sci USA 93:11746–11750 Keating MU, Jones RT, Worley NJ, Shively CA, Yunginger JW (1990) Immunoassay of peanut allergens in foodprocessing materials and ﬁnished foods. J Allergy Clin Immunol 86:41–44 Kerridge PC, Hardy B (eds) (1994) Biology and agronomy of forage Arachis. CIAT, Cali, Colombia. CIAT Publication No. 240 Kinney AJ, Knowlton S (1998) Designer oils: the high oleic acid soybean. In: Roller S, Harlander S (eds) Genetic Modiﬁcation in Food Industry. Blackie, London, pp 193–213 Kleber-Janke T, Crameri R, Appenzeller U, Schlaak M, Becker W-M (1999) Selective cloning of peanut allergens, including proﬁling and 2S albumins, by phage display technology. Allergy Immunol 119:265–274 Kochert G, Halward T, Branch WD, Simpson CE (1991) RFLP variability in peanut (Arachis hypogaea L.) cultivars and wild species. Theor Appl Genet 81:565–570 Konan KN, Viquez OM, Dodo HW (2002) Knocking down the major peanut allergen Ara h 2 in transgenic peanut plants. American Peanut Research Education Society (APRES) S93:19 Konieczny A, Ausubel FM (1993) A procedure for mapping Arabidopsis mutations using co-dominant eco-type speciﬁc PCR-based markers. Plant J 4:403–410 Koppelman SJ, Vlooswijk, Knippels LMJ, Hessing M, Knol EF, van Reijsen FC, Bruijnzeel-Koomen CAFM (2001) Quantiﬁcation of major peanut allergens Ara h1 and Ara h2 in the peanut varieties Runner, Spanish, Virginia, and Valencia, bred in different parts of the world. Allergy 56:132–137 Krapovickas A, Gregory WC (1994) Taxonomia del genero Arachis (Leguminosae). Bonplandia 8:1–186 Krishna GK, Zhang J, Yingzhi L, He G, Pittman RN, Burow MD, Delikostadinov SG, Puppala N (2003) Detection of genetic diversity in Valencia peanuts using

Chapter 3 Peanut microsatellite markers. http://www.apres.okstate.edu/ old%20proceedings/APRES%202003%20Proceedings%20 vol%2035.pdf#search=‘APRES%202003’ Kuck JA, St Angelo AJ (1980) Improved method for quantitative determination of oil content in peanuts and peanut products. J Am Oilseeds Chem Soc 57:128–129 Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP genetic linkage maps. Genetics 121:185–199 Liang P, Pardee AB (1992) Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction. Science 257:967–971 Livingstone JL, Hampton JL, Stiles AR, Phipps PM, Grabau DM (2003) Genetic transformation of peanut for resistance to Sclerotinia minor. http://www.apres.okstate.edu/ old%20proceedings/APRES%202003%20Proceedings%20 vol%2035.pdf#search=‘APRES%202003’ Lopez Y, Nadaf HL, Smith OD, Connell JP, Reddy AS, Fritz AK (2000) Isolation and characterization of the Δ12 -fatty acid desaturase in peanut (Arachis hypogaea L.) and search for polymorphisms for the high oleate trait in Spanish market-type lines. Theor Appl Genet 101:1131–1138 Lu CM, Zhang B, Kakihara F, Kato M (2001) Introgression of genes into cultivated Brassica napus through resynthesis of B. napus via ovule culture and the accompanying change in fatty acid composition. Plant Breed 120:405–410 Luo M, Dang P, Guo BZ, Holbrook CC (2003) Functional genomics of Arachis hypogaea L. for understanding host peanut and Aspergillus interactions (Abstr). In: Proc 3rd Fungal Genomics, 4th Fumonisin, and 16th Aﬂatoxin Elimination Workshops, 13–15 October 2003, Savannah, GA, p 56 Lynch RE, Mack TP (1995) Biological and biotechnical advances for insect management in peanut. In: Pattee HE, Pattee HT (eds) Advances in Peanut Science. American Peanut Research and Education Society, Inc., Stillwater, OK 74078, pp 95–159 Mehan VK (1989) Screening groundnuts for resistance to seed invasion by Aspergillus ﬂavus and to aﬂatoxin production. In: McDonald D, Mehan VK, Halls SD (eds) Proc Int Workshop on Afaltoxin Contamination of Groundnut. ICRISAT, Patancheru, AP, India, pp 323–334 Mehan VK, McDonald M (1980) Screening for resistance to Aspergillus ﬂavus invasion and aﬂatoxin production in groundnuts. ICRISAT Groundnut Improvement Program Occasional paper no. 2. ICRISAT, Patancheru, AP, India, pp 15 (limited distribution) Mehan VK, McDonald M, Ramakrishan N (1985) Varietal resistance in peanut to aﬂatoxin production. Peanut Sci 13:7–10 Melchinger AE, Utz HF, Schon CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149: 383–403

147

Meyers BC, Dickerman AW, Michelmore RW, Sivaramakrishnan S, Sobral BW, Young ND (1999) Plant disease resistance genes encode members of an ancient and diverse protein family within the nucleotide-binding superfamily. Plant J 20:317–332 Minja EM, van der Merwa PJA, Kimmins FM, Subrahmanyam P (1999) Screening of groundnut lines for resistance to aphids, Aphis craccivora Koch. Int Arachis Newslett 19:21–23 Misra JB, Mathur RS, Bhatt DM (2000) Near-infrared transmittance spectroscopy: a potential tool for non-destructive determination of oil content in groundnuts. J Sci Food Agric 80:237–240 Mixon AC (1986) Reducing Aspergillus species infection of peanut seed using resistant genotypes. J Environ Qual 15:101–103 Mixon AC, Rogers KM (1973) Peanut accessions resistant to seed infection by Aspergillus ﬂavus. Agron J 65:560–562 Moretzsohn M de C, Hopkins MS, Mitchell SE, Kresovich S, Valla JFM, Ferreira M (2004) Genetic diversity of peanut (Arachis hypogaea L.) and its wild relatives based on the analysis of hypervariable regions of the genome. BMC Plant Biol 4:11. http://www.biomedcentral.com/1471-2229/4/11 Mozingo RW, Coffelt TA, Wynne JC (1988) Quality evaluation of Virginia-type peanut varieties released from 1944–1985. South Coop Series Bull. 335:1–18 Muehlbauer GJ, Specht JE, Thomas-Compton MA, Staswick PE, Bernard RL (1988) Near-isogenic lines – a potential resource in the integration of conventional and molecular marker linkage maps. Crop Sci 28:729–735 Murant AF, Kumar IK (1990) Different variants of the satellite RNA of groundnut rosette virus are responsible for the chlorotic and green forms of groundnut rosette disease. Ann Appl Biol 117:85–92 Murant AF, Rajeshwari R, Robinson DJ, Raschke JH (1988) A satelliteRNA of groundnut rosette virus that is largely responsible for symptoms of groundnut rosette disease. J Gen Virol 69:1479–1486 Nageswara Rao RC, Wright GC (1994) Stability of relationship between speciﬁc leaf area and carbon isotope discrimination across environments in peanut. Crop Sci 34:98–103 Nageswara Rao RC, Nigam SN (2001) Genetic options for drought management in groundnut. In: Saxena NP (ed) Management of Agricultural Drought: Agronomic and Genetic Options. Oxford and IBH, New Delhi, pp 123–141 Nageswara Rao RC, Williams JH, Singh M (1989) Genotypic sensitivity to drought and yield potential of peanut. Agron J 81:887–893 Nageswara Rao RC, Williams JH, Wadia KDR, Hubick KT, Farquhar GD (1993) Crop growth, water-use efﬁciency, and carbon isotope discrimination in groundnut Arachis hypogaea L.) genotypes under end-of-season drought conditions. Ann Appl Biol 122:357–367 Nageswara Rao RC, Talwar HS, Wright GC (2001) Rapid assessment of speciﬁc leaf area and leaf nitrogen in peanut

148

S.L. Dwivedi et al.

(Arachis hypogaea L.) using a chlorophyll meter. J Agron Crop Sci 186:175–182 Naidu RA, Robinson DJ, Kimmins FM (1998) Detection of each of the causal agents of groundnut rosette disease in plants and vector aphids by RT-PCR. J Virol Methods 76:9–18 Negi MS, Devic M, Delseny M, Lakshmikumaran M (2000) Identiﬁcation of AFLP fragments linked to seed coat color in Brassica juncea and conversion to SCAR marker for rapid selection. Theor Appl Genet 101:146–152 Nelson SC, Simpson CE, Starr JL (1989) Resistance to Meloidogyne arenaria in Arachis spp. germplasm. J Nematol (suppl) 21(4S):654–660 Nevil DJ (1981) Components of resistance to Cercospora arachidicola and Cercosporodium personatum in groundnut. Ann Appl Biol 99:77–86 Nigam SN, Bock KR (1990) Inheritance of resistance to groundnut rosette virus in groundnut (Arachis hypogaea L.). Ann Appl Biol 117:553–560 Nigam SN, Upadhyaya HD, Chandra S, Nageswara Rao RC, Wright GC, Reddy AGS (2001) Gene effects for speciﬁc leaf area and harvest index in three crosses of groundnut (Arachis hypogaea L.). Ann Appl Biol 139:301–306 Nigam SN, Chandra S, Manhoar B, Talwar HS, Reddy AGS, Kanchi R (2003a) Evaluation of trait-based and empirical selections for drought resistance at ICRISAT Center, Patancheru, AP, Inida. In: Cruickshank AW, Rachupati NC, Wright GC, Nigam SN (eds) Breeding for Drought Resistant Peanuts. Proc Collaborative Review Meeting held on 25–27 Feb 2002, Hyderabdad, AP, India; ICRISAT, Queensland Department of Primary Industries (QDPI), and Indian Council of Agriculatural Research (ICAR), New Delhi, pp 43–51 Nigam SN, Nageswara Rao RC, Wright GC (2003b) Breeding for increased water-use Efﬁciency in groundnut. pp 305–318. In: Rai M, Singh H, Hegde DM (eds) National Seminar on Stress Management in Oilseeds for Attaining SelfReliance in Vegetable Oils: Thematic papers. Indian Society of Oilseeds Research, Directorate of Oilseed Research, Rajendranagar, Hyderabad, AP, India Norden AJ, Gorbet DW, Knauft DA, Young CT (1987) Variability in oil quality among peanut genotypes in the Florida breeding program. Peanut Sci 14:7–11 Olorunju PE, Kuhn CW, Demski JW, Misari SM, Ansa OA (1991) Disease reaction and yield performance of peanut genotypes grown under groundnut rosette and rosette-free ﬁeld environments. Plant Dis 75:1269–1273 Olorunju PE, Kuhn CW, Demski JW (1992) Inheritance of resistance in peanut to mixed infection of groundnut rosette virus (GRV) and groundnut rosette assistor virus and a single infection of GRV. Plant Dis 76:95–100 Olson M, Hood L, Cantor C, Botstein D (1989) A common language for physical mapping of the human genome. Science 245:1434–1435 Ozias-Akins P, Yang H, Perry E, Akasaka Y, Niu C, Holbrook C, Lynch RE (2002) Transegnic peanut for preharvest aﬂatoxin reduction. Proc 1st Fungal Genomics, 2nd Fumonisin

Elimination, and 14th Aﬂatoxin Elimination Workshops, p 141 Padagham DE, Kimmins FM, Rao GVR (1990) Resistance in groundnut (Arachis hypogaea L.) to Aphis craccivora (Koch). Ann Appl Biol 117:285–294 Panford JA (1990) Determination of oil content of seeds by NIR: inﬂuence of fatty acid composition on wavelength selection. J Am Oil Chem Soc 67:1627–1634 Paran I, Michelmore RW (1993) Development of a reliable PCRbased markers linked to downy mildew resistance genes in lettuce. Theor Appl Genet 85:985–993 Paterson AH, Lander ES, Hewitt JD, Peterson S, Lincon SE, Tanksley SD (1988) Resolution of quantitative traits into Mendelian factors, using a complete linkage map of restriction fragment length polymorphism. Nature 335:721–726 Paterson AH, Stalker HT, Gallo-Meagher M, Burow MD, Dwivedi SL, Crouch JH, Mace ES (2004) Genomics and genetic enhancement of peanut. In: Wilson RF, Stalker HT, Brummer EC (eds) Legume Crop Genomics. AOCS, Champaign, IL, pp 97–109 Podlich DW, Winkler CR, Cooper M (2004) Mapping as you go: an effective approach for marker-assisted selection of complex traits. Crop Sci 44:1250–1571 Pomes A, Helm RM, Bannon GA, Burks AW, Tsay A, Chapman MD (2003) Monitoring peanut allergen in food products by measuring Ara h1. J Allergy Clin Immunol 111:640–645 Rafalski JA, Vogel JM, Morgante M, Powell W, Andre C, Tingey SV (1996) Generating and using DNA markers in plants. In: Birren B, Lai E (eds) Analysis of Non-mammalian Genomes – A Practical Guide. Academic, New York, pp 75–134 Raina SN, Rani V, Kojima T, Ogihara Y, Singh KP, Devarumath RM (2001) RAPD and ISSR ﬁngerprints as useful genetic markers for analysis of genetic diversity, varietal identiﬁcation, and phylogenetic relationships in peanut (Arachis hypogaea) cultivars and wild species. Genome 44:763–772 Rajeshwari R, Murant AF, Massalski PR (1987) Use of monoclonal antibody to potato leaf roll virus for detecting groundnut rosette assistor virus and virus diseases: distribution, identiﬁcation and control. Ann Appl Biol 111:353–358 Rajgopal K, Chandran K, Bhagat NR, Bhalodia PK (1997) Morphological characterization of Valencia and Virginia bunch peanut (Arachis hypogaea L.) germplasm. Plant Genet Resource Newslett 109:27–29 Ram BP, Hart LP, Cole RJ, Pestka JJ (1986a) Application of ELISA to a retail survey of aﬂatoxin B1 in peanut butter. J Food Prot 49:792–795 Ram BP, Hart LP, Shotwell O, Pestka JJ (1986b) Enzyme-linked immunosorbent assay of aﬂatoxin B1 in naturally contaminated corn and cotton seed. J Assoc Off Anal Chem 69:904–907 Rao MJV, Upadhyaya HD, Mehan VK, Nigam SN, McDonald D, Reddy NS (1995) Registration of peanut germplasm ICGV 88145 and ICGV 89104 resistant to seed infection by Aspergillus ﬂavus. Crop Sci 35:1717

Chapter 3 Peanut Rao GVR, Wightman JA (1999) Status of the integrated management of groundnut pests in India. In: Upadhyaya RK, Mukerji KG, Rajak RL (eds) IPM System in Agriculture 5. Aditya, New Delhi, pp 435–459 Rao RDVJP, Reddy DVR, Nigam SN, Reddy AS, Waliyar F, Reddy TY, Subramanyam K, Sudheer MJ, Naik KSS, Bandhyopdahyay A, Desai S, Ghewande MP, Basu MS, Somasekhar (2003a) Peanut stem necrosis: a new disease of groundnut in India. Information Bull. No. 67. ICRISAT, Patancheru, AP, India, p 12 Rao NK, Reddy LJ, Bramel PJ (2003b) Potential of wild species for genetic enhancement of some semi-arid food crops. Genet Resource Crop Evol 50:707–721 Ratna AS, Rao AS, Nolt BL, Reddy DVR, Vijayalakshmi, McDonald D (1991) Studies on the transmission of Indian peanut clump virus disease by Polymyxa graminis. Ann Appl Biol 118:71–78 Reddy DVR, Rajeshwari R, Izuka N, Lesemann DE, Norlt BL, Goto T (1983) The occurrence of Indian peanut clump, a soil-borne virus disease of groundnut (Arachis hypogaea L.) in India. Ann Appl Genet 102:305–310 Savage GP, Keenen JL (1994) The composition and nutritive value of groundnut kernels. In: Smart J (ed) The Groundnut Crop: A Scientiﬁc Basis of Improvement. Chapman and Hall, London, pp 173–213 Seib JC, Wunder L, Gallo-Meagher M, Carpentieri-Pipolo V, Gorbet DW, Dickson DW (2003) Marker-assisted selection in screening peanut for resistance to root-knot nematode. APRES 35:90 (abstr) Seijo JG, Lavia GI, Fernandez A, Krapovickas A, Ducasse D, Moscone EA (2004) Physical mapping of the 5s and 18s-25s rRNA genes by FISH as evidence that Arachis duranensis and A. ipaensis are the wild diploid progenitors of A. hypogaea (Leguminosae). Am J Bot 91:1294–1303 Seetharama N, Dwivedi SL, Saxena NP (2003) Enhancing productivity of rainfed oilseed crops in India by mitigating effects of drought. In: Rai M, Singh H, Hegde DM (eds) National Seminar on Stress Management in Oilseeds for Attaining Self-Reliance in Vegetable Oils: Thematic papers. Indian Soc Oilseeds Res, Directorate of Oilseed Research, Rajendranagar, Hyderabad, AP, India, pp 305–318 Sharma KK, Anjaiah V (2000) An efﬁcient method for the production of transgenic plants of peanut (Arachis hypogaea L.) through Agrobacterium temefaciens-mediated genetic transformation. Plant Sci 159:7–19 Sharma HC, Pampapathy G, Kumar R (2002) Technique to screen peanut for resistance to the tobacco armyworm, Spodoptera litura (Lepidoptera: Noctuidae) under nochoice cage conditions. Peanut Sci 29:35–40 Sharma HC, Pampapathy G, Dwivedi SL, Reddy LJ (2003) Mechanisms and diversity of resistance to insect pests in wild relatives of groundnut. J Econ Entomol 96:1886–1897 Shen KA, Meyers BC, Islam-Faridi MN, Chin DB, Stelly DM, Michelmore RW (1998) Resistance gene candidates identiﬁed by PCR with degenerate oligonucleotide primers map

149

to clusters of resistance genes in lettuce. Mol Plant-Micr Interact 11:815–823 Shoemaker R, Keim P, Vodkin L, Retzel E, Clifton SW, Waterston R, Smoller D, Coryell V, Khanna A, Erpelding J, Gai X, Brendel V, Raph-Schmidt C, Shoop EG, Vielweber CJ, Schmatz M, Pape D, Bowers Y, Theising B, Martin J, Dante M, Wylie T, Granger C (2002) A compilation of soybean ESTs: generation and analysis. Genome 45:329–338 Sicherer SH, Munoz-Furlong A, Burk AW, Sampson HA (1999) Prevalence of peanut and tree nut allergy in the US determined by a random digit dialtelephone survey. J Allergy Clin Immunol 103:559–562 Simpson CE (2001) Use of wild Arachis species/introgression of genes into A. hypogaea L. Peanut Sci 28:114–116 Simpson CE, Starr JL (2001) Registration of ‘COAN’ Peanut. Crop Sci 41:918 Simpson CE, Nelson SC, Starr JL, Woodard KE, Smith OD (1993) Registration of TxAG-6 and TxAG-7 peanut germplasm lines. Crop Sci 33:1418 Simpson CE, Krapovikas A, Valls JFM (2001) History of Arachis including evidence of A. hypogaea L. progenitors. Peanut Sci 28:78–80 Simpson CE, Starr JL, Church GT, Burow MD, Paterson AH (2003) Registration of ‘NemaTAM’ Peanut. Crop Sci 43:1561 Singh AK, Simpson CE (1994) Biosystemics and genetic resources. In: Smartt J (ed) The Groundnut Crop: A Scientiﬁc Basis for Improvement. Chapman and Hall, London, pp 96– 137 Singh AK, Nigam SN (1997) Groundnut. In: Fuccillo D, Sears L, Stapleton P (eds) Biodiversity in Trust. Cambridge University Press, Cambridge, pp 114–127 Singh U, Jambunathan R (1980) Evaluation of rapid methods for the estimation of protein in chickpea (Cicer arietinum L.). J Sci Food Agri 31:247–254 Singh AK, Subrahmanyam P, Gurtu S (1996) Variation in wild groundnut species, Arachis duranensis Krapov. & W.C. Gregory. Genet Resour Crop Evol 43:135–142 Singh AK, Mehan VK, Nigam SN (1997) Sources of resistance to groundnut fungal and bacterial wilt diseases: an update and appraisal. Information Bulletin No. 50, ICRISAT, Patancheru, AP, India, p 48 Smartt J (1978) Makulu Red – A ‘green revolution’ groundnut variety? Euphytica 27:605–608 Specht JE, Williams JH, Weidenbenner CJ (1986) Differential response of soybean genotypes subjected to a seasonal soil water gradient. Crop Sci 26:922–934 St Angelo AJ, Ory RL (1973) Investigations of causes and prevention of fatty acid peroxidation in peanut butter. J Am Peanut Res Edu Assoc 5:128–133 Staden R, Judge DP, Bonﬁeld JK (2003a) Analysing sequences using the Staden package and EMBOSS. In: Stephen AK, Womble DD (eds) Introduction to Bioinformatics. A Theoretical and Practical Approach. Human Press, Totowa, NJ Staden R, Judge DP, Bonﬁeld JK (2003b) Managing sequencing projects in the GAP4 environment. In: Stephen AK, Womble

150

S.L. Dwivedi et al.

DD (eds) Introduction to Bioinformatics. A Theoretical and Practical Approach. Human Press, Totowa, NJ Stalker HT, Lynch RE (2002) Registration of four insectresistant peanut germplasm lines. Crop Sci 42:312–313 Stalker HT, Mozingo LG (2001) Molecular markers of Arachis and marker-assisted selection. Peanut Sci 28:117–123 Stalker HT, Simpson CE (1995) Germplasm resources in Arachis. In: Pattee HE, Stalker HT (eds) Advances in Peanut Science. APRES, Stillwater, OK, pp 14–53 Stanley JS, King N, Burks AW, Huang SK, Sampson H, Cockrell G, Helm RM, MC West, Bannon II(1997) Identiﬁcation and mutational analysis of the immunodominant IgE binding epitopes of the major peanut allergen Ara h2. Arch Biochem Biophys 342:244–253 Starr JL, Simpson CE, Lee Jr TA (1995) Resistance to Meloidogyne arenaria in advanced generation breeding lines of peanut. Peanut Sci 22:59–61 Subrahmanyam P, McDonald D, Waliar F, Reddy LJ, Nigam SN, Gibbons RW, Rao VR, Singh AK, Pande S, Reddy PM, Rao PVS (1995) Screening methods and sources of resistance to rust and late leaf spot of groundnut. Information Bulletin no 47, ICRISAT, Pattancheru, AP, India, p 20 Subrahmanyam P, Hildebran GL, Naidu RA, Reddy LJ, Singh AK (1998) Sources of resistance to groundnut rosette disease in global groundnut germplasm. Ann Appl Biol 132:473–485 Subrahmanyam P, Naidu RA, Reddy LJ, Kumar PL, Ferguson ME (2001) Resistance to groundnut rosette disease in wild Arachis species. Ann Appl Biol 139:45–50 Subramanian V, Gurtu S, Nageswara Rao RC, Nigam SN (2000) Identiﬁcation of DNA polymorphism in cultivated groundnut using random ampliﬁed polymorphic DNA (RAPD) assay. Genome 43:656–660 Taneja SL, Leuschner K (1985) Methods of rearing, infestation, and evaluation for Chilo partellus resistance in sorghum. In: Proc Int Sorghum Entomol Workshop, 15– 21 July 1984, Texas A&M University, College Station, TX. ICRISAT, Patancheru, AP, India, pp 178–185 Tanhuanpaa P, Vilkki J (1999) Marker-assisted selection for oleic acid content in spring turnip rape. Plant Breed 118:568–570 Tanksley SD, Nelson JS (1996) Advance backcross QTL analysis: a method for simultaneous discovery and transfer of valuable QTL from unadapted germplasm to elite breeding lines. Theor Appl Genet 92:191–203 Tanksley SD, Young ND, Paterson AH, Bonierbale MW (1989) RFLP mapping in plant breeding: new tools for an old science. Bio/Technology 7:257–264 Taylor AL, Sasser JN (1978) Biology, identiﬁcation and control of root-knot nematodes (Meloidogyne species). North Carolina State University Graphics, Raleigh, NC, p 111 Thakur RP, Rao VP, Reddy SV, Ferguson M (2000) Evaluation of wild Arachis germplasm accessions for in vitro seed colonization and aﬂatoxin production by Aspergillus ﬂavus. Int Arachis Newslett 20:44–46

Tian A-G, Wang J, Cui P, Han Y-J, Xu H, Cong L-J, Huang XG, Wang X-L, Ziao Y-Z, Wang B-J, Wang Y-J, Zhang JS, Chen S-Y (2004) Characterization of soybean genomic features by analysis of its expressed sequence tags. Theor Appl Genet 108:903–913 Timper P, Holbrook CC, Xue HQ (2000) Expression of nematode resistance in plant introductions of Arachis hypogaea. Peanut Sci 27:78–82 Torrance L, Mayo MA (1997) Proposed reclassiﬁcation of furoviruses. Archives Virol 142:435–439 United Nations University (1980) Analytical methods for the determination of nitrogen and amino acids in foods. In: Pallett PL, Young VR (eds) Nutritional Evaluation of Protein Foods. A report of a working group sponsored by the International Union of Nutritional Sciences and the United Nations University World Hunger Programme. United Nations University, Japan Upadhyaya HD (2005) Variability for drought resistance related traits in the mini core collection of peanut. Crop Sci 45:1432–1440 Upadhyaya HD, Ortiz R (2001) A minicore subset for capturing diversity and promoting utilization of chickpea genetic resources in crop improvement. Theor Appl Genet 102:1292–1298 Upadhyaya HD, Ferguson ME, Bramel PJ (2001a) Status of Arachis germplasm collection at ICRISAT. Peanut Sci 28:89–96 Upadhyaya HD, Ortiz R, Bramel PJ, Singh S (2001b) Development of Asia region groundnut core collection. Paper presented in the Diamond Jubilee Symposium on Hundred Years of Post-Mendelian Genetics: Retrospect and Prospects. 6–9 Nov 2001. Indian Agricultural Research Institute, New Delhi Upadhyaya HD, Nigam SN, Mehan VK, Reddy AGS, Yellaiah N (2001c) Registration of Aspergillus ﬂavus seed infection resistant peanut germplasm ICGV 91278, ICGV 91283, and ICGV 91284. Crop Sci 41:599–600 Upadhyaya HD, Bramel PJ, Ortiz R, Singh S (2002a) Developing a mini core of peanut for utilization of genetic resources. Crop Sci 42:2150–2156 Upadhyaya HD, Nigam SN, Reddy AGS, Yellaiah N (2002b) Registration of early maturing, rust, late leaf spot, and low temperature tolerant peanut germplasm line ICGV 92267. Crop Sci 42:2220–2221 Upadhyaya HD, Ortiz R, Bramel PJ, Singh S (2003) Development of a groundnut core collection using taxonomical, geographical and morphological descriptors. Genet Resource Crop Evol 50:139–148 Upadhyaya HD, Mallikarjuna Swamy BP, Goudar PVK, Kullaiswamy BY, Singh S (2005) Identiﬁcation of diverse accessions of groundnut through multienvironment evaluation of core collection for Asia. Field Crops Res 93:293–299

Chapter 3 Peanut Upadhyaya HD, Reddy LJ, Gowda CLL, Singh S (2006) Identiﬁcation of diverse groundnut germplasm: sources of earlymaturity in a core collection. Field Crop Res 97:261–267 USDA-ARS (2002) Germplasm Resources Information Network – (GRIN). (Online database) National Germplasm Resource Laboratory, Beltsville, MD. (http://www.ars-grin.gov/cgibin/npgs/html/site_holding.pl?S9) Utomo SD, Anderson WF, Wynne JC, Beute MK, Hagler WM Jr, Payne GA (1990) Estimates of heritability and correlation among three mechanisms of resistance to Aspergillus parasiticus in peanut. Proc Am Peanut Res Edu Soc 22:26 (abstract) Valls JFM, Simpson CE (1994) Taxonomy, natural distribution, and attributes of Arachis. In: Kerridge PC, Hardy B (eds) Biology and Agronomy of Forage Arachis. Cali, CIAT, pp 1–18 Vogt W (1984) An evaluation of immunological methods based on the requirements of the clinical chemist. J Clin Chem Clin Biochem 22:927–934 Waliyar F, McDonald D, Rao PVS, Reddy PM (1993) Components of resistance to an Indian source of Cercospora arachidicola in selected peanut lines. Peanut Sci 20:93–96 Weber JL (1990) Informativeness of human (dC-dA) (dG-dT)n polymorphism. Genomics 7:524–530 Wicking C, Williamson B (1991) From linked marker to gene. Trends Genet 7:288–293

151

Wightman JA, Rao GVR (1994) Groundnut pests. In: Smartt J (ed) The Groundnut Crop: A Scientiﬁc Basis for Improvement. Chapman and Hall, London, pp 395–479 Wright GC, Nageswara Rao RC, Farquhar GD (1994) Wateruse efﬁciency and carbon isotope discrimination in peanut under water deﬁcit conditions. Crop Sci 34:92–97 Wright GC, Nageswara Rao RC, Basu MS (1996) A physiological approach to the understanding of genotype by environment interactions – a case study on improvement of adaptation in groundnut. In: Cooper M, Hammer GL (eds) Plant Adaptation and Crop Improvement. CABI, Wallingford, UK, pp 247–267 Yang H, Ozias-Akins P, Gulbreath AK, Gorbet DW, Weeks JR, Mandal B, Pappu HR (2004) Field evaluation of tomato spotted wilt virus resistance in transgenic peanut (Arachis hypogaea). Plant Dis 88:259–264 Yanhao S, Caibin W (1990) Factors contributing to high yields of groundnut in Shandong, China. Int Arachis Newslett 8:7–9 Young ND, Mudge J, Ellis TH (2003) Legume genomes: more than peas in a pod. Curr Opin Plant Biol 6:199–204 Yu J, Bhatnagar D, Cleveland TE, Nierman WC (2002) Aspergillus ﬂavus EST technology and its applications for eliminating aﬂatoxin contamination. Mycopathologia 155:6

CHAPTER 4

4 Sunflower Norma Paniego, Ruth Heinz, Paula Fernandez, Paola Talia, Veronica Nishinakamasu, and H. Esteban Hopp Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina e-mail: [email protected]

4.1 Introduction 4.1.1 Brief History The center of diversity of sunﬂower is localized in northern Mexico and southwestern USA (Heiser 1954). Domestication occurred at least 3000 BC by pre-Hispanic American cultures. It became a common crop from which they harvested the calcium-rich seeds to be grounded into ﬂour or cracked for direct consumption (Putt 1997). As most crops grown in earlier times, sunﬂower had many alternative uses. It was used in medical applications as it has known diuretic and expectorant effect in pulmonary and laryngeal affections. The yellow dye was extracted from the petals for coloration, oil was extracted for ceremonial body painting, and the rest of the plant was used as a building material (after drying the stalk). The ﬂower was revered as documented in representations showing priestesses crowned with sunﬂowers. Reports from early Spanish explorers indicate the presence of sunﬂower decorations in the temples of the sun that were made of gold. Spanish explorer Monardes brought the plant to Europe in 1569, where it was widely adopted as an ornamental plant. Peter the Great is credited for introducing sunﬂower to Russia (Selmeczi-Kovacs 1975), where it became the main source of edible vegetable oil until the present times. The ﬁrst indication of a food use comes from a patent for squeezing oil from sunﬂower seeds, which was granted in 1716 in England. However, it was not until 1830 that sunﬂower oil was commercially manufactured in Russia, where it became a widely cultivated crop. Ukrainian immigrants of Jewish origin reintroduced the crop into America in the late 18th century. By 1892, 315 ha were commercially cultivated in the province of Buenos Aires, Argentina (Jewish

Colonization Association 1914). In North America, the ﬁrst commercial use of sunﬂower was for poultry feeding, and it was not until 1926 that its processing for oil started. World wars produced a general lack of traditional edible oils and triggered a substitution process of traditional oils by sunﬂower oils. However, at that time the common cultivated sunﬂower plant had a long cycle and relatively low oil content. This led to important breeding efforts at Krasnodar (Pustovoit 1964), with a drastic rise in oil content as seed yield remained constant. In 1940, the average oil content of the main cultivar in the Soviet Union was 330 g/kg, and by 1965 the USSR program was testing strains with 550 g/kg oil (Putt 1997). However, the ﬁrst ofﬁcial sunﬂower-breeding programs started in North and South America. In Canada, during the 1930s germplasm material from Mennonite (Russian immigrants) gardens was used, and shortly afterwards in Argentina a short-cycle and high-oil-content (39%) variety called Klein was bred in 1938. After that sunﬂower cultivation area steadily increased in both North and South America, reaching 1.8 million ha in 1948 in Argentina and 2.2 million ha in the late 1970s, leading ﬁnally to its ﬁrst rank in the world in sunﬂower production. In the USA, however, the preponderance of early use was not for oil but for silage (Putt 1997). In the 1960s the use of sunﬂowers with high oil content was started after the reintroduction of cultivars from the USSR. The discovery of genetic male sterility (Leclercq 1969) and cytoplasmic male sterility sources allowed efﬁcient production of hybrid seed in the late 1970s with a twofold increase in relative yields. 4.1.2 Botanical Description The commercial crop is a predominantly outcrossing annual erect plant with a long stem of up to 3 m, with a large ﬂower head (inﬂorescence called capitulum)

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

154

N. Paniego et al.

reaching a diameter of up to 30 cm, well known for its peculiarity of turning its face toward the sun. This property baptizes its denomination in languages like French (tournesol) and Spanish (girasol). The capitulum disc is composed of numerous ﬂowers called disc ﬂorets that are crowded together. The outer ﬂowers are called ray ﬂorets. They are sterile and can be yellow, maroon, orange, or other colors. The arrangement of ﬂorets forms a pattern of spirals where the number of left spirals and the number of right spirals are successive Fibonacci numbers. Anthesis starts in the periphery and proceeds to the center of the disc. The disc ﬂorets give rise to the fruits (botanically named achenes), which constitute what we commonly called the “seeds,” the true seeds being encased in a husk. Some recently developed varieties have drooping heads. These varieties are less attractive to gardeners growing the ﬂowers as ornamentals but appeal to farmers because they reduce bird damage and losses from some plant diseases. The scientiﬁc name of sunﬂower is Helianthus annuus L. Helianthus derives from two Greek words: helios, meaning sun, and anthos, meaning ﬂower. It is a diploid species (2n = 2x = 34) that belongs to the Helianthinae subtribe, Asteroideae subfamily, and Compositae family (Seiler and Rieseberg 1997). The genus Helianthus includes 12 annual and 36 perennial species. The Jerusalem artichoke (Helianthus tuberosa) is related to the sunﬂower. Helianthus is also related to another genus (Lactuca) to which lettuce belongs. The Mexican sunﬂower is Tithonia rotundifolia. False sunﬂower refers to plants of the genus Heliopsis. H. annuus comprises three subspecies: H. annuus ssp. macrocarpus, which is the cultivated sunﬂower, and H. annuus ssp. lenticularis and H. annuus ssp. annuus, which are wild relatives of the cultivated crop. 4.1.3 Economic Importance Total world production of sunﬂower seed is about 25 million metric tons in recent years (Table 1). World total production oscillated during the last years between 22 and 29 million tons. The whole seed contains about 40% oil and about 25% protein (which can reach up to 42% after removal of the husk), which is well suited for animal feeding and used as such in many countries. The meal remaining after the seeds have been processed for oil is used as a livestock feed for ruminants, pigs, and poultry.

It is rich in ﬁbers, with lower caloric content and lysine than soybean but with larger methione content than soybean. Some varieties of sunﬂowers have large striped seeds, which are roasted for snack food or blended with other grains to make birdseed. Special oilseed varieties produce small black seeds that contain up to 50% oil. Thus, from their breeding objectives and applications, there are at least three varieties of sunﬂowers: 1. Oilseed sunﬂowers (divided into three varieties according to their relative oleic acid composition). 2. Confectionery sunﬂowers: used for raw, roasted or salted snack food, as well as for food for birds and small animals. The seeds are typically larger than the oilseed variety. 3. Ornamental sunﬂowers. By far, the major portion of sunﬂower production is devoted to oil extraction (Dorrell and Vick 1997). Thus, the most important objectives of breeding are dedicated to this trait. Sunﬂower oil is the world’s third most important vegetable oil accounting for about 13% of the total world’s edible oil production. Only soybean oil and palm oil are produced in greater abundance. Sunﬂower oil is considered premium oil due to its high unsaturated FA composition and low content of linolenic acid (see comparisons with other oil seeds in Table 2). There are three qualities of sunﬂower oil composition depending on the genotype, some of which are specially suited for cooking since they do not need hydrogenation to be used in frying (mid-oleic and high oleic variants developed in the 1980s). Their stability makes them suitable for the elaboration of baby foods, for example. The comparatively high price of sunﬂower oil prevents some other potential applications of industrial use, which are not exploited. However, it was investigated and corroborated that it could be used advantageously for the fabrication of certain paints and plastics. Its relative transparency is much better than other oils with high linolenic content. Due to its relative abundance it is used in the elaboration of soaps and detergents in eastern Europe. It is also used as “carrier” in the production of agrochemicals, particularly hydrophobic pesticides, surfactants, adhesives, plastics, softeners, and lubricants. Sunﬂower oil is also used in massage oil blends and aromatherapy. It has great potential to produce biodiesel (as a replacement for diesel fuel), for which it is less expensive than the olive product.

Chapter 4 Sunflower

155

Table 1. Sunﬂower seed production (in 1,000 metric tons)

Item

Sunﬂower seed World supply & disappearance (in 1,000 metric tons) 2000/01 2001/02 2002/03 2003/04

Area harvested (1,000 HA) yield (MT/HEC)

19,540 1.18

18,485 1.18

19,892 1.2

Seed Production Argentina Eastern Europe European Union China (Peoples Republic of) Russia/Ukraine United States India Turkey Other TOTAL

2,950 1,657 3,333 1,954 7,368 1,608 730 630 2,880 23,110

3,720 1,861 3,030 1,750 4,936 1,551 870 530 3,551 21,799

Seed Import Mexico European Union Other TOTAL

23 1,999 704 2,726

Oilseed crushed Seed export Argentina United States Russia/Ukraine Other TOTAL

2004/05 Revised

2005/06 Forecast

22,918 1.17

21,262 1.23

22,791 1.29

3,340 1,648 3,718 1,946 7,194 1,112 1,060 830 3,108 23,956

2,990 2,295 4,078 1,820 9,348 1,209 1,160 560 3,467 26,927

3,650 2,270 4,133 1,750 8,001 930 1,300 640 3,505 26,179

3,800 1,950 3,765 1,850 10,450 1,824 1,250 790 3,665 29,344

10 1,155 467 1,632

104 1,007 812 1,923

38 1,473 1,249 2,760

11 763 813 1,587

23 1,000 801 1,824

21,116

18,514

21,149

23,442

23,115

25,510

94 153 1,768 711 2,726

342 176 100 1,084 1,702

232 122 524 1,112 1,990

44 136 1,271 1,277 2,728

97 116 73 1,257 1,543

121 225 560 957 1,863

(Source http://www.sunﬂowernsa.com/stats/table.asp?contentID=109&htmlID=74&submit170=View&submit.x=57&submit.y=12)

4.1.4 Conventional Breeding Commercial sunﬂower breeding started in most of the producing countries (eastern and western Europe, North and South America) between 1920 and 1950. Selected traits included increased oil content, capitulum diameter, precocity, and resistance to Botrytis and mildew. Signiﬁcant yield increases came after the introduction of heterosis, which was ﬁrst described in 1966 by Leclercq (1966) and extensively utilized after incorporation of cytoplasmic male sterility (Leclercq 1969) following interspeciﬁc crossing with H. petio-

laris Nutt. Up to now, the cytoplasmic male sterility (CMS) source, PET1, has been effectively used worldwide for hybrid breeding in sunﬂower. The CMS was associated with the expression of a 16-kDa protein encoded by orfH522 in the PET1 cytoplasm, which is also present in other male-sterile cytoplasms of sunﬂower (Horn et al. 1996). It is associated with the atpA mitochondrial gene, which codes for a subunit of F1 ATPase. This feature has drastically changed breeding strategies since then. The most frequent breeding method consists of genealogic selection separately applied in the process of generation of male (called R, for restorer, maintainer, or androfertile line) and female

156

N. Paniego et al.

Table 2. Oil composition of sunﬂower and comparison to other oilseeds Source

Conventional sunﬂower oil Mid oleic sunﬂower oil High oleic sunﬂower oil Olive oil Canola oil Cottonseed oil

Contents (%) Oleic Linoleic

Saturated

20 65 82 77 62 18

11 9 9 14 16 27

69 26 9 8 21 54

Source: US Sunﬂower Crop Quality Report 2003 http://www.sunﬂowernsa.com/uploads/cqr/cqr2003.pdf

(called A, or androsterile) lines, during six or more successive inbreeding cycles accompanied by selection of the best plants. The most important traits for breeding are yield, relative oil and protein content, FA composition (linoleic and oleic acids), disease resistance (Sclerotinia, Phomopsis, mildew), insect resistance (Liriomyza spancerella, Rachiplusia nu, and Spilosoma virginica), and abiotic stress tolerance. The genetic basis for breeding programs comes from the different H. annuus subspecies and interspeciﬁc crossing with other Helianthus species.

4.2 Molecular Markers and Genetic Maps Following the steps of model plant species, the ﬁrst molecular markers used for constructing a denser linkage map in sunﬂower were restriction fragment length polymorphism (RFLP) (Berry et al. 1995, 1996, 1997; Gentzbittel et al. 1995, 1999; Jan et al. 1998), random ampliﬁed polymorphic DNA (RAPD) (Rieseberg et al. 1993; Rieseberg 1998), and ampliﬁed fragment length polymorphisms (AFLPs) (Peerbolte and Peleman 1996; Flores Berrios et al. 2000; Gedil et al. 2001; Al-Chaarani et al. 2002). However, as is known, RFLPs are technically laborious for routine use as molecular markers, and, while RAPD and AFLP markers have many advantages, they are mostly dominant, abundant, but often nonspeciﬁc and not very useful for comparison of a genomewide synteny of molecular markers for cross referencing genetic linkage maps. Until relatively recently, microsatellite markers (also called single sequence repeats, SSRs) and expressed sequence tag (EST)-derived single nucleotide

polymorphism (SNP) markers have been lacking in sunﬂower. Microsatellites are, when available, the markers of choice for linkage analysis due to the fact that they are highly polymorphic, usually inherited in a codominant manner, and, in most cases, chromosome speciﬁc. In recent years, development of microsatellites by South American researchers at INTA in Argentina (HAx), together with European (CARTISOL; CRS) and North American (ORSx) researchers, summed up 2,040 markers (Dehemer and Friedt 1998; Gedil 1999; Paniego et al. 2002; Tang et al. 2002; Yu et al. 2002).These markers were used for the development of reference maps using different F2 and recombinant inbred line (RIL) populations, which are almost pure lines derived from crosses between highly contrasting sunﬂower inbred lines. The recombinant inbred line parental crosses used were PAC2 × RHA266 and RHA280 × RHA801 and PHA × PHAB (Flores Berrios et al. 2000; Tang et al. 2002, 2003; Yu et al. 2003; Al-Chaarani et al. 2004). Until now, the map described by Al-Chaarani et al. (2004) on average is the longest map reported in the literature. It includes 371 AFLPs and 38 SSR markers selected from the CRS and ORS collections and covers a length of 2,915.9 cM with a marker density of 7.9 cM. However, the number of linkage groups (LGs) was higher than the number of total chromosomes (17) described for sunﬂower. In contrast, the maps constructed using only SSR_ORS and SSR_ORS plus INDELs respectively (Tang et al. 2002; Yu et al. 2003) were shorter but highly saturated as expected for these kinds of markers. Tang et al. (2002) demonstrated that a screening of 459 SSR marker loci is enough for a genomewide analysis of sunﬂower showing an average spacing of 3.1 cM and coverage of 1,368.3 cM. These authors provided the ﬁrst sunﬂower cross-referenced maps by mapping 701 SSR and 89 RFLP or INDEL marker loci into three populations derived from crosses between contrasting germplasms, such as confectionary, oilseed, fertility restorer, and sterility maintainer lines (Yu et al. 2003). In the same work, the researchers also presented the ﬁrst integrated map by adding 120 SSR loci from the public SSR map to the HA370 × HA372 RFLP map of Berry et al. (1997). Finally, they published a composite linkage map of sunﬂower that integrated 657 loci in a 1,423-cM long map with a mean density of 2.2 cM per locus. This is the most saturated map described to date, but it still has gaps longer than 30 cM on LGs 2 (31.3 cM), 4 (36.4 cM), 6 (32.6 cM), and 13 (30.7 cM) (Tang et al. 2003). This map allowed the selection of

Chapter 4 Sunflower

95 single-locus SSRs at an average spacing of 12.9 cM representing a near-genomewide collection for a ﬁrstpass scan of the sunﬂower genome. Seventy-eight single-locus SSRs from this standard genotyping set were selected regarding map position, heterozygosity level, allele-length ranges, and genotyping qualities to create a 13 six − locus PCR multiplex set spanning 1,067 cM and including three to ﬁve SSR markers per LG, thus increasing the genotyping throughput of the set (Tang et al. 2003). A second set of 78 SSRs for sunﬂower variety identiﬁcation and diversity assessment was described recently by Zhang et al. (2005). A uniﬁed consensus molecular genetic map integrating the independently developed linkage maps has been reported recently throughout the collaboration between our group and the group of Professor Dr. A Sarraﬁ (ENSAT, France) by mapping a selection of SSRs from the composite map (ORSx) plus a group of single-locus highly polymorphic SSRs from the collections of INTA (HAx) and GIE CARTISOL (SSLx and SSUx) markers on the RIL population derived from the cross RHA266 × PAC2. The new map integrates 161 SSR markers from a previous map described by Al-Chaarani et al. (2004) reaching a map length of 2,180.7 cM and density of 4.1 cM (S. Poormohammad Kiani et al. unpubl. data). These markers distribute themselves throughout all map LGs, allowing the complete cross reference to the public SSR map (Fig. 1). The inclusion of such diverse markers as SSRs and AFLPs in this framework has contributed to approximate to 96% the full genome coverage of sunﬂower genome. In addition, the incorporation of new SSRs to the PAC2 × RHA266 map allows the ﬁlling of some gaps present in previous maps; meanwhile no gaps longer than 19 cM (LG 17) were found in the entire map. The ﬁrst functional map for sunﬂower based on the mapping of expressed genes was described by Lai et al. (2005a). This map comprises 196 SSR framework markers from the RHA280 × RHA801 map (Tang et al. 2002; Yu et al. 2003) and 243 EST markers. These markers derive from a collection of 22,920 and 21,133 sequences from both RHA280 × RHA801 source materials; potential polymorphisms were ﬁrst detected by means of computational approaches and then conﬁrmed and genotyped via denaturing high-performance liquid chromatography (DHPLC) of SNPs and length polymorphisms. The resulting map covered a map distance of 1,349.3 cM and included ESTs with candidate functions in traits

157

related to development, cell transport, metabolism, plant defense, and tolerance to abiotic stress. Most of the maps were functional for mapping phenotypic and quantitative trait loci described later in this chapter (León et al. 1995, 1996, 2000, 2001, 2003; Lawson et al. 1998; Mestries et al. 1998; Lu et al. 1999; Flores Berrios et al. 1999a,b; Bert et al. 2001; Hervé et al. 2001; Al-Chaarani et al. 2002; Mokrani et al. 2002; Pérez-Vich et al. 2002; Tang et al. 2003; S. Poormohammad Kiani et al. unpubl. data).

4.3 Genomics and Transcriptomics In addition to the resources described above, other valuable tools have been developed recently for sunﬂower that facilitates the genomic study of this plant. In recent years two bacterial artiﬁcial chromosome (BAC) libraries have been described (Gentzbittel et al. 2002; Horn et al. 2002), and different EST sequencing programs have been carried out increasing the molecular information available for sunﬂower in public databases to 66,098 accessions for H. annuus plus another 23,127 from wild species. A small fraction of this collection represents transcription sequences that were characterized from organ-speciﬁc cDNA libraries constructed by suppressed subtractive hybridization (Diatchenko et al. 1996) as an alternative way to identify low-copy mRNA and differentially expressed sunﬂower transcripts (Fernández et al. 2003; Tamborindeguy et al. 2004). The ﬁrst one describes the characterization of 318 nonredundant organ-speciﬁc ESTs generated from leaf, stem, root, and ﬂower bud at two developmental stages (R1 and R4) with the aim of identifying novel genes differentially expressed in sunﬂower as a source of organ-speciﬁc genetic markers that can be functionally associated to important traits. This work helped to successfully isolate a signiﬁcant number of newly reported sequences related to responses to abiotic and biotic stresses as well as low abundant transcripts with high similarity to homeobox genes, transcription factors, and signaling components (Fernández et al. 2003). This collection is the base of a SNP/InDel discovery project that allowed the identiﬁcation of 25 polymorphisms by means of sequencing a panel of 16 representative sunﬂower accessions (work in progress, unpubl. data).

158

N. Paniego et al.

Fig. 1. Actual status of a consensus public composite genetic linkage map (map B, Poormohammad Kiani et al. unpubl. data) and the reference PAC2 × RHA266 SSR map (map A, Tang et al. 2003). The present map incorporates new markers including 58 HA-SSR (Paniego et al. 2002)

Chapter 4 Sunflower

Fig. 1. (continued)

159

160

N. Paniego et al.

Fig. 1. (continued)

Chapter 4 Sunflower

Fig. 1. (continued)

161

162

N. Paniego et al.

Fig. 1. (continued)

Chapter 4 Sunflower

The second report involves the production of 1502 unique sequences out of 2479 high-quality ESTs from the characterization of four cDNA libraries created using sunﬂower protoplasts growing under embryogenic and nonembryogenic conditions (Tamborindeguy et al. 2004). This analysis allowed the identiﬁcation of 821 previously uncharacterized sunﬂower sequences and a group of candidate genes putatively implicated in embryo polarity that are being conﬁrmed by functional genomic approaches using these libraries in the construction and evaluation of cDNA microarrays essays (unpubl. data). Comprehensive EST data are found at GenBank dbEST division, at the Compositae Genome Project (CGPDB; http://cgpdb.ucdavis.edu), and at the Compositae DataBase (http://compositdb.ucdavis.edu), while gene indexing is offered at GenBank UniGene division, at TIGR (http://www.tigr.org/tdb/tgi/ plant.shtml) and at SPUTNIK EST database (http://sputnik.btk.ﬁ/ests), comparative records against Lactuca sativa and Arabidopsis thaliana are available at CGPDB and at Interspeciﬁc Comparative Clustering and Annotation for EST (ICCARE, http://bioinfo.genopole-toulouse.prd.fr/iccare, Müller et al. 2004). SNPs were characterized for 81 genes previously mapped as RFLP markers and dispersed throughout the genome. DNA fragments representing the cDNA probes were ampliﬁed from 12 genotypes and 68 loci using long-distance PCR, and the amplicons were cloned and single-pass sequenced from each end to produce ∼1,000 bp of DNA sequence per locus per genotype. SNPs were found in every gene at a mean frequency of 1/68 bp in this collection, the mean insertion-deletion (INDEL) frequency was 1/200 bp, and the mean haplotype frequency was 12/kb (Kolkman et al. 2002). This set of informative markers has been enlarged with the identiﬁcation of 273 SNP/InDel out of 535 putative polymorphism inferred in silico within the public EST collections (Lai et al. 2005a). These functional markers were mapped in a RIL population (described above) and could progressively replace RFLP markers to launch last-generation genetic linkage maps. This will deﬁnitively help to compare and validate, for example, quantitative trait loci (QTL) and other traitmapping results, which cannot yet be reproduced between different locations and among research groups. The development of such a resource will considerably affect genomic research by adding expressed

163

sequence landmarks for in silico syntenic comparisons with genetic maps developed in other plant species.

4.4 Structural Analysis by in situ Hybridization The genus Helianthus is complex, involving both perennial and annual species as well as natural and artiﬁcial interspeciﬁc hybrids with different levels of wild species genomic introgression, polyploids in perennial species, and a wide geographic distribution that leads to a large phenotypic variation. Wild H. annuus subspecies have been described as being able to extend their range by crossing with wild species followed by introgression of the new gene variants (Seiler and Rieseberg 1997). Classical cytogenetic studies have been important for the analysis of interspeciﬁc hybrids in the genus. Chromosomal rearrangements have been important in the speciation process; thus different species differ in one or more translocations and/or inversions. It has been postulated that chromosomal rearrangements could play a key role in reproductive barriers (Chandler et al. 1986). The genomic size varies with species and also among cultivars (from 4.9 to 9.9 pg; Sims and Price 1985). Development of molecular cytogenetic techniques such as genomic in situ hybridization (GISH) and ﬂuorescence in situ hybridization (FISH) are helping genomic studies for application in genome organization, physical mapping of interesting genomic regions, diversity and evolutionary studies between species of the genus Helianthus (Rocco 2002, Rocco et al. 2003). It also offers an alternative to classical cytogenetics for chromosomal identity due to the lack of speciﬁc chromosomal characteristics at the morphological level and classical banding patterns for some of the chromosomes. The GISH technique that uses genomic DNA as probes on cytological preparations has been successfully used in different plant species to evaluate introgression of wild species in cultivated crops (Benabdelmouna 2003; Shigyo et al. 2003; Wei 2003) as well as in evolutionary studies using wild polyploids (Bennett 1995; Poggio et al. 1999). Preliminary molecular cytogenetic studies on sunﬂower showed that the wild species H. petiolaris is closely related to the cultivated sunﬂower, although a differential hybridization density among chromosomes could

164

N. Paniego et al.

indicate a divergence during the breeding process of cultivated sunﬂower (Rocco 2002). Comparative genetic linkage maps and colinearity studies indicate the presence of 11 rearrangements (8 translocations and 3 inversions) between the species H. petiolaris and H. annus (Burke at al. 2004). The detection and localization of speciﬁc genes using FISH technology has been used in different species to detect different genes (typically ribosomal genes, see Fig. 2), but also transgenes (Fransz et al. 1996; Ten Hoopen et al. 1996; Pedersen et al. 1997; Abranches et al. 2000; Leggett et al. 2000; Svitashev et al. 2000; Salvo-Garrido et al. 2001). In sunﬂower, FISH studies using repetitive sequences derived from retroelements from H. annuus allowed the detection of sequences of two families of retroelements dispersed along the length of all chromosomes in all species studied. However, the Ty3/gypsylike sequences were localized preferentially at the centromeric regions in most of the studied species, whereas Ty1/copialike sequences were less represented or absent around the centromeres and plentiful at the chromosome ends only in H. annus (Santini et al. 2002; Natali et al. 2006). These ﬁndings suggest that these two sequence families played a role in Helianthus genome evolution and species divergence, evolved independently in the same genomic backgrounds and in annual or perennial species, and acquired different possible functions in the host genomes. FISH studies using different repetitive sequences as the 45S ribosomal DNA sequences allowed the differentiation among diploids, tetraploid, and hexaploid species (Vanzela et al. 2002). The use of a rDNA from wheat including the 18S, 5.8S, and 26S ribosomal DNA in studies that compared different genotypes allowed the detection of six positive regions corresponding to primary constrictions (Fig. 2), while in the confectionery sunﬂower two of these signals are weak (Rocco 2002). The use of different repetitive sequences showed a differential hybridization pattern among different chromosomes allowing the identiﬁcation of those chromosomes that cannot be differentiated by other techniques (Fig. 3, Rocco 2002). The use of BACs containing large genomic DNA inserts in physical mapping by FISH technology enabled studies of genome diversity, evolutionary pathways, and chromosomal location of speciﬁc genes or gene families in different species (Nagaki et al. 2003; Wei et al. 2003; Ji et al. 2004; Zhang et al. 2004). In sunﬂower these techniques have not been widely used mainly due to difﬁculties in cytological preparations,

but new advances both in classical and molecular cytological techniques should contribute to the application of these tools for physical mapping of interesting genes or gene regions. Sunﬂower BAC libraries have been developed (Gentzbittel et al. 2002; Horn et al. 2002), and thus the application of these techniques should be explored to improve our knowledge regarding localization of agronomically interesting characters.

4.5 Resistance Genes in Cultivated and Wild Sunflowers Cultivated sunﬂower is susceptible to several economically important fungal and bacterial diseases, and the severity of the infections greatly depends on environmental conditions. Diseases that cause economical losses worldwide in most of the sunﬂowergrowing areas include wilt, middle stalk rot, and head rot (mainly Sclerotinia sclerotiorum), downy mildew (Plasmopara halstedii), stem canker (Phomopsis helianthi = Diaporthe helianthi), rust (Puccinia helianthi), and Verticillium wilt (Verticillium dahliae). Other diseases such as head rots (Rhizopus arrhizus, R. stolonifera, Botrytis cinerea), phomopsis black stem (Phoma macdonaldii), Alternaria leaf and stem spot (Alternaria helianthi or A. zinniae), Septoria leaf spot (Septoria helianthi), charcoal rot (Macrophomina phasiolina), bacterial infections (Pseodomonas syringae pv. Tagetis), and powdery mildew (Erysiphe cichoracearum) have local impacts in some productive areas (Seiler 1992; Pereyra and Escande 1994; Schneiter 1997). The development of resistant cultivars is the most efﬁcient and sustainable strategy for controlling the disease and pest impact, and genes that confer disease resistance have been introduced in cultivated sunﬂower through both conventional and transgenic breeding programs. Conventional breeding has produced commercial sunﬂower hybrids that are resistant to several races of rust and downy mildew. During the last two decades the use of DNA molecular markers has been successful in the development of genetic maps in sunﬂower allowing the detection of loci controlling quantitative trait variations, including the location of QTLs associated with resistance to downy mildew, white rot, and phomopsis black stem.

Chapter 4 Sunflower

165

Fig. 2. In situ hybridizationof ribosomal DNA sequences. Photograph of hybridization pattern of FISH using a ﬂuorescent ribosomal probe from wheat (pTa 71) over sunﬂower chromosomal preps stained with DAPI (from Rocco 2002). Six conspicuous hybridization signals (A–D). Confectionary sunﬂower variety showed weaker signals for two of the six loci (E–F)

166

N. Paniego et al.

Fig. 3. Chromosome identiﬁcation using FISH and retroposon-related sequences. In situ hybridization of clone c785 (from Rocco 2002) indicates that this repetitive sequence has a dispersed distribution in sunﬂower genome (A–B) and that is useful to distinguish between chromosomes of similar size and shape (C–E)

Chapter 4 Sunflower

The genetic basis of resistance to Plasmopara halstedii (Farl.) Berlese et de Toni has been extensively studied in cultivated and wild sunﬂower. The ﬁrst genetic studies of resistance to downy mildew have shown that dominant major genes, denoted Pl, control resistance to different races of P. halstedii. So far, 11 Pl genes have been described (Rahim et al. 2002) from both the cultivated sunﬂower (Vranceanu and Stoenescu 1970) and wild Helianthus species (Miller and Gulya 1991). Pl6 , Pl7 , and Pl8 , found in wild Helianthus species, confer resistance to almost all races of P. halstedii (Bert et al. 2001; Bouzidi et al. 2002). Pl6 was obtained from wild H. annuus, whereas Pl7 came from H. praecox and Pl8 from H. argophyllus. Pl6 has been described as a complex locus with at least two genetically distinct regions and 11 functional Pl genes conferring resistance to different Plasmopara races (Vear et al. 1997; Bouzidi et al. 2002). Numerous RGAs clustered and linked to the Pl1 , Pl6 (Genztbittel et al. 1998; Gedil et al. 2001; Bouzidi et al. 2002) and Pl5 /Pl8 loci have been described (Radwan et al. 2003, 2004), and recently a new source of resistance against P. halstedii has been detected and mapped on Helianthus argophyllus. The newly described locus, PlArg , was mapped to a LG different from all other Pl genes previously mapped with SSRs (Dussle et al. 2004). On the other hand, functional studies of the resistance to this pathogen reported induced expression of an RGA isolated from an inbred line resistant to Pl8 associated with the induction of a delay hypersensitive response marker in hypocotyls (Radwan et al. 2005a,b). Resistance to other diseases, such as white rot and phomopsis, is more complex, involving several loci with different effects and is highly dependent on environmental conditions (Castaño et al. 1993). Sunﬂower production is seriously affected by Sclerotinia sclerotiorum infection when plants are grown in temperate and humid environments. Although the fungus can infect different plant parts, including roots, stem bases, leaves, and terminal buds, the infection on capitula produces the greatest loss. To date, no complete resistance to S. sclerotiorum is available in cultivated sunﬂower, even if differences in susceptibility exist (Tourvieille et al. 1996). The number of QTLs associated with resistance to this pathogen varies according to the host-pathogen interaction considered, ranging from two (Landry et al. 1992) to seven (Schön et al. 1993) loci and also depends on the plant organ affected. Regarding this point, Mestries et al. (1998)

167

detected four QTLs for leaf and two for capitulum resistance. More recent studies involved six different genetic crosses between cultivated inbred lines bearing reported QTLs distributed in at least 14 LGs (Bert el al. 2004). The results showed that seven QTLs explained less than 10% of phenotypic variance and that four QTLs explained up to 10% of capitulum resistance. To add even more complexity to this situation, relative QTL effect levels vary between different years and locations considered. Thus, QTL effects studied during a speciﬁc year and location differ within a range of 23% (Bert et al. 2004) to 44% (Mestries et al. 1998) and also differ from those studied over two or more years in different locations for which the variation ranged from 13 to 16% (Gentzbittel et al. 1998; Bert et al. 2002). Regarding Sclerotinia midstalk rot resistance, three to four QTLs were detected for each resistance trait that explained between 40.8 and 72.7% of the genotypic variance (Micic et al. 2005). For stem lesion two genomic regions explained 26.5% of the genotypic variance (Micic et al. 2004, 2005). A candidate gene approach revealed that a protein kinaselike gene was a marker cosegregating with a locus that explained up to 50% of the phenotypic variation for capitulum resistance (Gentzbittel et al. 1998). Up to now, the main QTLs detected from different crosses appear to be different, thus suggesting that pyramiding QTLs for resistance to Sclerotinia is possible, although the nature of the interaction between allelic loci is still unknown. Colocalization studies with other disease resistance loci detected coincidences with a QTL affecting the resistance to D. helianthi (Bert at al. 2002) and with the Pl5 locus conferring resistance to P. halstedii (Bert at al. 2001). An alternative approach to molecular-marker-assisted breeding programs for resistance to Sclerotinia was the transgenic expression of an oxalate oxidase transgene from wheat (see Transgenic traits section, Hu et al. 2003), based on the detoxiﬁcation of the pathogenocity factor oxalacetic acid produced by the pathogenic fungus. Expression of this gene in sunﬂower looks promising as it induces activation of endogenous defense pathways leading to inhibition of S. sclerotiorum by antifungal proteins (Hu et al. 2003). A related strategy based on the increase of oxalate oxidase activity to enhance resistance to Sclerotinia focuses on the identiﬁcation of natural allelic sources in cultivated and wild germplasm lines. Preliminary studies suggested that oxalate oxidase tran-

168

N. Paniego et al.

script levels vary among sunﬂower lines (Peluffo et al. 2004; Fernández et al. 2005). Accumulation of soluble phenolic compounds in sunﬂower capitula was also correlated with resistance to S. sclerotiorum (Prats et al. 2003). Association studies of transcript expression of candidate genes with disease resistance represents an alternative strategy to ﬁnd new sources of resistance to Sclerotinia. The development of a large EST public database for sunﬂower (http://www.ncbi.nlm.nih.gov/ dbEST/dbEST_summary.html), including entries from sequenced differential cDNA libraries with highly represented defense genes (Fernández et al. 2003), enables the utilization of the candidate gene approach for searching new QTL determinants. Resistance to phomopsis stem canker also shows continuous variation, and different genotypes appear to harbor resistance loci to different phases of the disease cycle (Viguié at al. 2000). Three major LGs were involved, with common or very close location (Bert et al. 2002). In the case of black stem caused by Phoma macdonaldii, QTL analysis of resistance showed in one of the studies seven QTLs in different LGs that together explained 92% of the phenotypic variation (Al-Chaarani et al. 2002), while in another study four QTLs were detected. In both cases individual effects were moderate, ranging from 5 to 20% of total phenotypic variation (Bert et al. 2002). The most damaging insect pests of cultivated sunﬂower are those that infest developing seed heads (weevil, moth, and midge larvae) and those that transmit disease (e.g., stem weevils that transmit phoma black stem). In wild relatives of H. annuus, insect resistance is either absent or polygenic, and efforts to introgress strong resistance into the crop have been unsuccessful (Seiler 1992). A high priority for transgenic commercial hybrids is resistance conferred by Bt toxins (see Transgenic traits section), which are speciﬁc to different groups of insects such as Lepidoptera (moths, butterﬂies), Coleoptera (weevils, beetles), or Diptera (ﬂies, midges). Another serious problem of sunﬂower crop, mainly in the Mediterranean region, is the root parasite Orobanche cumana Wall, which depletes the plant of nutrients and water. Genetic studies suggest that resistance to broomrape in sunﬂower is controlled by a combination of quantitative race speciﬁc resistance affecting the presence or absence of broomrape and quantitative nonspeciﬁc resistance affecting their number (Pérez-Vich et al. 2004).

4.6 QTL Analysis for Developmental and Agronomic Traits Yield is a complex trait regulated by a number of factors that can be studied as component traits. The development of the different genetic maps for sunﬂower allowed the mapping of QTL for a wide range of characteristics such as somatic embryogenesis and in vitro organogenesis (Flores Berrios et al. 2000), photosynthesis, and water status (Hervé et al. 2001); seed-oil content, seed weight, height, and maturity days (Mestries et al. 1998; Mokrani et al. 2002; Bert et al. 2003). Seed oil concentration is a complex trait determined by the genotype and the environmental conditions. Search for seed-oil-concentration QTLs using a genetic map of 205 loci deﬁned by RFLP (León et al. 1995) and composite interval mapping resulted in the detection of eight QTLs on seven LGs that accounted for 88% of the phenotypic variation for seedoil concentration across environments (León et al. 2003). Gene action was additive for four QTLs and dominant or overdominant for the others. Four of the eight QTLs were detected in two or more environments, and the parental effects were the same across generations and environments. Another important determinant for crop adaptation is ﬂowering date. León et al. (2001) dissected the ﬂowering date into growing-degree days and photoperiod components in multiple environments and reported two QTLs for photoperiod colocated with two of the six QTLs associated with growing-degree days. Based on their chromosomal positions it has been suggested that some QTLs for photoperiod sensitivity and basic vegetative loci, which are the main determinants of this trait, are the same loci of major genes (Yano et al. 1997; Lin et al. 2000; Yamamoto et al. 2000; Zhou et al. 2001). It is important to stress that some QTLs seem to be genotype speciﬁc and that for some traits it will be important to compare maps to determine on a large number of crosses which QTLs are common to different genotypes. QTLs for photosynthesis traits (net photosynthesis, stomatal conductance, intercellular CO2 concentration, and transpiration) studied under well-watered (Hervé et al. 2001) and water stress conditions and their association to closely linked markers were reported recently (Poormohammad Kiani et al. unpubl. data). Studies of colocalization of developmental and agronomic traits with resistance to pathogens like

Chapter 4 Sunflower

Sclerotinia and D. helianthi have shown association in some cases. A QTL for percentage of plants attacked by S. sclerotiorum was detected in a LG close to one for ﬂowering date with one of the parental alleles that reduced the days to ﬂowering being linked with increases in resistance to this pathogen (Bert et al. 2003). Other colocalization was reported for QTLs for resistance to S. sclerotiorum and QTLs for seed weight and oil content (Mestries et al. 1998; Bert et al. 2003). Some morphology-related traits such as branching regulated by the gene b1 are being studied for their association with S. sclerotiorum resistance and agronomic characters (Bert et al. 2003). Another important agronomic trait recently incorporated in sunﬂower is herbicide resistance to imidazolinones. Resistance to two classes of acetohydroxyacid synthase (AHAS)-inhibiting herbicides, imidazolinones (IMIs) or sulfonylureas (SUs), have been discovered in wild sunﬂower populations treated with imazethapyr or chlorsulfuron, respectively. Three AHAS genes were isolated from herbicide-resistant (mutant) and susceptible (wild type) genotypes About 48 SNPs were identiﬁed in AHAS1 while a single six-base pair insertion-deletion and a single SNP were detected in AHAS2 and AHAS3, respectively. One of the AHAS1 genes from imazethapyr-resistant inbreds harbored a C-to-T mutation conferring resistance to IMI herbicides, whereas AHAS1 from chlorsulfuron-resistant inbreds harbored a C-to-T mutation conferring resistance to SU herbicides. SNP and single-strand conformational polymorphism markers for the three isolated genes were developed and genetically mapped. The C/T SNP cosegregated with a partially dominant gene for resistance to imidazolinone herbicides in two mutant × wild-type populations, thus providing new molecular markers to sunﬂower breeding programs (Kolkman et al. 2004). In an attempt to provide evidence of colocalization of ESTs and QTLs in sunﬂower, Lai el al. (2005a) reported different candidate genes that map in the population RHA280 × RHA801 coincident with QTLrelated traits such as salt tolerance, morphology and development, cell division, photomorphogenesis regulation, and plant growth. Although these associations need to be conﬁrmed experimentally, the approach described by these authors represents an important step toward identifying the genes underlying complex genetic traits. On the other hand, a high level of linkage disequilibrium has been reported in cultivated sunﬂower. Thus it has been suggested that

169

association-based approaches will provide a high degree of resolution for the mapping of functional variations in sunﬂower (Liu and Burke 2005). Interspeciﬁc QTL mapping has been reported for the wild annual species H. annus and H. petiolaris (Lexer et al. 2005) and for three hybrid sunﬂower species derived from them: H. anomalus, H. deserticola, and H. paradoxus (Lai et al. 2005b). QTL analysis in these studies indicated that karyotypic differences among species contributed to reproductive isolation and evaluated inter- and intraspeciﬁc QTL magnitudes.

4.7 In vitro Tissue-Culture-Aided Breeding In vitro tissue culture techniques were rapidly incorporated in sunﬂower breeding because immature embryo rescue allows the development of four to six generations per year notably accelerating inbreeding speed. Embryo rescue and protoplast fusion also helped to sort sterility or incompatibility barriers in wide interspecies crossings. Examples of protoplast fusion techniques applied to rescue of wide crossings are those between H. annuus and H. petiolaris or with H. debilis (Alibert et al. 1994). Plant regeneration was obtained by direct or indirect organogenesis (depending on the induction and passage through an undifferentiated callus stage), somatic embryogenesis, and, more recently, vegetative multiplication. First efforts to regenerate sunﬂower plants from shoot-tip meristems date to 1954 (Henrickson 1954). However, very soon it was realized that sunﬂower is far from being a model system for efﬁcient and reproducible plant regeneration and transformation (see, for example, Alibert et al. 1994). Paterson (1984) showed genotype and cytokinin concentration dependence in a medium after trying more than 100 different genotypes for shoot regeneration. Since then, much work was devoted to overcoming the difﬁculties. Indirect organogenesis and embryogenesis through callus stages were soon discarded because of seldom regenerated viable shoots or embryos (Greco et al. 1984; Paterson and Everett 1985; Wilcox McCann et al. 1988). The most successful approach seems to be direct organogenesis, which was reported to occur from different explant sources: shoot-tip meristems or embryo axes (Lupi et al. 1987; Knittel et al. 1994;

170

N. Paniego et al.

Moliner et al. 2002), immature embryos (Power 1987; Bronner et al. 1994; Jeannin et al. 1995), mature-seedderived cotyledons (Chraibi et al. 1991, 1992; Knittel et al. 1991; Ceriani et al. 1992; Deglene et al. 1997; Baker et al. 1999; Flores Berrios et al. 1999a,b; Dhaka and Kothari 2002; Mayor et al. 2003; Parody 2003), leaves (Konov et al. 1998), and protoplasts (Burrus et al. 1991; Krasnyanski and Menczel 1993), while somatic embryogenesis was mainly obtained from immature zygotic embryos (Finner 1987; Freyssinet and Freyssinet 1988; Jeannin and Hahne 1991; Bronner et al. 1994; Jeannin et al. 1995; Sujatha and Prabakaran 2001).

4.8 Genetic Transformation Genetic transformation was achieved by Agrobacterium tumefaciens-mediated techniques, by biolistic systems, and by a combination of both. However, as a reﬂection of regeneration difﬁculties, all the published protocols of transformation show a low efﬁciency (Bidney et al. 1992; Knittel et al. 1994; MaloneSchoneberg et al. 1994; Burrus et al. 1996; Alibert et al. 1999; Lucas et al. 2000; Müller et al. 2001; Hewezi et al. 2002; Weber et al. 2003; Parody 2003; Lewi 2004). Since regeneration usually has a multicellular origin, transgenic plants are often chimeric, which may or may not result in transgenic inheritance to descendents to foster transgenic regeneration (Schrammeijer et al. 1990) cotransformed with a cytokinin biosynthesis transgene ipt, which promotes cell division (Moliner et al. 2002). Different approaches were explored to increase transformation efﬁciency: to stimulate A. tumefaciens vir genes by adding phenolic compounds like acetosyringone, to hurt tissues by microparticle bombardment (Bidney et al. 1992), glass powder (Grayburn and Vick 1995), enzyme treatment (Alibert et al. 1999), sonication, or combinations of these treatments (Weber et al. 2003).

for breeding (see, among many others cited in the references, Everett et al. 1987; Schrammeijer et al. 1990; Escandón and Hahne 1991; Bidney et al. 1992; Knittel et al. 1994; Malone-Schoneberg et al. 1994; Grayburn and Vick 1995; Rao and Rohini 1999; Müller et al. 2001; Hewezi et al. 2002; Weber et al 2003; Lewi 2004). Three agronomically important transgenic traits were incorporated in sunﬂower by seed companies: glyphosate tolerance by expressing Agrobacterium EPSPS gene cp4, Bacillus thuringiensis enthomotoxin gene cry1A for the control of Lepidoptera (usually known as Bt), and the oxalate-oxidaseexpressing gene for the control of Sclerotinia (known as oxox), which are in the precommercial ﬁeld trial stage in the USA and in Argentina (see years 1999–2003 in http://www.sagpya.gov.ar/new/ 0-0/programas/conabia/liberaciones_ogm.php and http://www.isb.vt.edu). Transgenic Bt sunﬂower was also obtained by public research institutions (Lewi 2004). Bt-induced resistance to Coleoptera was ﬁrst ﬁeld-tested in the USA in 1996, and resistance to Lepidoptera was approved for ﬁeld testing in 1999 (http://www.sagpya.gov.ar/new/ 0-0/programas/conabia/liberaciones_ogm_1999.php and http://www.isb.vt.edu). Broad-spectrum resistance involving multiple Bt genes and other genes for insect resistance (e.g., Stewart et al. 2000) could also be developed. S. sclerotiorum synthesizes and secretes large amounts of oxalacetic into infected host tissues. This acid is a mobile toxin that causes a wilting syndrome in infected sunﬂower (Noyes and Hancock 1981) suppressing the oxidative burst associated with a hypersensitive response (Cessna et al. 2000). Transgenic oxalate oxidase expression is able to convert S. sclerotiorum oxalic acid toxin and O2 to CO2 and hydrogen peroxide (H2 O2 ) activating defense and conferring disease resistance (Hu et al. 2003).

4.8.2 Transgenic Sunflowers: Biosafety Concerns 4.8.1 Transgenic Traits

Cultivated sunﬂower in North America is partially sympatric with some of its ancestors and related Initially transgenic plants were developed with selec- species. Some wild subspecies and relatives are tive markers and reporter genes like kanamycin re- also present in other parts of the world partially sistance gene nptII, phosphinotricin resistance gene overlapping with cultivated sunﬂower areas. Pollen bar, glucouronidase (GUS) gene uidA, green ﬂuores- from cultivated sunﬂower can spread to adjacent cent protein (GFP) gene egfp, which were not meant wild populations carried by foraging insects, espe-

Chapter 4 Sunflower

cially bees. Biosafety regulation in Argentina has established that distances required for transgenic sunﬂower ﬁeld trials need 3 of isolation between the assay and the closest production ﬁeld and/or wild sunﬂower to prevent contamination by “foreign” pollen (http://www.sagpya.gov.ar/new/0-0/programas/ conabia/condiciones_de_aislamiento.php). Pollen movement capacity is greatest at the crop edge, where up to 42% of hybrid seeds can generate, diminishing to nearly zero at distances of 800 to 1,000 m (Arias and Rieseberg 1994; Whitton et al. 1997). Thus, distances between populations determine the relative frequency and extent of gene exchange. Hybrids of the crop with wild H. annuus subspecies are fertile, but they typically produce fewer ﬂower heads per plant than purely wild genotypes (Snow et al. 1998). Once crop genes enter wild populations, they can spread farther by both pollen and seed dispersal. Whitton et al. (1997) and Linder et al. (1998) have documented long-term persistence of crop genes in populations of wild sunﬂower. Wild H. annuus is an outcrossing annual widespread throughout much of the USA, reaching its greatest abundance in midwestern states (Heiser 1954). This species occurs as a common but relatively manageable weed. Populations are typically patchy and ephemeral, relying on the soil seed bank and long-distance dispersal for opportunities to become established in available clearings. The imminent intended release of transgenic sunﬂowers requires a previous analysis of the population biology of wild relatives to assess the potential added ﬁtness or detrimental effects that agronomic traits might have on the ecosystem of the wild relatives. In the USA and in Argentina (Poverene et al. 2002), baseline information of reproduction and population dynamics of the wild relatives is under study. It is important to predict how a given transgene might affect the abundance and dispersion of wild populations, particularly their weed behavior. This information will help in making decisions about risk management of the different transgenic events, including their potential release to the environment. The recent release of imidazolinone-resistant sunﬂowers by conventional breeding (see above) gives an excellent opportunity to study the potential effect of similar traits derived from genetic engineering (like glyphosate and gluphosinate tolerance or resistance) because it will have a similar environmental impact showing the implicit contradiction of regulat-

171

ing GMO release while equivalent traits are released without regulation if obtained by conventional breeding. As expected, gene ﬂow to cultivated and wild sunﬂower relatives was rapidly demonstrated. Gene ﬂow from imidazolinone-resistant hybrids to common and prairie sunﬂower showed that gene ﬂow occurred and could be detected up to 30 m from the pollen source for both species and decreased as distance from the pollen source increased (Massinga et al. 2003). Since gene ﬂow is expected to occur, it is a matter of time to evaluate if the weedy properties of wild sunﬂower will result in a drastic increase in its abundance becoming a problem in farms using imidazolinone-based weed control. However, as shown for other crop species, it is hardly expected that herbicide resistance traits will signiﬁcantly affect ecosystems other than those subjected to agricultural practices involving the given herbicide. Resistance to diseases and insects poses a more challenging problem for risking evaluation since they may confer clear Darwinian selective advantages to wild populations and thus potentially disturb natural ecosystems. Resistance to Sclerotinia was addressed by Seiler (1992), who surveyed hundreds of wild sunﬂower populations without detecting Sclerotinia symptoms in mature plants, thus suggesting that Sclerotinia very unlikely regulates or limits the abundance of wild sunﬂowers in nature. Accordingly, no ﬁtness beneﬁt of oxalate oxidase transgene for white mold resistance was detected (Burke and Rieseberg 2003). In contrast, transgenic resistance to insect seed predators might be beneﬁcial to wild plants, which sometimes lose as many as 20 to 30% of their seeds to these insects (Pilson 2000). Fitness beneﬁts of Bt transgene (cry1A) in wild sunﬂowers showed 14 to 55% more seeds per plant depending on the geographic location (Snow et al. 2003). Assuming that Bt genes lead to greater survival or fertility of wild sunﬂower, the most relevant biosafety question is whether their transfer will thus cause wild populations to become more troublesome weeds, change natural ecosystems, or colonize new cultivated or wild habitats.

Acknowledgement. Dr. N. Paniego and Dr. R. Heinz are career members of the Consejo Nacional de Investigaciones Cientíﬁcas y Técnicas (CONICET, Argentina), and Dr. H. E. Hopp is a career member of the Comisión de Investigaciones Cientíﬁcas

172

N. Paniego et al.

de la Provincia de Buenos Aires (CIC) and Professor at the Facultad de Ciencias Exactas y Naturales, University of Buenos Aires (UBA). This research was supported by the ANPCyT/FONCYT, BID 1201 AC/AR PID 024 and BID 267 and ANPCyT/FONCYT, PICTO ASAGIR 13165.

References Abranches R, Santos AP, Wegel E, Willams S, Castillo A, Christou P, Shaw P, Stoger E (2000) Widely separated multiple transgene integration sites in wheat chromosomes are brought together at interphase. Plant J 24:713–723 Al-Chaarani GR, Roustaee A, Gentzbittel L, Mokrani L, Barrault B, Dechamp-Guillaume G Sarraﬁ A (2002) A QTL analysis of sunﬂower partial resistance to downy mildew (Plasmopara halstedii) and black stem (Phoma macdonaldii) by the use of recombinant inbred lines (RILs). Theor Appl Genet 104:490–496 Al-Chaarani GR, Gentzbittel L, Huang XQ, Sarraﬁ A (2004) Genotypic variation and identiﬁcation of QTLs for agronomic traits, using AFLP and SSR markers in RILs of sunﬂower (Helianthus annuus L.). Theor Appl Genet 109:1353–1360 Alibert B, Lucas O, Le Gall V, Kallerhoff J, Alibert G (1999) Pectolytic enzyme treatment of sunﬂower explants prior to wounding and cocultivation with Agrobacterium tumefaciens, enhances efﬁciency of transient beta glucuronidase expression. Physiol Plant 106:232–237 Alibert G, Aslane-Chanabé, Burrus M (1994) Sunﬂower tissue and cell cultures and their use in biotechnology. Plant Physiol Biochem 32:31–44 Arias DM, Rieseberg LH (1994) Gene ﬂow between cultivated and wild sunﬂower. Theor Appl Genet 89:655–660 Baker C, Muñoz-Fernandez N, Carter C (1999) Improved shoot development and rooting from mature cotyledons of sunﬂower. Plant Cell Tiss Org Cult 58:39–49 Benabdelmouna A, Gueritaine G, Abirached-Darmency M, Darmency H (2003) Genome discrimination in progeny of interspeciﬁc hybrids between Brassica napus and Raphanus raphanistrum. Genome 46:469–472 Bennett MD (1995) The development and use of genomic in situ hybridization (GISH) as a new tool in plant biosystematics. In: Bransham PE, Bennett MD (eds) Kew Chromosome Conference IV, Royal Botanical Gardens, Kew, UK, pp 167–183 Berry ST, Leon AJ, Challis P, Livin C, Jones R, Hanfrey CC, Grifﬁths S, Roberts A (1996) Construction of a high density, composite RFLP linkage map for cultivated sunﬂower Helianthus annuus. In: Proc 14th Int Sunﬂower Conf, 12–20 June 1996, Beijing, China, 2:1150–1160 Berry ST, Leon AJ, Hanfrey CC, Challis P, Burkholz A, Barness S, Rufener GK, Lee M, Caligari PDS (1995) Molecular marker analysis of Helianthus annuus L.: 2. Construction of an

RFLP linkage map for cultivated sunﬂower. Theor Appl Genet 91:195–199 Berry ST, Leon, AJ, Peerbolte R, Challis C, Livini C, Jones R, Feingold S (1997) Presentation of the Advanta sunﬂower RFLP linkage map for public research. In: Proc 19th Sunﬂower Res Workshop, 9–10 Jan 1997, Fargo, ND, pp 113–118 Bert PF, Tourvielle De Labrouhe D, Philippon J, Mouzeyar S, Jouan I, Nicolas P, Vear F (2001) Identiﬁcation of a second linkage group carrying genes controlling resistance to downy mildew (Plasmopara halstedii) in sunﬂower (Helianthus annuus L.). Theor Appl Genet 103:992–997 Bert PF, Jouan I, Tourvieille de Labrouhe D, Serre F, Nicolas P, Vear F (2002) Comparative genetic analysis of quantitative traits in sunﬂower (Helianthus annuus L.). 1. Characterisation of QTL involved in resistance to Sclerotinia sclerotiorum and Diaporthe helianthi. Theor Appl Genet 105:985–993 Bert PF, Jouan I, Tourvieille de Labrouhe D, Serre F, Nicolas P, Vear F (2003) Comparative genetic analysis of quantitative traits in sunﬂower (Helianthus annuus L.). 2. Characterisation of QTL involved in developmental and agronomic traits. Theor Appl Genet 107:181–189 Bert PF, Dechamp-Guillaume G, Serre F, Jouan I, De Labrouhe DT, Nicolas P, Vear F (2004) Comparative genetic analysis of quantitative traits in sunﬂower (Helianthus annuus L.). 3. Characterisation of QTL involved in resistance to Sclerotinia sclerotiorum and Phoma macdonaldii. Theor Appl Genet 109:865–874 Bidney D, Scelonge C, Martich J, Burrus M, Sims L, Hufmann G (1992) Microprojectile bombardment of plant tissues increases transformation frequency by Agrobacterium tumefaciens. Plant Mol Biol 18:301–313 Bouzidi MF, Badaoui S, Cambon F, Vear F, Tourvielle de Labrouhe D, Nicolas P, Mouzeyar S (2002) Molecular analysis of a major locus for resistance to downy mildew in sunﬂower with speciﬁc PCR-based markers. Theor Appl Genet 104:592–600 Bronner R, Jeannin G, Hahne G (1994) Early cellular events during organogenesis and somatic embryogenesis induced on immature zygotic embryos of sunﬂower (Helianthus annuus). Can J Bot 72:239–248 Burke JM, Lai Z, Salmaso M, Nakazato T, Tang S, Heesacker A, Knapp SJ, Rieseberg LH (2004) Comparative mapping and rapid karyotypic evolution in the genus helianthus. Genetics 167:449–457 Burke JM, Rieseberg LH (2003) The ﬁtness effects of transgenic disease resistance in wild sunﬂowers. Science 300:1250 Burrus M, Chanabe C, Alibert G, Bidney D (1991) Regeneration of fertile plants from protoplasts of sunﬂower (Helianthus annuus L.). Plant Cell Rep 10:161–166 Burrus M, Molinier J, Himber C, Hunold R, Bronner R, Rousselin P, Hahne G (1996) Agrobacterium-mediated transformation of sunﬂower (Helianthus annuus L.) shoot apices: transformation patterns. Mol Breed 2:329–338

Chapter 4 Sunflower Castaño F, Vear F, Touvielle de Labrouhe D (1993) Resistance of sunﬂower inbred lines to various forms of attack by Sclerotinia sclerotiorum and relations with some morphological characters. Euphytica 68:85–98 Ceriani MF, Hopp HE, Hahne G, Escandón AS (1992) Cotyledons: an explant for routine regeneration of sunﬂower plants. Plant Cell Physiol 33:157–164 Cessna SG, Sears VE, Dickman MB, Low PS (2000) Oxalic acid, a pathogenicity factor for Sclerotinia sclerotiorum, suppresses the oxidative burst of the host plant. Plant Cell hbox12:2191–2200 Chandler JM, Chan J, Beard BH (1986) Chromosomal differentiation among the annual Helianthus species. Syst Bot 11:354–371 Chraibi KM, Latche A, Roustan JP, Fallot J (1991) Stimulation of shoot regeneration from cotyledons of Helianthus annuus by the etilene inhibitors, silver and cobalt. Plant Cell Rep 10:204–207 Chraibi KMB, Castelle J-C, Latche A, Roustan J-P, Fallot J (1992) A genotype-independent system of regeneration from cotyledons of sunﬂower (Helianthus annuus L.). The role of ethylene. Plant Sci 86:215–221 Deglene L, Lesignes P, Alibert G, Sarraﬁ A (1997) Genetic control of organogenesis in cotyledons of sunﬂower (Helianthus annuus). Plant Cell Tiss Org Cult 48:127–130 Dehemer KJ, Friedt W (1998) Evaluation of different microsatellite motifs for analyzing genetic relationships in cultivated sunﬂower (Helianthus annuus L.). Plant Breed 1:39–42 Dhaka N, Kothari SL (2002) Phenylacetic acid improves bud elongation and in vitro plant regeneration efﬁciency in Helianthus annuus L. Plant Cell Rep 21:29–34 Diatchenko L, Lau YF, Campbell AP, Chenchick A, Moqaddam F, Huang B, Lukyanov S, Konstantin L, Gurskaya N, Sverdlov E, Siebert PD (1996) Suppression subtractive hybridization: a method for generating differentially regulated or tissuespeciﬁc cDNA probes and libraries. Proc Natl Acad Sci USA 93:6025–6030 Dorrell DG, Vick BA (1997) Properties and processing of oilseed sunﬂower. In: Schneiter AA (ed) Sunﬂower Technology and Production. Agronomy Series. ASA, CSSA, and ASSA, Madison, WI, 35:709–745 Dussle CM, Hahn V, Knapp SJ, Bauer E (2004) Pl(Arg) from Helianthus argophyllus is unlinked to other known downy mildew resistance genes in sunﬂower. Theor Appl Genet 109:1083–1086 Escandón A, Hahne G (1991) Genotype and composition of culture medium are factors important in the selection for transformed sunﬂower (Helianthus annuus) callus. Physiol Plant 81:367–376 Everett NP, Robinson KEP, Masarenhas D (1987) Genetic engineering of sunﬂower (Helianthus annuus L.). Bio/Technology 5:1201–1204 Fernández P, Paniego N, Lew S, Hopp HE, Heinz RA (2003) Differential representation of sunﬂower ESTs in enriched

173

organ-speciﬁc cDNA libraries in a small scale sequencing project. BMC Genom 4:40 Fernández P, Peluffo L, Hopp E, Paniego N, Heinz R (2005) Identiﬁcation and characterization of candidate genes for resistance to Sclerotinia head rot in sunﬂower. BAIRESBIOTEC 2005. Congreso internacional-GRUPO BIOTECNOLOGÍA – VI Simposio Nacional de Biotecnología-REDBIO Argentina 2005-Encuentro Binacional REDBIO ArgentinaChile-Uruguay, 7–10 May 2005, Buenos Aires, Argentina Finner J (1987) Direct somatic embryogenesis and plant regeneration from immature embryos of hybrid sunﬂower (Helianthus annuus L.) on a high sucrose medium. Plant Cell Rep 6:372–374 Flores Berrios E, Gentzbittel L, Alibert G, Griveau Y, Bervillé A, Sarraﬁ A (1999a) Genetic control of in vitro organogenesis in recombinant inbred lines of sunﬂower (Helianthus annuus L.). Plant Breed 118:359–361 Flores Berrios E, Gentzbittel L, Kayyal H, Alibert G, Sarraﬁ A (2000) AFLP mapping of QTLs for in vitro organogenesis traits using recombinant inbred lines in sunﬂower (Helianthus annuus L.) Theor Appl Genet 101:1299–1306 Flores Berrios E, Gentzbittel L, Serieys H, Alibert G, Sarraﬁ A (1999b) Inﬂuence of genotype and gelling agents on in vitro regeneration by organogenesis in sunﬂower. Plant Cell Tiss Org Cult 59:65–69 Fransz PF, Stam M, Montijn BM, Hoopen RT, Wiegrant J, Kooter JM, Oud O, Naumingga N (1996) Detection of single-copy genes and chromosome rearrangements in Petunia hybrida by ﬂuorescence in situ hibridization. Plant J 9:676–774 Freyssinet M, Freyssinet G (1988) Plant regeneration from sunﬂower (Helianthus annuus L.) immature embryos. Plant Sci 56:177–181 Gedil MA (1999) Marker development, genome mapping, and cloning of candidate disease resistance genes in sunﬂower, Helianthus annuus L. PhD Thesis, Oregon State University, Corvallis, OR Gedil MA, Wye C, Berry S, Segers B, Peleman J, Jones R, Leon A, Slabaugh MB, Knapp SJ (2001) An integrated restriction fragment length polymorphism-ampliﬁed fragment length polymorphism linkage map for cultivated sunﬂower. Genome 44:213–221 Gentzbittel L, Abbott A, Galaud JP, Georgi L, Fabre F, Liboz T, Alibert G (2002) A bacterial artiﬁcial chromosome (BAC) library for sunﬂower, and identiﬁcation of clones containing genes for putative transmembrane receptors. Mol Genet Genom 266:979–987 Gentzbittel L, Mestries E, Mouzeyar S, Mazeyrat F, Badaoui S, Vear F, De Labrouhe DT, Nicolas P (1999) A composite map of expressed sequences and phenotypic traits of the sunﬂower Helianthus annuus L. genome. Theor Appl Genet 99:218–234 Gentzbittel L, Mouzeyar S, Badaoui S, Mestries E, Vear F, Tourvieille D, Nicolas P (1998) Cloning of molecular markers for disease resistance in sunﬂower, Helianthus annuus L. Theor Appl Genet 96:519–525

174

N. Paniego et al.

Gentzbittel L, Vear F, Zhang YX, Berville A (1995) Development of a consensus linkage RFLP map of cultivated sunﬂower (Helianthus annuus L.). Theor Appl Genet 90:1079–1086 Grayburn WS, Vick BA (1995) Transformation of sunﬂower (Helianthus annuus L.) following wounding with glass beads. Plant Cell Rep 14:285–289 Greco B, Tanzarella OA, Carozzo G, Blanco A (1984) Callus induction and shoot regeneration in sunﬂower (Helianthus annuus L.). Plant Sci Lett 36:73–77 Heiser CB (1954) Variation and subspeciation in the common sunﬂower, Helianthus annuus. Am Midland Naturalist 51:287–305 Henrickson CE (1954) The ﬂowering of sunﬂower explants in aseptic culture. Plant Physiol 29:536–538 Herve D, Fabre F, Flores Berrios E, Leroux N, Al-Chaarani G, Planchon C, Sarraﬁ A, Gentzbittel L (2001) QTL analysis of photosynthesis and water status traits in sunﬂower (Helianthus annuus L.) under greenhouse conditions. J Exp Bot 362:1857–64 Hewezi T, A Perrault G, Alibert J, Kallerhoff J (2002) Dehydrating immature embryo split apices and rehydrating with Agrobacterium tumefaciens: a new method for genetically transforming recalcitrant sunﬂower. Plant Mol Biol Rep 20:335–345 Horn R, Hustedt J, Horstmeyer A, Hahnen J, Zetsche K, Friedt W (1996) The cms-associated 16 kDa protein encoded by orfH522 in the PET1 cytoplasm is also present in other male-sterile cytoplasms of sunﬂower. Plant Mol Biol 30:523–538 Horn R, Kusterer B, Lazarescu E, Oezdemir L, KraeuterCanham R, Steinmetz A, Friedt W (2002) New results on reproduction in sunﬂower and mapping of the restorer gene rf1. In: Plant, Animal & Microbe Genome X Conf, San Diego. http://www.intl-pag.org/10/abstracts/ PAGX_W77.html Hu X, Bidney DL, Yalpani N, Duvick JP, Crasta O, Folkerts O, Lu G (2003) Overexpression of a gene encoding hydrogen peroxide-generating oxalate oxidase evokes defense responses in sunﬂower. Plant Physiol 133:170–181 Jan CC, Vick BA, Miller JF, Kahler AL, Butler ET III (1998) Construction of an RFLP linkage map for cultivated sunﬂower. Theor Appl Genet 96:15–22 Jeannin G, Bronner R, Hahne G (1995) Somatic embryogenesis and organogenesis induced on the immature zygotic embryo of sunﬂower (Helianthus annuus L) cultivated in vitro: role of the sugar. Plant Cell Rep 15:200–204 Jeannin G, Hahne G (1991) Donor plant growth conditions and regeneration of fertile plants from somatic embryos induced on immature zygotic embryos of sunﬂower (Helianthus annuus L.). Plant Breed 107:280–287 Jewish Colonization Association (1914) L’Atlas des Colonies et des Domaines de la J.C.A. en Argentine et au Brasil. Paris

Ji Y, Pertuze R, Chetelat RT (2004) Genome differentiation by GISH in interspeciﬁc and intergeneric hybrids of tomato and related nightshades. Chrom Res 12:107–116 Knittel N, Escandón A, Hahne G (1991) Plant regeneration at high frequency from mature sunﬂower cotyledons. Plant Sci 73:219–226 Knittel N, Gruber V, Hahne G, Lénée P (1994) Transformation of sunﬂower (Helianthus annuus L.): a reliable protocol. Plant Cell Rep 14:81–86 Kolkman JM, Berry S, León AJ, Olungu C, Shintani DK, Slabaugh MB, Knapp SJ (2002) SNP discovery, diversity and genotyping in sunﬂower. In: Plant, Animal & Microbe Genomes X Conf, San Diego. http://www.intlpag.org/10/abstracts/PAGX_W75.html Kolkman JM, Slabaugh MB, Bruniard JM, Berry S, Bushman BS, Olungu C, Maes N, Abratti G, Zambelli A, Miller JF, León A, Knapp SJ (2004) Acetohydroxyacid synthase mutations conferring resistance to imidazolinone or sulfonylurea herbicides in sunﬂower. Theor Appl Genet 109:1147–1159 Konov A, Bronner R, Skryabin K, Hahne G (1998) Formation of epiphyllous buds in sunﬂower (Helianthus annuus L.): induction in vitro and cellular origin. Plant Sci 135:77–86 Krasnyanski S, Menczel L (1993) Somatic embryogenesis and plant regeneration from hypocotyl protoplasts of sunﬂower (Helianthus annuus L.). Plant Cell Rep 12:260–263 Lai Z, Livingstone K, Zou Y, Church SA, Knapp SJ, Andrews J, Rieseberg LH (2005a) Identiﬁcation and mapping of SNPs from ESTs in sunﬂower. Theor Appl Genet 111(8):1532– 1544 Lai Z, Nakazato T, Salmaso M, Burke JM Tang S, Knapp SJ, Rieseberg LH (2005b) Extensive chromosomal repatterning and the evolution of sterility barriers in hybrid sunﬂower species. Genetics 171:291–303 Landry BS, Hubert N, Crete R, Chang MS, Lincoln SE, Etoh T (1992) A genetic map for Brassica oleracea based on RFLP markers detected with expressed DNA sequences and mapping of resistance genes to race 2 of Plasmodiophora brassicae (Woronin). Genome 35:409–420 Lawson WR, Goulter KC, Henry RJ, Kong GA, Kochman JK (1998) Marker-assisted selection for two rust resistance genes in sunﬂower. Mol Breed 4:227–234 Leclercq P (1966) Une stérilité mâle utilisable pour la production d’hybrides simples de tournesol. Ann Amelior Plantes 16:135–144 Leclercq P (1969) Une stérilité mâle cytoplasmique chez le tournesol. Ann Amélior Plantes 19:99–106 Leggett JM, Perret SJ, Harper J, Morris P (2000) Chromosomal localization of cotransformed transgenes in the hexaploid cultivated oat Avena sativa L. using ﬂuorescence in situ hybridization. Heredity 84:46–53 León AJ, Lee M, Rufener GK, Berry ST, Mowers RP (1995) Use of RFLP markers for genetic linkage analysis of oil percentage in sunﬂower seed. Crop Sci hbox35:558–564

Chapter 4 Sunflower León AJ, Lee M, Rufener GK, Berry ST, Mowers RP (1996) Genetic mapping of a locus (hyp) affecting seed hypodermis color in sunﬂower. Crop Sci 36:1666–1668 León AJ, Andrade FH, Lee M (2000) Genetic mapping of factors affecting quantitative variation for ﬂowering in sunﬂower (Helianthus annuus L.). Crop Sci 40:404–407 León AJ, Lee M, Andrade FH (2001) Quantitative trait loci for growing degree days to ﬂowering and photoperiod response in sunﬂower (Helianthus annuus L.). Theor Appl Genet 102:497–503 León AJ, Andrade FH, Lee M (2003) Genetic analysis of seedoil concentration across generations and environments in sunﬂower (Helianthus annuus L.). Crop Sci 43:135–140 Lewi D (2004) Transformación genética del girasol. PhD Thesis, Faculty of Exact and Natural Sciences, University of Buenos Aires, Argentina Lexer C, Rosenthal DM, Raymond O, Donovan LA, Rieseberg LH (2005) Genetics of species differences in the wild annual sunﬂowers, Helianthus annuus and H. petiolaris. Genetics 169:2225–2239 Lin HX, Yamamoto T, Sasaki T, Yano M (2000) Characterisation and detection of epistatic interactions of three QTLs, Hd1, Hd2 and Hd3, controlling heading date in rice using nearly isogenic lines. Theor Appl Genet hbox101:1021–1028 Linder CR, Taha I, Seiler GJ, Snow AA, Rieseberg LH (1998) Long-term introgression of crop genes into wild sunﬂower populations. Theor Appl Genet 96:339–347 Liu A, Burke JM (2005) Patterns of nucleotide diversity in wild and cultivated sunﬂower. Genetics. 2005 Dec 1 [Epub ahead of print] Lu YH, Gagne G, Grezes-Besset B, Blanchard P (1999) Integration of a molecular linkage group containing broomrape resistance gene Or5 into an RFLP in sunﬂower. Genome 42:453–456 Lucas O, Kallerhoff J, Alibert G (2000) Production of stable transgenic sunﬂowers (Helianthus annuus L.) from wounded immature embryos by particle bombardment and co-cultivation with Agrobacterium tumefaciens. Mol Breed 6:479–487 Lupi MC, Bennici A, Locci F, Gennai D (1987) Plantlet formation from callus and shoot-tip culture of Helianthus annuus (L.). Plant Cell Tiss Org Cult 11:47–55 Malone-Schoneberg JB, Scelonge CJ, Burrus M Bidney DL (1994) Stable transformation of sunﬂower using Agrobacterium and split embryonic axis explants. Plant Sci 103:199–207 Massinga R, Al-Khatib K, St Amand P, Miller JF (2003) Gene ﬂow from imidazolinone-resistant domesticated sunﬂower to wild relatives. Weed Sci 51:854–862 Mayor ML, Nestares G, Zorzoli R, Picardi LA (2003) Reduction of hyperhydricity in sunﬂower tissue culture. Plant Cell Tiss Org Cult 72:99–103 Mestries E, Gentzbittel L, Tourvielle de Labrouhe D, Nicolas P, Vear F (1998) Analyses of quantitative trait loci associated with resistance to Sclerotinia sclerotiorum in sun-

175

ﬂower (Helianthus annuus L.) using molecular markers. Mol Breed 4:215–226 Micic Z, Hahn V, Buer E, Schon CC, Knapp SJ, Tang S, Melchinger AE (2004) QTL mapping of Sclerotinia midstalk rot resistance in sunﬂower. Theor Appl Genet 109:1474–1484 Micic Z, Hahn V, Buer E, Schon CC, Melchinger AE (2005) QTL mapping of resistance to Sclerotinia midstalk rot in RIL of sunﬂower population NDBLOSsel × CM625. Theor Appl Genet 110:1490–1498 Miller JF, Gulya TJ (1991) Inheritance of resistance to race 4 of downy mildew derived from interspeciﬁc crosses in sunﬂower. Crop Sci 31:40–43 Mokrani L, Gentzbittel L, Azanza F, Fitamant L, Al-Chaarani G, Sarraﬁ A (2002) Mapping and analysis of quantitative trait loci for grain oil content and agronomic traits using AFLP and SSR in sunﬂower (Helianthus annuus L.). Theor Appl Genet 106:149–156 Moliner J, Thomas C, Brignou M, Hahne G (2002) Transient expression of ipt gene enhances regeneration and transformation rates of sunﬂower shoot apices (Helianthus annuus L.). Plant Cell Rep 21:251–256 Müller A, Markus I, Hess D (2001) Stable transformation of sunﬂower (Helianthus annuus L.) using a non-meristematic regeneration protocol and green ﬂuorescent protein as a vital marker. Transgen Res 10:435–444 Müller C, Denis M, Gentzbittel L, Faraut T (2004) The Iccare web server: an attempt to merge sequence and mapping information for plant and animal species. Nucleic Acids Res 32:W429–W434 Nagaki K, Song J, Stupar RM, Parokonny AS, Yuan Q, Ouyang S, Liu J, Hsiao J, Jones KM, Dawe RK, Buell CR, Jiang J (2003) Molecular and cytological analyses of large tracks of centromeric DNA reveal the structure and evolutionary dynamics of maize centromeres. Genetics 163:759–770 Natali L, Santini S, Giordani T, Minelli S, Maestrini P, Cionini PG, Cavallini A (2006) Distribution of Ty3-gypsy- and Ty1copia-like DNA sequences in the genus Helianthus and other Asteraceae. Genome 49(1):64–72 Noyes RD, Hancock JG (1981) Role of oxalic acid in the Sclerotinia wilt of sunﬂower. Physiol Plant Pathol 18:123–132 Paniego N, Echaide M, Muñoz M, Fernández L, Torales S, Faccio P, Fuxan I, Carrera M, Zandomeni R, Suárez I, Hopp EH (2002) Microsatellite isolation and characterization in sunﬂower (Helianthus annuus L.). Genome 45:34–43 Parody B (2003) Cultivo in vitro de girasol (Helianthus annuus L.): optimización de sus distintas etapas. Licenciate Thesis. Faculty of Exact and Natural Sciences, University of Buenos Aires, Argentina, p 180 Paterson KE (1984) Shoot tip culture of Helianthus annuus – ﬂowering and development of adventitious and multiple shoots. Am J Bot 71:925–931 Paterson KE, Everett NP (1985) Regeneration of Helianthus annuus inbred plants from callus. Plant Sci 42:126–132

176

N. Paniego et al.

Pedersen C, Zimny J, Becker D, Jähne-Gärtner, Lörz H (1997) Localization of introduced genes on the chromosomes of transgenic barley, wheat and triticale by ﬂuorescence in situ hybridization. Theor Appl Genet 94:749–757 Peerbolte RP, Peleman J (1996) The Cartisol sunﬂower RFLP map (146 loci) extended with 291 AFLP markers. In: Proc 18th Sunﬂower Res Workshop, 11-12 Jan 1996, Fargo, ND, pp 174–178 Peluffo L, Raimondi JP, Fernández P, Troglia C, Paniego N, Pereyra V, Hopp E, Heinz R, Escande A (2004) Identiﬁcación y caracterización de fuentes de resistencia a la Podredumbre Húmeda del Capítulo (PHC) causada por S. sclerotiorum en girasol mediante herramientas genómicas In: REDBIO 2004. V Encuentro Latinoamericano y del Caribe de Biotecnología Agrícola, Boca Chica, Dominican Republic Pereyra VR, Escande A (1994) Manual de reconocimiento de enfermedades del Girasol en la Argentina. Ed del INTA CERBAS, Balcarce, Buenos Aires, Argentina, pp 122–130 Perez-Vich B, Akhtouch B, Knapp SJ, Leon AJ, Velasco L, Fernandez-Martinez JM, Berry ST (2004) Quantitative trait loci for broomrape (Orobanche cumana Wallr.) resistance in sunﬂower. Theor Appl Genet 109:92–102 Pilson D (2000) Herbivory and natural selection on ﬂowering phenology in wild sunﬂower, Helianthus annuus. Oecologia 122:72–82 Poggio L, Confalioneri V, Comas C, González, Naranjo C (1999) Genomic afﬁnities of Zea luxurians, Z. diplopernnis and Z perennies: meiotic behavior of their hybrids and genomic “in situ hybridization” (GISH). Genome 42:993–1000 Poverene MM, Cantamutto MA, Carrera AD, Ureta MS, Salaberry MT, Echeverria MM, Rodríguez RH (2002) El girasol silvestre (Helianthus spp.) en la Argentina: Caracterización para la liberación de cultivares transgénicos. Rev Investig Agropec INTA 31:97–116. http://www.inta.gov.ar/ ediciones/ria/31_2/007.pdf Power CJ (1987) Organogenesis from Helianthus annuus inbreds and hybrids from the cotyledons of zygotic embryos. Am J Bot 74:497–503 Prats E, Bazzalo ME, León A, Jorrin JV (2003) Accumulation of soluble phenolic compounds in sunﬂower capitula correlates with resistance to Sclerotinia sclerotiorum. Euphytica 132:321–329 Pustovoit VS (1964) Conclusions of work on the selection and seed production of sunﬂowers. Agrobiology 5:662–697 Putt ED (1997) Early history of sunﬂower. In: Schneiter AA (ed) Sunﬂower Technology and Production. Agronomy Series. ASA, CSSA, and ASSA, Madison, WI, 35:1–19 Radwan O, Bouzidi MF, Vear F, Philippon J, Tourvieille de Labrouhe D, Nicolas P, Mouzeyar S (2003) Identiﬁcation of non-TIR-NBS-LRR markers linked to PL5/PL8 locus for resistance to downy mildew in sunﬂower. Theor Appl Genet 106:1438–1446 Radwan O, Bouzidi MF, Nicolas P, Mouzeyar S (2004) Development of PCR markers for the Pl5/Pl8 locus for resistance

to Plasmopara halstedii in sunﬂower, Helianthus annuus L. from complete CC-NBS-LRR Sequences. Theor Appl Genet 109:176–185 Radwan O, Mouzeyar S, Nicolas P, Bouzidi MF (2005a) Induction of a sunﬂower CC-NBS-LRR resistance gene analogue during incompatible interaction with Plasmopara Halstedii. J Exp Bot 56:567–575 Radwan O, Mouzeyar S, Venisse JS, Nicolas P Bouzidi MF (2005b) Resistance of sunﬂower to the biotrophic oomycete Plasmopara halstedii is associated with a delayed hypersensitive response within the hypocotyls. J Exp Bot 56:2683–2693 Rahim M, Jan CC, Gulya TJ (2002) Inheritance of resistance to sunﬂower downy mildew races 1, 2 and 3 in cultivated sunﬂower. Plant Breed 121:57–60 Rao KS, Rohini VK (1999) Agrobacterium-mediated transformation of sunﬂower (Helianthus annuus L.): a simple protocol. Ann Bot 83:347–354 Rieseberg LH (1998) Genetic mapping as a tool for studying speciation. In: Soltis DE, Soltis PS, Doyle JJ (eds) Molecular Systematics of Plants, 2nd edn. Chapman and Hall, New York, pp 459–487 Rieseberg LH, Choi H, Chan R, Spore C (1993) Genomic map of a diploid hybrid species. Heredity 70:285–293 Rocco M (2002) Citogenética clásica y molecular en girasol. Licenciate Thesis, Faculty of Exact and Natural Sciences, University of Buenos Aires, Argentina Rocco M, Hopp HE, Poggio L (2003) Citogenética clásica y molecular en girasol. Segundo Congreso Argentino de Girasol ASAGIR, Buenos Aires, Argentina. http:// www.asagir.org.ar/2_congreso/Murales/Rocco/rocco.pdf Salvo-Garrido HG, Travella S, Schwarzacher T, Harwood WA, Snape JW (2001) An efﬁcient method for the physical mapping of transgenes in barley using in situ hybridization. Genome 44:104–110 Santini S, Cavallini A, Natali L, Minelli S, Maggini F, Cionini PG (2002) Ty1 /copia- and Ty3 /gypsy-like DNA sequences in Helianthus species. Chromosoma 111:192–200 Schneiter AA (ed) (1997) Sunﬂower Technology and Production. Agronomy Monographs, No. 35. ASA, CSSA, Madison, WI Schön CC, Lee M, Melchinger AE, Guthrie WD, Woodman WL (1993) Mapping and characterization of quantitative trait loci affecting resistance against second-generation European corn borer in maize with the aid of RFLPs. Heredity 70:648–659 Schrammeijer B, Sijmons PC, Van den Elzen PJM, Hoekama A (1990) Meristem transformation of sunﬂower via Agrobacterium. Plant Cell Rep 9:55–60 Seiler G, Rieseberg LG (1997) Systematics, origin, and germplasm resources of the wild and domesticated sunﬂower. In: Schneiter AA (ed) Sunﬂower Science and Technology. Agronomy Series. ASA, CSSA, and ASSA, Madison, WI, pp 21–66

Chapter 4 Sunflower Seiler GJ (1992) Utilization of wild sunﬂower species for the improvement of cultivated sunﬂower. Field Crops Res 30:195–230 Selmeczi-Kovacs A (1975) Akklimatisation und Verbreitung der Sonnenblume in Europa. Acta Ethnograph Acad Sci Hung 24:47–88 Shigyo M, Wako T, Kojima A, Yamauchi N, Tashiro Y (2003) Transmission of alien chromosomes from selfed progenies of a complete set of Allium monosomic additions: the development of a reliable method for the maintenance of a monosomic addition set. Genome 46:1098–1103 Sims LE, Price HJ (1985) Nuclear DNA content variation in Helianthus (Asteraceae). Am J Bot 72:1213–1219 Snow AA, Morán-Palma P, Rieseberg LH, Wszelaki A, Seiler GJ (1998) Fecundity, phenology, and seed dormancy of F1 wild-crop hybrids in sunﬂower (Helianthus annuus, Asteraceae). Am J Bot 85:794–801 Snow AA, Rieseberg LH, Alexander HM, Cummings C, Pilson D (2003) Assessment of gene ﬂow and potential effects of genetically engineered sunﬂowers on wild relatives. In: Proc 5th Intl Biosafety Symp, Braunschweig, Germany Stewart CN Jr, Richards HA 4th, Halfhill MD (2000) Transgenic plants and biosafety: science, misconceptions and public perceptions. Biotechniques 29:838–843 Sujatha M, Prabakaran AJ (2001) High frequency embryogenesis in immature zygotic embryos of sunﬂower. Plant Cell Tiss Org Cult 65:23–29 Svitashev S, Ananiev E, Pawlowski WP, Somers DA (2000) Association of transgene integration sites with chromosome rearrangements in hexaploid oat. Theor Appl Genet 100:872–880 Tamborindeguy C, Ben C, Liboz T, Gentzbittel L (2004) Sequence evaluation of four speciﬁc cDNA libraries for developmental genomics of sunﬂower. Mol Genet Genom 271:367–375 Tang S, Kishore VK, Knapp SJ (2003) PCR-multiplexes for a genome-wide framework of simple sequence repeat marker loci in cultivated sunﬂower. Theor Appl Genet 107:6–19 Tang S, Yu JK, Slabaugh MB, Shintani DK, Knapp SJ (2002) Simple sequence repeat map of the sunﬂower genome. Theor Appl Genet 105:1124–1136 Ten Hoopen R, Robbins TP, Fransz PF, Montijn BM, Oud O, Gerats A, Nanninga N (1996) Localization of T-DNA insertions in petunia by ﬂuorescence in situ hybridization: physical evidence for suppression of recombination. Plant Cell 8:823–830 Tourvieille D, Mestries E, Vear F (1996) Multilocal Sclerotinia sclerotiorum resistance tests. ISA Symp “Disease Tolerance in Sunﬂower”, 13 June 1996, Beijing, PR China, pp 71–79 Vanzela AL, Ruas CF, Oliveira MF, Ruas PM (2002) Characterization of diploid, tetraploid and hexaploid Helianthus species by chromosome banding and FISH with 45S rDNA probe. Genetica 114:105–111

177

Vear F, Garreyn M, Tourvieille de Labrouhe D (1997) Inheritance of resistance to phomopsis (Diaporthe helianthi) in sunﬂowers. Plant Breed 116:277–281 Viguié A, Touvieille de Labrouhe D, Vear F (2000) Inheritance of several sources of resistance to phomopsis stem canker (Diaporthe helianthi Munt.-Cvet.) in sunﬂower (Helianthus annuus L.). Euphytica 116:167–179 Vranceanu V, Stoenescu F (1970) Immunity to sunﬂower downy mildew due to a single dominant gene. Probl Agric 22:34–40 Weber S, Friedt W, Landes N, Molinier J, Himber C, Rousselin P, Hahne G, Horn R (2003) Improved Agrobacteriummediated transformation of sunﬂower (Helianthus annuus L.): assessment of macerating enzymes and sonication. Plant Cell Rep 21:475–482 Wei WH, Zhao WP, Song YC, Liu LH, Guo LQ, Gu MG (2003) Genomic in situ hybridization analysis for identiﬁcation of introgressed segments in alloplasmic lines from Zea mays × Zea diploperennis. Hereditas 138:21–26 Whitton J, Wold DE, Arias DM, Snow AA, Rieseberg LH (1997) The persistence of cultivar alleles in wild populations of sunﬂowers ﬁve generations after hybridization.Theor Appl Genet 95:33–40 Wilcox McCann A, Cooley G, van Dresser J (1988) A system for routine plantlet regeneration of sunﬂower (Helianthus annuus L.) from immature embryo-derived callus. Plant Cell Tiss Org Cult 14:103–110 Yamamoto T, Lin H, Sasaki T, Yano M (2000) Identiﬁcation of heading date quantitative trait locus Hd6 and characterization of its epistatic interactions with Hd2 in rice using advanced backcross progeny. Genetics 154:885–891 Yano M, Harushima Y, Nagamura Y, Kurata N, Minobe Y, Sasaki T (1997) Identiﬁcation of quantitative trait loci controlling heading date in rice using a high-density linkage map. Theor Appl Genet 95:1025–1032 Yu JK, Mangor J, Thompson L, Edwards KJ, Slabaugh MB, Knapp SJ (2002) Allelic diversity of simple sequence repeat markers among elite inbred lines in cultivated sunﬂower. Genome 45:652–660 Yu JK, Tang S, Slabaugh MB, Heesacker A, Cole G, Herring M, Soper J, Han F, Chu W-C, Webb DM, Thompson L, Edwards KJ, Berry S, Leon AJ, Grondona M, Olungu C, Maes N, Knapp SJ (2003) Towards a saturated molecular genetic linkage map for cultivated sunﬂower. Crop Sci 43:367–387 Zhang P, Li W, Fellers J, Friebe B, Gill BS (2004) BAC-FISH in wheat identiﬁes chromosome landmarks consisting of different types of transposable elements. Chromosoma 112:288–299 Zhang LS, Le Clerc V, Li S, Zhang D (2005) Establishment of an effective set of simple sequence repeat markers for sunﬂower variety identiﬁcation and diversity assessment. Can J Bot 83:66–72 Zhou Y, Li W, Wu W, Chen Q, Mao D, Worland AJ (2001) Genetic dissection of heading time and its components in rice. Theor Appl Genet 102:1236–1242

CHAPTER 5

5 Indian Mustard D. Edwards1 , P.A. Salisbury2 , W.A. Burton3 , C.J. Hopkins1 , and J. Batley1 1

2 3

Plant Biotechnology Centre, Primary Industries Research Victoria, Department of Primary Industries, Victorian AgriBiosciences Centre, 1 Park Drive, Bundoora, Victoria 3083, Australia e-mail: [email protected] Grains Innovation Park, Private Bag 260, Horsham, Victoria 3401, Australia Faculty of Land and Food Resources, The University of Melbourne, Victoria 3010, Australia

5.1 Introduction

species B. rapa and B. nigra have sympatric distribution (Gomez-Campo and Prakash 1999). This would account for the various centers of diversity observed in the species.

5.1.1 Brief History of Brassica juncea Brassica juncea L. Czern. and Coss., alternatively known as Indian, Oriental or Brown mustard, is widely believed to be one of the earliest domesticated plants, with mustard known as a condiment (spice) since early times. It is described in Sanskrit and Sumerian texts from as early as 3000 BC (Hemingway 1995). B. juncea initially spread to Europe in the Middle Ages as a medicinal crop and was later grown as a vegetable for human consumption. Today, B. juncea is used worldwide as an oilseed, a condiment and a vegetable. U (1935) demonstrated that Brassica crop species comprise three diploid species, B. rapa (genome AA, 2n = 20), B. nigra (BB, 2n = 16) and B. oleracea (CC, 2n = 18), plus three amphidiploid species, B. napus (AACC, 2n = 38), B. juncea (AABB, 2n = 36) and B. carinata (BBCC, 2n = 34). The amphidiploid species originated through interspeciﬁc hybridization between two of the three diploid species. B. juncea is an amphidiploid species that combines the genomes of both B. rapa and B. nigra. The center of origin of B. juncea is uncertain, with China and the Middle East the most favored options (Gomez-Campo and Prakash 1999). Due to the sympatric range of distribution of its two diploid progenitors, B. juncea could have originated anywhere between eastern Europe and China. Prakash and Hinata (1980) proposed that it ﬁrst evolved in the Middle East, where B. rapa and B. nigra had geographic sympatry. Various forms of all three crops still grow on the plains there today. It is possible that there have been multiple origins for B. juncea where the parental

5.1.2 Botanical Description B. juncea is an annual crop which grows as cultivated, weedy escapes or wild forms in coastal lowlands, sandy beaches, plateaus and mountainous regions. It has a wide geographical range, spanning the continents of Europe, Africa, Asia, America and Australia. B. juncea is closely related botanically to canola (B. napus) and turnip rape (B. rapa) and has a similar growth habit (Hemingway 1976). The cytogenetic relationship between the Brassica species established by U (1935) was later conﬁrmed by chromosome pairing and artiﬁcial synthesis (Axelsson et al. 2000), nuclear DNA content, DNA analysis and the use of genome-speciﬁc chromosome markers (Hasterok et al. 2001). Röbbelen (1960) proposed that the three diploid species originated from one common ancestor. However, recent molecular investigations summarised by Gomez-Campo and Prakash (1999) point to a common origin for B. rapa and B. oleracea, with B. nigra evolved from a separate progenitor. B. juncea is an amphidiploid containing conserved diploid genomes from the species B. rapa and B. nigra (Axelsson et al. 2000). This polyphyletic origin of B. juncea is supported by a study utilising random ampliﬁed polymorphic DNA (RAPD) markers (Demeke et al. 1992). The genome size of B. juncea has been determined using two different methods. Bennett and Smith (1976) used the Feulgen reaction to estimate a genome size of 1,495 Mbp. This was later revised by Arumuganathan and Earle (1991), who utilised ﬂow

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

180

D. Edwards et al.

cytometry to estimate the genome size of B. juncea to be 1,105 Mbp. Brassica species have small chromosomes which are morphologically similar, making it extremely difﬁcult to differentiate the chromosomes using traditional staining methods, and studies of this type are often limited to diploid Brassica species. Hasterok and Maluszynska (2000) performed morphometric analysis based on the length of the chromosome and the position of the centromere. This allowed rough classiﬁcation into median (Nr 1–6) and submedian (Nr 7– 15) groups; however two nuclear organising regions (NORs) bearing chromosomes can also be identiﬁed with unusually prominent secondary constrictions in the short arm (Nr 17–18). The relatively large variation in chromosome size and morphology observed in B. juncea species is due to the chromosomes from the A genome, which is the most asymmetric among the three genomes (Kulak et al. 2002). Kulak et al. (2002) applied a combined morphometric and multicolor FISH, with 5S and 25S rRNA probes, to amphidiploid species. This study was based on eight different rDNAbearing chromosomal types as described in Hasterok et al. (2001). It was found that the mitotic complement comprises 36 chromosomes, equivalent to the diploid progenitors, with chromosome lengths ranging from 1.38 to 3.25 μm. Ten sites of 5S hybridization and 16 sites of 25S hybridization were identiﬁed, which, combined with the morphometric characterization, allowed clear identiﬁcation of chromosomes (Nr 4, 8, 10, 12, 14 and 16). Chromosomes 4, 10 and 16 have co-localised 5S and 25 S rRNA genes, which, among the three progenitor Brassica genomes, is a feature of the A genome. 5.1.3 Economic Importance B. juncea is cultivated worldwide as a condiment, an oilseed and a vegetable. B. juncea is one of the two main species used for condiment mustard production worldwide, along with Sinapis alba (white mustard or yellow mustard). Seed color is important in mustard, with both brown-seeded (brown mustard) and yellow-seeded (oriental mustard) B. juncea cultivars used. Brown mustard seed is ground into ﬂour which is used to produce a hot mustard in European products (Skrypetz 2003), including dijon mustard (Lionneton et al. 2002). The ﬂour is also used in mayonnaise, salad dressing and sauces (Skrypetz 2003). Oriental mustard cultivars have been bred with a range of oil

Table 1. B. juncea types used for vegetable production (adapted from Labana and Gupta 1993) Subspecies or variety

Common name

Use

Capitata Crispifolia Faciliﬂora Lapitata Multiceps Rapifera Rugosa Spicea Tsa-tsai

Head mustard Cut leaf mustard Broccoli mustard Large petiole mustard Multi-shoot mustard Root mustard Leaf mustard Mustard Big stem mustard

Vegetable Vegetable/fodder Vegetable Vegetable Vegetable/fodder Vegetable Vegetable Pickle/vegetable

content and volatility to meet alternative market requirements. Low-volatility, low-oil-content cultivars are suitable for dry milling purposes (Skrypetz 2003). Canada is the dominant exporter and the second largest producer of mustard seed in the world. It is the largest producer of mustard for condiment purposes. Oriental and brown mustard are both produced in Canada, along with yellow mustard. Annual Canadian condiment mustard seed production has ranged from 105,000 to 319,000 tonnes in the last decade (Skrypetz 2003). The relative production of each type varies from year to year. Brown and oriental mustard combined are typically 50 to 70% of the Canadian production, with yellow mustard 30 to 50% (Skrypetz 2003). India produces the bulk of the world mustard seed, where it is predominantly used for oilseed production. The forecast world oilseed production of rapeseed and mustard for 2004 was 42 million tonnes (http://www.fas.usda.gov/wap/circular/2004/04-08/ Oils.xls). While production ﬁgures from India do not differentiate between B. juncea and B. rapa, B. juncea is the major oilseed crop in India, accounting for around 80% of the 4 to 6 million ha of oilseed Brassica production annually (Kumar et al. 2000; Negi et al. 2004). Production is primarily in the north-western part of the country during the winter season. The oil extracted from mustard seed is largely used for edible purposes in India and other South Asian countries (Sharma et al. 2002b). B. juncea is also widely used for vegetable production, particularly in Asia. Due to eco-geographic variation and human selection, a number of morphologically distinct vegetable forms are available (Table 1).

Chapter 5 Indian Mustard

5.1.4 Breeding Objectives and Progress For breeding purposes, two distinct B. juncea germplasm groups have been identiﬁed (Oram et al. 1999). The China-Eastern Europe-Canada geographic B. juncea is characterized by brown or yellow seeds containing predominantly allyl (propenyl) glucosinolate. These plants require long days for ﬂowering and are resistant to leaf blight caused by Pseudomonas syringae. In contrast to this, the India-Pakistan geographic group has brown seeds containing a combination of butenyl and allyl glucosinolates, and plants which are relatively day neutral and susceptible to leaf blight. The traits required for adaptation can differ signiﬁcantly between environments. From a breeding perspective, desired traits are often found in nonadapted backgrounds. For example, B. juncea lines from Canada, Australia and eastern Europe are poorly adapted to Indian agroclimatic conditions but constitute a rich source of agronomically important traits such as yellow seed, oil and meal quality, oil content, disease resistance and yield components such as pod branching, density and number. Exploitation of these traits in India through conventional plant breeding has been relatively unsuccessful due to nonavailability of desirable segregants in the F2 and subsequent generations from crosses between Indian and exotic germplasm (Pradhan et al. 2003). Australian adapted B. juncea needs the combination of the early ﬂowering and reduced height of the Indian germplasm with the superior disease resistance and quality attributes of the European germplasm. The breeding objectives for B. juncea can be broken down into three major areas of interest: yield and adaptation, oil and meal quality and disease and insect resistance. Yield and Adaptation B. juncea has a number of advantages over B. napus for production in lower-rainfall, marginal growing environments. It exhibits higher seedling vigour, improved heat and drought tolerance and increased pod-shatter resistance relative to B. napus (Kirk and Oram 1978; Woods et al. 1991; Burton et al. 1999; Oram et al. 1999). This has led to its development as an alternative oilseed Brassica species for these regions.

181

Development of high-yielding cultivars is a high priority for all breeding programs. In India, yields of mustard and rapeseed have increased two- to three-fold since the 1950s (Singh 2003; Yadava and Yadav 2003). Fifty B. juncea cultivars had been released in India to the year 2000, comprising half of all oilseed Brassica cultivars released (Katiyar and Chamola 2003). Further yield improvements are being sought through the use of hybrids. Several hybrid systems (based on cytoplasmic or genetic male sterility) are being evaluated and developed in B. juncea (Banga et al. 2003b; Kaur et al. 2004), although to date each system has had some weakness or deﬁciency to be corrected (Katiyar and Chamola 2003; Singh 2003). The Moricandia cytoplasmic male sterility (CMS) system is the most complete system and has performed well in a range of locations (Singh 2003). Commercial hybrids are possible within 3 to 4 years (S. Banga, pers. comm.). Diverse parental combinations are required for high levels of heterosis in hybrid breeding. In B. juncea, Negi et al. (2004) demonstrated that F1 hybrids derived from more genetically diverse genotypes are more productive than genotypes that are closely related. Studies of genetic diversity in B. juncea (Burton et al. 2004) can be used to identify distinct genetic pools for hybrid breeding. Separate B. juncea cultivars speciﬁcally selected for frost tolerance, drought tolerance or salinity tolerance have also been released in India (Singh 2003; Yadava and Yadav 2003).

Oil and Meal Quality The required oil and meal quality is determined by the end use of the product. For condiment mustard production, a seed very high in allyl glucosinolates (160 to 200 μmoles of glucosinolates per gram of seed at 8.5% moisture), with moderate levels of erucic acid in the oil (typically 10 to 25%), is required. For oilseed production, quality requirements differ. In India, China and eastern Europe, B. juncea cultivars high in erucic acid (40 to 50%) and glucosinolates (80 to 160 μmoles per gram of seed) have traditionally been used for oil production. This is changing, as lines with improved quality have been identiﬁed (Agnihotri and Kaushik 1998). These countries are in the process of converting to canola-quality (low glucosinolates, low erucic acid) cultivars. A number of low erucic acid B. juncea lines have performed well in Indian trials

182

D. Edwards et al.

(Katiyar and Chamola 2003; Yadava and Yadav 2003). Canola-quality B. juncea is in relatively early stages of development in India and is also being developed in China. These changes to oil and meal quality occurred because diets high in erucic acid were known to be associated with problems in myocardium tissue in laboratory animals (Beare et al. 1963; Gopalan et al. 1974; Sauer and Kramer 1983), while glucosinolates caused palatibility and nutritional problems when meal was fed to non-ruminant animals (Bille et al. 1983; Bell 1984). In western countries, oil and meal quality similar to canola-quality B. napus and B. rapa is required for commercial oilseed production of B. juncea. In Canada, canola-quality B. juncea cultivars can only be registered if they meet certain quality and agronomic performance standards as speciﬁed by the Western Canada Canola/Rapeseed Recommending Committee (Anonymous 2002). The cultivars must contain less than 1 μmole of allyl glucosinolate and less than 12 μmoles of total glucosinolates per gram of seed at 8.5% moisture (equivalent to ca. 20 μmoles of total glucosinolate in seed meal), the seed oil must have less than 2% erucic acid (C22:1) and contain 55% or more oleic acid (C18:1), and the total oil and protein content cannot be signiﬁcantly less than current canola cultivars. The release of canola-quality B. juncea has taken signiﬁcant time and effort over 20 years (Rakow et al. 1995; Oram et al. 1999) with the ﬁrst cultivars, ‘Arid’ and ‘Amulet’, released in Canada by the Saskatchewan Wheat Pool in 2002. The ﬁrst Australian canolaquality cultivars are due for release in 2007. Development of low erucic acid (Kirk and Oram 1981) and low glucosinolate (Love et al. 1990b) B. juncea provided the necessary building blocks for breeding canola-quality B. juncea. The ﬁrst low erucic acid lines were discovered in Australia in a mixture of high and low erucic types in two commercial mustard samples (Kirk and Oram 1981). The zero erucic/low eicosenoic acid components of these two samples were designated Zem 1 and Zem 2 and distributed to breeders around the world (Oram et al. 1999). Kirk and Hurlstone (1983) reported that the two geographic groups of China-Eastern EuropeCanada and India-Pakistan differed greatly in erucic acid content, with 25% on average in the ﬁrst group and 49% on average in the second. In the F2 of crosses with Zem 1 and Zem 2 to representitives of both groups, the proportions of low erucic acid plants were 25% and 6.25%, suggesting that the

groups were homozygous for dominant alleles controlling the synthesis of erucic acid at one or two loci repectively (Kirk and Hurlstone 1983). Gupta et al. (2004) conﬁrmed that the inheritance of erucic acid in B. juncea was controlled by two genes with additive effects, zero erucic acid being recessive in expression. Love et al. (1990b) developed the low glucosinolate B. juncea line 1058 through an interspeciﬁc cross between an Indian B. juncea selection containing butenyl glucosinolate and a low glucosinolate B. rapa, followed by backcrossing to the B. juncea parent. This line was found to be genetically stable for the low glucosinolate trait but was later revealed to be nullisomic, with 2n = 34 chromosomes (Cheng et al. 2001). Further crossing and selection over many generations has reduced the glucosinolate levels in Canadian and Australian lines to canola-quality standards (Burton et al. 1999). Love et al. (1990a) suggested that two loci may control the synthesis of allyl and butenyl glucosinolates, one in each of the A and B genomes of B. juncea, while (Stringham and Thiagarajah 1995) estimated that ﬁve to eight loci were involved in the low glucosinolate character. In order for canola-quality B. juncea to be used interchangeably with B. napus in the market place, the entire B. juncea fatty acid (FA) proﬁle required attention (Woods et al. 1991). It has been important to increase oleic acid levels to match the B. napus level of 60%. Most of the early Canadian and Australian canola-quality breeding B. juncea lines had oleic acid levels in the 40 to 52% range (Burton et al. 1999). High oleic acid (60 to 65%) B. juncea lines developed in Canada have been used as a source of high oleic in the Australian breeding program. The inheritance of this trait is reported to be controlled by a single dominant gene in Canadian germplasm (Potts et al. 1999). Worldwide, a series of further modiﬁcations to oilseed Brassica quality, targeted at speciﬁc end uses (both edible and industrial) are under development, using both conventional breeding methods and genetic engineering. A report by Green and Salisbury (1998) identiﬁed more than 20 such modiﬁcations. Improved oil and protein contents are also primary objectives in B. juncea breeding programs worldwide. Yellow-seeded B. juncea generally has higher oil content, lower crude ﬁber content in the seed hull and higher protein content in the seed meal relative to dark-seeded lines. In India, all commercial

Chapter 5 Indian Mustard

oilseed varieties of B. juncea are dark seeded. In Canada, canola-quality B. juncea cultivars are yellow seeded. Yellow seed is also essential for oriental mustard condiment cultivars. The inheritance of seed coat color in B. juncea has been evaluated by Vera et al. (1979), Vera and Woods (1982) and Anand et al. (1985). An exclusive maternal inheritance was observed and brown/black seed coat color was dominant over yellow. A segregation ratio of 15 brown to 1 yellow indicated digenic control of the trait.

Disease and Insect Resistance The relative importance of different diseases and insect pests in B. juncea varies between regions and countries. Some of the more signiﬁcant disease problems worldwide include white rust (Albugo candida), Alternaria blight (Alternaria brassicae, A. brassicicola, A. raphani), downy mildew (Perenospora parasitica), sclerotinia (Sclerotinia sclerotiorum) and blackleg (Leptosphaeria maculans). White rust is one of the most important diseases of B. juncea in Canada and India (Saharan and Verma 1992; Katiyar and Chamola 2003; Yadava and Yadav 2003). It infects both vegetative and reproductive tissues and has two distinct phases, a white rust phase and a staghead phase. Signiﬁcant yield losses are seen annually in India, with yield losses of 30 to 90% sometimes reported (Verma and Bhowmik 1989; Subhudi and Raut 1994). Most commercial cultivars in India are susceptible to white rust (Yadava and Yadav 2003; Varshney et al. 2004). There is therefore a signiﬁcant breeding focus on white rust resistance (Mukherjee et al. 2001). The predominant race of white rust on B. juncea in Canada (Rimmer et al. 2000) and Australia (A.M. Gurung, unpubl.) has been race 2A. Resistance to race 2A is available in Canadian and Australian germplasm; however, recent studies in Canada have shown that there has been a shift from race 2A to race 2V as the predominant pathotype on B. juncea (Rimmer et al. 2000). Resistance to race 2A in B. juncea has been reported to be controlled by a single dominant gene (Tiwari et al. 1988; Sachan et al. 1995) or a dominant gene with a second recessive or partially dominant gene (Somers et al. 1999). There have also been reports of variation in sporulation intensity and latent period exhibited among genotypes, suggesting that other genes are involved in the control of white rust severity (Bansal et al. 1999).

183

Alternaria blight causes yield losses ranging from 10 to 70% in India (Saharan and Chand 1988). No perceptible breeding progress for Alternaria tolerance has been made (Katiyar and Chamola 2003). In contrast, sources of resistance for downy mildew have been identiﬁed and are being utilised in India (Katiyar and Chamola 2003). In India, severe sclerotinia infection in B. juncea can cause seed yield losses of 37 to 92%, depending on the time of disease appearance (Shivpuri et al. 1999). Prelimiary identiﬁcation of Indian genotypes with possible resistance to sclerotinia (Shivpuri et al. 1999) appears to have been unfounded, as Yadava and Yadav (2003) stated that no resistance sources have been found against the emerging sclerotinia rot problem. Blackleg is a major disease of oilseed Brassica crops worldwide. B. juncea has proved to be much more resistant than B. napus to the blackleg fungus (Purwantara et al. 1998; Marcroft et al. 2002). Although Ballinger and Salisbury (1996) have identiﬁed Australian ﬁeld isolates that can attack B. juncea under ﬁeld conditions, B. juncea remains signiﬁcantly more resistant than B. napus. Mustard aphid (Lipaphis erysimi) and painted bug (Bagrada hilaris) are signiﬁcant insect pests in India. However, there is currently no dependable resistance against these pests (Katiyar and Chamola 2003; Yadava and Yadav 2003).

5.1.5 Overcoming Limitations of Classical Endeavours Lack of available variability within B. juncea has limited breeding progress for a number of key characters, including disease and insect resistance, adaptation to stress environments, quality and hybrid systems. Where this within-species variability has been insufﬁcient to make progress, breeders have turned to other methods to create or incorporate the required variability. Methods being used include mutation, resynthesis of B. juncea, interspeciﬁc transfer from other cultivated species within the triangle of U, interspeciﬁc or intergeneric transfer from other Brassicaceae species, protoplast fusion and genetic engineering (Table 2). These methods have varied in their effectiveness. Mutation and resynthesis have both contributed some important traits to the B. juncea germplasm pool. The artiﬁcial resynthesis of B. juncea was ﬁrst reported

184

D. Edwards et al.

Table 2. Recent examples of different breeding techniques being used with a view to creating or incorporating new variability in the B. juncea genome Breeding technique

Character

Reference

Mutation

CMS Reduced linolenic acid High oil content Several/unspeciﬁed

Bhat et al. 2001 Haque and Sharma 2002 Singh et al. 2003 Bhat and Sarla 2004 Negi et al. 2004 Srivastava et al. 2004 Pal et al. 1999 Chauhan and Raut 2002 Kumar et al. 2002

Resynthesis of B. juncea

Interspeciﬁc transfer from triangle of U

Interspeciﬁc/intergeneric transfer from Brassicaceae

Somatic hybridization Genetic engineering

White rust resistance (B. napus) White rust resistance (B. carinata) Alternaria and powdery mildew resistance (B. carinata) Alternaria resistance (B. carinata) Several (B. napus) Low glucosinolates (B. rapa) CMS (Enarthrocarpus lyratus) CMS (Moricandia arvensis) Alternaria resistance (8 resistant species) Aphid tolerance and drought tolerance (Brassica tournefortii) General (Diplotaxis catholica) General (Diplotaxis siifolia) General (Diplotaxis virgata) General (Eruca sativa) General (Erucastrum virgatum) General (Raphanus sativus) CMS (barnase and barstar genes from Bacillus) Disease resistance Alternaria resistance (rubber tree lectin) Aphid resistance (wheat germ agglutinin) Aphid resistance (cowpea lectin) Salt resistance (glycinebetaine biosynthesis gene from Arthrobacter) Increase oleic acid (co-suppression) Gamma-linolenic acid (Δ6 desaturase from Pythium)

by Howard (1942). Resynthesis offers opportunities for the introduction of new genes from the diploid progenitors (Prakash 1980). Mattson (1988) resynthesised B. juncea using a range of subspecies of B. rapa. Likewise, Negi et al. (2004) have studied diversity among B. nigra accessions as part of a program for the resynthesis of B. juncea. Direct commercial utilisation of resynthesised B. juncea lines has been limited due to problems with fertility and low seed yield relative to existing cultivars

Krishnia et al. 2000 Patil et al. 2003 Zhang et al. 2003 Love et al. 1990b Banga et al. 2003b Kaur et al. 2004 Sharma et al. 2002a Kumar et al. 2001 Banga et al. 2003a Ahuja et al. 2003 Inomata 2003 Bijral and Sharma 1999 Goswami and Devi 2002 Inomata 2001 Muller et al. 2001 Jagannath et al. 2002 Grover 2003 Kanrar et al. 2002b Kanrar et al. 2002a Datta and Koundal 2003 Prasad et al. 2000 Stoutjesdijk et al. 2000 Haiping et al. 2002

(Prakash 1980). However, repeated selection within such lines, or crossing with other B. juncea lines, has resulted in the development of promising material. One trait introduced into B. juncea through resynthesis was earliness (Shpota and Konovalov 1978). Interspeciﬁc transfer from other cultivated Brassica species within the triangle of U has been an effective way to introgress new variation, particularly when the two species have a common genome. Important recent successful examples include the introgression

Chapter 5 Indian Mustard

of low glucosinolates and white rust resistance into B. juncea (Table 2). Many potentially valuable traits have been identiﬁed in other Brassicaceae species (Salisbury and Kadkol 1989). However, a number of barriers have been identiﬁed to their successful introgression into B. juncea. Hybridization has often been ineffective due to pre- and postfertilization barriers, including failure of fertilization and abortion of hybrid embryos (Prakash and Hinata 1980). Where crosses have been successful (or embryo rescue has been used), some degree of homoeologous pairing between the chromosomes of the crop Brassica species and the wild Brassicaceae species has often been reported. However, sterility in the F1 and subsequent generations has regularly limited successful gene transfer (Heyn 1977; Kumar et al. 1988). Despite the large number of successful crosses reported between cultivated Brassica and Brassicaceae species and the occurrence of homoeologous pairing, there is very little published information on the successful introgression of useful nuclear genes from wild species to cultivated species (Salisbury and Kadkol 1989). In contrast to the lack of successful nuclear gene transfer, the cytoplasm of weedy Brassicaceae species has been successfully transferred into B. juncea as a component of a CMS system (Banga et al. 2003b). Protoplast fusion can overcome sexual barriers (Glimelius et al. 1986) and has allowed somatic intergenomic hybrids to be created where sexual hybrids have not been reported. Somatic hybrids can be symmetric (with a full chromosome complement from both species) or asymmetric (with spontaneous, but preferential, elimination of many of the chromosomes of one parent). Cytoplasmic hybrids (cybrids) can also be created which contain the nucleus from one parent and any mitochondrial and chloroplast combination different from the nucleus donor parent (Kemble and Barsby 1988). These techniques have been used particularly in the development of CMS hybrid systems. For example, protoplast fusion was used to create symmetric and asymmetric hybrids in a Diplotaxis muralis × B. juncea cross by Chatterjee et al. (1988). Genetic engineering enables overcoming of natural species barriers and allows effective gene transfer where not previously possible. Provided this technology gains widespread public acceptance, it offers B. juncea breeders access to an incredible range of new

185

sources of variability, plus it allows breeders to switch off unwanted existing genes. Several recent examples are included in Table 2.

5.1.6 Classical Mapping Efforts Isozyme work in B. juncea has been very limited compared to the other Brassica oilseed species, the majority concentrating on peroxidase isozyme patterns. Peroxidase isozyme patterns were used by Kumar and Gupta (1985) as a tool to characterize different genotypes of B. juncea, based on differences in the number, placement and intensity of the bands. Further work on peroxidase and acid phosphatase isozymes in B. juncea by Chen and Tong (1985) revealed separate morphotypes of the species. They could also distinguish hybrid B. juncea from parental lines. Isozyme markers have also been used in B. juncea, as genetic markers in breeding programs in association with agronomic traits. However, little has been published in this ﬁeld. Thukral et al. (1985) studied peroxidase isozymes in B. juncea and found that band number and activity were higher in drought-tolerant genotypes, indicating they could be used to screen breeding material. The lack of publications reﬂects that these markers were quickly superseded by molecular genetic markers.

5.1.7 Utility of Molecular Mapping The application of molecular markers to advanced plant breeding is now well established. Modern breeding is dependent on molecular markers, from trait identiﬁcation and introgression to marker-assisted breeding and selection. Molecular markers can be used to select parental genotypes in breeding programs, eliminate linkage drag in back-crossing and select for traits which are difﬁcult to score using phenotypic markers. Molecular markers are complementary tools to traditional selection. They can help in obtaining knowledge of selected characters and their genetic association, which may modify the breeding objectives. Molecular genetic markers have the advantage that they can replace unreliable phenotypic analysis. Phenotype-based selection for desired oil levels is not always reliable because of interactions and environ-

186

D. Edwards et al.

mental factors such as temperature and day length. Low erucic acid mustard can be developed by the introduction of recessive alleles from donor varieties such as Zem 1 or its derivative line Heera, through backcross breeding with an Indian variety as the recurrent parent. However, this is a lengthy process and necessitates selﬁng of every backcross generation and identifying zero erucic plants in the segregating population through analysis of the FA proﬁle of single cotyledons from individual seeds using gas chromatography (GC). The development of molecular markers tightly linked to the erucic acid trait will allow for the selection of lines with low erucic acid alleles in the heterozygous state during backcrossing, removing the need for selﬁng every backcross generation and extensive GC analysis. Breeding for resistance to the white rust pathogen would also be greatly assisted by the development and application of tightly linked genetic markers for this important trait (Varshney et al. 2004). Many traits that are important in crop improvement exhibit continuous variation. It has been established that the quantitative pattern of inheritance of these traits arises from the segregation of the alleles of multiple genes which are often modiﬁed by environmental factors. The systematic mapping of quantitative trait loci (QTLs) contributing to a continuously variable trait was not feasible before the use of molecular markers, such as restriction fragment length polymorphisms (RFLPs), because the inheritance of an entire genome could not be studied with phenotypic genetic markers. The existence of linkage maps, covering the entire genome, has enabled QTLs to be mapped. To map QTLs, two lines are crossed that differ substantially in a quantitative trait and the trait scored in the segregating population.

5.2 Construction of Genetic Maps 5.2.1 Genetic Mapping in B. juncea In the mid- to late 1990s, the status of molecular marker technology development for B. juncea was considerably less advanced than for the other major Brassica oilseed species. High-resolution genetic linkage maps, with a multiplicity of molecular marker types, had been constructed for B. napus, B. oleracea

and B. rapa and reviewed by Lakshmikumaran et al. (2003). These genetic maps had been utilised for mapping genes and QTLs for multiple phenotypic characters associated with yield, oil quality and disease resistance (Kole et al. 1996, 2002; Thormann et al. 1996; Cheung et al. 1997, 1998a,b; Prabhu et al. 1998; Camarago et al. 1999). By contrast, only rudimentary reference genetic maps of B. juncea based on a small number of RFLP and random ampliﬁcation of polymorphic DNA (RAPD) markers were available (Sharma et al. 1994). Limited QTL information was available for characters such as oil content (Cheung et al. 1998b) and days to ﬂowering (Sharma et al. 1994). Among various maps of this species reported to date, the ﬁrst was by Sharma et al. (1994) based on an F2 population. It had only 15 markers on six linkage groups (LGs). Later maps described in B. juncea were more extensive, such as the RFLP map, based on double haploid (DH) lines with 343 loci in 18 LGs and 4 unlinked loci covering 2,073 cM published by Cheung et al. (1997). Recently, additional maps have been produced using RFLPs (Axelsson et al. 2000; Mahmood et al. 2003a) and RAPDs (Sharma et al. 2002b). Only two maps in B. juncea have been constructed using AFLPs; the ﬁrst was described by Lionneton et al. (2002), followed by Pradhan et al. (2003) (Table 3).

5.2.2 First-Generation Maps The ﬁrst genetic map in B. juncea was produced by Sharma et al. (1994). This was a cross between the cultivar Varuna, a popular Indian variety, and BEC144, an exotic Polish variety, based on an F2 population. Linkage analysis was performed using Mapmaker. This was only a partial map, with 15 RFLP markers covering 6 LGs in a total map length of 173.9 cM. These cultivars were chosen based on their morphological and molecular differences, and 89.5% of the applied RFLP probes were found to be polymorphic between the cross. Upadhyay et al. (1996) published a second partial map in B. juncea. Using the computer package Mapmaker, 25 out of 44 RFLP markers were aligned into 9 LGs covering a total of 243.3 cM. This was an F2 population from an intervarietal cross. Cheung et al. (1997) constructed an RFLP linkage map of canola-quality mustard using a segregating F1 derived DH population. The cross was between J904317, a canola-quality, white-rust-susceptible mustard line as the female parent, and J90-2733, a high-

Pradhan et al. 2003

Mahmood et al. 2003b

61 DH and 51 from 7 F1 (2 maps) 123 F1 derived DH

120 F1 94 RILs 131 F1 derived DH

F2 F2 119 F1 derived DH 60 F1

Varuna × BEC-144 Intervarietal cross J90-4317 × J90-2733 Resynthesised B. juncea × Natural B. juncea 2 × Natural B. juncea Varuna × BEC-144 BJ-70 (Indian) × BJ-99 (Russian) RLM-514 × canola-quality inbred Varuna (Indian) × Heera (canola quality)

Sharma et al. 1994 Upadhyay et al. 1996 Cheung et al. 1997 Axelsson et al. 2000

Axelsson et al. 2000 Sharma et al. 2002b Lionneton et al. 2002

No./Type progeny

Cross

Publication

Table 3. Summary of genetic maps produced in B. juncea

996 AFLP, 33 RFLP

183 RFLP 114 RAPD 264 AFLP, 9 RAPD 280 RFLP

15 RFLP 25 RFLP 343 RFLP 296 RFLP

No. loci mapped

18

18

18 21 18

6 9 18 18

No. LGs

20 markers in 7 groups and 16 unlinked 0

4 16 13

– – 5 unassigned groups/13 markers 2

No. unlinked markers

1629

1564

1266 790.4 1641

173.9 243.3 2073 1041

Map length (cM)

3.5

5.2

7.7 6.9 6.3

11.6 9.7 6.6 3.7

Average marker interval (cM)

Chapter 5 Indian Mustard 187

188

D. Edwards et al.

oil-content, white-rust-resistant mustard line as the male parent. The RFLP probes were from anonymous B. napus cDNA markers. The map consisted of 343 loci in 18 LGs, covering 2,073 cM. In addition to the 18 LGs, 13 unlinked markers were assigned to an additional 5 groups. The authors therefore concluded that more than 343 markers are required for generation of a complete map of B. juncea. They observed that 62% of the loci were duplicated, mostly in inter-LG duplications, which the authors believed illustrated genomic duplications and rearrangements. This map was the most detailed linkage map published available in B. juncea and the ﬁrst that could be used efﬁciently for QTL mapping. The overall average marker interval of this B. juncea map was 6.6 cM, which is suitable for efﬁcient use in breeding applications. This cross was later used to locate many markers and QTLs involved in seed quality and disease resistance traits (Cheung et al. 1998a,b; Prabhu et al. 1998). Axelsson et al. (2000) constructed two linkage maps based on RFLP markers in B. juncea. One map was generated from a cross termed BjSYN, between a resynthesised B. juncea (a chromosome doubled interspeciﬁc B. rapa × B. nigra hybrid) and a natural B. juncea cultivar. The second map was produced from a cross termed BjNAT, between two natural B. juncea cultivars, J-o-3DH1 and J-o-7DH1. The use of a common cultivar in both crosses allowed the two maps to be unambiguously integrated. Eighteen LGs were assembled in both maps, corresponding to the 18 chromosome pairs of the B. juncea genome. The BjNAT map consisted of 183 loci, covering 1,266 cM, with an average marker interval of 7.7 cM. In this map four markers were unlinked. The map based on BjSYN included 296 loci, covering 1,041 cM, with two unlinked loci and an average marker interval of 3.7 cM. It was noted that a signiﬁcantly higher proportion of loci were polymorphic in the BjSYN cross. In this cross all loci exhibited disomic inheritance of the parental alleles, suggesting that the B. rapa chromosomes paired exclusively with their A genome homologues in B. juncea and the B. nigra chromosomes likewise paired with their B genome homologues. The maps derived from the two crosses were perfectly colinear at the level of resolution provided by the 137 common loci. These maps were also colinear with maps of the diploid progenitor species, B. rapa and B. nigra, produced using the same set of RFLP probes. These results indicate a high degree of conservation between the A and B genomes of B. juncea and their respective genomes in B. rapa and B. nigra.

5.2.3 Second-Generation Maps Two more genetic maps were produced independently in 2002. Sharma et al. (2002b) employed RAPD markers to construct a linkage map between the cultivars Varuna and BEC144. The mapping population consisted of 94 recombinant inbred lines (RILs). Only 30% of the 235 primers were polymorphic and reproducible. One hundred and fourteen markers were assigned to 21 LGs, with an average marker interval of 6.9 cM and covering a total map length of 790.4 cM. Sixteen markers remained unlinked. The map was incomplete as six of the LGs had only two loci assigned to them (Fig. 1). Lionneton et al. (2002) developed a genetic linkage map of B. juncea based on AFLP and RAPD markers. Up to that point, only RAPD and RFLP maps had been published for B. juncea. The mapping population consisted of 131 F1 -derived DH plants, from a cross between an Indian (BJ-70, a short and early ﬂowering Indian type with brown seeds) and a Russian (BJ-99, a tall, late ﬂowering Russian type known as oriental mustard in Canada with yellow seeds) mustard line. The map included 273 markers (264 AFLP, 9 RAPD) arranged on 18 LGs, covering a total genetic distance of 1,641 cM. There were 13 unlinked markers and an average marker interval of 6.3 cM. This framework map was thought to cover only 72.9% of the estimated B. juncea genome length; however, it is twice the length of that described by Sharma et al. (2002b). This cross was also used to map QTLs involved in the control of oil and FA content in mustard seeds. Mahmood et al. (2003b) constructed an RFLP linkage map of B. juncea, comprising 300 linked loci and 16 unlinked loci. The mapping population originated from a cross and its reciprocal between two B. juncea lines, RLM-514 (a highly inbred non-canola Indian cultivar with high erucic acid and high glucosinolate content) and a canola-quality inbred line (with low erucic acid and low glucosinolate content). Sixty-one DH lines were produced from a single F1 plant from this original cross, called the S population, and an additional 51 DH lines from 7 F1 plants were produced for a reciprocal cross, called the R population. The R and S populations together were called the C population. Overall 276 loci could be mapped in the R population and 307 in the S population, with 276 markers common between the two populations.

Chapter 5 Indian Mustard

189

Fig. 1. Linkage map of B. juncea (L.). Names of markers are indicated on the right side of the LGs. The markers were named after the Operon primers and the size of the ampliﬁed product is expressed as one-tenth its actual size and separated by a decimal point. The numbers on the left side of the LGs denote centiMorgan distances between adjacent markers. LGs have been arranged in descending order of their length. Markers showing segregation distortion (P < 0.01) are underlined. (Adapted and reprinted with kind permission of NRC Research Press from Sharma et al. 2002b)

The maps derived from the two populations were homogeneous and were therefore combined to produce a single map. In the C population map, 280 of the linked loci were organised into 18 LGs and the remaining 20 organised into 7 unlinked segments, covering a total map distance of 1,564 cM, with an average distance of 5.2 cM per locus. The homeologous relationships between the A and B genomes were shown. The A genome (N1-N10) of B. napus (Cheung et al. 1997; Butruille et al. 1999) was used to identify the A genome of B. juncea. Gene order was conserved in most cases; however, rearrangements were also observed. Frequent rearrangements could also be seen in intragenomic comparisons. This was the ﬁrst study to evaluate sex-based differences in recombination fractions in B. juncea, and results suggested an absence of sex-based differences of recombination. This has important implications for genetic analysis and breeding strategies. Since recombination is independent of the cross, either parent can be used as male or female in a breeding program. Furthermore, in-

tegrated maps of B. juncea could be developed from different crosses without consideration of male or female meioses. The most recent genetic map to be produced in B. juncea was described by Pradhan et al. (2003) (Fig. 2), who constructed a high-density genetic linkage map of B. juncea with 996 AFLP and 33 RFLP markers using an F1 -derived DH population of 123 individuals. This was the second AFLP map reported in B. juncea. The mapping population was developed by crossing a well-adapted, extensively grown Indian variety (Varuna) and a canola-quality line (Heera). These two lines are highly divergent and contain a number of contrasting qualitative and quantitative traits of high agronomic value. The 1,029 markers were aligned in 18 LGs, producing a total map length of 1,629 cM with an average marker interval of 3.5 cM. No marker was left stranded out of the 18 LGs, indicating that the map is saturated and 1,029 markers is sufﬁcient to saturate a map. Based on the constructed map, a subset of 26 primer combinations

190

D. Edwards et al.

Fig. 2. A B. juncea linkage map with AFLP and RFLP markers. The map shows the distribution of 1029 markers among 18 LGs (LG1–LG18). LGs are arranged in descending order of length. Cumulative recombination distances are shown on the left and marker loci on the right of the LG bar diagram. RFLP markers have been underlined. Maker loci showing segregation distortion are indicated with an asterisk (∗), and the letter following the asterisk indicates skewness toward a particular parent (v, Varuna; h, Heera (Adapted from Fig. 1 in Pradhan et al. 2003 with kind permission of Springer Science and Business Media)

Chapter 5 Indian Mustard

Fig. 2. (continued)

191

192

D. Edwards et al.

Fig. 3. (continued)

Chapter 5 Indian Mustard

were identiﬁed that would allow extensive coverage of the mapped part of the genome, with the minimum number of AFLP primer combinations. The 384 markers generated by these primer combinations could cover 1,560 cM (96%) of the mapped genome.

5.2.4 Comparative Mapping Comparative genetic mapping allows the transfer of knowledge from one genome to another, related, genome. Comparative mapping is of particular relevance to the breeding of the allotetraploid Brassica crops, where conservation between the three progenitor genomes permits transfer of knowledge to the more complex polyploids. RFLP markers are frequently applied for comparative genetic mapping since they often cross-hybridize to DNA of related species. Therefore, the linkage arrangement of markers in closely related species can be compared if the same set of RFLP markers is used for genetic mapping. The availability of RFLP-based genetic maps of the three diploid genomes allows for detailed comparative analysis. It has been demonstrated that the linear order of genes is conserved over a large evolutionary timescale and this synteny has been observed between the amphidiploid AB and AC genomes and the diploid progenitor genomes. In the study by Axelsson et al. (2000), two RFLP maps of B. juncea were developed and compared. One of the maps was generated using a synthetic B. juncea (a chromosome-doubled interspeciﬁc hybrid of B. rapa and B. nigra) crossed to a natural B. juncea. The second map was generated using two natural B. juncea cultivars. A comparison of these two maps showed that the genomic segments derived from the A and B genomes were perfectly conserved in the AB genome and the two maps were colinear, showing that synteny could extend throughout the entire genome. They concluded that the genomes of B. juncea and its diploid progenitor have remained essentially unchanged since polyploidy speciation. In contrast, Song et al. (1993) suggested that natural Brassica amphidiploids were much more divergent from their progenitor diploid species than resynthesised amphidiploid species. This was supported using genetic linkage mapping by Cheung et al. (1997) (Fig. 4), who reported complex rear-

193

rangements in the B. juncea genome. These experiments suggested that the B. juncea genome has undergone numerous rearrangements since polyploid formation. Colinearity, with few rearrangements, was observed when a B. juncea map was compared to the map of B. napus (Butruille et al. 1999). This strengthens the suggestion by Axelsson et al. (2000) that the complex rearrangements observed by Cheung et al. (1997) might have resulted from the inability to distinguish between inter- and intragenomic comparisons. Comparative genetic mapping may be extended to more divergent species. Brassica species are in the same family (Brassicaceae) as Arabidopsis thaliana, and these genera diverged ca. 15 to 21 million years ago (Koch et al. 2000). DNA sequences of homologous genes in the two taxa are similar; therefore it is possible to use clones for one species as RFLP probes to map loci in the other species. Comparative mapping in B. rapa, B. napus and Arabidopsis suggest possible single locations in the A and C genomes syntenic with resistance clusters on Arabidopsis chromosome 5 (Kole et al. 2002). Comparative genetic mapping between Arabidopsis and the Brassica species B. nigra, B. oleracea, B. rapa and B. juncea in the genomic region controlling ﬂowering time has revealed extensive duplication in the Brassica genome. Axelsson et al. (2001) used QTL analysis to study the evolution of genes controlling ﬂowering time in four genomes: AA, BB, AABB and CC. Comparative mapping showed that a chromosomal region from the top of chromosome ﬁve in Arabidopsis corresponded to six homeologous copies in B. juncea. The segment in Arabidopsis contained three genes known to be important to ﬂowering: CO (CONSTANS), FY and FLC (FLOWERING LOCUS C). CO encodes a putative transcription factor and is a regulator in the photoperiod promotion pathway (Osborn and Lukens 2003) and FLC encodes a MADS domain containing transcription factor and is a key regulator of the autonomous ﬂowering pathway. QTLs were detected in three of these six replicated segments. This indicates that for ﬂowering time, multiple QTLs resulting from genome duplication are the rule, not the exception. Brassica homologues to a candidate gene CO, identiﬁed from the corresponding Arabidopsis region, mapped close to the QTL peaks. FLC mapped further away for six of the seven QTLs while FC was not tested. The ﬂowering time QTLs were also mapped in B. nigra, B. oleracea and B. rapa, and results suggested that the QTLs detected in

194

D. Edwards et al.

Fig. 4. Linkage map of B. juncea (B.j). Linkage group (LG) numbers are indicated above the LGs. Recombination distances between markers are in Kosambi centiMorgans (left). The 343 RFLP loci are assembled into 18 major groups assigned arbitrarily as group 1–18, and 5 smaller segments are labelled as unassigned segments A–E. Thirteen loci remained unlinked to any other marker and are listed at the bottom of the ﬁgure. Intra-LG duplicated loci are indicated by a box around the name of the locus. (Reprinted with kind permission of Springer Science and Business Media from Fig. 1 in Cheung et al. 1997)

Chapter 5 Indian Mustard

the different species could be the result of duplicated copies of the same ancestral gene, probably the ancestor of CO. Comparative mapping suggests that three homeologous regions should occur in diploid species and the amphidiploid should contain six copies. In B. juncea the three QTLs were detected on J2, J3 and J18. A model based on the three QTLs explained a total of 56% of phenotypic variation in ﬂowering time.

5.3 Gene Mapping and Marker-Assisted Selection B. juncea genetic linkage maps have been used to link qualitative and quantitative traits to molecular genetic markers and precisely locate genes for important agronomic traits. These associations and tags are essential for marker-assisted breeding and for selection programs for crop improvement. In B. juncea the main traits to be tagged and used for marker-assisted breeding and selection reﬂect the breeding objectives. These are principally white rust resistance, seed coat color and FA and oil content and quality.

5.3.1 White Rust Resistance Resistance to white rust in B. juncea is believed to be governed by a simple Mendelian inheritance. It is reported that there is monogenic dominant resistance to the A. candida race 2 white rust pathogen, which infects B. juncea (Tiwari et al. 1988; Sachan et al. 1995). The development of marker-assisted selection (MAS) breeding strategies for white rust resistance will be valuable in identifying resistant plants from among segregating populations. DNA-based tests can replace more time-consuming pathology testing and therefore permit the analysis of more plants at a reduced cost. A marker-assisted breeding strategy for the development of white rust resistance cultivars would be useful in both condiment and canola-quality mustard. The locus conferring resistance to white rust has been mapped in B. juncea, and markers linked to this trait have been identiﬁed (Table 4). Some of the earlier work in identifying markers linked to white rust resistance was performed by Cheung et al. (1998a), who identiﬁed RFLPs linked to

195

the white rust resistance gene (Acr). Using a cross from a white-rust-susceptible cultivar (J90-4317) and a white-rust-resistant cultivar (J90-2733), they assessed resistance to A. Candida race 2 in 119 F1 derived DH progeny lines (Cheung et al. 1997). Three markers were identiﬁed on B. juncea LG7, linked to the Acr gene that confers resistance to white rust race 2. These were a co-segregating dominant RFLP marker, X140a, and two closely linked RFLP markers, X42 (dominant) and X83 (co-dominant), 2.3 cM and 4 cM from the Acr locus respectively (Table 4). Further work on this cross was performed by Prabhu et al. (1998), who applied bulk segregant analysis (BSA) in the DH population to identify two RAPD markers, WR2 and WR3, ﬂanking the white rust resistance locus designated Ac21 by Liu et al. (1996) (Table 4). These markers are speciﬁc to resistance from a Russian source, imparting resistance to the predominant Canadian isolate of A. candida and were effective in identifying the presence/absence of the resistance gene in the DH population. Mukherjee et al. (2001) performed molecular mapping of a locus conferring resistance to the white rust pathogen using RAPDs and BSA. In this study, the cultivar Varuna was used as the white-rust-susceptible parent and the cultivar BEC-144 was the white-rust-resistant parent. The researchers identiﬁed 11 RAPD markers, which were able to distinguish the parental lines and the bulked populations. Five of these 11 markers showed linkage with the rust resistance locus, Ac2(t). Somers et al. (2002) identiﬁed this as a different locus to the Ac21 locus identiﬁed by Lui et al. (1996), as this locus was not polymorphic in the Varuna and BEC-144 cross. Two of the ﬁve linked markers were linked in coupling (OPN011000 ) and repulsion (OPB061000 ) phases at 9.9 cM and 5.5 cM respectively on either side of the locus. The work of Mukherjee et al. (2001) was furthered by Varshney et al. (2004), who developed a cleaved ampliﬁed polymorphic sequence (CAPS) marker converted from the RAPD marker (OPB061000 ). This CAPS marker was validated in different F2 populations of B. juncea as being able to distinguish between homozygous and heterozygous white rust resistance. However, the presence of recombinants between the marker and the gene revealed that 3 to 4% of the segregants would be misclassiﬁed based on the marker alone. Therefore the authors developed a tightly linked marker for the gene controlling resistance to the white rust pathogen using AFLP markers. They identiﬁed

Cross

Linkage criteria

Mapmaker 3, minimum LOD threshold = 3, maximum recombination fraction = 0.3 Mapmaker 2, minimum LOD threshold = 6, maximum recombination fraction = 0.4 Mapmaker 3, minimum LOD threshold = 4, maximum recombination fraction = 0.3 Mapmaker 3, minimum LOD threshold = 2.5, maximum recombination fraction = 0.3 Mapmaker 3

Linkage criteria

Negi et al. 2000

No linkage information Skorospieka (female parent, yellow seeded) × Seven AFLP markers linked to either brown or yellow seed color identiﬁed, of RH30 (male parent, brown seeded) which one AFLP8 (E-ACC/M-CTC235 ) was found to be very tightly linked. This was converted to a SCAR marker, SCM-08, and mapped to LG2 by Sabharwal et al. (2004) Sabharwal et al. 2004 Skorospieka (female parent, yellow seeded) × 15 AFLP markers linked to trait identiﬁed. Marker E-ACA/M-CTG350 explained Mapmaker 3, minimum RH30 (male parent, brown seeded) LOD threshold = 3, 69% of variation in seed coat color and with markers E-AAC/M-CTC235 and maximum recombination E-AAC/M-CTA250 explained 89% of total variation. The 15 markers together exfraction = 0.4 plained 99% of the trait. E-ACA/M-CTG350 co-segregated with Gene1 controlling seed coat color on LG1. Seven other markers were also on LG1 with a density of 6 cM, including E-AAC/M-CTA300 (1.6 cM from Gene1) and E-AAC/M-CTA250 (4.5 cM from Gene1). The remaining three markers mapped to LG2 at a density of 3.6 cM. The mendelian trait locus Bjc1 co-segregated with the marker E3 M3_7 Mapmaker 3, minimum Lionneton et al. 2004 BJ-99 (tall, late ﬂowering oriental type on LG3 and Bjc2 co-segregated with the marker E8 M7_4 on LG6 LOD threshold = 4, with yellow seeds) × BJ-70 (short early maximum recombination ﬂowering Indian type with brown seeds) fraction = 0.3

Publication

Marker information

Varuna (highly susceptible) × BEC-144 (resistant) A CAPS marker, developed from the RAPD marker OPB061000 , and an AFLP marker E-ACC/M-CAA350 these ﬂank the Ac2(t) gene at 3.8 cM and 6.8 cM respectively

Varshney et al. 2004

Table 5. Markers linked to seed coat color in B. juncea

J90-4253 (B. juncea susceptible) × S86-69 (B. napus resistant)

Somers et al. 2002

Eight AFLP markers linked to Ac2V 1 B. napus resistance gene locus, introgressed into B. juncea

J90-4317 (female parent, white rust susceptible) × Three RFLP markers: X140a (dominant, co-segregating), J90-2733 (male parent, white rust resistant) X42 (dominant 2.3 cM away), X83 (co-dominant, 4 cM away) with Acr locus on LG7 Varuna (highly susceptible) × BEC-144 (resistant) Two RAPD markers OPN011000 (9.9 cM away) and OPB061000 (5.5 cM away) show linkage with Ac2(t) locus

Cheung et al. 1998a

Mukherjee et al. 2001

J90-4317 (female parent, white rust susceptible) × Two RAPD markers, WR2 (7 cM away) and WR3 (1.4 cM away) linked J90-2733 (male parent, white rust resistant) to white rust resistance locus Ac21 , speciﬁc to Russian source

Prabhu et al. 1998

Marker information

Cross

Reference

Table 4. Markers linked to white rust resistance in B. juncea

196 D. Edwards et al.

Chapter 5 Indian Mustard

an AFLP marker, E-ACC/M-CAA350 , 5 cM away from the Ac2(t) gene. When used in combination with the CAPS marker, observed misclassiﬁcation was reduced to only 0.25%. Research has also been conducted on partial resistance of white rust race 2 (Bansal et al. 1999). These authors found that the parent DH line, with partial resistance, had some plants with complete resistance, and that this complete resistance was carried over into subsequent generations. The segregation of the DH lines showed no distortion from the expected 1:1 Mendelian ratio, suggesting that partial resistance for white rust disease is controlled by a single gene and is simply inherited; however, only 11 lines were tested in total. The authors suggest that partial resistance may be advantageous over complete resistance, as the pathogen may have limited reproduction capacity and may therefore be under reduced selective pressure for the emergence of new virulent strains. Another variant of race 2, 2V, for which there is no natural resistance in B. juncea, has been identiﬁed (Rimmer et al. 2000). Somers et al. (2002) have reported that the development of canola-quality B. juncea, via interspeciﬁc crosses of B. juncea and B. napus, has led to the introgression of white rust resistance, to race 2V from B. napus into B. juncea. They phenotyped and screened a BC3 F2 population of condiment B. juncea mustard for AFLP markers associated with the race 2V resistance using BSA. Eight markers, linked to white rust resistance, were identiﬁed, all derived from B. napus. The B. napus chromosome segment, carrying the white rust resistance gene (Ac2V 1 ), appeared to have recombined with the B. juncea DNA as recombinant individuals were identiﬁed. Subsequent comparative mapping of the eight B. napus-derived AFLP markers in a B. napus mapping population was inconclusive, and therefore the size of the introgressed fragment could not be determined. The authors conclude that if multiple genes controlling resistance to 2V are in B. napus, then it is possible that not all the resistance genes would be introgressed into B. juncea. The resistance genes on the A genome of B. napus may more readily be introgressing into the B. juncea A genome, while any C genome resistance genes from B. napus would be rarely transmitted due to the very low frequency of pairing observed between the C genome chromosome of B. napus and the B genome chromosomes of B. juncea (Attia et al. 1987). The recombination and introgression observed by Somers et al. (2002) are likely to represent segments from the A genome.

197

5.3.2 Seed Coat Color The genetics of seed coat color has been studied in order to localise areas of the genome that control this trait. Vera et al. (1979) and Vera and Woods (1982) demonstrated that seed coat color in B. juncea is under the control of two genes at two loci, which are under exclusive maternal inheritance. Later work by Thiagarajah and Stringam (1993) showed that seeds are yellow when both alleles are recessive and brown when a single dominant gene is present. As the yellow seed coat color is recessive and maternally inherited, it would be advantageous to ﬁnd molecular markers linked to the seed coat color loci for application in MAS. The molecular markers identiﬁed to date are described in Table 5. Negi et al. (2000) used a combination of AFLPs and BSA to identify markers linked to seed coat color in B. juncea. Seven AFLP bands speciﬁc to either brown or yellow seed coat color were identiﬁed in the pooled samples of homozygous yellow and brown coated lines. One of these bands, AFLP8 (E-ACC/M-CTC235 ), was found to be very tightly linked. Sequences adjacent to this marker were isolated and characterized using PCR walking and converted to a PCR-based codominant sequence-characterized ampliﬁed region (SCAR) marker: SCM-08. This marker produced two fragments in the yellow-seeded varieties, indicating the presence of two loci. Furthermore, a 15:1 segregation of brown:yellow coat was observed, suggesting two genes as proposed previously by Anand et al. (1985). This SCAR marker distinguished yellow and brown lines and between the heterozygous and homozygous brown seeded in different accessions of B. juncea. Sabharwal et al. (2004) performed association mapping of the seed coat color using AFLPs in 39 B. juncea lines. These lines had genetically diverse parentage and varied for seed coat color and other morphological characters. The 335 polymorphic AFLP bands detected were analysed for association with seed coat color using multiple regression analysis, revealing 15 markers associated with seed coat color, from eight primer combinations. Of these 15 AFLP markers, six ampliﬁed only in the yellow lines and the remaining nine ampliﬁed only in the brown lines. The marker E-ACA/M-CTG350 explained 69% of the variation in seed coat color. Along with markers E-AAC/M-CTC235 and E-AAC/M-CTA250 , these three markers explained 89% of the total

198

D. Edwards et al.

variation while the 15 markers together explained 99% of the variation in the trait. These 15 associated AFLP markers were also validated for linkage with the seed coat color loci using a RIL mapping population, and the position of one of the genes controlling seed coat color was mapped. The two parents of the mapping population represent two genetically and morphologically diverse accessions that differ for a number of traits. One parent, Skorospieka, is tall, yellow seeded, late ﬂowering and resistant to white rust, whereas the second parent, RH30, is short, brown seeded, early ﬂowering and susceptible to white rust. A total of 144 segregating F2 individuals were obtained, and these were selfed to generate F3 families. Eleven of the 15 associated markers mapped onto two LGs, designated LG1 and LG2. The marker E-ACA/M-CTG350 , speciﬁc for brown seed coat color, co-segregated with Gene1 controlling seed coat color on LG1. Seven other markers were placed on LG1, and the entire interval mapped with AFLP markers around Gene1 was 41.5 cM, with a marker density of 6 cM. Markers E-AAC/M-CTA300 and E-AAC/M-CTA250 mapped to a distance of 1.6 and 4.5 cM from Gene1 respectively. The remaining three markers mapped to LG2 at a marker density of 3.6 cM. These included the marker E-AAC/M-CTC235 , which had been studied previously (Negi et al. 2000). Lionneton et al. (2004) have studied the genetic control and relationships of several characters of B. juncea in order to develop an efﬁcient breeding program. Characters assessed include days to ﬂowering, plant height, thousand-seed weight, FA composition, seed oil content, sinigrin, gluconapin and the effect of seed coat color in a DH population grown in the ﬁeld over 2 years. Seed coat color was found to be associated with two Mendelian trait loci on two distinct LGs: Bjc1 co-segregated with the marker E3 M3_7 on LG3 and Bjc2 co-segregated with the marker E8 M7_4 on LG6. The hypothesis of two loci controlling this trait was consistent with reports of other authors (Negi et al. 2000).

to genes for various FAs such as oleic and erucic acids (Table 6). Several QTLs associated with FA proﬁles have been mapped in B. juncea (Cheung et al. 1998b; Lionneton et al. 2002; Mahmood et al. 2003b). Early work on markers associated with FA traits was performed by Cheung et al. (1997), who constructed an RFLP linkage map of canola-quality mustard using a segregating F1 -derived DH population. The locations of QTLs associated with oil content were identiﬁed in this population (Cheung et al. 1998b). Three further studies undertaken in 2002 identiﬁed markers and QTLs associated with FA content (Bhat et al. 2002; Lionneton et al. 2002; Sharma et al. 2002b). Through work in B. carinata, it was thought that in the three amphidiploid Brassica species, a higher level of erucic acid was attributable to alleles at two loci, E1 and E2, which act in an additive manner (Getinet et al. 1997). Recognising the amphidiploid nature of B. juncea, the genes for high erucic acid in this crop may have come from both diploid progenitors. This was conﬁrmed in B. juncea by Bhat et al. (2002), who suggested that, despite the long history of amphidiploidy, both genes remain active. It was further shown that these genes do not contribute uniformly to total erucic acid content. Experimental data showed that the gene, E2, associated with the A genome, provided a greater contribution to the total erucic acid content in B. juncea than the gene, E1, residing on the B genome, supporting previous suggestions of unequal contributions of two dominant genes. The genes E1 and E2 are thought to contribute 12% and 20% erucic acid levels respectively, implying ca. 64% erucic acid in a high erucic acid genotype (E1E1E2E2) in amphidiploid Brassica species. Using this theory, in the absence of dominance, erucic acid in the F1 (E1e1E2e2) cross between low erucic acid and high erucic acid genotypes should be ca. 32%. The study of Bhat et al. (2002) proved this theory true, with a demonstrated mean of 29.3% erucic acid in the F1 . The majority of published markers linked to genes for FAs in B. juncea have been concentrated on those associated with erucic acid. The ﬁrst detailed linkage of markers and localisation of QTLs associated with 5.3.3 oleic acid was presented by Mahmood et al. (2003b). Fatty Acid/Oil Content In a population of 94 RILs, 114 RAPD markers were As B. juncea is grown in many countries for the pro- assigned to 21 LGs, covering a total length of 790.4 cM duction of oil, the manipulation of oil quality and with an average marker distance of 6.93 cM. This popquantity has been a primary objective of many B. ulation was used to locate QTLs for oleic acid level and juncea breeding programs. There has been a concen- signiﬁcant association was found with seven markers tration of studies to generate markers and QTLs linked for three QTLs. A QTL on LG9 was located between the

Varuna (Indian, high erucic acid) × Heera (canola quality, zero erucic acid)

Gupta et al. 2004

Mahmood et al. 2003b RLM-514 × canola quality inbred

BJ-99 (tall, late ﬂowering oriental type with yellow seeds) × BJ-70 (short early ﬂowering Indian type with brown seeds)

Lionneton et al. 2002

–

Linkage criteria

Mapmaker 3, minimum Three QTLs, associated with seven markers controlling oleic acid content, were LOD threshold = 3, identiﬁed. These markers were located on LGs 1, 7 and 9. The QTLs explained between 12.4 and 28.5% of phenotypic variance individually and 32.2% collectively maximum recombination fraction = 0.3. SYSTAT and MAPMAKER/QTL 1.1 Mapmaker 3, minimum Two QTLs for total oil content were identiﬁed, explaining 17.1% and 9.2% LOD threshold = 5 variation individually. QTLs were also identiﬁed for all the individual fatty acids, explaining between 10.3 and 51.8% phenotypic variation individually. Seven of these QTLs were associated with the same marker – Two Fatty Acid Elongase genes were characterized, these were associated with 2 QTLs. FAE1.1 originates from the A genome and contributes 60% phenotypic variation. FAE1.2 originates from the B genome and contributes 38% phenotypic variation Mapmaker 3, minimum Two QTLs associated with erucic acid content, explaining 32 to 53% of LOD threshold = 2.4 variation individually and 85.8% collectively were found. These also had association with oleic and linolenic acid content, explaining 97% and 85% variation respectively. Three further QTLs were found to associate with linolenic acid explaining 76.5% variation collectively and 4.2 to 35.5% individually

Two genes linked controlling erucic acid levels were identiﬁed. The gene E1, from the B genome, and the gene E2, from the A genome, contribute 12% and 20% of variance respectively

Three crosses: QM14 (zero erucic acid) × RL1359 (high erucic acid), QM11 (zero erucic acid) × PBR91 (high erucic acid) and QM11 × CCWF 16 (intermediate erucic acid) Varuna (Indian, low erucic acid) × BEC144 (high erucic acid)

Bhat et al. 2002

Sharma et al. 2002b

Marker and QTL information

Cross

Publication

Table 6. Markers and QTLs associated with fatty acid and oil content in B. juncea

Chapter 5 Indian Mustard 199

200

D. Edwards et al.

markers OPF 081000 and OPI 101000 , with the marker OPK 121000 also in linkage. This QTL explained 28.5% of the trait variance and was considered the major locus. Another conﬁrmed QTL was positioned in the marker interval between the linked markers OPJ 181300 and OPG 091000 on LG1. This QTL had a smaller effect, explaining 12.4% of the variance in the trait. These two conﬁrmed QTLs together explain 32.2% of the variance in the oleic acid level of mustard seed. The third QTL was associated with the markers OPA 11400 and OPD 06600 on LG17; however, the position could not be determined in this study. By using ﬂanking markers for the markers OPF 081000 and OPI 101000 simultaneously, the QTLs can be identiﬁed in segregants with a misidentiﬁcation rate of 0.4%. Lionneton et al. (2002) produced a preliminary study analysing the phenotypic and genetic relationship of FA content, describing a genetic map of a DH population. By performing QTL analysis of oil content, palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid, eicosenic acid and erucic acid, they identiﬁed several genomic regions associated with the traits. Two QTLs controlling oil content in mustard seeds were located. One of these was on LG11 linked to marker E7M4_7a, which explained 17.1% of the variation of oil content. The second QTL was located on LG18, associated with the marker E1M5_3, which explained 9.2% of the trait variation. These two QTLs were shown to have opposite effects, with the oil alleles being derived from the different parents of the cross. The mapping of FA QTLs identiﬁed a major QTL location on LG2 associated with the marker E4M1_4, which was associated with all individual FA content. These major QTLs for palmitic, stearic, oleic, linoleic, linolenic, eicosenic and erucic acids explained 26.1%, 15.8%, 51.8%, 41.2% 10.3%, 21.3% and 24% of the phenotypic variation respectively. Another independent major QTL for palmitic acid, on LG6 at marker E1M2_11, was also mapped explaining 14.1% of the phenotypic variation of this trait. A minor QTL for oleic acid, explaining 9.5% of the variation, was found on LG6. For linoleic acid a major QTL (41.2%) was detected on LG2. A QTL explaining 8.1% of linolenic acid variation located on LG6 was found. Two further QTLs for eicosenoic acid were detected, one on LG2 explaining 6.9% of the variation and the other on LG3 explaining 10.8% of the variation. Two further QTLs for erucic acid, on LG2 and LG1, were also identiﬁed. The correlation analysis showed that palmitic, stearic, oleic, linoeic and linolenic acid are all positively correlated with

each other and are negatively correlated with both eicosenoic and erucic acids. The QTLs for oleic acid conﬁrmed the work of (Sharma et al. 2002b), with the presence of one major and one minor locus, suggesting that the QTL corresponds to the Fatty Acid Elongase 1 gene (FAE1). Further analysis of the FAE1 gene was performed by Gupta et al. (2004). The 123 DH lines previously used for the construction of a linkage map in B. juncea were used for detecting QTLs for erucic acid variation (Pradhan et al. 2003). This study found two FAE1 genes, FAE1.1 and FAE1.2, in high and low erucic acid mustard lines, with four substitution single nucleotide polymorphisms (SNPs) in FAE1.1 and three in FAE1.2. These seven SNP markers were used to map the two genes to independent loci that co-segregated with the two QTLs governing the erucic acid trait. FAE1.1 mapped to LG17 at a position coinciding with the highest LOD value of the QTL, explaining 60% of the phenotypic variance, and FAE1.2 mapped to LG3 at a position coinciding with the highest LOD value of that QTL, explaining 38% of the phenotypic variance. All seven SNPs could distinguish low from high erucic acid types and the heterozygotes were found to be intermediate between the two phenotypes. High sequence identity between FAE1.1 and the FAE1 gene from B. napus suggested that B. juncea FAE1.1 is homologous to the FAE1 gene from B. rapa and FAE1.2 in B. juncea is homologous to the FAE1 gene from B. nigra. This is the ﬁrst public description of the application of SNPs in B. juncea, and the applicability of the SNPs in marker-assisted manipulation of the erucic acid trait was veriﬁed by genotyping a set of contrasting germplasm of B. juncea belonging to two distinct gene pools (Indian and East European). The RFLP linkage map described by Mahmood et al. (2003a) was applied for the mapping of genes controlling the FA proﬁle of B. juncea. Two QTLs were identiﬁed, E1a and E1b , which signiﬁcantly affected erucic acid content. They individually explained 53.7% and 32.1% of variation respectively and collectively explained 85.8% of the phenotypic variation in the population. These two QTLs showed epistasis, and the full model, including epistasis, explained nearly all of the phenotypic variation in the population. These QTLs were also found to be associated with oleic, linoleic and linolenic acid content, working in the opposite manner, explaining 97% and 85% of the oleic and linoleic variation respectively. Three additional QTLs signiﬁcantly in-

Chapter 5 Indian Mustard

ﬂuenced linolenic acid content, LN2, LN3 and LN4. Collectively the ﬁve QTLs explained 76.4% and individually 4.2 to 35.4% of the phenotypic variation. These results conﬁrm the previous ﬁndings of a two-gene model for the inheritance of erucic acid content with additive gene action in B. juncea (Kirk and Hurlstone 1983; Bhat et al. 2002). Mahmood et al. (2003b) propose that the same two genes controlling erucic acid content also affect the quantity of linoleic acid; however, there appears to be three additional genes controlling linolenic acid in B. juncea, corresponding to the three extra QTLs identiﬁed.

5.3.4 Glucosinolate Content Studies are limited on the genetics and inheritance of glucosinolates in B. juncea. Early work was performed by Love et al. (1990a), who reported on the genetic control of the synthesis of glucosinolates 2-propenyl and 3-butenyl in mustard. Later work by Stringam and Thiagarajah et al. (1995) indicated that there were ﬁve to nine genes controlling aliphatic glucosinolates in B. juncea. Breeding for low glucosinolate content in B. juncea was initiated in Canada. In this work Love et al. (1990a) transferred genes for low 3-butenyl glucosinolate content to B. juncea by crossing an Indian B. juncea strain, with high 3-butenyl glucosinolate, with B. rapa canola to produce the low glucosinolate B. juncea breeding line 1058. However, this line demonstrated poor fertility, and Cheng et al. (2001), using cytogenic studies, later found that the line was nullisomic, missing one pair of B-genome chromosomes. A genetic analysis of total glucosinolate content in B. juncea was reported by Sodhi et al. (2002). Using high-performance liquid chromatography (HPLC) they analysed the glucosinolate content and composition of B. juncea and found that varieties grown and bred in India had a high glucosinolate content characterized by the presence of 2-propenyl (allyl) and 3-butenyl fractions. In contrast, germplasm from other countries was characterized by the presence of 2-propenyl as the major glucosinolate fraction with little 3-butenyl glucosinolate. In order to transfer the low glucosinolate trait to Indian B. juncea, Sodhi et al. (2002) investigated the inheritance of total glucosinolates using DH

201

populations derived from a cross between Varuna (the most widely cultivated high glucosinolate variety of India) and Heera, a non-allyl-type low glucosinolate line. The 752 DH1 and 1263 BC1 DH produced gave rise to 7 and 11 low glucosinolate individuals respectively. On the basis of the frequency of these low glucosinolate individuals, the total glucosinolate level was estimated to be under the control of seven genes. The authors concluded that, due to the large number of genes involved, incorporation of the low glucosinolate trait into Indian B. juncea should be approached through DH breeding. The mapping of markers and the detection of QTLs associated with seed aliphatic glucosinolates in B. juncea was performed independently by Cheung et al. (1998b) and later by Mahmood et al. (2003a). Using the mapping population previously described by Cheung et al. (1997), Cheung et al. (1998b) identiﬁed two QTLs for 2-propenyl glucosinolate that explained 89% of the phenotypic variance in the population and three QTLs for 3-butenyl glucosinolate that explained 81% of the variance in the population. Mahmood et al. (2003b) performed QTL analysis across three locations over 2 years. Using MAPQTL version 3 (Van Ooijen and Maliepaard 1996) and a LOD value of 2.4, eight QTLs controlling aliphatic glucosinolate inheritance were identiﬁed using their previously described genetic linkage map (Mahmood et al. 2003a). Five of these QTLs (GSL-A3, GSL-B5, GSLA7, GSL-B3 and GSL-F) were found to signiﬁcantly affect total glucosinolate content. Individually these QTLs explained 6.7 to 20.9%, and collectively 29.5 to 45.1%, of the total phenotypic variance in the different environments. However, only the QTLs GSL-B3 and GSL-F were signiﬁcant across all environments. In this study, QTL analysis was also performed for the individual glucosinolates. Four QTLs were shown to signiﬁcantly inﬂuence 2-propenyl glucosinolates in different environments. These four QTLs collectively explained ca. 57.9 to 78.2% of the total phenotypic variance in different environments, and individually they explained 5.8 to 49.8% of the observed variation. Only one of these QTLs, GSL-A2a was not shown to affect total glucosinolate content. This and another major QTL, GSL-A2b, were only associated with individual and not total glucosinolate content when performing QTL analysis for 3-butenyl glucosinolate content. These two QTLs were consistent across all environments and explained 35.3 to 41.6% and 19.7 to 33.1% of the phenotypic variance respec-

202

D. Edwards et al.

tively. It was also observed that several QTLs were inconsistent across different locations. Mahmood et al. (2003a) propose a MAS strategy based on QTLs associated with individual glucosinolates rather than total aliphatic glucosinolates for germplasm enhancement in B. juncea.

5.4 Marker-Assisted Breeding The evaluation of genetic divergence and relatedness of breeding materials is important for crop plant improvement. Besides predicting estimates of genetic variation within a species, they facilitate planning of new breeding approaches for cultivar development. Diverse parental combinations can either be used to create segregating progenies with maximum genetic variability for selection or for heterosis breeding. Cultivar improvement of B. juncea has mainly followed breeding methodologies for self-pollinating grain crops. Over the past two decades, breeding programs, especially in India, have made selections from segregating populations of crosses amongst varieties from regional programs. Crossing within such germplasm has resulted in only marginal improvements in the productivity of B. juncea. To realise further gains in productivity, it is important to utilise new sources of variation, which will lead to broadening the genetic base of the existing varieties. Molecular genetic markers are increasingly being recognised as useful tools for assessing genetic diversity amongst germplasm as they are least inﬂuenced by the environment. Kumar and Gupta (1985) used isozymes in an early study of genetic diversity in B. juncea. Jain et al. (1994), using 32 RAPD primers, conducted later studies and found that 378 of the 500 identiﬁed RAPD fragments were polymorphic, with an average of 11.8 polymorphic loci per primer. The cultivars of Indian B. juncea are bred from a narrow genetic pool (Pradhan et al. 1993), which limits improvement through crop breeding. Resynthesised B. juncea increases the genetic variation, which may be useful for breeding programs and may produce breeding material for transferring desirable characters such as yellow seed color, earliness, pest and disease resistance to new crop cultivars. Resynthesis of Brassicas has been used for the introgression of desirable traits and genes and for the generation of morphological and physical variation in

B. napus and B. juncea. Srivastava et al. (2001) calculated genetic diversity in 21 agronomically important natural and newly synthesised lines of B. juncea using AFLP molecular markers. These 21 lines originated from Asia, Australia, Canada, eastern Europe and Russia. Seven hundred and seventy-eight of the 1251 scorable bands were shown to be polymorphic and the 21 lines clustered into three distinct groups. All the Indian lines, Chinese lines and previously developed B. juncea synthetics formed one group, the recently developed B. juncea synthetics formed a separate cluster, and lines from Australia, Canada, Eastern Europe and Russia grouped into the third cluster. This indicates that the diversity exhibited by newly synthesised B. juncea lines might act as a new source of variation, as these may be developed using diverse B. rapa and B. nigra lines. The researchers extended this study (Srivastava et al. 2004) and resynthesised B. juncea through interspeciﬁc crosses between B. rapa and B. nigra in order to broaden the genetic base of B. juncea. This was performed using ten diverse parental lines of B. rapa and two lines of B. nigra, of Indian and exotic origin. Recently, two independent assessments of the genetic diversity of Brassica species and B. juncea have been performed (Bornet and Branchard 2004; Burton et al. 2004). Bornet and Branchard (2004) used intersimple sequence repeat (ISSR) ﬁngerprints to detect microsatellites and genetic diversity in several related Brassica taxa and A. thaliana including one B. juncea accession. SSRs were found to be less abundant in B. napus and B. juncea than in B. oleracea, B. carinata, B. nigra and B. rapa. Brassica species formed two groups: the ﬁrst comprised of B. juncea, B. nigra and B. rapa and the second group comprised of B. carinata, B. napus and B. oleracea lines. This study concluded that the diploid C genome showed a higher degree of conservation than the A or B genomes. Burton et al. (2004) used AFLP markers to assess the genetic diversity of 77 breeding lines from three of the world’s major canola-quality B. juncea breeding programs. These lines originated from Canada (Agriculture and Agri-Food Canada and Saskatchewan Wheat Pool; AAFC, SWP) and Australia (Agriculture Victoria). Fifteen lines of mustard-quality B. juncea from India, China, Russia and Australia were also included in the investigation. Seven hundred and ﬁfty-one scorable fragments were produced, with an average of 26 polymorphic bands per primer pair (35%). Analysis of the dendrogram produced

Chapter 5 Indian Mustard

203

by the Unweighted Pair Group Method with Arithmatic Mean (UPGMA) method, indicated partitioning of the germplasm into ﬁve main groups. Group 1 consisted mainly of Agriculture Victoria mid-oleic acid B. juncea. Group 2 contained Russian and Canadian mustard-quality B. juncea lines. Group 3 included a mixture of mid- and high oleic B. juncea from Agriculture Victoria, SWP and AAFC. Group 4 consisted of high oleic acid B. juncea lines from AAFC, and Group 5 contained Indian mustard-quality B. juncea lines. The two mustard-quality B. juncea lines of Chinese origin examined in the study both grouped with the Russian and Canadian mustard group (Group 2). This study by Burton et al. (2004) is the ﬁrst time the world’s elite canola-quality B. juncea germplasm has been evaluated for genetic variability. There was more variation identiﬁed in the lines screened from the Agriculture Victoria and SWP programs compared to the AAFC program. The AAFC elite selected lines were the least similar to the two released cultivars Arid and Amulet, whereas the SWP elite lines were the most similar, as would be expected. Some of the SWP canola-quality B. juncea base germplasm was derived from the AAFC germplasm. Agriculture Victoria and SWP have more recently exchanged germplasm as part of a collaborative project and Agriculture Victoria’s source of high oleic acid was from the SWPreleased cultivar Arid (which has shown adaptation and good quality in western Canada) and has since been extensively used in the Australian program. The Australian program differs from the Canadian programs in that Indian germplasm has been used more extensively as a parental source of earliness and reduced height. Understanding genetic variability has implications for future breeding and collaboration for each of the individual canola-quality B. juncea breeding programs. Maintaining diversity will be important once a hybrid system becomes available in canolaquality B. juncea, and diversity between the programs will also be important in obtaining genetic gain for yield, disease, quality and agronomic characteristics.

ment. Yao et al. (2003) detail the application of genetic engineering to reduce the saturated FA level in B. juncea. An Arabidopsis gene, ADS1, was overexpressed in B. juncea in order to assess gene function. Analysis of the resulting FA proﬁle in transgenic plants suggested that the gene encoded a FA desaturase. There was no cross-hybridization of the Arabidopsis ADS1 cDNA probe to the B. juncea genome, indicating that any B. juncea gene homologue shares little or no sequence identity with the Arabidopsis gene. Due to the potential value of the ADS1 gene on seed oil modiﬁcation, the performance of the transgenic plants was assessed in ﬁeld trials. Transgenic ADS1 plants demonstrated a dramatic decrease in saturated FA content compared to wild type B. juncea. The reduction in saturated FA level is accompanied by an increase in oleic acid but not in palmitoleic acid. Oleic acid has proven effective in lowering cholesterol in human blood plasma, and oils containing higher oleic acid content can be heated to a higher temperature without smoking and exhibit greater oxidative stability. In contrast, palmitoleic acid may have some health disadvantages, such as behaving as a saturated FA in its effect on LDL cholesterol levels and an association with high blood pressure. A major goal of phytoremediation is to transform fast-growing plants with genes from plant species that hyperaccumulate toxic trace elements. LeDuc et al. (2004) over-expressed a gene encoding a selenocysteine methyltransferase (SMT) from the selenium hyperaccumulator Astragalus bisulcatus in both Arabidopsis and B. juncea. SMT detoxiﬁes selenocysteine by methylating it to methylselenocysteine, diminishing the toxic misincoproration of selenium into protein. B. juncea transgenic plants expressing SMT accumulated more selenium in the form of methylselenocysteine than the wild type, and SMT transgenic seedlings demonstrated a greater tolerance to selenium than the wild type.

5.5 Transgene-Assisted Breeding

5.6.1 Gene Discovery and Expression

5.6 Advanced Works

The GenBank database currently contains sequences There have been few cases of the application of trans- for just over 200 B. juncea expressed genes. This genic technologies for B. juncea germplasm improve- is compared to 322,645 expressed sequence tags

204

D. Edwards et al.

(ESTs) for Arabidopsis, 37,159 for B. napus and over 7,000 for B. rapa. However, many proprietary EST sequences exist for B. juncea. At the Plant Biotechnology Centre, Melbourne, there are 4,503 B. juncea ESTs maintained within a BASC format database (http://bioinformatics.pbcbasc.latrobe.edu.au/), along with ESTs for B. nigra and B. napus (Love et al. 2004b). While these sequences are not available to the public directly, the sequences have been electronically mapped onto the complete Arabidopsis genome sequence and can be identiﬁed through their homologous Arabidopsis location. It would be expected that increasing numbers of ESTs for B. juncea will become publicly available over the next few years. EST sequences derived from Brassica species which share the A and B progenitor genomes, B. nigra (BB), B. napus (AACC), B. rapa (AA) and B. carinata (BBCC), may be expected to share a signiﬁcant sequence identity to gene sequences from B. juncea. These sequences may be used to identify B. juncea homologues through hybridization or PCR-based methodologies. The high degree of sequence conservation within the different Brassica species enables the transfer of tools and knowledge between each of the Brassica species and beyond to related species. Simple sequence repeat (SSR) molecular genetic markers are generally considered to be transferrable between Brassica species and frequently share syntenic positions on each of the respective genomes. Recent tools for the discovery of SSR markers from sequence data have been applied to Brassica EST and genomic sequences (Robinson et al. 2004). Mining of the 61,700 Brassica sequences currently maintained at the Plant Biotechnology Centre, Melbourne, has identiﬁed a total of 2,370 EST-SSR molecular markers (Love et al. 2004a,b). A further 47,000 SSR molecular markers have been identiﬁed through screening 450,000 B. oleracea genome shotgun sequences from TIGR. These markers may be used for comparative mapping in Brassica. The comparison of these marker sequences with the complete genome sequence of A. thaliana further extends the comparison between these species. Tools for gene expression studies may also be shared between related species. Gene expression cDNA microarrays developed from one Brassica species may be readily applied to related species. Oligonucleotide microarrays may also be applied between related species, though the results need to be analysed cautiously due to the increased speciﬁcity

of oligonucleotide arrays (Lee et al. 2004). There are currently few reports of the development of Brassica microarrays. A 3,000 feature B. rapa microarray has been produced by Yang et al. (2005) and applied to identify changes in gene expression in response to low temperatures. A 7,000 unigene Brassica cDNA array has been produced which includes expressed sequences from B. napus, B. nigra and B. juncea (Kaur et al. 2005). This microarray has been applied to assess B. napus gene expression in response to infection with the blackleg fungus Leptosphaeria maculans. However, there are currently no speciﬁc reports of either a B. juncea speciﬁc microarray or the application of microarray technology to study gene expression in B. juncea.

5.7 Future Scope of Works Members of the Brassicaceae offer especially excellent opportunities for comparative genome analysis. The release of the complete sequence of the genome of Arabidopsis has had a major impact on Brassica genomics through the identiﬁcation of the complete set of genes required for the growth and development of a plant. The close phylogenetic relationship of Arabidopsis to Brassica crops presents a unique chance to transfer information and resources developed within the Arabidopsis Genome Project to crop Brassica species. Several hundred RFLP markers for Arabidopsis are available for comparative mapping. As the complete genome sequence of Arabidopsis is available, the chromosomal position and copy number for each marker can be determined. The Arabidopsis genome may act as an anchor genome, markers positioned on it can be utilised for reciprocal localisation of markers on Brassica species (Lakshmikumaran et al. 2003). The knowledge of the position of genes controlling qualitative and quantitative traits can also be used to predict the location of homologous genes for these traits in related species. While it is unlikely that there will be any sequencing of the amphidiploid Brassica species in the near future, there are several efforts aimed at gaining sequence information for the A and C diploid Brassica genomes. The Institute for Genome Research (TIGR), in collaboration with the Cold

Chapter 5 Indian Mustard

Spring Harbor Laboratory, has undertaken a partial genome shotgun sequencing of the Brassica C genome with the aim of using this information to further annotate the complete Arabidopsis genome (http://www.tigr.org/tdb/e2k1/bog1/). While this sequence information is of greatest value to understand the C genomes of B. oleracea and the AC amphidiploid B. napus, the information gained from this study may be applied to research on all Brassica genomes in providing Brassica-speciﬁc gene and gene-promoter information. Of greater interest to B. juncea researchers would be the current plan to sequence the complete A genome of B. rapa by a multinational consortium by the end of 2007. The approach being taken in the Multinational Brassica Genome Sequencing Project is a robust BAC-by-BAC method which aids the assembly of a complex genome such as Brassica which has undergone many rounds of gene and genome duplication. While it is expected that there has been some sequence and genomic divergence between the A genome from B. rapa which is being sequenced and the A genome present within the amphidiploid B. juncea, differences are expected to be minimal, with a signiﬁcant degree of microsynteny, conserved gene function and expression. The availability of a complete Brassica genome offers major beneﬁts for crop improvement. Candidate genes for valuable traits may be readily identiﬁed and perfect molecular markers for traits designed and applied for germplasm improvement. The identiﬁcation of genes responsible for traits also provides a greater level of understanding of the fundamental biology underlying traits, permitting novel means of trait improvement through transgenic approaches with native or modiﬁed genes or through the breeding selection of divergent genes from broad crosses. With the ever-reducing cost of genome sequencing and the forthcoming availability of the B. rapa genome sequence which may be used as a framework for comparison, it would be expected that efforts to determine the complete B. juncea genome sequence will be undertaken at some point in the future. The B genome, being the most diverse of the three diploid Brassica genomes, offers a valuable source of novel gene information which could be applied both to improvement of B. nigra, B. juncea and B. carinata as well as related Brassica crops. The availability of the complete B. juncea genome sequence, combined with broad gene expression and proteomic

205

data, will one day permit greatly advanced methods for germplasm improvement for this agronomically important crop.

References

Agnihotri A, Kaushik N (1998) Transgressive segregation and selection of zero erucic acid strains from intergeneric crosses of Brassica. Int J Plant Genet Resource 11:251–255 Ahuja I, Bhaskar P, Banga S, Banga S (2003) Synthesis and cytogenetic characterisation of intergeneric hybrids of Diplotaxis siifolia with Brassica rapa and B. juncea. Plant Breed 122:447–449 Anand I, Reddy W, Rawat D (1985) Inheritance of seed color in mustard. Int J Genet 45:34–37 Anonymous (2002) Procedures of the Western Canada Canola/Rapeseed Recommending Committee for the evaluation and recommendation for registration of canola/rapeseed candidate cultivars in western Canada, p 31 Arumuganathan K, Earle ED (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9:211–215 Attia T, Busso C, Robbelen G (1987) Digenomic triploids for an assessment of chromosome relationships in the cultivated diploid Brassica species. Genome 29:326–330 Axelsson T, Bowman CM, Sharpe AG, Lydiate DJ, Lagercrantz U (2000) Amphidiploid Brassica juncea contains conserved progenitor genomes. Genome 43:679–688 Axelsson T, Shavorskaya O, Lagercrantz U (2001) Multiple ﬂowering time QTLs within several Brassica species could be the result of duplicated copies of one ancestral gene. Genome 44:856–864 Ballinger DJ, Salisbury PA (1996) Seedling and adult plant evaluation of race variability in Leptosphaeria maculans on Brassica species in Australia. Aus J Exp Agric 36:485–488 Banga S, Bhaskar P, Ahuja I (2003a) Synthesis of intergeneric hybrids and establishment of genomic afﬁnity between Diplotaxis catholica and crop Brassica species. Theor Appl Genet 106:1244–1247 Banga S, Deol J, Banga S (2003b) Alloplasmic male-sterile Brassica juncea with Enarthrocarpus lyratus cytoplasm and the introgression of gene(s) for fertility restoration from cytoplasmic donor species. Theor Appl Genet 106:1390–1395 Bansal VK, Thiagarajah MR, Stringam GR, Tewari JP (1999) Inheritance of partial resistance to race 2 of Albugo candida in canola-quality mustard (Brassica juncea) and its role in resistance breeding. Plant Pathol 48:817–822 Beare JL, Campbell JA, Youngs CG, Craig BN (1963) Effects of saturated fats in rats fed rapeseed oil. Can J Biochem Physiol 41:605–612 Bell J (1984) Nutrients and toxicants in rapeseed meal: a review. J Anim Sci 58:996–1010

206

D. Edwards et al.

Bennett M, Smith J (1976) Nuclear DNA amounts in angiosperms. Philos Trans R Soc Lond 274:227–274 Bhat S, Sarla N (2004) Identiﬁcation and overcoming barriers between Brassica rapa L. em. Metzg. and B. nigra (L.) Koch crosses for the resynthesis of B. juncea (L.) Czern. Genet Resource Crop Evol 51:455–469 Bhat MA, Gupta ML, Banga SK, Raheja RK, Banga SS (2002) Erucic acid heredity in Brassica juncea – some additional information. Plant Breed 121:456–458 Bhat S, Haque A, Chopra V (2001) Induction of mutations for cytoplasmic male sterility and some rare phenotypes in Indian mustard (Brassica juncea L.). Int J Genet 61:335–340 Bijral J, Sharma T (1999) Brassica juncea-Eruca sativa hybrids. Cruciferae Newslett 21:33–34 Bille N, Eggum BO, Jacobsen I, Olseno O, Sorensen N (1983) Antinutritional and toxic effects in rats of individual glucosinolates (+) myrosinases added to a standard diet. I. Effects on protein utilization and organ weights. Tierphysiol Tierer nahar Futtermittelkd 49:195–210 Bornet B, Branchard M (2004) Use of ISSR ﬁngerprints to detect microsatellites and genetic diversity in several related Brassica taxa and Arabidopsis thaliana. Hereditas 140:245–247 Burton W, Pymer S, Salisbury P, Kirk J, Oram R (1999) Performance of Australian canola quality Indian mustard breeding lines. In: GCIRC 10th Int Rapeseed Congr, Canberra, Australia Burton W, Ripley V, Potts D, Salisbury P (2004) Assessment of genetic diversity in selected breeding lines and cultivars of canola quality Brassica juncea and their implications for canola breeding. Euphytica 136:181–192 Butruille DV, Guries RP, Osborn TC (1999) Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of Brassica napus L. Genetics 153:949–964 Camarago L, Williams PH, Osborn TC (1999) Mapping loci controlling ﬂowering time in Brassica oleracea. Theor Appl Genet 92:610–616 Chatterjee G, Sikdar S, Das S, Sen S (1988) Intergeneric somatic hybrid production through protoplast fusion between Brassica juncea and Diplotaxis muralis. Theor Appl Genet 76:915–922 Chauhan S, Raut R (2002) A source of white rust resistance in Indian mustard derived from Brassica carinata. Cruciferae Newslett 24:79–80 Chen S, Tong N (1985) A preliminary study of different morphotypes in mustard (Brassica juncea). Cruciferae Newslett 10:37 Cheng BF, Seguin-Swartz G, Somers DJ, Rakow G (2001) Low glucosinolate Brassica juncea breeding line revealed to be nullisomic. Genome 44:738–741 Cheung WY, Friesen LJ, Rakow G, Seguin-Swartz G, Landry BS (1997) An RFLP based linkage map of mustard Brassica juncea (L.) Czern. and Coss. Theor Appl Genet 94:841–851 Cheung WY, Gugel RK, Landry BS (1998a) Identiﬁcation of RFLP markers linked to the white rust resistance gene (Acr)

in mustard (Brassica juncea (L.) Czern. and Coss.). Genome 41:626–628 Cheung WY, Landry BS, Raney P, Rakow GFW (1998b) Molecular mapping of seed quality traits in Brassica juncea L. Czern. & Coss. Acta Hort 459:139–147 Datta S, Koundal K (2003) Genetic transformation of Brassica juncea (L.) with an insecticidal lectin gene from cowpea Vigna unguiculata (L.Walp). Brassica 5:33–40 Demeke T, Adams RP, Chibbar R (1992) Potential taxonomic use of random ampliﬁed polymorphic DNA (RAPD) – a casestudy in Brassica. Theor Appl Genet 84:990–994 Getinet A, Rakow G, Raney JP, Downey RK (1997) The inheritance of erucic acid content in Ethiopian mustard. Can J Plant Sci 77:33–41 Glimelius K, Mats D, Hugo F (1986) Selection and enrichment of plant protoplast heterokaryons of Brassicaceae by ﬂow sorting. Plant Sci 45:133–141 Gomez-Campo C, Prakash S (1999) Origin and Domestication. In: Gomez-Campo C (ed) Biology of Brassica Coenospecies. Elsevier, Amsterdam, p 58 Gopalan C, Krishnamurthy D, Shenolikar I, Krishnamurthy K (1974) Myocardial changes in monkey fed on mustard oil. Nutr Metabol 16:352–365 Goswami R, Devi J (2002) Intergeneric hybridization of Indian mustard (Brassica juncea) with taramira (Eruca sativa). Int J Agric Sci 72:436–438 Green A, Salisbury P (1998) Genetically modiﬁed oilseeds: The impact of gene technology on the Australian oilseeds industry. Australian Oilseeds Federation Grover A (2003) Different emerging strategies to develop fungus-resistant transgenic plants. Brassica 5:24–32 Gupta V, Mukhopadhyay A, Arumugam N, Sodhi YS, Pental D, Pradhan AK (2004) Molecular tagging of erucic acid trait in oilseed mustard (Brassica juncea) by QTL mapping and single nucleotide polymorphisms in FAE1 gene. Theor Appl Genet 108:743–749 Haiping H, Datla N, Reed D, Covello P, MacKenzie S, Xiao Q (2002) High-level production of gamma-linolenic acid in Brassica juncea using a Δ6 desaturase from Pythium irregulare. Plant Physiol 129:354–362 Haque A, Sharma G (2002) Mutant Brassica juncea lines with reduced linolenic acid. J Genet Breed 56:309–315 Hasterok R, Maluszynska J (2000) Cytogenetic analysis of diploid Brassica species. Acta Biol Cracov Bot 42:145–153 Hasterok R, Jenkins G, Langdon T, Jones RN, Maluszynska J (2001) Ribosomal DNA is an effective marker of Brassica chromosomes. Theor Appl Genet 103:486–490 Hemingway JS (1976) Mustards, Brassica species and Sinapsis alba (cruciferae). In: Simmonds NW (ed) Evolution of Crop Plants, 1st edn. Longman, London, 56–59 Hemingway J (1995) The mustard species: condiment and food ingredients use and potential as oilseed crops. In: Kimber DS, McGregor DI (eds) Brassica Oilseeds: Production and Utilization. CAB, Wallingford, UK, pp 373–383

Chapter 5 Indian Mustard Heyn F (1977) Analysis of unreduced gametes in the Brassicaceae by crosses between species and ploidy levels. Z Pﬂanz 78:13–30 Howard HW (1942) The effect of polyploidy and hybridity on seed size in crosses between Brassica chinensis, B. carinata, amphidiploid B. chinensis-carinata and autotetraploid B. chinensis. J Genet 43:106–119 Inomata N (2001) Intergeneric hybridization between Brassica juncea and Erucastrum virgatum and the meiotic behaviour of F1 hybrids. Cruciferae Newsletter 23:17–18 Inomata N (2003) Production of intergeneric hybrids between Brassica juncea and Diplotaxis virgata through ovary culture, and the cytology and crossability of their progenies. Euphytica 133:57–64 Jagannath A, Arumugam N, Gupta V, Pradhan A, Burma K, Pental D (2002) Development of transgenic barstar lines and identiﬁcation of a male sterile (barnase)/restorer (barstar) combination for heterosis breeding in Indian oilseed mustard (Brassica juncea). Curr Sci 82:46–52 Jain A, Bhatia S, Banga SS, Prakash S, Lakshmikumaran M (1994) Potential use of random ampliﬁed polymorphic DNA (RAPD) technique to study the genetic diversity in Indian mustard (Brassica juncea) and its relationship to heterosis. Theor Appl Genet 88:116–122 Kanrar S, Venkateswari J, Kirti PB, Chopra VL (2002a) Transgenic Indian mustard (Brassica juncea) with resistance to the mustard aphid (Lipaphis erysimi Kalt.). Plant Cell Rep 20:976–981 Kanrar S, Venkateswari JC, Kirti PB, Chopra VL (2002b) Transgenic expression of hevein, the rubber tree lectin, in Indian mustard confers protection against Alternaria brassicae. Plant Sci 162:441–448 Katiyar R, Chamola R (2003) Accomplishments and new research priorities for improvement of oilseeds: rape and mustard. Brassica 5:7–15 Kaur G, Banga S, Gogna K, Joshi S, Banga S (2004) Moricandia arvensis cytoplasm based system of cytoplasmic male sterility in Brassica juncea: Reappraisal of fertility restoration and agronomic potential. Euphytica 138:271–276 Kaur J, Webster T, Felitti S, Nguyen N, Batley J, Howlett B, Salisbury P, Spangenberg GC, Edwards D (2005) Microarray analysis of gene expression in canola in response to Blackleg fungus (Leptosphaeria maculans) infection. In: Plant & Animal Genome XIII Conf, San Diego Kemble R, Barsby T (1988) Use of protoplast fusion systems to study organelle genetics in a commercially important crop. Biochem Cell 66:665–676 Kirk JTO, Hurlstone CJ (1983) Variation and inheritance of erucic acid content in Brassica juncea. Z Pﬂanz 90:331–338 Kirk JTO, Oram RN (1978) Mustard as possible oil and protein crops for Australia. J Austr Inst Agric Sci 44:143–156 Kirk JTO, Oram RN (1981) Isolation of erucic acid-free lines of Brassica juncea: Indian mustard now a potential oilseed crop in Australia. J Aust Inst Agric Sci 47:51–52

207

Koch M, Haubold B, Mitchell-Olds T (2000) Comparative evolutionary analysis of the chalcone synthase and alcohol dehydrogenase loci among different lineages of Arabidopsis, Arabis and related genera. Mol Biol Evol 17:1483–1498 Kole C, Teutonico R, Williams PH, Osborn TC (1996) Molecular mapping of a locus controlling resistance to Albugo candida in Brassica rapa. Phytopathology 86:367–369 Kole C, Williams PH, Rimmer SR, Osborn TC (2002) Linkage mapping of genes controlling resistance to white rust (Albugo candida) in Brassica rapa (syn. campestris) and comparative mapping to Brassica napus and Arabidopsis thaliana. Genome 45:22–27 Krishnia S, Saharan G, Singh D (2000) Genetics of Alternaria blight resistance in inter and intraspeciﬁc crosses of Brassica juncea and Brassica carinata. Ann Biol 16:211–216 Kulak S, Hasterok R, Maluszynska J (2002) Karyotyping of Brassica amphidiploids using 5S and 25S rDNA as chromosome markers. Hereditas 136:144–150 Kumar R, Gupta V (1985) Isozyme studies in Indian mustard (Brassica juncea). Theor Appl Genet 79:109–110 Kumar N, Shivanna K, Prakash S (1988) Wide hybridization in Brassica. Crossability barriers and studies on the F1 hybrid and synthetic amphidiploid of B. fruticulosa × B. campestris. Sex Plant Reprod 1:234–239 Kumar PR, Chauchan J, Singh A (2000) Rapeseed mustard genetic resources: status and priorities. Ind J Plant Genet Resource 13:207–218 Kumar R, Chowdhury J, Jain R (2001) Interspeciﬁc hybridization in Brassica juncea and Brassica tournefortii through embryo rescue and their evaluation for biotic and abiotic stress tolerance. Int J Exp Biol 39:911–915 Kumar S, Saharan G, Singh D (2002) Inheritance of resistance in inter and intraspeciﬁc crosses of Brassica juncea and Brassica carinata to Albugo candida and Erysiphe cruciferum. J Mycol Plant Pathol 32:59–63 Labana K, Gupta ML (1993) Importance and origin. In: Labana KS, Banga S, Banga SK (eds) Breeding Oilseed Brassicas. Narosa, New Delhi Lakshmikumaran M, Das S, Srivastava PS (2003) Application of molecular markers in Brassica coenospecies: comparative mapping and tagging. In: Nagata T, Lorz H, Widholm J (eds) Biotechnology in Agriculture and Forestry. Springer, Berlin Heidelberg New York LeDuc DL, Tarun AS, Montes-Bayon M, Meija J, Malit MF, Wu CP, AbdelSamie M, Chiang CY, Tagmount A, de Souza M, Neuhierl B, Bock A, Caruso J, Terry N (2004) Overexpression of selenocysteine methyltransferase in Arabidopsis and Indian mustard increases selenium tolerance and accumulation. Plant Physiol 135:377–383 Lee H-S, Wang J, Tian L, Jiang H, Black MA, Madlung A, Watson B, Lukens L, Pires EJC, Wang JJ, Comai L, Osborn TC, Doerge RW, Chen ZJ (2004) Sensitivity of 70mer oligonucleotides and cDNAs for microarray analysis of gene expression in Arabidopsis and its related species. Plant Biotechnol J 2:45–57

208

D. Edwards et al.

Lionneton E, Ravera S, Sanchez L, Aubert G, Delourme R, Ochatt S (2002) Development of an AFLP-based linkage map and localization of QTLs for seed fatty acid content in condiment mustard (Brassica juncea). Genome 45:1203–1215 Lionneton E, Aubert G, Ochatt S, Merah O (2004) Genetic analysis of agronomic and quality traits in mustard (Brassica juncea). Theor Appl Genet 109:792–799 Liu JQ, Parks P, Rimmer SR (1996) Development of monogenic lines for resistance to Albugo candida from a Canadian Brassica napus cultivar. Phytopathology 86:1000–1004 Love H, Rakow G, Raney J, Downey R (1990a) Genetic control of 2-propenyl and 3-butenyl glucosinolate synthesis in mustard. Can J Plant Sci 70:425–429 Love HK, Rakow G, Raney JP, Downey RK (1990b) Development of low glucosinolate mustard. Can J Plant Sci 70:419–424 Love CG, Batley J, Edwards D (2004a) Applied computational tools for crop genome research. J Plant Biotechnol 5:193–195 Love CG, Batley J, Lim G, Robinson AJ, Savage D, Singh D, Spangenberg GC, Edwards D (2004b) New computational tools for Brassica genome research. Comp Funct Genom 5:276–280 Mahmood T, Ekuere U, Yeh F, Good AG, Stringam GR (2003a) Molecular mapping of seed aliphatic glucosinolates in Brassica juncea. Genome 46:753–760 Mahmood T, Ekuere U, Yeh F, Good AG, Stringam GR (2003b) RFLP linkage analysis and mapping genes controlling the fatty acid proﬁle of Brassica juncea using reciprocal DH populations. Theor Appl Genet 107:283–290 Marcroft SJ, Purwantara A, Wratten N, Salisbury PA, Potter TD, Barbetti MJ, Khangura R, Howlett BJ (2002) Reaction of a range of Brassica species under Australian conditions to the fungus, Leptosphaeria maculans, the causal agent of blackleg. Aus J Exp Agri 42:587–594 Mattson B (1988) Interspeciﬁc crosses within the genus Brassica and some related genera. Veriges Utsadesforenings Tidskrift 98:187–212 Mukherjee AK, Mohapatra T, Varshney A, Sharma R, Sharma RP (2001) Molecular mapping of a locus controlling resistance to Albugo candida in Indian mustard. Plant Breed 120:483–487 Muller J, Sonntag K, Rudloff E (2001) Somatic hybridization between Brassica spp. and Raphanus sativus. In: Proc 4th Int Symp In Vitro Cult Hort Breed, Tampere, Finland, pp 219–220 Negi MS, Devic M, Delseny M, Lakshmikumaran M (2000) Identiﬁcation of AFLP fragments linked to seed coat color in Brassica juncea and conversion to a SCAR marker for rapid selection. Theor Appl Genet 101:146–152 Negi MS, Sabharwal V, Bhat SR, Lakshmikumaran M (2004) Utility of AFLP markers for the assessment of genetic diversity within Brassica nigra germplasm. Plant Breed 123:13–16 Oram R, Salisbury P, Kirk J, Burton W (1999) Brassica juncea breeding. In: Salisbury PA, Potter TD, McDonald G, Green

AG (eds) Org Comm 10th Intl Rapeseed Congr, Canberra, ACT, Australia, pp 37–40 Osborn T, Lukens L (2003) The molecular genetic basis of ﬂowering time variation in Brassica species. In: Nagata T, Tabata S (eds) Brassicas and Legumes, from Genome Structure to Breeding. Springer, Berlin Heidelberg New York, pp 69–86 Pal S, Gupta T, Kumar V, Dhaliwal H (1999) Transfer of white rust resistance from Brassica napus to B. juncea cv. RLM 198. Crop Improv 26:249–251 Patil S, Khambalkar V, Gawande P, Charjan S (2003) Studies on interspeciﬁc hybridization in Brassica species. J Soils Crops 13:343–347 Potts D, Rakow G, Males D (1999) Canola quality Brassica juncea, a new oilseed crop for the Canadian prairie. In: GCIRC 10th Int Rapeseed Congr, Canberra, Australia Prabhu KV, Somers DJ, Rakow G, Gugel RK (1998) Molecular markers linked to white rust resistance in mustard Brassica juncea. Theor Appl Genet 97:865–870 Pradhan A, Sodhi YS, Mukhopadhyay A, Pental D (1993) Heterosis breeding in Indian mustard (Brassica juncea L. Czern & Cross): analysis of component characters contributing to heterosis for yield. Euphytica 69:219–229 Pradhan AK, Gupta V, Mukhopadhyay A, Arumugam N, Sodhi YS, Pental D (2003) A high-density linkage map in Brassica juncea (Indian mustard) using AFLP and RFLP markers. Theor Appl Genet 106:607–614 Prakash S (1980) Cruciferous oilseeds in India. In: Tsunoda S, Hinata K, Gómez-Campo C (eds) Brassica Crops and Wild Allies, Biology and Breeding. Japanese Science Society Press, Tokyo, Japan, pp 151–163 Prakash S, Hinata K (1980) Taxonomy, cytogenetics and origin of crop Brassicas – a review. Opera Bot 55:1–57 Prasad K, Sharmila P, Kumar PA, Saradhi PP (2000) Transformation of Brassica juncea (L.) Czern with bacterial codA gene enhances its tolerance to salt stress. Mol Breed 6:489–499 Purwantara A, Salisbury P, Burton W, Howlett B (1998) Reaction of Brassica juncea (Indian mustard) lines to Australian isolates of Leptosphaeria maculans under glasshouse and ﬁeld conditions. Eur J Plant Pathol 104:895–902 Rakow G, Raney JP, Males D (1995) Field performance of canola quality Brassica juncea. In: Proc 9th Int Rapeseed Congr, Cambridge, UK, pp 428–430 Rimmer S, Mathur S, Wu C (2000) Virulence of isolates of Albugo candida from western Canada to Brassica species. Can J Plant Pathol 22:229–235 Röbbelen G (1960) Beiträge zur Analyse des Brassica-Genomes. Chromosoma 11:205–228 Robinson AJ, Love CG, Batley J, Barker G, Edwards D (2004) Simple sequence repeat marker loci discovery using SSR primer. Bioinformatics 20:1475–1476 Sabharwal V, Negi MS, Banga SS, Lakshmikumaran M (2004) Mapping of AFLP markers linked to seed coat color loci in Brassica juncea (L.) Czern. Theor Appl Genet 109:160–166 Sachan J, Kolte S, Singh B (1995) Genetics of resistance to white rust (Albugo candida race 2) in mustard (Brassica juncea

Chapter 5 Indian Mustard (L.) Cern. & Coss.). In: GCIRC 9th Int Rapeseed Congr, Cambridge, UK, pp 1295–1297 Saharan G, Chand J (1988) Diseases of oilseed crops. Haryana Agricultural University, Hisar, India Saharan G, Verma P (1992) White rusts – a review of economically important species. International Development Research Centre, Ottawa, Canada Salisbury P, Kadkol G (1989) Utilisation of alien genes in oilseed Brassica breeding. Genetics (Life Sci Adv) 8:65–87 Sauer F, Kramer J (1983) The problems associated with the feeding of high erucic acid rapeseed oils and some ﬁsh oils to experimental animals. In: Kramer JKG, Sauer FD, Pigdem WJ (eds) High and Low Erucic Acid Rapeseed Oils. Academic, Toronto, pp 254–292 Sharma A, Mohapatra T, Sharma R (1994) Molecular mapping and character tagging in Brassica juncea. I. Degree, nature and linkage relationship of RFLPs and their association with quantitative traits. J Plant Biochem Biotechnol 3:85–89 Sharma G, Kumar V, Haque A, Bhat S, Prakash S, Chopra V (2002a) Brassica coenospecies: a rich reservoir for genetic resistance to leaf spot caused by Alternaria brassicae. Euphytica 125:411–417 Sharma R, Aggarwal RAK, Kumar R, Mohapatra T, Sharma RP (2002b) Construction of an RAPD linkage map and localization of QTLs for oleic acid level using recombinant inbreds in mustard (Brassica juncea). Genome 45:467–472 Shivpuri A, Sharma K, Chipa H (1999) Some studies on the stem rot (Sclerotinia sclerotiorum) disease of rapeseed/mustard in Rajasthan, India. In: GCIRC 10th Int Rapeseed Congr, Canberra, Australia Shpota V, Konovalov (1978) Selskokhoztaustvenna 13:617 Singh N (2003) Accomplishment and challenges in rapeseedmustard research. Brassica 5:1–11 Singh K, Kumar A, Sareen P (2003) Podding mutants of raya Brassica juncea (L.) Czern & Coss for higher oil content under dryland conditions. Crop Res (Hisar) 25:316–318 Skrypetz S (2003) Mustard seed: situation and outlook. In: Agriculture and Agrifood Canada Bi-weekly Bulletin, p 9 Sodhi YS, Mukhopadhyay A, Arumugam N, Verma JK, Gupta V, Pental D, Pradhan AK (2002) Genetic analysis of total glucosinolate in crosses involving a high glucosinolate Indian variety and a low glucosinolate line of Brassica juncea. Plant Breed 121:508–511 Somers DJ, Rakow G, Raney P, Prabhu V, Séguin-Swartz G, Rimmer R, Gugel R, Lydiate D, Sharpe A (1999) Developing marker-assisted breeding for quality and disease resistance traits in Brassica oilseeds. In: GCIRC 10th Int Rapeseed Congr, Canberra, Australia Somers DJ, Rakow G, Rimmer SR (2002) Brassica napus DNA markers linked to white rust resistance in Brassica juncea. Theor Appl Genet 104:1121–1124 Song K, Tang K, Osborn TC (1993) Development of synthetic Brassica amphidiploids by reciprocal hybridization and comparison to natural amphidiploids. Theor Appl Genet 86:811–821

209

Srivastava A, Gupta V, Pental D, Pradhan AK (2001) AFLPbased genetic diversity assessment amongst agronomically important natural and some newly synthesized lines of Brassica juncea. Theor Appl Genet 102:193–199 Srivastava A, Mukhopadhyay A, Arumugam N, Gupta V, Verma JK, Pental D, Pradhan AK (2004) Resynthesis of Brassica juncea through interspeciﬁc crosses between B. rapa and B. nigra. Plant Breed 123:204–206 Stoutjesdijk P, Hurlestone C, Singh S, Green A (2000) Higholeic Australian Brassica napus and B. juncea varieties produced by co-suppression of endogenous Δ 12-desaturases. Biochem SocTrans 28:938–940 Stringham G, Thiagarajah M (1995) Inheritance of alkenyl glucosinolates in traditional and microspore-derived doubled haploid populations of Brassica juncea (L.) Cern & Coss. In: GCIRC 9th Int Rapeseed Congr, Cambridge, UK, pp 804–806 Subhudi P, Raut R (1994) White rust resistance and its association with parental type and leaf waxiness in Brassica juncea L. Czern and Coss. × Brassica napus L. crosses under the action of EDTA and gamma-ray. Euphytica 74:1–7 Thiagarajah MR, Stringam GR (1993) A comparison of genetic segregation in traditional and microspore-derived populations of Brassica juncea L. Czern and Coss. Plant Breed 111:330–334 Thormann C, Romero J, Mantet J, Osborn TC (1996) Mapping loci controlling the concentration of erucic acid and linolenic acids in seed oil of Brassica napus L. Theor Appl Genet 93:282–286 Thukral S, Behl R, Kumar R (1985) Water stress effects on some important physiomorphological attributtes in oilseed Brassica. Ann Biol 85:209–215 Tiwari A, Petrie G, Downey R (1988) Inheritance of resistance to Albugo candida race 2 in mustard (Brassica juncea (L.) Czern). Can J Plant Sci 68:297–300 U N (1935) Genome analysis in Brassica with special reference to the experimental formation of B. napus and peculiar mode of fertilization. Jpn J Bot 7:389–452 Upadhyay A, Mohapatra T, Pai R, Sharma R (1996) Molecular mapping and character tagging in Indian mustard (Brassica juncea) II. RFLP marker association with seed coat color and quantitative traits. J Plant Biochem Biotechnol 5:17–22 Van Ooijen J, Maliepaard C (1996) MapQTL version 3.0: Software for the calculation of QTL positions on genetic maps. In: Plant & Animal Genome IV Conf, San Diego Varshney A, Mohapatra T, Sharma RP (2004) Development and validation of CAPS and AFLP markers for white rust resistance gene in Brassica juncea. Theor Appl Genet 109:153–159 Vera C, Woods D (1982) Isolation of independent gene pairs at two loci for seed coat color in Brassica juncea. Can J Plant Sci 62:47–50 Vera C, Woods D, Downey R (1979) Inheritance of seed coat color in Brassica juncea. Can J Plant Sci 59:635–637

210

D. Edwards et al.

Verma U, Bhowmik T (1989) Inheritance of resistance to a Brassica juncea pathotype of Albugo candida in B. napus. Can J Plant Pathol 11: 443–444 Woods D, Capcara J, Downey R (1991) The potential of mustard (Brassica juncea (L.) Coss) as an edible oil crop on the Canadian Prairies. Can J Plant Sci 71:195–198 Yadava J, Yadav R (2003) Breeding objectives in rapeseedmustard in India: Present and future. Brassica 5:1–6 Yang KA, Lim CJ, Hong JK, Jin ZL, Hong JC, Yun DJ, Chung WS, Lee SY, ChoMJ, Lim CO (2005) Identiﬁcation of Chinese

cabbage genes up-regulated by prolonged cold by using microarray analysis. Plant Sci 168:959–966 Yao K, Bacchetto RG, Lockhart KM, Friesen LJ, Potts DA, Covello PS, Taylor DC (2003) Expression of the Arabidopsis ADS1 gene in Brassica juncea results in a decreased level of total saturated fatty acids. J Plant Biotechnol 1:221–229 Zhang GQ, Zhou WJ, Gu HH, Song WJ, Momoh EJJ (2003) Plant regeneration from the hybridization of Brassica juncea and B. napus through embryo culture. J Agron Crop Sci 189:347–350

CHAPTER 6

6 Brassica Rapa Pablo Quijada1 , Jiashu Cao2 , Xiaowu Wang3 , M. Hirai4 , and C. Kole5 1

2

3

4 5

Laboratory of Genetics, University of Wisconsin-Madison, 425-G Henry Mall, Madison, WI 53706, USA e-mail: [email protected] Laboratory of Cell & Molecular Biology, Institute of Vegetable Science, Zhejiang University, No. 268 Kaixuan Road, Hangzhou, 310029, China Institute of Vegetables and Flowers, Chinese Academy of Agricultural Science, No. 12, Zhongguancun Nandajie, Beijing, 100081, China National Research Institute of Vegetables, Ornamental Plants & Tea, Ano, Mie 5142392, Japan Department of Horticulture, The Pennsylvania State University, University Park, PA 16802, USA

6.1 Introduction 6.1.1 History of the Crop Brassica rapa L. (syn. Brassica campestris L.) seems to have grown naturally from the West Mediterranean region to Central Asia and is still present throughout this area, in general associated with weedy habitats. A large number of important subspecies in B. rapa are recognized. All these subspecies have a wide range of variation and were cultivated in China even before the Common Era. The earliest reference pertains to yellow sarson in ancient Sanskrit literary works such as the Upanisadas and the Brahamanas (c. 1500 BCE), where it was referred to as “Siddhartha” (Prakash 1961; Watt 1989). B. rapa has the widest distribution, with secondary centers of diversity in Europe, western Russia, Central Asia, and the Near East (Vavilov 1949; Mizushima and Tsunoda 1967; Zeven and Zhukovsky 1975). Its wide availability made it probably the ﬁrst domesticated Brassica crop. The wide range of subspecies in B. rapa is believed to have resulted from varied selection pressures in different geographic regions. Turnip (ssp. rapifera) is probably of European origin. In the East, the selection for leafy vegetables resulted in great diversity of Chinese cabbage. Burkill (1930) regarded Europe as the place where B. rapa was ﬁrst domesticated as a biennial plant from which annual forms later evolved. Evidence from morphology, geographic distribution, isozymes (Denford and Vaughan 1977), restriction fragment length polymorphisms (RFLPs) (Song et al. 1988b), and ampliﬁed fragment length polymor-

phisms (AFLPs) (Zhao et al. 2004) indicate two independent centers of origin. Europe constitutes the primary center for oleiferous forms and turnip. Eastern forms evolved in the northwest of India in the oleiferous direction, while Chinese forms differentiated as leafy vegetables in south China. It is believed that European forms of oleiferous B. rapa developed in the Mediterranean area (Sinskaia 1928), and Asian forms originated in the region comprising Central Asia, Afghanistan, and adjoining northwest India. In the Indian subcontinent there are three ecotypes of oleiferous B. rapa: brown sarson, yellow sarson, and toria. Brown sarson is thought to be the oldest among them (Singh 1958). Toria is an early-maturing crop very similar to brown sarson in morphology except for the growing period and plant size. Yellow sarson is characterized by yellow seed color and self-compatibility. It is believed to have evolved from brown sarson as a mutant and to have survived because of its selfcompatible nature. It might haven been selected by farmers for its attractive yellow seed color and bigger seed size. 6.1.2 Botanical Description B. rapa belongs to the family Cruciferae and genus Brassica. For a time, the species category was widely applied (B. chinensis, B. pekinensis, B. japonica), but it is now considered an excessive license. The subspecies rank is only recommended for the most signiﬁcant variants (Oost 1985) though an intensiﬁcation of the use of the names according to the International Code of Nomenclature for Cultivated Plants (Trehane et al. 1995) would be desirable. The priori-

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

212

P. Quijada et al.

tary name for the species is B. rapa while the name subsp. campestris should be reserved for nonspecialized semiwild forms with slender root (Toxopeus et al. 1984). In the Far East, domestication led to a wide variety of forms, such as leafy vegetables B. rapa subsp. chinensis Makino (Chinese cabbage-pak-choi), B. rapa subsp. pekinensis Oleeon (Chinese cabbagepe-tsai, heading Chinese cabbage), and root vegetable B. rapa subsp. rapifera Metzg. (turnip). The number of names used, from either the East or the West, is very high, but Oost (1985) suggests that only 10 to 12 should merit subspeciﬁc rank. An internationally accepted criterion in the nomenclature of Brassica crops seems highly necessary. Oleiferous forms can be divided into “winter” and “spring” types. The winter type of B. napus tends to be grown in areas with mild winter, as growing points are usually better protected from frost (Torssell 1959). The spring type of B. rapa is normally earlier ﬂowering and maturing than B. napus, hence it tends to be grown in shorter-season areas. The oil-bearing B. rapa plant is profusely branched and attains a height of 1 to 1.5 m at maturity. The leaves are lyrate and leaf blade encircles the stem. Inﬂorescence is racemose with yellow ﬂowers. The anthers are six in number and arranged in tetradynamous (4 + 2) condition. The carpel is superior and is bi- or tetralocular and divided by a false septum. The number of ovules in an ovary varies from 10 to 50. The anthers are at a lower level than the stigmas at the bud stage, but before ﬂower opening, four of them elongate and carry the anthers as high as or above the stigma level. The surface of siliqua and seed is smooth. The plants come to ﬂowering in about 90 to 140 d after planting. The genome of B. rapa, A genome, consists of ten chromosomes. Pachytene chromosome analysis by Röbbelen (1960) and Venkateswarlu and Kamala (1973) revealed that diploid Brassica species had six basic chromosomes. The B. rapa genome is represented by AABCDDEFFF (tetrasomic for chromosomes A and D and hexasomic for chromosome F), B. oleracea by ABBCCDEEF (tetrasomic for chromosomes B, C and E), and B. nigra by AABCDDEF (tetrasomic for chromosomes A and D). The meiotic behavior indicated secondary association of chromosome in B. rapa, the number ranging from 1 to 4. The cytological basis of this could be secondary balanced polyploidy. Using a method based on the condensation pattern, quantitative chromosome maps of three Brassica species with basic genomes A (B. rapa), B (B. nigra)

and C (B. oleracea) have been developed successfully (Fukui et al. 1998). Although the overall chromosomal characteristics are similar within the three species, demonstrating prominent condensed regions at the centromeres, species-speciﬁc characteristics are also revealed. The most prominent species-speciﬁc characteristic that appears consistently is the difference in the relative size of the condensed regions at the centromeric regions. In chromosomes of B. rapa, the larger heterochromatic regions usually occur at the proximal region of the long arms, whereas the opposite tendency occurs in B. oleracea, but for B. nigra almost even-sized heterochromatic blocks appear on both arms of chromosome (Fukui 2003). The oilseed Brassica comprises four species, namely, B. rapa, B. juncea, B. napus, and B. carinata. Initial cytogenetic researches demonstrated that crop brassicas comprised three elementary diploid species, namely, B. rapa (2A = 20; AA), B. nigra (2n = 16; BB), and B. oleracea (2n = 18; CC), and three amphidipoids, which originated through interspeciﬁc hybridization between any two of the three diploid species. These are B. juncea (2n = 36; AABB), B. napus (2n = 38; AACC), and B. carinata (2n = 34; BBCC) (Fig. 1) (Morinage 1934; U 1935). The earlier view held that they evolved from a common progenitor species with a basic chromosome number of n = 6, and that the diploid species, with n = 8, 9, and 10, resulted from a secondary balanced polyploidy (Röbbelen 1960). Evidence in support of this view comes from a secondary association of

Fig. 1. Cytogenetic relationships of crop brassicas (U 1935)

Chapter 6 Brassica Rapa

bivalents, chromosome pairing in haploids (Prakash and Hinata 1980), and the presence of duplicated loci for rDNA genes (Quiros et al. 1987). However, recent investigation on nuclear, mitochondrial, and cholorplast DNA RFLPs established their evolution from two prototypes: B. rapa and B. oleracea evolved from one progenitor, B. nigra from another (Palmer 1988; Warwick and Black 1991; Pradhan et al. 1992). Furthermore, evidence from the development of microsatellite markers (simple sequence repeats, SSRs) also supports this evolutionary relationship. The transferability of SSRs is higher between the A and C genomes than between this group and the B genome (Lowe et al. 2004). The size of B. rapa genome varies from 468 to 516 Mb in different cultivars (Arumuganathan and Earle 1991). The RFLP linkage map for B. rapa was published in 1991 in which 36% of their genomic clones produced segregating RFLPs at more than one locus and 41% detected sequences segregating as single locus; additional monomorphic fragments were also shown in the map (Song et al. 1991). Hoenecke and Chyi (1991) disclosed intergenomic recombination using comparative mapping between B. rapa and B. napus. They found signiﬁcant linkage arrangement differences between the A genome of diploid and amphidiploid species.

6.1.3 Economic Importance Rapeseed is the traditional name for the group of oilseed crops in the Brassicaceae family. Rapeseed (B. napus or B. rapa) can be divided into two types: canola and industrial rapeseed. The two types are distinguished based on their individual chemical or fatty acid (FA) proﬁles. Canola is the name for the edible oil crop that is characterized by low erucic acid (LEAR), with less than 2% erucic acid, and less than 30 μmol g−1 oil-extracted, air-dried meal glucosinolates. Industrial rapeseed has high erucic acid (HEAR) content, with more than 45% erucic acid and high or low in glucosinolates. Canola oil is second only to olive oil, among the common edible fats and oils, in oleic acid content (55 to 60%) and, together with soybean oil, is the only common edible oil that contains a signiﬁcant amount of linolenic acid (8 to 10%) (McDonald 1995). Brassica oilseeds contain 20 to 30% protein on a whole-seed basis, which adds to the value of the seed. The meal

213

byproduct of oil extraction contains between 36 and 44% crude protein and a good balance of essential amino acids (Miller et al. 1962). It is generally used as an animal feed. Some work has explored the preparation of protein for human consumption (Rubin et al. 1990). In some Asian areas it has also been used as a fertilizer. There is uncertainty as to when Brassica was ﬁrst used as a source of oil. The history can be traced back to 17th century in Holland, where B. napus may ﬁrst have been grown as a source of oil. Linnaeus suggested in the mid-1700s that mustard or turnip would produce as good a source of oil (Appelqvist 1972). Rapeseed oil was primarily used for making soap and for illumination in Europe. In Asia and the Indian subcontinent rapeseed oil has been widely accepted as cooking oil for centuries, but in Europe it has only been used mainly as edible oil since the Second World War. Progress in breeding for quality of both oil and meal ensures that use as edible oil now exceeds all other uses. Canada is one of the four regions with the highest oilseed production. In the 1970s, 75% of the rapeseed area in Canada was of spring B. rapa cultivars, later in the 1990s the proportion decreased to 50%. B. rapa is the main species for oilseed production with the largest cultivation area in China before introduction of B. napus in the 1940s from Japan and Europe. This species is also one of the two traditional oilseed crops in the Indian subcontinent. Since the 1960s, B. napus has gradually substituted B. rapa in China because of its higher seed yield and disease resistance. However, the short growing period makes B. rapa still an optimal choice in some areas as spring cultivar. For example, in 2002 the cultivation area of rapeseed in China was about 7 million ha in which B. napus, B. nigra, and B. rapa accounted for 80, 5, and 15%, respectively (He et al. 2002).

6.1.4 Breeding Objectives and Achievements The achievements in oilseed rape breeding have greatly contributed to the continuing increased production of rapeseed. During the last two decades, the need for modiﬁcation in the FA composition of the oil and the elimination of the glucosinolates from the seed meal attracted great attention from Brassica breeders. In Asian countries, greater seed yield and stability were the primary objectives besides quality

214

P. Quijada et al.

breeding. Overall, the major breeding aims are was possible to change the FA proﬁle dramatically by focusing on increasing seed yield, improving quality, introducing these genes into adapted cultivars. This and improving agronomic traits. led to the ﬁrst low erucic acid B. rapa cultivar Span (Downey et al. 1975) and resulted in the development of nutritionally superior canola cultivars. By 1980 low Increase in Seed Yield erucic acid cultivars were superior in performance to In rapeseed breeding, the ﬁrst and ever important task the old high erucic acid ones, and breeders selecting is to increase the seed harvest per unit of land surface. for low linolenic or high oleic genotypes have had to However, this trait is neither simple nor independent compete with them. Lower linolenic acid is desired of environmental inﬂuences and agronomic practices. to improve the storage characteristics of the oil while Primarily, the seed yield is the resultant of some deterhigher linolenic acid content may be nutritionally demining components, i.e., the number of siliques per sirable. But these further changes in FA composition unit area (determined as the number of siliques per have not been so easy. It has been difﬁcult to ﬁnd plant and the number of plants per unit area), branch genotypes that confer low linolenic or high oleic stanumber, number of seeds per silique, and seed size. tus and, even when they have been found, inheritance Further, seed yield increase could result from increase has been more complex (Brunklaus-Jung and Robbein biomass and/or harvest index. Increased biomass len 1987; Diepenbrock and Wilson 1987; Pleins and can result from reduced photorespiration and an inFriedt 1989). Increased content of FAs with shorter creased light saturated rate of photosynthesis. chain lengths is also of interest. Swedish researchers A number of studies have shown that there is conhave selected B. rapa lines with 10 to 12% of palmisiderable heterosis for the yield in B. rapa (Schuler toleic acids compared to 4 to 5% in the unselected et al. 1992), but the problem has been to ﬁnd a pollipopulation (Persson 1985). On the other hand, denation control system that allows for the production mand for higher levels of erucic acid for the industrial of F1 hybrid seeds. Success has been limited so far, oil market has encouraged breeders to try to produce although different approaches have been developed. cultivars with levels greater than 50%. The problem in oilseed B. rapa breeding has been to Brassica oilseeds contain 20 to 30% protein on ﬁnd an effective cytoplasmic male sterility (CMS) sysa whole-seed basis, which adds to the value of the tem. Several systems are available, but each has probseed. The meal byproduct of oil extraction contains lems, mainly reduction in yield and irregular growth between 36 and 44% proteins. Protein content has gendue to interaction between foreign cytoplasm and the erally shown an inverse relationship with oil content, host nucleus and instability of the male sterility. Infor example, protein content being higher and oil concomplete male sterility is another problem for some tent lower when the seed is grown under warm, dry systems. conditions. Glucosinolates are sulphur-containing substances that are broken down by the enzyme myrosinase to Improvement in Seed Quality The seed of B. rapa is used for two main products, oil give bitter-tasting, toxic, and goitrogenic compounds. and meal. Oil quality is determined by its FA composi- The glucosinolate content of rape meal in animal feed tion, while the levels of antinutritional factors, partic- is important for two reasons. The bitter taste they imularly glucosinolates and the proportions of protein part to the meal reduces its palatability and hence reand ﬁber, determine meal quality. Traditional Bras- stricts the animal’s food input and growth rate. Howsica oilseeds differed from other edible oils in their ever, more important is the health hazard involved content of long-chain monoenoic FAs and eicosenoic since oxazolidinethione byproducts have been shown and erucic acids. In the 1950s and early 1970s feeding to inhibit the function of the thyroid gland. The disexperiments with laboratory animals indicated that covery of the reduced aliphatic glucosinolate trait in the nutritional value of rapeseed oil would be sub- the B. napus cultivar Bronowski (Krzymanski 1970) stantially improved if the erucic acid content could resulted in the development of B. napus and B. rapa be reduced to less than 5% of the total FA content cultivars with less than 30 μmoles of glucosinolates (Sauer and Kramer 1983). Since erucic acid content per gram of oil-free meal. The ﬁrst low glucosinois determined by the embryo genotype and is con- late summer rape, cv. Tower, was registered in 1974 trolled by one gene in B. rapa and two genes in both (Downey et al. 1975) and the ﬁrst summer turnip rape, B. napus and B. juncea (Kirk and Hurlstone 1983), it cv. Candle, was registered in 1977 (Anonymous 1977).

Chapter 6 Brassica Rapa

TR4 is the ﬁrst B. rapa strain essentially free of glucosinolates derived from subsequent breeding efforts (Hutcheson et al. 1999). The oil is the most valuable fraction of the seed. Within established cultivars grown on a large scale, the oil content in the air-dried seed varies between 36 and 44% for B. rapa. Winter forms surpass the seed oil content of the corresponding spring sown forms. Yellow seed coat has been shown to be associated with low ﬁber content and therefore higher oil and protein content (Stringam et al. 1974). Spring cultivars of B. rapa with either “semiyellow” or pure yellow seed have been bred in Canada and Europe, and there is an expectation that future cultivars will be yellow. Oil content is inﬂuenced by the environment, particularly temperature, moisture stress, and soil nitrogen, but there is also genetic variation in B. rapa. Selection for oil content has led to slow but steady improvement. As there is a negative correlation between yield and oil content, it is more effective to select for the sum of oil and protein percentages.

Biotic and Abiotic Factors In Brassica breeding, a great effort is always devoted to improving plant resistance against diseases. Stem rot (Sclerotina sclerotiorum), stem canker (Leptosphaeria maculans), and white rust (Albugo candida) are the most important diseases afﬂicting B. rapa worldwide. Since there is a wide range of host of S. sclerotiorum, it is unlikely that durable resistance can be established in cultivars, although moderate tolerance has been reported to occur in some Brassica lines (Kolte 1985; Sedun et al. 1989); therefore apetalous rape may be an alternative of reducing ascospore infection. Other means of protection may be provided by biotechnology (Freyssinet et al. 1995; Thompson et al. 1995). Stem canker is a serious disease of Brassica crops and has caused yield loss in vegetable brassicas for many years. The importance of the disease on oilseed rape is more recent and often associated with the increase in production areas. Breeders have been able to identify sources of resistance and to incorporate them into commercial cultivars (Rimmer and van den Berg 1992). White rust is a serious disease of B. juncea and B. rapa, but most cultivars of B. napus are resistant to the prevalent races of this fungus. In Canada, newer cultivars of B. rapa are resistant or tolerant to the predominant races but it is premature to claim that the disease is under control. There is enough variation in

215

B. rapa to allow selection for tolerance to white rust (Tiwari et al. 1988). Among the insects, mustard aphid (Lipaphis erysimi) is the most devastating insect pest. Breeding for pest resistance has yielded very little success due to failure in identifying sources of resistance. Only selection for rapid seedling development or shorter ﬂowering period has contributed to reduce insect damage. Seed yield is affected for many abiotic stresses associated with low temperatures, frost, drought, alkalinity and salinity. Winter hardiness is very important for winter types of B. rapa. In general, seed yield, production stability, and product quality will remain the pillars of the rapeseed industry. However, new objectives will be made accessible to breeding and production of Brassica. Herbicide tolerance as well as disease and pest resistance produced by alien gene transfer will have an economic impact on B. rapa production.

6.2 Construction of Genetic Maps 6.2.1 Brief History of Mapping Efforts Genetic linkage maps in plants are very useful tools for studying genome structure and evolution, identifying introgression between different genomes, and localizing genes of interest (Beckmann and Soller 1986; Tanksley et al. 1989). Genetic maps have been developed and used for most major crop species and can readily be constructed for additional taxa as needed. The Brassica genus comprises six crop species each with considerable morphological variation. Many studies on biochemical data, cytological studies, DNA sequence, and phenotypic analysis of B. rapa and other Brassica species have provided insight on genome relationships within or between the amphidiploid and dipoid species (Dass and Nybom 1967; Coulthat and Denford 1982; Erickson et al. 1983; Takahata and Hinata 1986; Williams 1989; Song et al. 1990; Pradhan et al. 1992; Lanner et al. 1997; Murren et al. 2002). The development of genetic maps in Brassica will be helpful for understanding the origin and relationship among the genomes of the cultivated Brassica diploid species and for use in applied genetics and breeding of the Brassica crops

216

P. Quijada et al.

lation to use for linkage mapping must be considered. The simplest are F2 populations derived from F1 hybrids and backcross (BC) populations. For most plant species, populations such as these are easy to develop, although sterility in the F1 hybrid may limit some combinations of parents (Quiros 2001). The major drawback of F2 and BC populations is that they are ephemeral. The best solution to this problem is the use of inbred populations or doubled-haploid (DH) populations that provide a permanent mapping resource. Use of populations of recombinant inbred lines (RILs), which are derived from F2 or BC, is an excellent strategy (Burr et al. 1988). DH populations are developed by culturing microspores or anthers and represent another type of immortal mapping population. However, this type of population is difﬁcult to develop for some Brassica species, especially for B. rapa (Kole et al. 1997a). Another valuable mapping population is the inbred BC population (Bernacchi et al. 1998), in which germplasm development and quantitative trait locus (QTL) mapping proceed simultaneously. The maps produced in B. rapa are based mainly on F2 populations. Song et al. (1991) developed the ﬁrst B. rapa genetic linkage map from one F2 population of 95 individuals derived from the cross between a Chinese cabbage cultivar and an accession of spring broccoli. These parents represent two diverse groups that are polymorphic for both morphological traits and restriction fragment lengths (Figdore et al. 1988; Song et al. 1988b). Another F2 segregating population was developed from a cross between the yellow sarson type R500 and the canolar type Horizon (Chyi et al. 1992); a genetic linkage map of RFLP loci was created based on this mapping population. Matsumoto et al. (1998) reported the construction of a genetic map from an F2 population of 104 individuals derived from the cross between two Chinese cabbage DH lines, T136-8 and Q5. Other F2 progenies derived from different B. rapa cultivar groups have been developed in several laboratories (McGrath and Quiros 1991; Teutonico and Osborn 1994; Ajisaka et al. 1995; Zhang et al. 2000; Lu et al. 2002a). The ﬁrst immortal mapping population of B. rapa reported is a population of RILs (Kole et al. 1997a), which was derived from a cross between 6.2.2 the biennial cultivar Per and the annual cultivar Mapping Populations R500. These RILs were developed by advancing Genetic map construction requires researchers to se- 95 individual F2 plants to an F6 generation by lect the most appropriate mapping populations. Once single seed descent. Other RIL populations have suitable parents have been chosen, the type of popu- been developed by crossing Chinese cabbage and

(Quiros 2001). B. rapa (AA; 2n = 20) is an important vegetable crop and to a minor extent also an oilseed crop. There is variation for the plant organs that are used, which resulted in the selection of different morphotypes depending on local preferences. Because B. rapa has been cultivated for many centuries in different parts of the world, this further increased the variation within the species due to ongoing breeding. Based on the organs used and, secondly, on their morphological appearance, a number of major cultivar groups, which have been given subspecies names in the past, can be distinguished. This species includes vegetable, oilseed, salad, condiment, and fodder crops such as Chinese cabbage-petsai, Chinese cabbage-pak-choi, wuta-cai, broccoletto, oilseed turnip rape, sarson, mizuna, caixin, and fodder turnip. The oilseed types (B. rapa ssp. oleifera) fall into different subgroups based on their growth habit (spring and winter types). The Chinese turnip rape is possibly developed from Chinese cabbage-pak-choi in south China (Li 1981; Liu 1984) and shows strong branching. The separate breeding tradition in India led to the development of the sarson types, which are very early, self-compatible, and often yellow seeded (GomezCampo and Prakash 1999). Research on some morphological markers or their inheritance has been reported in this diploid species (Yarnell 1956; Hawk 1982a,b; Aslam and Bechyne 1983; Williams and Hill 1985; Anand 1987; Zaman 1989). At the DNA level, the origin and evolution of B. rapa also has been explored using RFLPs, random ampliﬁed polymorphic DNA (RAPDs), and ampliﬁed fragment length polymorphisms (AFLPs) (Song et al. 1988a, b; Demeke et al. 1992; Das et al. 1999; Chen et al. 2000; Guo et al. 2002; He et al. 2003; Zhao et al. 2004b). These studies have generated information on the evolution and origin of B. rapa and also on the high degree of polyporphism at the molecular level. These have allowed the construction of genetic linkage maps of B. rapa, which provided new information on the organization and evolution of the genome structure of this species.

Chapter 6 Brassica Rapa

Mizuna and Chinese cabbage and Chinese cabbage, respectively (Novakova et al. 1996; Yu et al. 2003a–c). More recently, some DH populations have been reported (Lim et al. 1998; Suwabe et al. 2004; Wang et al. 2004; Zhao et al. 2004a). The DH population created by Lim research group was generated from a cross between two morphologically diverse Chinese cabbage inbred lines, Chiifu and Kenshin. This population will serve as a useful reference to undertake genetic mapping and genome sequencing of B. rapa.

6.2.3 Mapping Resources The main mapping resources developed for Brassica crops are genetic markers such as RFLPs and polymerase chain reaction (PCR)-based markers and cytogenetic stocks, which provide the means to assign genes and linkage groups (LGs) to speciﬁc chromosomes. Genetic Markers In B. rapa, morphological markers and isozyme loci have been used for genetic analysis (Yarnell 1956; Williams and Hill 1985; Chevre et al. 1995); however, they have had a minimal impact on gene mapping because of their small numbers or paucity. Recently, the advent of RFLP and PCR-based genetic markers (Quiros et al. 1994; Kresovich et al. 1995; SzeweMcFadden et al. 1996) has provided sufﬁcient markers to develop comprehensive maps for Brassica genomes and related applications. RFLP Markers RFLPs are genetic markers detected by hybridizing cloned DNA sequences to DNA fragments from restriction enzyme digests. The genetic variation in the plant genome is reﬂected in the variable lengths of these DNA fragments. RFLPs have been derived in Brassica from various sources (Quiros 2001). RFLP markers have been used to study genome evolution and taxonomy in B. rapa and related species (Song et al. 1988a,b, 1990). RFLPs have also been used to construct extensive genetic linkage maps in B. rapa (McGrath and Quiros 1991; Schilling 1991; Song et al. 1991; McGrath and Quiros 1991; Chyi et al. 1992; Teutonico and Osborn 1994; Novakova et al. 1996; Kole

217

et al. 1997a; Matsumoto et al. 1998; Choi et al. 2004; Suwabe et al. 2004). PCR-Based Markers RAPD Markers. The technology of RAPD was one of the ﬁrst modiﬁcations of PCR for genome scanning and analysis (Williams et al. 1990). RAPD markers have been extensively used for genetic diversity analysis (Demeke et al. 1992; Chen et al. 2000; He et al. 2002) and map construction in B. rapa (Ajisaka et al. 1995; Tanhuanpää et al. 1996a; Nozaki et al. 1997; Ajisaka et al. 1999; Zhang et al. 2000; Lu et al. 2002a; Yu et al. 2003a–c; Suwabe et al. 2004). AFLP Markers. The use of AFLP markers is increasing in Brassica (Quiros 2001). The development of AFLP technology has been useful for genetic diversity studies in B. rapa and has considerable potential for generating a large number of polymorphic loci (Das et al. 1999; Guo et al. 2002; Zhao et al. 2004a,b). Recently some genetic maps of B. rapa have been developed using AFLP markers (Lim et al. 1998; Lu et al. 2002a; Yu et al. 2003a–c; Choi et al. 2004; Wang et al. 2004). SSR Markers. Microsatellite or simple sequence repeat (SSR) markers based on di-, tri-, and tetranucleotide tandem repeats were ﬁrst developed in Brassica species by Kresovich et al. (1995). SSRs are a valuable tool for characterizing germplasm in Brassica species because they are numerous, highly informative, technically simple, robust, and suitable for automated allele detection and sizing (Rafalski and Tingey 1993). Due to the economic importance of cultivated Brassica species, large investments have been made in the development of Brassica SSRs, many of which are available to the scientiﬁc community (http://ukcrop.net/perl/ace/search/BrassicaDB). More recently this type marker has been used to establish linkage maps in B. rapa (Choi et al. 2004; Suwabe et al. 2004). Other PCR-Based Markers Primers are designed based on unique sequences and used to amplify a single locus or a few loci. The resultant products are codominant in nature and thus applied to mapping. These marker systems include cleaved ampliﬁed polymorphic sequences (CAPS; Konieckzny and Ausubel 1993), expressed sequence tags (ESTs; Adams et al. 1991), sequencecharacterized ampliﬁed regions (SCARs; Paran and Michelmore 1993), and sequence tagged sites (STSs;

218

P. Quijada et al.

Thomas and Scott 1994). More recently, some of them have been used for map construction in B. rapa (Choi et al. 2004). Another novel simpliﬁed PCRbased marker technique called sequence-related ampliﬁed polymorphism (SRAP) has also been developed; its utility was demonstrated for mapping and tagging of genes responsible for glucosinolate desaturation (Li and Quiros 2001). Cytogenetic Stocks Cytogenetic stocks are primarily alien addition lines in both allotetraploid and diploid backgrounds. In Brassica species, a few maps have been added to the existing ones for B and C genomes (Quiros et al. 1994; Hu et al. 1999; Quiros 2001); and Shen et al. (2000) have developed some primary trisomics of Chinese cabbage.

6.2.4 Genetic Maps Most of mapping work in Brassica rapa has taken place during the past 10 years. More than 20 maps (Table 1) have been developed independently in various labs for this species involving crossing between different cultivar groups, which will require their integration for a more efﬁcient use in the future. The ﬁrst map of B. rapa was developed using an F2 mapping population from Michihili × Spring Broccoli (Song et al. 1991). A total of 188 genomic clones identiﬁed 280 RFLP loci that covered 1,850 cM in 10 LGs. The average distance between markers was 6.6 cM (Fig. 2). An updated version of this map was used to analyze the segregation of 220 RFLP loci and locate genes determining 28 phenotypic traits. The total map length was 1,593 cM in 10 LGs (Song et al. 1995). Another earlier extensive map developed using an F2 population of sarson × canola was reported by Chyi et al. (1992). It included 360 RFLP loci covering 1876 recombination units in 10 LGs with average intervals of 5.2 units. McGrath and Quiros (1991) reported a small map developed from an F2 population of turnip × Pak Choi. This map included 49 RFLP loci and isozyme markers in 8 LGs and covered a total of 262 cM. Schilling (1991) also built a linkage map based on F2 progenies from a cross between the oilseed cultivar Candle and a rapid cycling strain. Matsumoto et al. (1998) reported the construction of an RFLP genetic map of Chinese cabbage

using two DH parental lines. The map spanned 735 cM with 63 loci distributed among 10 LGs. This map was also used to perform the linkage analysis of RFLP markers to clubroot resistance (CR) and pigmentation. Maps based on F2 populations have also been reported by Nozaki et al. (1997) employing 52 RAPD markers distributed among 10 LGs and spanned 733 cM and by Ajisaka et al. (1995) using RAPD and isozyme markers that spanned 860 cM. A series of loci affecting microspore culture efﬁciency in B. rapa were mapped (Ajisaka et al. 1999) using AFLP and RAPD markers. Lu et al. (2002a) constructed a map based on an F2 mapping population derived from a cross between Chinese cabbagepak-choi Aijiaohuang and turnip Qisihai; this map spanned 1,810.9 cM with 131 loci distributed among 10 LGs and two small groups. Zhang et al. (2000) produced a genetic map based on the segregation of 99 RAPD markers from 84 10-base random primers. This map spanned 1,632.4 cM with average interval between markers of 16.5 cM. Teutonico and Osborn (1994) also produced a map consisting of 139 RFLP loci using an F2 population derived from a cross from winter turnip rape Per and the spring yellow sarson R500. More recently, permanent mapping populations were used for map construction of B. rapa. Kole et al. (1996b, 1997a) obtained a map (Fig. 3) based on an RIL population derived from the same F2 plants used by Teutonico and Osborn (1994). The total map distance was 890 cM, with an average distance of 6.0 cM between loci. This map represented the ﬁrst framework map for an immortal population of B. rapa. A total of 144 RFLP loci were assembled into 10 major LGs and two short groups. Some of these loci were used for testing associations with stress-related traits and disease resistance (Kole et al. 1996a, 2002a,b). Other maps based on RIL populations have been developed in B. rapa (Novakova et al. 1996; Yu et al. 2003a). Novakova et al. (1996) created a map that spanned 1,138.1 cM assembled in 10 LGs. The map constructed by Yu et al. (2003a) consisted of 265 AFLP and 87 RAPD markers covering a length of 2,665.7 cM with an average genetic distance of 7.6 cM between loci and distributed among 17 groups. Genetic maps from DH populations have been reported (Choi et al. 2004; Suwabe et al. 2004; Wang et al. 2004). Wang et al. (2004) constructed a molecular genetic map of Chinese cabbage based on AFLP markers by screening 64 AFLP primer combinations; 263 polymorphic

Chapter 6 Brassica Rapa

219

Table 1. Summary of genetic linkage maps in Brassica rapa Populations (parents)

Marker types

Markers number

Map distance

Groups

Reference

F2 (Chinese cabbage, spring broccoli) F2 (Turnip, pak choi)

RFLP RFLP Isozyme RFLP RFLP RFLP

280 49

1850 262

10 8

58 360 139

700 1876 1785

– 10 11

220 117

1593 860

10 16

Song et al. 1991 McGrath and Quiros 1991 Schilling 1991 Chyi et al. 1992 Teutonico and Osborn 1994 Song et al. 1995 Ajisaka et al. 1995

144 22 83 126 144 52 63 –

519

10

Tanhuanpää et al. 1996a

1138.1 821 890 733 735 –

10 12 12 10 10 –

Novakova et al. 1996 Kole et al. 1996b Kole et al. 1997a Nozaki et al. 1997 Matsumoto et al. 1998 Lim et al. 1998

851

16

Ajisaka et al. 1999

99 131

1632.4 1810.9

13 12

Zhang et al. 2000 Lu et al. 2002a

352

2665.7

17

Yu et al. 2003a

255 262

883.7 1005.5

10 10

Wang et al. 2004 Suwabe et al. 2004

644

1131

10

Choi et al. 2004

F2 (Oilseed cultivar, rapid cycling) F2 (Sarson, canola) F3 (Turnip rape, yellow sarson) F2 (Chinese cabbage, spring broccoli) F2 (Chinese cabbage, Chinese cabbage) F2 (Turnip rape, turnip rape) RIL (Chinese cabbage, Chinese cabbage) RIL (Turnip rape, yellow sarson) RIL (Turnip rape, yellow sarson) F2 (Chinese cabbage, mizuna) F2 (Chinese cabbage, Chinese cabbage) DH (Chinese cabbage, Chinese cabbage) F2 (Chinese cabbage, Chinese cabbage)

F2 (Turnip, Chinese cabbage) F2 (Turnip, pak choi) RIL (Chinese cabbage, Chinese cabbage) DH (Chinese cabbage, Chinese cabbage) DH (Chinese cabbage, Chinese cabbage)

DH (Chinese cabbage, Chinese cabbage)

RFLP RAPD Isozyme RAPD RFLP RFLP RFLP RFLP RAPD RFLP AFLP RAPD RAPD RFLP Isozyme RAPD AFLP RAPD AFLP RAPD AFLP SSR RFLP RAPD AFLP RFLP ESTP CAPS SSR

–

DH = doubled haploid lines RIL = recombinant inbred lines

bands were obtained from 20 primer pairs and assembled in 10 LGs that spanned 883.7 cM. The map developed by Suwabe et al. (2004) used a DH population in combination with 113 SSR, 87 RFLP, and 62 RAPD loci. The resultant map contained 10 LGs that spanned a total length of 1,005.5 cM with an average spacing of 3.7 cM between loci. This map has been used to identify three loci for CR and do

comparative analysis between B. rapa and A. thaliana. With the synteny map data, two major QTL regions were found to be aligned to the same region. The Lim research group (Choi et al. 2004) also created a DH population by crossing two morphologically diverse Chinese cabbage inbred lines, Chiifu and Kenshin. A genetic linkage map of the B. rapa subsp. pekinensis was constructed based on DNA markers such as

220

P. Quijada et al.

Fig. 2. Linkage map of Brassica rapa built with 280 marker loci identiﬁed with 188 random genomic DNA clones. Marker loci that were mapped based on segregation of a single band are underlined. Adjacent duplicated loci are marked with brackets. Loci with segregation ratios that deviated from expected 1:2:1 ratio are indicated with ∗ (0.05 > P > 0.025) and ∗∗ (0.025 > P > 0.01) (Song et al. 1991)

AFLP, PCR-RFLP, ESTP, CAPS, and SSR segregating in this population. A set of 644 markers was mapped on 10 LGs covering 1,131 cM with an average distance of 1.8 cM between loci. These markers were assigned to LGs of Chinese cabbage based on the SSR map of B. na-

pus. Twenty-one polygenic traits including yield and morphological traits were studied for QTL analysis. This map will serve as a useful reference to undertake physical mapping and genome sequencing of B. rapa under the aegis of the Multinational Brassica Genome

Chapter 6 Brassica Rapa

221

Fig. 2. (continued)

Project. So far, the most extensive maps created for The availability of genetic maps of the diploid Brasthis species are proprietary. The integration of these sica genomes allowed for their comparative analysis. maps is an ongoing effort by various laboratories. Based on developed maps, a comparative analysis of the three diploid genomes using a common set of RFLP probes was performed by Lagercrantz and Lydiate (1996). Distinct chromosomal structures differ6.2.5 entiated by a large number of rearrangements, but Comparative Mapping colinear regions involving virtually the whole of each High-density maps have become a potent tool in of the three genomes, were identiﬁed (Fig. 4). Comthe study of genome evolution and rearrangement. parative analysis of the A genome from B. rapa and

222

P. Quijada et al.

Fig. 3. RFLP linkage map of Brassica rapa. Map distances in centiMorgans are on left side of linkage groups; locus names are in italics on right. Loci marked ∗ and ∗∗ deviated signiﬁcantly from a 1:1 ratio at P < 0.05 and P < 0.01, respectively. P and R indicate that P/P or R/R genotypes predominated, respectively (Kole et al. 1997a)

the A genome from B. napus revealed 11 conserved linkage segments with at least four loci in common (Hoenecke and Chyi 1991). Teutonico and Osborn (1994) also reported comparisons between the two species using RFLP linkage maps constructed with

a common set of DNA probes. The results indicated that nine of the B. rapa LGs had conserved linkage arrangements with B. napus LGs. Similar conservation of LGs was observed when they compared A and C genomes from B. rapa and B. oleracea.

Chapter 6 Brassica Rapa

223

Fig. 4. Comparative map of Brassica A, B, and C genomes showing alignment of Brassica nigra LGs (G1 to G8) with corresponding chromosome segments from B. oleracea (O1 to O9) and B. rapa (R1 to R10). Lines connecting B. nigra LGs with chromosome segments from B. oleracea and B. rapa indicate positions of homoeologous loci. Dotted lines indicate positions of loci not detected in B. nigra but homoeologous in B. oleracea and B. rapa. Round ends on chromosome segments indicate that these are internal to corresponding LGs. X: inversions in B. oleracea with respect to B. nigra; : inversions in B. nigra; : inversions in both B. oleracea and B. rapa with respect to B. nigra (Lagercrantz and Lydiate 1996)

Recently, two RFLP linkage maps, one from a cross between a resynthesized B. juncea (B. rapa × B. nigra) and a natural B. juncea cultivar and the other from a cross between two natural B. juncea cultivars, were compared (Axelsson et al. 2000). The comparison showed that B. rapa chromosomes paired exclusively with their A-genome homologs in B. juncea and that B. nigra chromosomes likewise paired with their B-genome homologs and the two maps were highly colinear. Synteny has also been found between B. rapa and A. thaliana, as observed for genes controlling ﬂowering time (Osborn et al. 1997; Kole et al. 2001; Schranz et al. 2002), the incompatibility response locus S (Conner et al. 1998), and resistance to white rust genes (Kole et al. 2002a). Two

QTLs controlling ﬂowering time (VFR1 and VFR2) in B. rapa were analyzed in an F2 population (Teutonico and Osborn 1995) and a recombinant inbred population (Osborn et al. 1997). The two genomic regions containing these QTLs showed homology to two regions in B. napus, which contain QTLs (VFN1 and VFN2) controlling vernalizationresponsive ﬂowering time in segregating populations derived from annual and biennial oilseed cultivars (Osborn et al. 1997; Butruille et al. 1999). The Brassica regions containing VFR2 and VFN2 were also found to be homologous to a region at the top of chromosome 5 in the related crucifer A. thaliana, where several ﬂowering-time genes are located (Osborn et al. 1997).

224

P. Quijada et al.

Based on comparative linkage maps, Kole et al. (2001) concluded that VFR2 was homologous to ﬂowering locus C (FLC), a repressor of ﬂowering that is required for the winter-annual habit of late-ﬂowering ecotypes of A. thaliana; therefore, it might control ﬂowering time through a mechanism similar to that in A. thaliana. Futhermore, Schranz et al. (2002) found four B. rapa FLC homologs, three of which were located in genomic regions that are syntenic with the top of chomosome 5 of A. thaliana. More recently the dwarf gene DWF2 was mapped to the bottom of LGR6, in a region having homology to the top of A. thaliana chromosome 2. The results from comparative mapping showed that the B. rapa R6 and had high colinearity to a 784-kb segment of A. thaliana chromosome 2 (Muangprom and Osborn 2004). Comparative genome studies are important contributors to our understanding of genome evolution. Genetic map comparison would also allow the analysis of genome rearrangements, duplications, deletions, inversions, and translocations of genetic segments (Hu and Quiros 1991; Sharpe et al. 1995). The comparative physical mapping between A. thaliana and B. rapa using ﬂuorescence in situ hybridization (FISH) techniques revealed that chromosomal duplications played a major role in the evolution of the B. rapa genome (Jackson et al. 2000). Comparative mapping between A. thaliana and Brassica species showed that a chromosomal region from the top of A. thaliana chromosome 5 corresponded to three homoeologous copies in each of the diploid species B. nigra, B. oleracea, and B. rapa. QTLs were detected in two of the three replicated segments in each diploid genome. All the major QTLs detected in the different species of Brassica could be the result of duplicated copies of the same ancestral gene (Axelsson et al. 2001).

to important traits and for the discovery of qualitative or quantitative trait loci and genes. The molecular tags, which are linked to the target traits such as disease resistance and morphological and physiological characters, are a prerequisite for any markerassisted selection (MAS) program for crop improvement and are also important for map-based cloning. Bulked segregant analysis (BSA) was developed to rapidly identify markers linked to any speciﬁc gene or trait (Michelmore et al. 1991). It has been a successful approach for tagging desirable traits by screening various molecular markers on bulked samples. Many markers for oleic acid content, disease resistance, selfincompatibility, dwarﬁsm, and other important traits have been obtained using this efﬁcient approach in B. rapa. Agronomic traits of interest in B. rapa can be divided into four categories: (1) traits such as oil content and oil quality and crushed meals, (2) disease resistance, (3) traits of importance in F1 hybrid seed production, and (4) morphological traits. Molecular markers linked to some of these traits have been reported (Table 2), and a number of them are now successfully integrated into oilseed breeding programs. Some of these are discussed here with special reference to the seed coat color, disease resistance, and oil composition.

6.3.1 Seed Coat Color

The seed coat color in B. rapa varies from yellow to brown, with intermediate shades. The yellow-seeded varieties have inherent advantages over the darkseeded varieties in both qualitative and quantitative terms, with lower crude ﬁber content in the seed (Stringam et al. 1974; Daun and DeClercq 1988). Such characteristics are desirable for the oil industry. However, most varieties grown for commercial cultivation 6.3 are dark-seeded with very few yellow/light-seeded vaGene Mapping rieties. An example in B. rapa is the variety Candle, which was released in Canada (Stringam 1980), and One of the most important applications of genetic YsPb (yellow sarson) and Y1D, which have been commaps and DNA markers is to identify markers asso- mercially released in India. Attempts have been made ciated with qualitatively and quantitatively controlled to develop artiﬁcially synthesized yellow-seeded variimportant agronomic traits, which can assist breeders eties in B. rapa (Jönsson 1975, 1977; Chen et al. 1988). in making more efﬁcient selections in breeding proThe seed coat coloration results from deposition of grams. A number of practical examples have demon- condensed polyphenols or polymers of leukocyanidin strated the power of high-density genetic maps for pigments in the palisade layer and partially in the the identiﬁcation of genetic markers closely linked parenchymal layer of the testa in Brassica species (van

Gene symbol

fad2 fad3

Eru

Crr1, Crr2

Crr3

CRB

CRa

–

–

–

Dwf2 –

ht-1–ht5

– VFR2

Traits

Oleic acid content Linolenic acid content

Erucic acid content

Clubroot resistance

Clubroot resistance

Clubroot resistance

Clubroot resistance

Clubroot resistance

White rust resistance

TuMV-resistance

Dwarf Microspore embryogenic ability

Heat tolerance

Late bolting Flowering time

F2 BC3 S1

RIL

F2 , BC2 F2

F2

RIL

DH

F2

F2

F3

F2

F3

F2 F2

Population

Table 2. Some of the traits mapped in Brassica rapa

RAPD dominant RFLP codominant

AFLP, RAPD dominant, codominant

RFLP codominant RAPD dominant

AFLP dominant

RFLP codominant

RAPD dominant

RLFP codominant

STS (dominant, codominant) SCAR

SSR codominant

RFLP codominant

SCAR codominant RAPD dominant

Marker types

W01.600 (0.9) CT-AC179 (1.5) CA-AG193 (1.0 cM) CC-AT64 (2.4) CT-TT170 (0.1) BN007-1 tg1g9 (0.44)

tg1f8 (7.2) GAP-b (14.2) BRMS-088 (1.75) BRM-096 (0.88) OPC11-1S OPC11-2S TCR09 (0.78) TCR05 (1.92) HC352b (3) HC181 (12) RA12-75A WE22B WE49B wg6c1a-Pub1 wg2d11-ec5a6a CAG 150 (7.5) CAC 150 (8.4) At2g 01810 (0.5) OPE 03-1600 OPA 13-1200 OPB 70-1400

OPH-17 (11.5) OPS-01∼OPJ-20 OPP-05∼OPG-16

Flanking markers (distance in cM)

R6 3 11 13 17 3 8 8 9 9 – 8 (R10)

4 2 –

–

3

–

–

–

6 3 9 10 1

Linkage group

Ajisaka et al. 2001 Kole et al. 2001

Yu et al. 2003c

Muangprom and Osborn 2004 Zhang et al. 2003

Han et al. 2004

Kole et al. 2002a

Kuginuki et al. 1997

Matsumoto et al. 1998

Piao et al. 2004

Hirai et al. 2004

Suwabe et al. 2003

Teutonico and Osborn 1994

Tanhuanpää et al. 1996b Tanhuanpää et al. 1996b

Reference

Chapter 6 Brassica Rapa 225

–

Lob Pub Pub1

Leaf lobes

Number of leaf lobes Pubescence Pubescence

Seed coat color Seed coat color

S-glycoprotein F2 NS-glycoprotein – BC3 Yls F3

Self-incompatibility

F2 F2 F3

F2

BC3 S1

BrFLC2

Flowering time

Population

Gene symbol

Traits

Table 2. (continued)

RFLP codominant RFLP codominant RFLP codominant

RAPD, AFLP

RAPD dominant RFLP codominant

RAPD dominant

RFLP, SSR codominant

Marker types

wg3h2 (6.7) COR6.6 (7.2) F09-1040 ACP-1 B06-600 M456b (6.5) ec3c8b (7.8) T11-500∼H14-2000 E17-240∼f-14-650 J03-880∼A16-1000 004-500∼M04-1400 48 116b, 145 ec2b3 (8.4) ec2e12 (29.2)

Flanking markers (distance in cM)

2 1 4 6 4A 9A 4

2 1 – 5

R2

Linkage group

Song et al. 1995 Song et al. 1995 Teutonico and Osborn 1994

Lu et al. 2002

Chen et al. 1997 Teutonico and Osborn 1994

Nozaki et al. 1997

Schranz et al. 2002

Reference

226 P. Quijada et al.

Chapter 6 Brassica Rapa

Caseele et al. 1982). The difference in seed colors has been attributed to variable amounts of polyphenols in the seed coat, the lowest amount being detected in yellow seeds (Theander et al. 1977). Leukocyanidins are derivatives of the ﬂavonoid biosynthesis pathway, which has been shown to affect seed coat color in different plant species (Sparvoli et al. 1994). The seed coat color in B. rapa has been shown to be controlled by one (Ahmed and Zuberi 1971; Teutonico and Osborn 1994), two (Stringam 1980), and multiple genes (Schwetka 1982). A study on B. napus has led to a similar conclusion, a three-gene model with maternal genotypic control of the seed coat color, and the brown seed coat as dominant over yellow. Environmental inﬂuence on seed coat color has also been reported (Schwetka 1982). Since yellow/light seed is recessive, maternally inherited, and inﬂuenced by environmental factors, it is desirable that the locus responsible for seed coat color be linked to molecular markers. The seed coat color gene has been tagged using RFLP, RAPD, and AFLP markers. Teutonico and Osborn (1994) reported a 3:1 segregation ratio of brown:yellow seed in B. rapa in an F2 population derived from the cross between Per and R500. The locus controlling seed color (Yls) mapped to LG5 (also known as R9) ﬂanked by the m456b and ec3c8b loci (Fig. 5). In another study, RAPD markers linked to the seed coat color in B. rapa were developed by Chen et al. (1997) using B. rapa-alboglabra addition lines. The B. rapa background of the addition lines was the Indian yellow sarson accession K-151, which produces purely yellow seeds, while the alien C-genome chromosome was from the black-seeded B. alboglabra accession No4003. Twenty BC3 and 20 BC4 progeny plants and the parental materials were

Fig. 5. Yellow seed coat locus (Yls) on LG5 (Teutonico and Osborn 1994)

227

the subject of the study. RAPD analysis revealed 19 markers speciﬁc to the alien C-genome chromosome of the addition lines. One of these markers (B06-600) was inferred to be close to the seed color locus, which mapped in a terminal region of the alien chromosome. Studies on mitotic prometaphase chromosomes of the addition lines indicated that the alien chromosome was apparently chromosome 1 of B. alboglabra (Fig. 6).

6.3.2 Disease Resistance Selection for disease resistance is one of the major components in most plant-breeding programs. Tight linkage between DNA markers and disease resistance genes is useful to follow gene transfer from one genetic background to another in a breeding program, which allows early selection and avoids difﬁcult multiple screening with pathogen strains. Fungal diseases like white rust caused by Albugo candida and clubroot caused by Plasmodiophora brassicae have fueled intense mapping research in B. rapa, resulting in the identiﬁcation of several useful DNA markers. AFLP

Fig. 6. Distribution of RAPD markers and black seed color gene (Blc) on alien C-genome chromosome of B. campestrisalboglabra addition line. Markers within groups are not ordered (Chen et al. 1997)

228

P. Quijada et al.

markers that map near the CR gene CRb, which confers resistance to the P. brassica races 2, 4, and 8, were found in an F2 population consisting of 143 plants derived from the cross of Shinki, a CR DH line of Chinese cabbage, and the susceptible line, 94SK (Piao et al. 2004). A reliable conversion procedure allowed ﬁve AFLP markers to be successfully converted into CAPS and SCAR markers. A genetic map around CRb covering a total distance of 6.75 cM was constructed. One dominant marker, TCR09, was located 0.74 cM from CRb. The remaining markers were located on the other side of CRb, and the nearest of these was TCR05, at a distance of 1.97 cM (Fig. 7). Suwabe et al. (2003) identiﬁed two loci, Crr1 (linked to SSR marker BRMS-088) and Crr2 (linked to SSR marker BRMS-096), in an analysis of 114 F2 individuals from a cross between clubroot-resistant (G004) and susceptible (A9709) lines of B. rapa. Each

locus segregated independently in this F2 population. Genetic analysis showed that each locus had little effect on CR by itself, indicating that these two loci are complementary for CR. The resistance to clubroot was much stronger when both loci were homozygous for resistant alleles than when they were heterozygous. These results indicate that CR in B. rapa is under oligogenic control and at least two loci are necessary to confer resistance. An inbred turnip line, N-WMR-3, was crossed with the clubroot-susceptible DH line A9709; the segregating F3 population was obtained by single-seed descent of F2 plants and used for a genetic analysis (Hirai et al. 2004). Segregation of CR in the F3 population suggested that a major gene controls it. Two RAPD markers, OPC11-1 and OPC11-2, were considered as candidate markers by BSA, and were converted to STS markers, named OPC11-1S and OPC11-2S, respectively. These two marker loci were linked to each other at a distance of ca. 10 cM. The frequency distribution of disease index among OPC11-2S genotypes in the F3 population clearly showed that a gene closely linked to OPC11-2S had a large effect on the CR of N-MWR-3. Frequency distributions and statistical analyses indicate the presence of a major dominant CR gene linked to these two markers. The present marker for CR was independent of the previously found CR loci, Crr1 and Crr2 found by Suwabe et al. (2003), and it was named Crr3. Linkage of RAPD markers with genes resistant to clubroot in B. rapa was studied in a DH population (Kuginuki et al. 1997). Thirty-six DH lines were derived from F1 plants of a cross between susceptible Homei P09 and resistant Siloga S2 plants. Three RAPD markers, RA12-75A, WE22B, and WE49B, were found linked to a clubroot-resistance locus (Fig. 8). In another study, a dominant major gene (CRa) was mapped on LG3 (Fig. 9), between RFLP loci HC352b and HC181 (Matsumoto et al. 1998). B. rapa is the primary host of race 7 (AC7) of A. candida (Verma et al. 1975). Albugo candida race 2

Fig. 7. Genetic mapping of CRb gene. (a) Linkage map showing AFLP markers and previously identiﬁed SCAR marker, TCR01, based on 138 F2 plants. (b) Linkage map showing SCAR and CAPS marker loci, based on 143 F2 plants (Piao et al. 2004)

Fig. 8. Linkage of RAPD markers to clubroot resistance in Brassica rapa. Distances between markers are shown in centiMorgans. This ﬁgure was established from 36 DH lines derived from crossing Homei P09 and Siloga S2 (Kuginuki et al. 1997)

Chapter 6 Brassica Rapa

(AC2) is compatible primarily with B. juncea (Pound and Williams 1963), but it also has been found to severely infect many genotypes of B. rapa (Petrie 1988). Pathotypes that combine the virulence of race 2 on B. juncea and the virulence of race 7 on B. rapa have been isolated from western Canada (Rimmer et al. 2000). Thus, resistance to both races may be an important requirement of future cultivars. Resistance to AC2 in B. rapa and other oilseed Brassica spp., including B. juncea, B. napus, and B. carinata, is conferred by dominant alleles at single loci (Delwiche and Williams 1974, 1981; Ebrahimi et al. 1976; Tiwari et al. 1988; Kole et al. 1996a), although evidence for minor genes controlling resistance has also been reported (Edwards and Williams 1987; Kole et al. 1996a). Inheritance of resistance to AC7 has not been reported in its primary host, B. rapa; however, dominant alleles at three unlinked loci were found to confer resistance in B. napus (Fan et al. 1983). Information on the genetics and chromosomal location of resistance to these two races of A. candida in the B. rapa genome would be useful to develop resistant varieties

229

by marker-assisted breeding and also to clone the resistance genes. A single locus controlling resistance to AC2 in B. rapa was mapped previously using RFLP markers and a segregating F2 population from a Per × R500 cross (Kole et al. 1996a). Genes for resistance to white rust in oilseed B. rapa were mapped using a population of recombinant inbred lines (RIL) and a genetic linkage map consisting of 144 RFLP markers and three phenotypic markers (Kole et al. 2002a). Young seedlings were evaluated by inoculating cotyledons with A. candida race 2 (AC2) and race 7 (AC7) and scoring the interaction phenotype (IP) on a scale of 0 to 9. The IP of each line was nearly identical for the two races and the population showed bimodal distributions, suggesting that a single major gene (or tightly linked genes) controlled resistance to the two races. A QTL mapping approach using the IP scores detected the same major resistance locus for both races, plus a second minor QTL effect for AC2 on LG2. These results indicate that either a dominant allele at a single locus (Aca1) or two tightly linked loci control seedling resistance to both races of white rust in the biennial turnip rape cultivar Per. The map positions of white rust resistance genes in B. rapa and B. napus were compared and the results indicate the possible location of additional loci that have not been mapped. Alignment of these maps to the physical map of the Arabidopsis genome identiﬁed regions to target for comparative ﬁne mapping using this model organism. An F2 population derived from the cross between Brp0058 and Brp0181 was analyzed using AFLP markers in combination with BSA, and two AFLP loci linked to TuMVresistance were identiﬁed (Han et al. 2004).

6.3.3 Vernalization Requirements and Flowering Time

Fig. 9. Dominant major clubroot resistance gene (CRa) on LG 3 (Matsumoto et al. 1998)

Although ﬂowering can be recorded as a quantitative trait, it involves some major genes, which contribute easily detectable effects and segregate as a single gene. Molecular markers have been used to map ﬂoweringtime QTLs in populations of B. rapa (Song et al. 1995; Teutonico and Osborn 1995; Osborn et al. 1997; Axelsson et al. 2001). Genes controlling the vernalization requirement were identiﬁed in a B. rapa F2 population derived from a cross between Per and R500 using an RFLP linkage map and QTL analysis for ﬂowering time in F3 lines. Two regions, COR6.6a–wg3h2a (VFR1) on LG2

230

P. Quijada et al.

Fig. 10. A portion of Brassica rapa LG 8 (Br8) around a vernalization-responsive ﬂowering time gene (VFR2) and corresponding region in Arabidopsis thaliana. The names of RFLP loci are on the right and linkage distance, in cM, is on left. The positions of RFLPs detected by four A. thaliana DNA clones (FLC, mi174, CO, and mi438) in B. rapa are indicated by lines connecting their positions on chromosome 5 of A. thaliana (At 5). The map positions of loci on At 5 are from linkage analyses in a recombinant inbred population (nga 249, mi174, and mi438) or from physical map data (Lister and Dean 1993). CO and FLC are cloned ﬂowering-time genes. An RFLP locus detected in B. rapa by exons 2–6 of a FLC cDNA cosegregated exactly with the VFR2 phenotype in 414 gametes (Kole et al. 2001)

(R2) and ec5f3–ec5a7a (VFR2) on LG8 (R10), were identiﬁed containing putative QTLs with large effects on days to ﬂowering (DTF). The two-QTL model explained 75.2% of the variation in DTF, with the QTL on R2 accounting for 44.6% of the variation and the QTL on R10 accounting for 21.7% of the variation (Teutonico et al. 1995). To determine precisely the allelic effects and map position of VFR2, a BC3 S1 population was evaluated for ﬂowering time and leaf number in growth chamber and ﬁeld experiment. In the growth chamber, one quarter of the population had a distinctly ealier ﬂowering time; in the ﬁeld, the population segregated into three distinct ﬂowering-time categories in a 1:2:1 ratio. The plants from these distinct ﬂowering time classes were genotyped with 12 marker loci near VFR2, including DNA probes from A. thaliana such as FLC and constans (CO). The result from this analysis

showed that VFR2 does not correspond to CO because these loci were 13 cM apart, but it cosegregated exactly (resolution of <0.24 cM) with an RFLP locus detected by FLC (Kole et al. 2001; Fig. 10). The ﬂowering time difference between homozygous VFR2 genotypes in this population was 95 d days in the growth chamber and 43 d in the ﬁeld, and the late-ﬂowering effect of the Per allele was overridden by vernalization. Heterozygous genotypes were almost exactly intermediate to the homozygous genotypes, indicating complete additive gene action for the late-ﬂowering allele (Kole et al. 2001). Four B. rapa homologs of the MADs-box ﬂowering-time regulator FLC (BrFLC) were cloned by Schranz et al. (2002). BrFLC1 was determined to be the FLC locus mapped onto LG R10 (Kole et al. 2001). The position of BrFLC2 was mapped onto R2 using 78 BC3 S1 plants; it corresponded

Chapter 6 Brassica Rapa

to the major QTL between the ﬂanking markers wg3h2 and COR6.6 and explained 80.6% of the variation. Both BrFLC3 and BrFLC5 were mapped onto R3 using 100 BC1 S1 plants derived from the cross of a Per × R500 RI line with R500. However, only BrFLC5 had an effect on ﬂowering time in this BC1 S1 population. Moreover, Schranz et al. (2002) developed an F2 population from the cross between vfr1vfr1VFR2VFR2 and VFR1VFR1vfr2vfr2 to test the main and interaction effects of two BrFLC loci on ﬂowering time, which were subjected to a two-factor analysis of variance. The full genetic model explained 87% of the ﬂowering-time variation. Ninety-eight percent of this genetic variation was due to the individual additive effects of BrFLC1 (72.2%) and BrFLC2 (25.4%), similar to the results for the populations with each gene segregating alone. Dominance at BrFLC1 was signiﬁcant in the F2 population, as were some of the epistatic interactions, but in total these nonadditive effects explained only 2.4% of the genetic variation. This result supports the hypothesis that BrFLC1 and BrFLC2 are duplicate copies of the same gene that have maintained a similar function.

6.3.4 Fatty Acid Content Brassica species are grown as a major oilseed crop, and manipulation of oil quality and quantity has been a primary objective of crop-improvement programs. Several studies have been undertaken to generate markers linked to genes controlling FA content, such as linolenic, linoleic, oleic, palmitic, and erucic acid. Erucic acid locus was mapped to LG1 (also known as R5), ﬂanked by loci tg1f8 and Gap-B in B. rapa using RFLP markers (Teutonico and Osborn 1994; Fig. 11). A QTL in a LG of six markers associated to oleic acid content was detected by Tanhuanpää et al. (1996b) in B. rapa. OPH-17, a codominant RAPD marker, was the best for selection of this trait. It was converted to a SCAR marker for better reproducibility. The oleic acid QTL also affected palmitic and linoleic acid content, which indicates that it controls either chain elongation or desaturation steps. The previously identiﬁed oleic-acid-content QTL observed in an F2 population from the B. rapa ssp. oleifera cross Jo4002 × Jo4072 (a high-oleic-acid individual) was mapped more precisely by adding markers to the LG that harbors the locus. The fad2 gene,

231

Fig. 11. Erucic acid locus (Eru) on LG1 (Teutonico and Osborn 1994)

which is known to encode the 18:1 desaturase in Arabidopsis, was also mapped in Brassica. The QTL located exactly at the fad2 RFLP marker (Tanhuanpää et al. 1998). A population of 90 F2 plants, derived from a cross between two spring turnip rape individuals, 93651-2 and Sv3402, segregating for linolenic acid content was used to identify genes and develop markers for linolenic acid content in spring turnip rape (B. rapa ssp. oleifera) by Tanhuanpää and Schulman (2002). A candidate gene approach for the rapeseed fad3 gene and BSA with RAPD markers was used. A total of 27 markers were distributed in three LGs, LG3, LG9, and LG10, which contained 6, 7, and 14 markers, respectively. The three QTLs accounted jointly for 73.7% of the variation in linolenic acid content. The fad3 gene was mapped near one QTL that explained 23.5% of the phenotypic variation. RAPD markers tagged to genes controlling linolenic acid concentration in B. rapa had also been identiﬁed by Hu et al. (1995) using BSA.

6.3.5 Self-Incompatibility Self-incompatibility (SI) as well as male sterility has been successfully used in the production of F1 hybrid seeds in Brassica. One of the common methods is the use of self-incompatibility through S-alleles from B. oleracea and B. rapa, which are responsible for pollen rejection at the stigma surface wherever the pollen and stigma bear identical S-alleles (Nasrallah et al. 1991). Multiple alleles in a single locus controlled the self-incompatibity in B. rapa, and 30 alleles have been identiﬁed in the S-locus (Nou

232

P. Quijada et al.

et al. 1993). DNA markers linked to some of the Salleles have been identiﬁed (Nishio et al. 1994, 1996, 1997). F2 populations from a hybrid between an inbred line of Chinese cabbage (B. rapa var. pekinensis) and an inbred line of Mizuna (B. rapa var. japonica) were analyzed with RAPD and isozyme markers to construct a linkage map. A total of 52 RAPD markers were integrated into 10 LGs. Genes for selfincompatibility, S-glycoprotein and NS-glycoprotein, which have a high degree of structural homology with the S-glycoprotein, were found linked to the RAPD markers by QTL analyses. The locus for selfincompatibility (S-glycoprotein) was located on LG2, and a RAPD marker (F09-1040) showing linkage to this locus showed a recombination value of 20.2%. The NS-glycoprotein locus was located on LG1 and linked with ACP-1 with a recombination value of 26.2% (Nozaki et al. 1997). In another study, two of the main genes SLG1 and SRK were highly polymorphic and multiallelic, serving to identify speciﬁc S-lines in B. oleracea and B. rapa based on their DNA restriction or ampliﬁcation proﬁles (Nishio et al. 1997; Okazaki et al. 1999).

6.3.6 Dwarfism Dwarf genes have been valuable for improving harvestable yield of several crop plants and may be useful in oilseed Brassica. A dwarf gene, dwf2, from B. rapa was evaluated by Muangprom and Osborn (2004) in order to determine its phenotypic effects and genetic characteristics. The dwf2 mutant was insensitive to exogenous GA3 for both plant height and ﬂowering time, suggesting that it is not a mutation in the gibberellin biosynthesis pathway. The dwarf phenotype was controlled by a semidominant allele at a single locus. Near-isogenic lines (NILs) that were homozygous or heterozygous for dwf2 had 47.4% or 30.0% reduction in plant height, respectively, compared to the tall wild-type line, and the reduction was due to reduced internode length and number of nodes. The dwf2 homozygous and heterozygous lines had the same or signiﬁcantly higher numbers of primary branches than the wild-type line, but did not differ in ﬂowering time. The DWF2 gene was mapped to the bottom of LGR6, 0.5 cM from the At2g01810 locus (Fig. 12), in a region having homology to the top of A. thaliana chromosome 2. The map position of DWF2 in comparison to markers in A. thaliana sug-

Fig. 12. Comparison of physical map for segment of Arabidopsis thaliana chromosome 2 (At 2) and chromosome 5 (At 5), and genetic map for segment of B. rapa LG 6 (R6) containing DWF2. Horizontal lines show the same markers in B. rapa and A. thaliana chromosome segments. Arrows indicate direction to top of chromosomes; physical distances are shown in kilobases for A. thaliana genes as estimated from SeqViewer in Arabidopsis genome database. Genetic distances are shown in centiMorgans for B. rapa as estimated from analysis of 410 BC5 plants segregating for DWF2 (Muangprom and Osborn 2004)

gests it is a homolog of RGA (repressor of ga1–3), which is a homolog of the wheat “Green Revolution” gene. This dwarf gene could be used to gain more insight on the gibberellin pathway and to reduce lodging problems in hybrid oilseed Brassica cultivars.

Chapter 6 Brassica Rapa

6.3.7 Markers Linked to Microspore Embryogenic Ability In order to identify markers linked to microspore embryogenic ability in B. rapa, RAPD segregation analyses were performed in a microspore-derived population and an F2 population derived from an F1 between Chinese cabbages Ho Mei and 269, and between the oilseed cultivars Lisandra and Kamikita. When the relationship between the 15 markers and embryo yields was investigated in the Chinese cabbage F2 population, seven markers (OPB 15-700, OPB20-1400, oph061200, OPD10-550, OPA 13-1200, RA1273c, OPE131600) were found to be related to embryo yield. In the oilseed rape F2 population, three markers (A2-1600, OPA 10-1200, OPA13-1200) were linked to microspore embryogenic ability (Zhang et al. 2003).

6.4 QTL Mapping A QTL is a region in the genome containing one or several genes affecting a quantitative trait. QTLs can be mapped, and the effect of individual QTLs can be estimated. Experimental designs for mapping of QTLs are based on linkage disequilibrium between alleles at marker locus and alleles at the linked QTLs. One of the requirements for mapping QTLs is a linkage map of polymorphic marker loci that adequately covers the genome. Marker loci should preferably be highly polymorphic, abundant, neutral, and codominant. Restriction fragment length polymorphism (RFLP), randomly ampliﬁed polymorphic DNA (RAPD), simple sequence repeat (SSR), and single nucleotide polymorphism (SNP) are all suitable genetic markers. Ampliﬁed fragment length polymorphisms (AFLPs) are also widely used in QTL analysis. Generally, to identify a QTL, we need to search for statistical association between marker genotypes and trait phenotype. Most commonly, parental inbreds are crossed to produce the F1 generation, which is then either backrossed to one or both parental lines (the BC design) or crossed or selfed to produce the F2 generation (the F2 design); recombinant inbred lines (RILs) can be constructed by taking an F1 line through multiple rounds of selﬁng or multiple generations of brothersister mating. DH lines are also used for QTL analysis.

233

In the simplest case (single-marker analysis), the mapping population is divided based on the genotype at a marker locus, and the phenotypic values are compared statistically between genotypes at the marker locus. This step is repeated for each marker locus on the map. If QTL analysis identiﬁes the association between a marker genotype and a phenotype, one can assume that a QTL is located near the marker. However, it is difﬁcult to distinguish whether the detected effect, is due to tight linkage between the marker and a QTL with a small effect or loose linkage between the marker and a QTL with a large effect. In this case interval mapping can be used to estimate both the QTL effects and their map position. In interval mapping, the genotype at a large number of positions along the chromosome is predicted based on the observed genotypes of markers ﬂanking an interval and the distance between markers. Composite interval mapping that includes additional markers as cofactors can also be used to reduce variation associated with other QTLs in the genome. For QTL analysis, two softwares, MAPMAKER/QTL program (Lander and Botstein 1989; Lincoln et al. 1992) and QTL Cartographer V1.15 (Schranz et al. 2002), have been generally used in Brassica. Most studies have used the interval mapping method, while some have used composite interval mapping (Schranz et al. 2002; Yu et al. 2003b). So far, much progress has been made on QTL studies in B. rapa, including morphological traits, heat resistance, disease resistance, linolenic acid content, and ﬂowering time. Recent progress is presented here. Features like population used, marker types, strategy and software, QTL position and number, allele effects, and variance explained by QTL are mentioned if available. Finally, mendelization of QTL in B. rapa is discussed.

6.4.1 Morphological Traits Although there have been active breeding programs in B. rapa, very limited information is available on the inheritance of many morphological traits in this species. The genetic control of many quantitative traits is unknown due to their complex inheritance patterns. To date, there are several studies on marker analysis of genes controlling morphological variation. Song

234

P. Quijada et al.

et al. (1991) constructed a detailed genetic linkage map of B. rapa based on 280 RFLP loci, which provides an opportunity to detect and measure the effects of genes controlling quantitative traits in B. rapa. Song et al. (1995) made further exploration related to this work; the Chinese cabbage cultivar Michihili and an accession of spring broccoli were selected as parents of 95 F2 individuals that were analyzed for segregation at 220 RFLP loci and for variation in leaf and stem traits. The number, location, and magnitude of genes were determined by using the interval mapping method. There were unequal gene effects on the expression of many traits, and the inheritance patterns of traits ranged from those controlled by a single major gene plus minor genes to those controlled by polygenes with small and similar effects The MAPMAKER/QTL program (Lander and Botstein 1989; Lincoln et al. 1992) was used to identify putative QTL controlling quantitative traits. A LOD score of 2.8 was chosen as the threshold for declaring putative QTLs. Results suggested that a dominant gene controlled the presence of pubescence, designated Pub, and mapped on the LG9A, ﬂanked by marker loci 116b and 145. The degree of pubescence was controlled by polygenes. Based on pubescence 2 (P2) data, a QTL with a major effect at the Pub locus and a QTL having a minor effect on LG7A were found. After excluding hairless plants, the major effect at the Pub locus was not observed and three putative QTLs having small effects were uncovered on LGs 9A, 7A, and 4A (Table 3). The mode of inheritance of leaf lobes was similar to that of pubescence. A dominant gene, designated Lob, was mapped at the upper end of LG4A. Number of leaf lobes (NLL2) analysis revealed a major gene effect at the Lob locus and an additional minor gene on LG7A. For the trait NLL3, three small and almost equal effects were detected at the Lob locus and on LGs 7A and 10A. Petiole length was controlled by a major gene (Lob) and several minor genes, depending on how the trait was measured. Other petiole traits, including petiole width (PW), petiole thickness (PT), and the ratio of width to thickness (PI), were controlled by several QTLs located on LGs 4A, 5A, and 6A. Leaf length was controlled by at least two QTLs located on LGs 9A and 10A. Lamina length and ratio of lamina length to width were controlled mainly by a major gene at the Lob locus and a minor QTL on the LG 7A. All of the QTLs controlling plant height showed small and equal effects on the phenotypes. At least two QTLs for PH1 were de-

tected on LGs 4A and 9A, and one QTL for PH2 was detected on LG5A. Two and three QTLs were found for stem length and the ratio of length to diameter, respectively. Lu et al. (2002b) identiﬁed 24 QTLs associated with 8 morphological traits based on an F2 population derived from an intersubspeciﬁc cross between B. rapa ssp. chinensis Aijiaohuang and B. rapa ssp. rapifera Qisihai, using interval mapping with the software Mapmaker/QTL v1.1. Two QTLs were identiﬁed for leaf length and leaf number, three QTLs for leaf width and plant height, four QTLs for number of leaf lobes and petiole width, and ﬁve QTLs for petiole length. For leaf width, two QTLs with major effects were found on LG2 and their variances were 35.8% and 43.1%, respectively. For number of leaf lobes, four QTLs were found with major effects, including one on LG4 between marker J03−880 and A16−1000 and 3 on LGs 1, 2, and 6, respectively. Yu et al. (2003b) identiﬁed 50 putative QTLs in an RIL population, including ﬁve for plant growth habit, six for plant height, ﬁve for plant diameter, seven for leaf length, four for leaf width, six for leaf length/leaf width ratio, seven for petiole length, four for petiole width, and six for bolting character. They were mapped on 14 LGs. There were unequal gene effects and unequal variation explained on the expression of many morphological traits. For leaf width, two possible QTLs, lw-1 and lw-2, were found with major effects. Fifty-one QTLs related to 12 morphological traits were found by Zhang (1999) using an F2 population genotyped with RAPD markers. A multinational Brassica Genome Project was initiated by Lim et al. (2004) based on B. rapa. A genetic linkage map of the Chinese cabbage-pe-tsai (B. rapa ssp. pekinensis) was constructed based on DNA markers including AFLP, RFLP, EST, CAPS, and SSR segregating in a DH population. This population was generated by crossing two morphologically diverse Chinese cabbage inbred lines, Chiifu and Kenshin. Twenty-one complex traits including yield and morphological attributes were studied for QTL analysis. Data recorded on these traits, based on replicated trials over many years, were subjected to interval mapping using MapQTL v4.0. QTLs with signiﬁcant effect for head weight (16.7%), leaf blade width (37.4%), head compactness (32.5%), and head length (23.1%) were mapped on LG1, LG7, LG2, and LG1, respectively.

Chapter 6 Brassica Rapa Table 3. Summary of putative QTL controlling morphological traits (from Song et al. 1995) Traita

Flanking marker

LOD score

Additive effectb

Dominant effectb

R2 (%)c

Linkage group

Pubescence 2

Pub-145 79a-32b 153b-95b 32b-54a 27-33 136-153b 213b-15b Lob-48 19b-29d Lob-48 19b-29d Lob-48 19b-29d Lob-48 79a-32b 9a-95a Lob-48 202b-207 Lob-48 Lob-48 Lob-48 43-79a 154-85 Lob-48 22a-67 32c-327 325-55b 22b-319 54c-32c Lob-48 22b-319 22a-67 32c-327 10a-190b 114-24b 67-91a 113-102 54d-54b 79a-32b 20-25b 186-46b 200a-29a 207-54c 105-42 29c-36a

19.84 3.53 2.92 3.12 2.85 3.84 3.05 44.83 2.88 47.96 2.88 32.90 3.27 3.99 3.61 2.83 5.80 5.06 38.75 3.71 32.34 3.86 5.06 10.52 3.76 3.45 2.87 5.51 3.75 8.07 3.34 4.30 5.73 3.38 3.64 2.86 6.88 4.59 3.96 3.19 2.86 7.05 9.43 3.94 3.28

−1.68 0.49 −0.85 0.49 −0.66 4.20 3.67 −19.00 −7.43 −19.80 −7.52 4.76 1.86 4.60 −0.38 0.67 3.13 −3.71 18.87 2.50 16.61 −5.45 3.41 −0.86 −0.66 0.46 0.10 0.14 −0.07 −1.47 −1.13 −1.35 0.97 3.45 3.61 −6.37 −9.27 −6.57 −6.45 −3.26 −4.88 −11.87 3.52 3.21 1.78

0.73 1.08 −0.11 0.81 0.32 3.32 3.02 −14.21 −4.30 −14.42 −4.77 2.58 1.47 2.95 −2.06 1.52 4.12 −0.75 13.17 3.29 8.90 −8.98 7.29 −0.55 −0.63 −0.63 0.00 −0.02 0.11 −1.02 −0.11 −1.23 −1.09 2.03 2.41 −5.21 −8.88 −5.72 −4.42 −7.41 −6.25 −9.16 −4.46 1.84 −3.81

70.5 20.3 27.8 23.0 21.3 17.0 15.9 88.9 13.4 90.8 13.4 82.5 15.2 26.6 20.7 16.9 31.8 21.9 85.6 20.7 79.8 19.2 33.6 46.6 26.5 15.4 13.3 26.4 18.3 40.5 18.1 31.1 24.3 15.1 16.2 13.1 40.1 20.0 18.6 14.7 13.8 29.7 39.7 19.5 14.7

9 7 9 7 4 9 10 4 7 4 7 4 7 4 7 10 4 6 4 4 4 7 9 4 5 6 4 4 6 4 4 5 6 4 9 5 3 6 7 7 8 3 6 7 8

Pubescence 3

Leaf length Lamina length Lamina index Number of leaf lobe 2 Number of leaf lobe 3

Petiole length 1 Petiole length 2 Petiole length 3 Petiole length 5 Petiole length 6 Petiole width

Petiole thickness

Petiole index

Plant height 1 Plant height 2 Days to bud

Days to ﬂower Stem length

a

For details of trait description, see Table 1 of Song et al. 1995 Additive and dominant effects of allele from male parent spring broccoli in units of trait measurement c R2 indicates percentage of phenotypic variation explained by putative QTL b

235

236

P. Quijada et al.

Table 3. (continued) Traita

Flanking marker

LOD score

Additive effectb

Stem index

207-54c 105b-42 29c-36a

8.90 2.90 4.11

1.05 0.79 0.67

Dominant effectb −1.16 −0.52 −1.14

R2 (%)c

37.6 14.6 18.2

Linkage group 6 7 8

Table 4. QTLs controlling heat tolerance and its effect in Chinese cabbage (from Yu et al. 2003c) QTL Linkage group

hr-1 hr-2 hr-3 hr-4 hr-5

3 8 8 9 9

Flanking markers

QTL position (cM)

LR score

R2

Additive effect

Nearest marker

Distance from marker (cM)

V02.2200-W01.600 CT-AC179-AG-AA194 CA-AG193-CC-AA68 CA-TG540-CC-AT64 CC-TC208-CT-TT170

85.5 47.0 124.2 55.8 107.5

11.39 24.15 9.67 10.16 18.09

7.70 18.53 7.00 8.01 11.98

−4.50 7.29 −4.39 4.79 −6.18

W01.600 CT-AC179 CA-AG193 CC-AT64 CT-TT170

0.9 1.5 1.0 2.4 0.1

6.4.2 Heat Resistance Chinese cabbage-pe-tsai (B. rapa ssp. pekinensis) prefers coolness and coldness, which makes it difﬁcult for production in spring and summer. Thus heat resistance is one of the important hot spots for the breeding of Chinese cabbage-pe-tsai. Yu et al. (2003c) studied heat resistance using an RIL population from the cross between line 177 and line 276. Three hundred ﬁfty-two markers, 87 RAPD markers and 265 AFLP markers, were used to detect QTLs controlling heat tolerance, using the composite interval mapping method with the software Windows QTL Cartographer v2.0. Heat damage index in seedlings was used as phenotypic value to detect QTLs. Five QTLs were mapped in three LGs: LG3, LG8, and LG9. Three loci including ht-1, ht-3, and ht-5 showed positive additive effect, while ht-2 and ht-4 showed negative additive effect. Out of the ﬁve QTLs, phenotypic variation explained by ht-2 was the highest one. The distance from the nearest ﬂanking markers to QTLs ranged from 0.1 cM to 2.4 cM (Table 4).

6.4.3 Resistance to Clubroot

eases of Brassica crops worldwide. It was known as early as the 15th century in Spain. Earlier studies of clubroot resistance (CR) in turnips suggested the occurrence of three independent genes in B. rapa (Wit 1964; Williams 1966; Buczacki et al. 1975; Toxopeus and Janssen 1975; Crute et al. 1980). At the same time, some researchers considered that a single locus or gene was sufﬁcient to express CR (Fuchs and Sacristán 1996). Yoshikawa (1981), however, suggested the existence of a major gene and some genes with a minor effect on CR in B. rapa. A recent study clearly demonstrates that three CR loci, Crr1, Crr2, and Crr3, existed in B. rapa (Suwabe et al. 2003). Crr3 was identiﬁed in the genetic analysis for an F3 population, using the inbred turnip line, N-WMR-3, which carries CR from a European turnip, Milan White, and a clubrootsusceptible DH line, A9709, as parents. RAPD and STS markers were used in combination with BSA (Hirai et al. 2004). Frequency distributions and statistical analyses linked Crr3 to two STS markers, OPC11-1S and OPC11-2S.

6.4.4 Resistance to White Rust

Genes for resistance to white rust (Albugo candida) Clubroot disease, caused by the obligate parasite Plas- in oilseed B. rapa were mapped by Kole et al. (2002a) modiophora brassicae, is one of the most serious dis- using an RIL population and a genetic linkage map

Chapter 6 Brassica Rapa

237

Table 5. Summary of QTLs Traits

Flanking marker

Linkage group

Additive effect

LOD

R2 (% )

Population Reference

Clubroot

BRMS-088 BRMS-096 OPC11-1S – OPC11-2S wg6c1a – Pub1 wg2d11 – ec5a6a wg6c1a – Pub1 Z19a pCOE2 – pAt12E1 pN121E2 – pN102B1 BrFLC2 BrFLC2 BrFLC5 BN007-1 OS-01 – OPJ-20 – OPP-05 – OPG-16

– – – Br4 Br2 Br4 LG2 R2 R3 R2 R2 R3 – LG3 LG9 LG10

– – – −3.44 −0.49 −3.95 – −2.7 −2.5 9.4 4.1 8.0 69 – – –

– – – 34.4 2.5 49.3 4.2 5 5.7 34.7 8.1 10.64 24.3 5.8 5.3 5.2

– – – 89.8 1.7 96.3 18.4 21 23 80.6 14.0 39.0 77 32.8 37.6 23.8

F2 F2 F3 F2 F2 F2 F2 F2 F2 BC3 S1 BC3 S1 BC1 S1 F2 F2 F2 F2

Clubroot White rust

White rust Flowering time Flowering time

Flowering time Linolenic acid content

consisting of 144 RFLP markers and three phenotypic markers. The population was from the cross of B. rapa Per [a biennial turnip rape resistant to both A. candida race 2 and race 7 (AC2 and AC7)] and B. rapa R500 (an annual sarson susceptible to both AC2 and AC7). Young seedlings from the RIL population were evaluated by inoculating cotyledons with AC2 and AC7 and scoring the interaction phenotype (IP) on a scale of 0 to 9. Data were analyzed by Mapmaker v2.0 with a LOD score minimum of 3.0. QTLs were located by interval mapping analysis using the mean IP scores of the four replications and Mapmaker/QTL 1.1 (Lincoln et al. 1992). A LOD threshold of 2.0 was used to identify regions containing putative QTLs associated with resistance. Only additive effects were estimated because there were no heterozygous marker loci included in the analysis. Searches for additional marker loci associated with resistance were performed by ﬁxing the interval with the highest LOD score and rescanning the genome (Lincoln et al. 1992) using a LOD threshold of 2.0 above the baseline. QTL analysis suggested that a single major gene controlled resistance to the two races (Table 5). A second minor QTL effect for AC2 was located on LG2. The resistance locus mapped to B. rapa LG4 (BR4) and was ﬂanked by the RFLP locus wg6c1a (9.8 cM), as well as Pub1 (4.6 cM), a locus controlling the presence or absence of pubescence (Fig. 13). A locus for re-

Suwabe et al. 2003 Hirai et al. 2004 Kole et al. 2002a

Tanhuanpää 2004 Axelsson et al. 2001 Schranz et al. 2002

Ajisaka et al. 2001 Tanhuanpää and Schulman 2002

sistance to AC2 (designated ACA1) was previously mapped in the same interval and linked to the Pub1 locus (Kole et al. 1996a). These results indicate that either a dominant allele at a single locus (Aca1) or two tightly linked loci control seedling resistance to both races of white rust in the biennial turnip rape cultivar Per. Another recent QTL study on white rust resistance in B. rapa ssp. oleifera was done by Tanhuanpää (2004) on an F2 population. The strategy used was BSA with RAPD markers and linkage mapping. Quantitative trait data were analyzed using MAPMAKER/QTL 1.1. One QTL for white rust resistance was identiﬁed with a LOD score of 4.2 (Fig. 14) in LG2, which accounted for 18.4% of the variation for white rust resistance. This QTL was situated 0.1 cM from the RAPD marker Z19a.

6.4.5 Linolenic Acid Content The quality of rapeseed oil is determined by its FA composition. Linolenic acid (C18:3) is one of the main FAs in the oil. Owing to its three double bonds, linolenic acid is easily oxidized, resulting in shortened shelf life of the oil. Therefore, reducing the amount of linolenic acid content has been a target in rapeseed breeding. The predominant gene responsible for the

238

P. Quijada et al.

Fig. 13. Genetic maps of LGs 2 (BR2) and 4 (BR4) from Brassica rapa and LGs 9 (BN9, Ferreira et al. 1994; or N2, Butruille et al. 1999 and 19 (BN19, Ferreira et al. 1994; or N6, Butruille et al. 1999) from B. napus. Aca1 on BR4 controls resistance to Albugo candida race 2 [AC2 and race 7 (AC7)], and Aca1 on BN9 controls resistance to A. candida, B. carinata pathotype. Pub1 on BR4 controls leaf pubescence (Kole et al. 2002a)

Chapter 6 Brassica Rapa

239

05 and OPG-16, and 2.3 cM from fad3. This QTL accounted for 23.8% of the variation in linolenic acid content. Similar results had been obtained previously in B. napus (Tanhuanpää et al. 1995; Jourdren et al. 1996). Jointly the three QTLs accounted for 73.5% of the variation in linolenic acid level in this population.

6.4.6 Flowering Time

Fig. 14. LOD proﬁle for a locus controlling white rust resistance in F2 population from Brassica rapa ssp. oleifera cross Bor4109 × Bor4206. LOD scores were computed using MAPMAKER/QTL 1.1. Horizontal dashed line: LOD score threshold of 3.0 (Tanhuanpää 2004)

synthesis of linolenic acid in seed triacylglycerols is fad3 (FA desaturation), the structural gene for a microsomal 18:2 desaturase (Lemieux et al. 1990). The importance of fad3 in the control of seed linolenic acid content has been veriﬁed in B. napus (Jourdren et al. 1996; Thormann et al. 1996; Somers et al. 1998), soybean (Byrum et al. 1997), and A. thaliana (Browse et al. 1993). Tanhuanpää and Schulman (2002) identiﬁed three QTLs for linolenic acid. A population of 90 F2 plants, which segregated for linolenic acid, was derived from a cross between two spring turnip rape individuals (Laakso et al. 1999). A candidate gene approach applying the rapeseed fad3 gene and BSA with RAPD markers was used. Quantitative trait data were analyzed using MAPMAKER/QTL 1.1 and standard analysis of variance (ANOVA), which was run using SPSS v6.1. A total of 27 markers, one RFLP (fad3) marker and 26 RAPD markers, were distributed in three LGs, LG3, LG9, and LG10 (Fig. 15), each one having a QTL for linolenic acid. In LG3, a peak LOD score (5.8) was detected between markers OPS-01 and OPJ-20 and explained 32.8% of the phenotypic variance. The precise location of the linolenic acid QTL in LG9 remained unresolved because the maximum LOD score (5.3) was situated at the end of the LG. The proportion of the variance explained at this point was 37.6%. The peak LOD score (5.2) in LG10 was found exactly at the same position as markers OPP-

Genetic variation for ﬂowering time is important for the adaptation of plant species to different environments and for the selection of crop plants that meet speciﬁc cultivation and consumer needs. Brassica species, annuals and biennials, have been selected as two extreme forms of ﬂowering habit. Biennials have an absolute requirement for cold treatment to induce ﬂowering (vernalization). This ﬂowering behavior is critical for adaptation to certain agricultural practices, such as the fall planting of B. rapa. Teutonico and Osborn (1995) and Osborn et al. (1997) mapped QTLs for ﬂowering time in B. rapa. Axelsson et al. (2001) developed a B. rapa F2 population (FTBr) from crosses between an earlyﬂowering male parent (RC; Williams and Hill 1985) and a late-ﬂowering female parent. The female parent, Rc50a7, was an inbred line of B. rapa ssp. chinensis. Genotypic and phenotypic data from individuals in FTBr were analyzed by interval mapping with MAPMAKER-QTL 1.1 (Lander and Botstein 1989; Lincoln et al. 1992). A LOD-score threshold of 2.0 was used initially to detect QTLs. Epistasis was evaluated by selecting the marker locus closest to each QTL and performing a two-factor analysis of variance using JMP 3.02 (SAS Institute). Two QTLs were detected on two segments originating from LGs R2 and R3 (Fig. 16 and Table 5). A model including both QTLs explained 38% of the phenotypic variation. Bolting is a persistent problem in Chinese cabbage production in spring (Elers and Wiebe 1984; Mero and Honma 1985). The late bolting nature of a line derived from a Japanese local cultivar of the nonheading leafy vegetable Osaka Shirona Bansei was studied. Unlike common Chinese cabbage, the bolting of this line was induced without vernalization but promoted by a long day (Yui and Yoshikawa 1991). An F2 population derived from lines DH27 and G309 was used for the QTL analysis (Ajisaka et al. 2001). BSA based

240

P. Quijada et al.

Fig. 15. QTL likelihood maps for linolenic acid content in F2 population of B. rapa ssp. oleifera cross 93561–2 × Sv3402. LOD scores were computed using MAPMAKER/QTL 1.1 computer program (Tanhuanpää and Schulman 2002)

on RAPD markers and MAPMAKER/QTL 1.1 analysis with a threshold LOD of 2.0 identiﬁed one major QTL around BN007-1. The QTL was located 2.5 cM from the ﬂanking marker BN007-1. The results showed 24.3 as the log-likelihood (Fig. 17), 69 d as the additive effect, and 20 d as the dominance effect of the QTL. The QTL explained 77% of the phenotypic variation.

6.4.7 Abiotic Stresses: Winter Survival and Freezing Tolerance Oilseed B. rapa and B. napus include both annual and biennial types, the latter of which require vernalization to ﬂower and are grown as an overwintering crop in northern climates. Biennial forms generally have a higher frequency of winter survival, and winter survival was correlated with acclimated freezing tolerance in a study including annual and biennial cultivars (Teutonico et al. 1993). In B. rapa, Teutonico et al. (1995) mapped loci controlling nonacclimated and acclimated freezing tolerance to different regions

of the genome that were also different from those of ﬂowering-time genes. Most freezing-tolerance effects were due to overdominance for freezing sensitivity (the heterozygous class had less tolerance than either homozygous class). In a B. napus, none of the genome regions covered by markers was signiﬁcantly associated with freezing tolerance. Recently, Kole et al. (2002b) evaluated immortalized populations of oilseed B. rapa (recombinant inbred lines) and B. napus (DH lines) derived from crosses of annual and biennial types in order to compare the map positions and effects of QTLs controlling winter survival, nonacclimated and acclimated freezing tolerances, and ﬂowering time. For B. napus, they used the same population assayed previously for freezing tolerance (Teutonico et al. 1995), and they also reanalyzed the freezing-tolerance data using a more complete molecular marker linkage map. For B. rapa, they used a recombinant inbred population derived from the same parents studied before (Teutonico et al. 1995). This population, like the B. napus population of DH lines, segregated only for additive genetic variation; thus results from this pop-

Chapter 6 Brassica Rapa

241

Fig. 16. Comparative linkage maps and QTL likelihood maps for ﬂowering time (Axelsson et al. 2001)

Fig. 17. Partial map around BN007-1 and LOD score for bolting time. LOD shows a maximum value of 24.3 at 2.5 cM beside BN007-1 (Ajisaka et al 2001)

ulation are more directly comparable to those from B. napus. The B. rapa RI lines were also analyzed for freezing-tolerance QTL. The map positions of QTLs for winter survival and freezing tolerance and for ﬂowering time reported previously for these populations (Osborn et al. 1997) were compared both within and between species. The B. napus population was evaluated in multiple winters, and 6 of the 16 total signiﬁcant QTLs for winter survival were detected in

more than one winter. Correspondence in the map positions of QTLs controlling different traits within species provided evidence that some alleles causing greater acclimated freezing tolerance and later ﬂowering time also contributed to increased winter survival. Correspondence in the map positions of QTLs between species provided evidence for allelic variation at homologous loci in B. rapa and B. napus.

242

P. Quijada et al.

Fig. 18. Genetic mapping and QTL analyses of Brassica rapa chromosomes R2 and R3 (Schranz et al. 2002)

6.4.8 Mendelization of QTLs Isolation of a single QTL in near-isogenic lines (NILs) is referred to as the “mendelization” of the QTL (Kole et al. 1997b, 2001; Swarup et al. 1999). Advances in molecular technologies, together with the information provided by genome projects, have made possible the mendelization of QTL intervals, which are instrumental in positional cloning of the underlying genes. Recent examples are the fw2.2 gene of tomato (Frary et al. 2000) and the EDI locus of A. thaliana, a QTL that turned out to be a novel allele of the CRYPTOCHROME2 (CRY2) gene (El-Assal et al. 2001). So far, the populations used for QTL analysis in B. rapa are focused on F2 , backcross, and RIL. Only two studies have been done based on mendelization of QTLs. The ﬁrst was reported by Kole et al. (1997b, 2001) and resulted in the identiﬁcation of FLC as the gene responsible for the VFR2 QTL detected by Teutonico and Osborn (1995) and Osborn et al. (1997). The second ﬁne-mapping QTL was done by Schranz et al. (2002), who used 78 BC3 S1 plants segregating for FR1 (R2 population), 100 BC1 S1 plants segregating for FR2 (R3 population), and 326 F2 plants segregating for bothFR1 and VFR2; these were evaluated for

ﬂowering time by counting the number of days after sowing to the ﬁrst open ﬂower (DTF) and the number of leaves on the main axis at ﬂowering (LN). FR1 and FR2 were previously reported by Osborn et al. (1997). Schranz et al. (2002) used the linkage maps constructed for the R2 and R3 populations to ﬁne map QTLs for ﬂowering time using QTL Cartographer v1.15 (Basten et al. 2001) with a 10-cM covariate window for composite interval mapping (CIM). For the R2 population, 14 genetic marker loci, including 10 RFLP markers and 4 SSR markers, were used for QTL analysis (Fig. 18a). CIM revealed two QTLs. The major QTL (FR1, LOD = 34.7) centered on the BrFLC2 locus, explained 80.6% of the variation and had an additive effect of 9.4 DTF. A second, smaller QTL (VFR1, LOD = 8.1) centered at 53.9 cM and explained 14.0% of the variation in the population with an additive effect of 4.1 DTF. For R3 population, CIM gave a single QTL (LOD = 10.64) centered on the BrFLC5 locus (Fig. 18b). This QTL explained 39.0% of variation in ﬂowering time with an additive effect of DTF. These authors cloned four replicated FLC loci in these three genomic regions (R2, R3, and R10) and found that three of these replicated FLC loci (BrFLC1, BrFLC2, and BrFLC5) were the genes controlling the ﬂowering time in B. rapa.

Chapter 6 Brassica Rapa

243

6.5 Marker-Assisted Breeding

RAPDs have been developed for improving selection, they were well suited for a direct application in MAS. The RFLP technique is too complex and time conMolecular markers enhance the practicability of ge- suming, and RAPD markers are dominant, precludnetic markers in genomic researches and marker- ing the distinction of homozygous from heterozygous assisted selection (MAS), and this technology can be individuals. Therefore, molecular markers should be used for the distinction of rape varieties, location of converted into SCAR markers, which would be more genes, construction of genetic linkage maps, predic- reproducible and more reliable for genotyping. tion of heterosis, etc. (Janick 2001). Some molecular markers mainly used in research on B. rapa breeding are brieﬂy introduced here. 6.5.2 Germplasm Screening 6.5.1 Marker Conversions Molecular markers are of interest to plant breeders as a source of genetic information on crop species and for use in indirect selection of traits to which they are linked. In the classic plant-breeding approach, the markers were invariably visible morphological phenotypic traits, and breeders dedicated considerable effort and time to reﬁning crosses, as tight linkage of desired traits with the phenotypic markers was never unequivocally established. Furthermore, indirect selection for a trait using such morphological markers was not practical due to (1) a paucity of suitable markers, (2) the undesirable pleiotropic effects of many morphological markers on plant phenotype, and (3) the inability to score multiple morphological mutant traits in a single segregating population. With advances in molecular biology, the use of molecular markers in plant breeding has become very commonplace and has given rise to molecular breeding. Molecular breeding involves primarily “gene tagging,” followed by MAS of desired genes or genomes (Ranade et al. 2001). In order to perform gene tagging and MAS, genetic markers should be converted into SCARs or a gene determining a speciﬁc agronomic trait should be mapped. To date, several genetic linkage maps of oilseed crops including B. napus, B. rapa, and B. juncea are available (Landry et al. 1991; Ferreira et al. 1994; Uzunova et al. 1995; Butruille et al. 1999; Liu et al. 2000; Lombard et al. 2001; Mahmood et al. 2003; Udall et al. 2005). Since codominant SCAR markers are more reproducible and more reliable than dominant markers, some RAPD and AFLP molecular markers have been converted to SCAR markers to be used in MAS in oilseed crops (Hu et al. 1999; Gan et al. 2001). While molecular markers such as RFLPs and

In countries that are members of the International Union for Protection of Plant Varieties (UPOV 1978, 1994, 1996), new crop varieties must undergo distinctness, uniformity, and stability (DUS) testing and each candidate variety must be shown to be distinct from others of common knowledge and to be sufﬁciently uniform and stable in the diagnostic characteristics used to demonstrate distinctness. The current testing system, which is based on the assessment of a range of standardized morphological characters that are expensive and time consuming. It requires large areas of land to grow the crops and highly skilled personnel. An added complication with B. napus is the existence of various types of hybrid cultivars, including trangenics among them. It is impossible to adequately compare new varieties with those of common knowledge using the limited number of morphological characters available and then affect the quality of protection offered by the PBR system. Therefore, it is important to devise more rapid and cost-effective testing procedures to improve the current testing system (Cooke 1999). Some of them are described below. Isozymes and Physiological Indices The discovery of polymorphic isozyme markers (Hunter and Markert 1957) provided a basis for differentiation of B. rapa cultivars. Zhao and Becker (1998) analyzed the genetic variation in Chinese and European oilseed rape (B. napus) and turnip rape (B. rapa) using isozymes. The researchers compared 36 turnip rape (B. rapa) and 41 oilseed rape (B. napus) cultivars from China, Canada, and Europe using 8 isozyme loci comprising 36 alleles in B. rapa and 13 loci with 32 alleles in B. napus. In B. rapa, the largest proportion (about 70%) of the total gene diversity was due to the within cultivar variation. In B. napus, the largest contribution to

244

P. Quijada et al.

gene diversity was made by the variation among cultlvars (about 80%). Dendrograms based on cluster and principal coordinate analyses revealed two very clear-cut groups of Chinese and European origin in B. rapa. The European cultivars were further divided into winter and spring groups. In B. napus most European winter and spring type cultivars were grouped in two different clusters. Most Chinese “+” quality cultivars had large genetic distances from all other materials and from each other. The researchers concluded that the exchange of breeding materials between China and Europe in B. rapa and between Chinese + and European 00 materials in B. napus should be an efﬁcient approach to broaden the genetic base of these oilseed species. Interestingly, physiological indices [leaf water potential (ψw ), solute potential (ψs ), relative water content (RWC), turgor potential (ψp ), osmotic adjustment (reciprocal of slope b where ln RWC = a − b lnψs ), leaf diffusive conductance (K1 ), the difference between canopy and air temperature (Tc − Ta ), and water loss from excised leaves (WLL)] can be used to screen large numbers of germplasm lines of Brassica species (B. rapa, B. carinata, B. juncea, B. napus) for drought tolerance under ﬁeld conditions (Kumar and Singh 1998).

Random Amplified Polymorphic DNA (RAPD) The use of RAPD markers has become increasingly important in establishing genetic markers in B. rapa (Williams et al. 1990) and has replaced emphasis on isozyme polymorphism analyses. By clustering RAPD markers using unweighted pair group mathematics average (UPGMA), 106 landraces of B. rapa were separated into two major groups and 50 1andraces of B. juncea were divided into four different groups. The result suggested that genetic diversity is very rich in rapeseed landraces from Tibet (Dan et al. 2003). He et al. (2002) used RAPDs to investigate the genetic diversity among l72 landraces of B. rapa selected from China. Forty-three random l0-mer primers were tested and 248 polymorphic bands were scored. By using the dendrogram analysis, the landraces were separated into two major groups at the DNA level: 8 vegetable B. rapa and l64 oil-purpose B. rapa. The vegetable B. rapa group was very different from the oilseed group. Moreover, by UPGMA clustering, the l64 oilseed B. rapa were separated into 15 groups: 6 northern groups, 8 southern groups, and 1 mixed group. The result indicated that the grouping was related to geographical origins. The diversity level of

the spring type was higher than that of the winter type, and the genetic diversity of northern B. rapa was related closely to geographical regions. Furthermore, landraces from a speciﬁc area such as Yunnan – Guizhou – Hubei provinces and Qinghai – Gansu – Xinjiang provinces showed more genetic variations and exhibited a higher level of genetic diversity than landraces from other provinces, which might give some evidence about China being one of the center of origin of B. rapa. Li et al. (1997) assessed the genetic diversity among 36 B. juncea cultivars by RAPD technique. Among the ampliﬁed 128 DNA bands, polymorphic bands reached 88.2%. High genetic variation existed between spring and winter cultivars. Large genetic diversity among winter cultivars was detected. Twenty-ﬁve winter cultivars were divided into three groups (I, II, III), whereas spring cultivars clustered into a separated group (IV). A close relationship both between RLM198 from India and Gongxian Jinhuang Youcai from Sichuan province and between Australian cultivars and the spring-type cultivars of China was found.

Restriction Fragment Length Polymorphism (RFLP) RFLP markers have been useful for studying genetic diversity in Brassica. Figdore et al. (1988) reported a high degree of polymorphism within Brassica using RFLP markers. Song and Osborn (1992) surveyed 19 oilseed B. napus accessions with nuclear RFLP markers. The markers divided the accessions into two major groups, with one containing mostly winter cultivars and the other mostly spring cultivars. Subsequently, Diers and Osborn (1994) analyzed the genetic diversity of oilseed B. napus germplasm based on RFLPs. Eighty-three cultivar accessions were screened using 43 genomic DNA clones, which revealed 161 polymorphic fragments. Overall, there are three major groups of cultivars. The ﬁrst group included only spring accessions, the second mostly winter accessions, and the third rutabagas and oilseed rape accessions from China and Japan. Moreover, Thormann et al. (1994) reported the comparison of RFLP and RAPD markers for estimating genetic relationships within and among cruciferous species, including B. napus, B. rapa, B. nigra, B. oleracea, B. carinata, and Raphanus staivus. Based on a comparison of genetic-similarity matrices and cophenetic values, RAPD markers were very similar to

Chapter 6 Brassica Rapa

245

RFLP markers for estimating intraspeciﬁc genetic relationships; however, the two marker types gave different results for interspeciﬁc genetic relationships. Their results suggested that RAPD data might be less reliable than RFLP data when estimating the genetic relationships of accessions from more than one species.

acea; (2) B. rapa can be separated into four groups: B. rapa ssp. rapifera (Matzg.) Sinsk., B. rapa var. chinensis, B. rapa ssp. nipposinica (Bailey) Olsson, and B. rapa ssp. chinensis (L.) Makino; (3) the AFLP data allowed assigning the B. rapa L. ssp. chinensis var. pekinensis (Rupr.) into B. rapa ssp. chinensis (L.) Makino.

Amplified Fragment Length Polymorphism Ampliﬁed fragment length polymorphism (AFLP) is another PCR-based technology that has generated considerable interest as a means to rapidly identify genotypes and for producing large numbers of markers for mapping. Lombard et al. (2000) evaluated the discrimination power of 17 AFLP primer combinations tested on a collection of 83 spring and winter rapeseed cultivars. A total of 324 polymorphic markers were scored, with an average of 19.1 markers per primer combination. The use of only two primer combinations was sufﬁcient to identify uniquely all the cultivars. The analysis of the genetic structure of the diversity by cluster and principal component analyses and analyses of molecular variance (AMOVA) clearly delineated three signiﬁcant factors: the cultivar type (winter or spring), the country of origin (France or Germany), and the breeding company. The three measures of genetic distance were highly correlated (P < 0.001; r = 0.96 – 0.98) and led to similar groupings. AFLPs were shown to be a powerful method for identifying cultivars and analyzing the genetic structure of the diversity in rapeseed. Variation in AFLP markers has been used to establish the extent of genetic diversity and relationship among 46 landraces and cultivars of B. rapa L. (syn. B. campestris L.), one cultivar of B. juncea Czern. et Coss, and one cultivar of B. oleracea L. (Cao et al. unplubl. data). The results indicated abundant genetic diversity in Chinese cabbage and their closer groups. AFLP ﬁngerprints revealed that the number of ampliﬁed bands was different among cultivars, for example, B. juncea produced 201 bands, but B. campestris ssp. nipposinica (Bailey) Olsson produced 118 bands. The smallest genetic distance coefﬁcient among cultivars was between Shanxibaicaixingyoucai and Aidagan (0.1728), while the largest (0.9289) was between cv. Chixin-29 and B. olerarea L. Tree dendrograms were constructed based on the AFLP marker data by system cluster analysis of WPGMA. The results showed (1) differences of AFLP ﬁngerprints among B. rapa, B. juncea, and B. oler-

Simple Sequence Repeats and Intersimple Sequence Repeats Simple sequence repeats (SSRs), or microsatellites, are short segments of DNA consisting of a speciﬁc repeated motif of 1 to 6 bases. Microsatellites are ubiquitous and widely dispersed throughout eukaryotic genomes, highly polymorphic, and technically easy to use and they produce reproducible results, making them one of the markers of choice for ﬁngerprinting, assessment of genetic diversity, trait tagging, and genetic mapping (Gupta et al. 1996). In addition, intersimple sequence repeat (ISSR), a PCR-based technique, involves the use of microsatellite sequences as primers in a PCR to generate multilocus markers. It is a simple and quick method that combines most of the advantages of SSRs and AFLP to the universality of RAPDs. ISSR markers are highly polymorphic and are useful in studies on genetic diversity, phylogeny, gene tagging, genome mapping, and evolutionary biology (Reddy et al. 2002). Plieske and Struss (2001) demonstrated a high efﬁciency of SSR markers for monitoring genetic diversity in Brassica. One hundred twenty-one microsatellites were identiﬁed by screening a lambda phage library of B. napus. The distribution of these microsatellites within Brassicaceae species was estimated using 81 locus-speciﬁc primer pairs. Most of them (83%) ampliﬁed fragments either from B. oleracea or B. rapa, or both species, whereas less than 30% detected loci in B. nigra. The same was true (30 to 35%) for more distantly related crucifer species such as Diplotaxis ssp., B. tournefortii, Sinapis alba, Raphanus sativus, and Eruca sativa. Only 16 microsatellite-speciﬁc primer pairs (19.8%) ampliﬁed fragments from A. thaliana. Moreover, 61 of the primer pairs detecting 198 polymorphisms were used to estimate the extent of genetic diversity among 32 B. napus cultivars. On average, four alleles per locus were observed. The spring and winter types of oilseed rape could be clearly distinguished by using the microsatellite markers in a cluster analysis.

246

P. Quijada et al.

Single-Nucleotide Polymorphisms The vast majority of polymorphisms, which exist in DNA sequence, are single-base-pair differences. Recently, much effort has been focused on the exploitation of single-nucleotide polymorphisms (SNPs; Nikiforov et al. 1994; Marshall 1997). Although SNP detection in most cases currently still relies on techniques and equipment that are beyond the capbilities or resources of the majority of plant breeders, it can be expected that SNP markers will play a major role in B. rapa genetics and breeding in coming years.

6.5.3 Marker-Assisted Selection

With respect to sequence similarity, SLG genes have been classiﬁed into two groups: class I and class II (Chen et al. 1990). Interestingly, all of the class II haplotypes identiﬁed are recessive, whereas all of the class I S-haplotypes are dominant. Nozaki et al. (1997) developed an RAPD marker showing linkage to the SI locus and it migth be useful for developing MAS of SI lines in B. rapa. Another simple method for identifying S-haplotypes, called PCR-RFLP (Brace et al. 1994; Nishio et al. 1994, 1996), have proved to be very successful for S-haplotypes selection in B. rapa. This method consists of speciﬁc PCR ampliﬁcation of SLG alleles with a pair of SLG-speciﬁc primers designed according to the conserved regions near the 5 and 3 ends of the SLG/SRK coding sequence and electrophoretic analysis of the PCR products after cleavage with one or more restriction endonucleases (Table 6). Identiﬁcation of S-haplotypes in breeding lines became much easier with the analysis DNA polymorphism of S-locus genes than by a traditional cross-pollination test. According to the above results, PCR-RFLP can provide us with a more precise and practical MAS strategy in B. rapa breeding.

To date, there are 21 B. rapa molecular maps available for marker-assisted breeding (detailed in the linkagemap-construction section). These maps are used to locate genes determining important phenotypic traits (Song et al. 1995). Among genes being mapped by various laboratories and commercial plant breeding companies, self-incompatibility, disease resistance, ﬂowering time, and oilseed quality are the most prominent ones, which are on the verge of practical application Marker-Assisted Clubroot Resistance Selection for MAS. Clubroot (Plamodiophora brassicae) is one of the most damaging diseases in Brassica crops, and the majority MAS for Self-Incompatibility of commercial cultivars of B. rapa are highly suscepSelf-incompatibility (SI) is successfully used for the tible. Breeding for resistance to clubroot is an effecproduction of F1 hybrid seeds in Brassica crops. tive approach to eliminate the use of pesticides and SI in Brassica is controlled by multiple alleles in minimize the crop losses due to this disease. Moleca single locus S, and the recognition speciﬁcity of ular markers, as aided in breeding and genetic studthe alleles in S-locus is expressed sporophytically. ies, have been used in CR breeding for a long time. Thirty alleles have been identiﬁed in the S-locus of Landry et al. (1992) were the ﬁrst to report linkage B. rapa (Nou et al. 1993). Since the SI phenotype markers of CR genes with genetic markers in Brasis affected both by environmental factors and sica, and Kuginuki et al. (1997) were the ﬁrst to report physiological conditions of plants, the test crossing three dominant RAPD markers, RA12-75A, WE22B, should be repeated several times. This is a highly and WE49B, linked to a CR locus in B. rapa. So far, time-consuming and labor-intensive procedure. RAPD and RFLP markers linked to the B. rapa resisMolecular genetic study of S-haplotypes in B. rapa tance locus have been identiﬁed and mapped (Kugcan contribute to the development of the breeding inuki et al. 1997). These markers were not suitable for methodology for B. rapa. Three types of molecules MAS due to concerns about RAPD markers’ reproencoded at the S-locus have been identiﬁed, the ducibility as well as laborious and time-consuming S-locus receptor kinase (SRK; Stein et al. 1991), RFLP technology. And thus, many efforts have been S-locus glycoprotein (SLG; Nasrallah and Wallace made to convert these markers to PCR-based ones, like 1967), and S-locus cysteine-rich protein/S-locus STS (Matsumoto et al. 1998; Hirai et al. 2004), SCAR, protein 11 (SCR/SP11; Suzuki et al. 1999), which and CAPS (Piao et al. 2004). Among these, Piao et al. was shown to be necessary and sufﬁcient for the (2004) successfully converted ﬁve AFLP markers into determination of SI speciﬁcity in pollen (Takayama CAPS and SCAR markers (Table 7). The TCR09 and TCR10 markers ﬂanked the CRb gene by only three et al. 2000).

Chapter 6 Brassica Rapa

247

Table 6. Some PCR-RFLP primer combinations for S-haplotype selection Primer combination for MAS

Primer sequence origin

Suitable restriction endonuclease

Suitable for S-haplotypes

Reference

PS5 + PS15 PS18 + PS15 PS3 + PS21 PK1 + PK4

SLG SLG SLG SRK

Class I Class I Class II Class I

Nishio et al. 1996 Nishio et al. 1996 Nishio et al. 1996 Nishio et al. 1997

PK5 + PK4

SRK

MboI + MspI MboI + MspI MboI + MspI HinfI, AluI MboI + AfaI MboI + AfaI

Class I

Nishio et al. 1997

Table 7. Converted SCAR and CAPS markers linked to CRb (Piao et al. 2004) AFLP marker

SCAR and CAPS markers Name Primers (5 to 3b )

Size (bp)

PCR conditionsc

P2

TCR02a

447

62 ◦ C, 60 s

72 ◦ C, 90 s

P5

TCR05

279

62 ◦ C, 60 s

72 ◦ C, 90 s

P8

TCR08

131

62 ◦ C, 60 s

72 ◦ C, 60 s

P9

TCR09

101

53 ◦ C, 60 s

72 ◦ C, 60 s

GCTCCATTCAGTTACGGTGA GCAGAGAATTTTGGAAGAGGA AGAATCATGACCGGGGAAAT GCAGCTAAGTCATCGACCAA GCAGAATTATAACCTGAGCGTGT ATTACCGGAGTATGCGATCC GCAGCAACCGATAATATAAGGA AACCAGAAGAAGAAAAACAAAAA

a

PCR products were digested with HpaII Forward and reverse primers are listed in that order c PCR conditions for all primers were 5 min at 94 ◦ C, 35 cycles of 30 s at 94 ◦ C b

Table 8. SSR markers linked to the locus of clubroot resistance (From Suwabe et al. 2003) SSR marker

Primer sequence (5 to 3 )

BRMS-088

TATCGGTACTGATTCGCTCTTCAAC 233 ATCGGTTGTTATTTGAGAGCAGATT AGTCGAGATCTCGTTCGTGTCTCCC 189 TGAAGAAGGATTGAAGCTGTTGTTG

BRMS-096

Product size (bp)

and two recombinants among 143 F2 plants. Further, they can be easily detected on agarose gels. Therefore, these two markers can be successfully used in backcrosses to breed resistant lines carrying the resistance gene. Due to the genetic variation of P. brassicae and to the presence of a mixture of several races in a single ﬁeld, the current CR cultivars are confronted with breakdown (Kikuchi et al. 1999; Piao et al. 2002). Therefore combination of these genes in a single line

Annealing temperature (◦ C) 60 60

can improve resistance to a broad spectrum of races (Huang et al. 1997). These markers combined with others linked to other resistance genes (Matsumoto et al. 1998), would enable to introgress them into a single B. rapa line. In another case, two independent loci, crr1 and crr2, controlling resistance to clubroot in B. rapa were identiﬁed by Suwabe et al. (2003). The loci were detected by convenient codominant SSR markers (Table 8), which are useful in assisting to select a superior CR B. rapa cultivar with both resistant

248

P. Quijada et al.

loci. Summarizing, markers linked to CR resistance in B. rapa would reduce both time and labor required develop resistant genotypes and therefore increasing the breeding efﬁciency for CR. MAS for other Traits Markers for other important traits in B. rapa are also available for MAS such as AS1, AS2, and AS3 for linolenic acid content (Tanhuanpää and Schulman 2002) and fad2 for oleic acid (Tanhuanpää et al. 1998). All the markers linked to the loci affecting the important traits in B. rapa would play a critical role in MAS and thus make the breeder’s task easier, faster, and more effective. MAS for Quantitative Traits Another major issue that needs to be addressed is how to increase MAS efﬁciency for quantitative traits through better characterization of target genes. Fortunately, ﬁeld design and statistical approaches for QTL mapping have steadily progressed during the past decade. With the latest mathematical methods, such as composite interval mapping, ﬁeld data from different environments can be integrated into a joint analysis to evaluate the Q×E and thus identify “stable” QTLs across environments. Manipulation of QTLs in repulsion (linked QTLs at which the favorable allele comes from a different parental line) is a typical example of how DNA markers can be used very efﬁciently to select those genotypes that have broken the linkage between two QTL regions at an early stage of recombination. Although the process of QTL identiﬁcation has improved signiﬁcantly, MAS is still limited by three main factors (i) number of samples that can be analyzed, (ii) number of lines that can be improved within a given time, and (iii) the belief that QTL identiﬁcation is required whenever additional germplasm is used. Even though MAS now plays a prominent role in the ﬁeld of plant breeding, examples of successful, practical outcomes are rare. No experiment has clearly demonstrated whether using DNA markers for quantitative trait improvement is superior to conventional breeding selection, although some encouraging results have been published. Molecular breeders must reassess their research programs so that DNA marker work leads to useful selection tools and valuable germplasm (Young 1999). Dekkers and Hospital (2002) deem that the economic merit of MAS becomes questionable and more

difﬁcult to evaluate in cases in which MAS is expected to provide greater genetic gain at increased costs. This is particularly the case for selection schemes that rely on a combination of phenotype and molecular score, because molecular costs are in addition to, not in place of, phenotypic costs. In such cases, MAS might not be economically more advantageous than quantitative genetic selection. However, the economic merit of MAS could be restored by reducing the frequency of reevaluation of marker effects (Hospital et al. 1997). Another consideration is that the resources allocated to MAS could also be allocated to enhance phenotypic selection programs. For example, improvement by conventional selection could also be enhanced by increasing the number of individuals to be tested by phenotypic evaluation (Moreau et al. 2000). Further work on the economic evaluation and optimization of strategies using molecular genetics in breeding programs is required. It is likely that the economically optimal use of MAS necessitates a complete rethinking of the design of breeding schemes.

6.5.4 Marker-Assisted Introgression Introgression is a simple form of genotype building in which a target gene is introduced into an otherwise productive, recipient line. The method and procedures of marker-assisted introgression in plants were introduced in detail by Dekkers and Hospital (2002). Introgression is an important genetic improvement strategy, in particular in plants (Fig. 19). The aim of an introgression program is to introduce a “target” genomic region, which can be a single gene, a QTL, or a transgenic construct, from an otherwise lowproductivity line or breed (donor) into a productive line that lacks that particular gene (recipient; R). Introgression starts by crossing the donor and recipient lines, followed by repeated backcross (BC) to the recipient line to recover the recipient-line genome. The target gene is maintained in the BC generations through selection of donor gene carrier. Recovery of the recipient genome can be enhanced by the selection of BC individuals that have a high value for the recipient trait phenotype. It is to be noted that genetic improvement for this trait can be maintained by continuing recurrent selection in the recipient line (vertical arrows). Once a sufﬁcient proportion of the recipient genome is recovered, the BC line is intercrossed (to generate IC lines), and donor-gene homozygotes

Chapter 6 Brassica Rapa

are selected to ﬁx the target gene. This might require more than one generation to obtain sufﬁcient individuals for further breeding or if several target genes must be introgressed. The effectiveness of introgression schemes is limited by the ability to identify BC or intercross individuals that have a high proportion of the recipient genome, in particular in regions around the target gene. MAS can help address the problem. Moreover, the use of markers provides a better understanding of interactions between the introgressed genes. BC breeding is a well-known procedure for the introgression of a target gene from a donor line into the genomic background of a recipient line. The objective is to increase the recipient genome content (RGC) of the progenies by repeated BCs to the recipient line. Molecular markers can be used to control the target gene (foreground selection) and/or to hasten the return to the recipient genotype on chromosomal regions outside the target gene (background selection). Markers can be used in both the backcrossing and intercrossing phases of introgression programs. During the intercrossing phase, markers can be used to select individuals that are homozygous for the target gene. For multiple QTLs, introgression can be com-

Fig. 19. DNA introgression (Dekkers and Hospital 2002)

249

bined with gene pyramiding to decrease the number of individuals required (Hospital et al. 1997; Koudande et al. 2000). The efﬁciency of such marker-assisted introgression programs has been analyzed in a series of theoretical works (Visscher et al. 1996). One particular case of marker-assisted introgression is when the target locus is a QTL, which poses additional problems (Bouchez et al. 2002). Foreground selection is more difﬁcult for a gene for which exact chromosomal location is estimated with only a given imprecision than for a well-known gene (Visscher et al. 1996). This requires using more markers and optimizing the positions of these markers with respect to the uncertainty of the true QTL location (Hospital et al. 1997). Once the introgression is achieved, it must be checked that the effect of the QTL in the new genetic background is the same as the effect estimated originally. If the trait of interest is a complex trait, as is often the case in plant and animal breeding, then it is unlikely that one single QTL for that trait could explain enough genetic variation to justify the economic effort corresponding to the marker-assisted introgression program. In such a case, several QTLs should be introgressed simultaneously. This necessitates using larger population sizes of foreground selection and reduces the possibilities of background selection. However, such multiple introgression programs seem in theory feasible with reasonable population sizes for up to three or four QTLs (Hospital et al. 1997; Koudande et al. 2000). Gene introgression through recurrent backcrossing can be used in various circumstances: (i) in plant or animal breeding to improve the agronomic value of a commercial strain by introgressing a mono- or oligogenic trait (typically, a resistance trait) from a wild relative or from another – less productive – strain; (ii) to transfer a transgenic construction from one (transformed) strain to another (nontransformed) strain; or (iii) to construct near-isogenic or congenic lines, e.g., for the detection of QTLs and/or the validation of candidate genes for such QTLs. In examples (i) and (ii), a drastic reduction of the length of the donor segment surrounding the introgressed gene is important if undesirable genes are located close to the introgressed gene, as might be the case if the donor strain is a wild genetic resource. If introgression takes place between two commercial strains, then a drastic reduction of the length of the donor segment is not always important. In example (iii), such a reduction is always important. In cases where the reduction is important, one would like the donor segment remaining in the BC

250

P. Quijada et al.

progenies at the end of the program to be as short as possible. In addition to requiring extra resources, an introgression program diverts some selection pressure away from other traits of economic importance. To compensate for this, the beneﬁt of the target gene must be greater than that which could be achieved by regular selection over the same period. Only genes with a large effect will meet this requirement (Gama et al. 1992). There are still few reports of application of markerasisted introgression to rape crops, and successful reports are conﬁned to the introgression of genes for which the functional variant is known, or which have clearly identiﬁable phenotype effects. Examples are the Bt transgene into different maize genetic background (Ragot 1995). Marker-assisted introgression of such “known” genes is now widely used in plants, in particular by private companies (Dekkers and Hospital 2002). Another successful report is on the introgression of favorable alleles at three QTLs for earliness and grain yield among maize elite lines (Bouchez et al. 2002).

several parents (Fig. 20; Dekkers and Hospital 2002). The example shows how four genes (G1 to G4), which are present in four different lines (L1 to L4), can be combined into a single line in a two-step procedure. In the ﬁrst step, two lines are developed, each of which is homozygous for target genes (G1, G2 and G3, G4), by crossing pairs of lines. This is followed by construction of F2 , recombinant inbred line (RIL), or doubledhaploid (DH) progeny and selection of homozygotes. In the second step, such individuals are crossed to produce lines that are homozygous for all four targeted genes. This process can be expanded to more than four genes by expanding the pyramiding. Because the above strategies involve several generations of speciﬁc matings and the production of numerous offspring, they are more applicable to plants than animals. In the process of gene pyramiding, selection of homozygotes can be on the basis of linked markers and MAS is a very useful approach to maximizing utilization of the existent gene resources. Molecular

6.5.5 Gene Pyramiding Gene pyramiding, such as DNA introgression, is also a form of genotype building and is an important plant genetic improvement strategy especially on insectand disease- resistance breeding. A promising way is the incorporation of two or more resistance genes in the same cultivar or selection, the so-called pyramiding of resistance genes (MacHardy et al. 2001). This strategy should delay, or even prevent, the breakdown of the resistance gene and can hardly be achieved without the use of molecular markers, which greatly facilitate the identiﬁcation of favorable genetic combinations during the selection process (Gianfranceschi et al. 1996). Genes controlling different agronomic traits can be quickly brought together in an existing variety. Furthermore, genes responsible for resistance to different races or biotypes of a disease or insect pest can also be pyramided together to make a line have multirace or multibiotype resistance. Theoretically, these multirace or multibiotype resistances should be more durable than single-race or single-biotype resistance. Gene pyramiding is used to create individuals that are homozygous at all loci for a particular trait. Gene pyramiding involves multiple initial crosses between Fig. 20. Gene pyramiding scheme (Dekkers and Hospital 2002)

Chapter 6 Brassica Rapa

markers linked to major blast resistance genes offer a powerful tool for marker-assisted indirect selection of resistance loci in gene-pyramiding strategies. Servin et al. (2004) investigated the best way to combine into a single genotype a series of target genes identiﬁed in different parents and describe a general framework for the pyramiding of multiple genes into a single genotype. By combining these results with those available for various other aspects of MAS (Dekkers and Hospital 2002) it is now possible to optimize complex breeding schemes incorporating molecular information. Assuming that individuals can be selected and mated according to their genotype, the best method corresponds to an optimal succession of crossovers for several generations (pedigree). For each pedigree, Servin et al. (2004) computed the probability of success from the known recombination fractions between the target loci, as well as the number of individuals (population sizes) that should be genotyped over successive generations until the desired genotype is obtained, and provide an algorithm that generates and compares pedigrees on the basis of the population sizes they require and on their total duration (in number of generations) and ﬁnd the best gene-pyramiding scheme (Servin et al. 2004). Gene pyramiding has been successfully applied in several crop-breeding programs, and many varieties and lines possessing multiple attributes have been produced (Huang et al. 1997; Wang et al. 2001; Samis et al. 2002), including the “pyramiding” of several major disease resistance genes in rice (Xu et al. 1996; Huang et al. 1997; Hittalmani et al. 2000; Katiyar et al. 2001; Sandhu et al. 2003), barley (Raman et al. 1999), wheat (Dweikat et al. 1997; Wang et al. 2001), apple (Gygax et al. 2004), soybean (Wu et al. 2001), but few or none have been reported in oilseed rape crops.

251

sis) (Michaels and Amasino 1998). The high-density maps of B. rapa that are being developed are likely to speed up the process of positional cloning in this species (as aided by the molecular markers tagged to the locus/loci controlling various traits). There are now a variety of strategies available to create, identify, and analyze the affected locus/loci. (Ponce et al. 1999; Lukowitz et al. 2000; Springer 2000). These strategies include PCR-based technology, transposon tagging, and map-based or position cloning.

6.6.1 Principles of Map-Based Cloning

The ﬁrst step of map-based or positional cloning is to identify a molecular marker that lies close to a target gene of interest. Recently, several strategies have been developed that allow one to screen a large number of random, unmapped molecular markers in a relatively short time and to select just those few markers that reside in the vicinity of the target gene. These methods rely on two principles: (1) the development of highvolume marker technology, which allows hundreds or even thousands of potentially polymorphic DNA segments to be generated and visualized rapidly from single preparations of DNA; and (2) use of genetic stocks to identify, among these thousands of DNA fragments, those few that are derived from a region adjacent to the targeted gene. The next step is to saturate the region around that original molecular marker with other markers. At this stage, the population size could increase to at least 300 to 600 individuals based on the genome size of the target crop. The genome size of B. rapa is physically about 500 Mb and genetically about 1,000 cM. Theoretically, one recombinant in 1000 individuals counts for 0.1 cM or 50 kb on average, which should be enough for map-based cloning of a target gene. And the third step is to screen a large insert genomic library (BAC or YAC) with the marker 6.6 to isolate clones harboring the molecular markers. Map-Based Cloning Once the initial markers are identiﬁed that map near or, better yet, ﬂank the target gene and found a clone Map-based cloning or positional cloning is a strategy to which the markers hybridize, this is the way to deof gene cloning that makes use of tagging molecular termine where that gene resides. These steps can be markers (Nagata and Tabata 2003). The process of tar- summarized as follows: get locus/loci identiﬁcation has been hastened by the combined approaches of forward (phenotype to genotype) and reverse (genotype to phenotype) genetics – Identify a marker tightly linked to the target gene in a “large” mapping population. and high throughput (microarrays and SNP analy-

252

P. Quijada et al.

decreases the closer one gets to the target. Ideally, one would like to identify a marker at a physical distance from a target that is less than the average insert size of the BAC library from which one expects to isolate the gene. For example, working with the S. Korea B. rapa BAC library with an average insert size of 130 kb, it would be desirable to identify at least one marker within a 260-kb window containing the target (130 kb on each side). If such a marker were to be identiﬁed, it would be possible to screen the BAC library with the marker and to directly isolate a single BAC that contains the target gene without any need for chromosome walking. However, for B. rapa, which has a genome size about 500 Mb, the possibility of identi6.6.2 fying a random marker within 130 kb of a target gene Genetic Resources and Mapping Populations is very low. For the B. rapa genome containing 5.0 × 108 base pairs, the possibility would be 2.6 × 105 /5.0 As identifying molecular markers closely linked to × 108 = 5.2 × 10−4 . To have a 90% chance of identifya target gene is one of the most important steps for ing at least one marker within a 260-kb interval, ca. map-based cloning, a high-quality existing map will 6,000 markers would need to be sampled (Steven et al. be of great importance in the process of map-based 1995). cloning. Preferably such a map should be an immortal one, RI or DH populations. In B. rapa, a high-density molecular linkage map constructed using a DH popu- 6.6.3 lation was reported by Yong Pyo Lim’s research group BAC Libraries in South Korea. Using a DH population by crossing two morphologically diverse Chinese cabbage inbred Good BAC libraries are one of the most important lines, Chiifu and Kenshin, a genetic linkage map of resources for map-based cloning. Two top-quality the B. rapa ssp. pekinensis was constructed based B. rapa BAC libraries are publicly available. One on AFLPs, RFLPs, and SSRs. Out of the total 895 is JBr BAC library constructed by Ian Bancroft at markers, 494 mapped on 10 LGs, covering 1,046 cM John Innes Centre in the United Kingdom. The with an average distance of 2.1 cM (Lim et al. 1998). clones of the library were individually ﬁngerA very high-density molecular marker map with at printed and assembled into contigs using the Image least 1,000 markers for the 10 LGs is under construc- (http://www.sanger.ac.uk/Software/Image/) and FPC tion using the DH lines and more RILs. Such a map (http://www.agcol.arizona.edu/software/fpc/) prowill be of great importance in map-based cloning. grams developed at the Sanger Centre. High-density Sequence information of Brassica crops is accumu- gridded ﬁlters of the entire JBr library (comprising lating rapidly in public databases. Over 658,000 nu- more than 30,000 clones) were provided for colony cleotide entries are now available for Brassica. These hybridizations. A set of about 1300 GST probes, sequences can be used for developing large numbers or one every 100 kb, were hybridized in order to of SSRs or SNPs for rapid saturation of a targeted re- anchor within the Arabidopsis genome sequence each gion. Furthermore, B. rapa is a relatively easy crop of the thousand B. rapa mini contigs. To date, the for microspore culture. Large DH populations with ﬁngerprinted clones covers 462 Mb. BAC clones of thousands of lines could be created using a wide this library can be requested from John Innes Centre range of genotypes, which would allow for the identi- based on a material transfer agreement (MTA). ﬁcation of a very small chance of crossover events An online request can be made at http://brassica. with very good certainty. Such an advantage will bbsrc.ac.uk/IGF/body/request.htm. The other BAC also make B. rapa a good choice for map-based gene libraries are B. rapa subsp. pekinensis (Chinese cloning. cabbage-pe-tsai) variety Chiifu libraries KBrH and The probability of identifying one or more mark- KBrB constructed by Yong Pyo Lim’s group in S. ers within a B. rapa physical distance of a target gene Korea (Lim et al. 1998). Each library consists of – Find a YAC or BAC clone to which the marker probe hybridizes. – Create new markers from the large-insert clone and determine if they cosegregate with the target gene. – If necessary, rescreen the large-insert genomic library for other clones and search for cosegregating markers. – Identify a candidate gene from large-insert clone whose markers cosegregate with the gene. – Perform genetic complementation (transformation) to rescue the wild-type phenotype.

Chapter 6 Brassica Rapa

144 × 384-well plates, made using HindIII (KBrH) or BamHI (KBrB) digested genomic DNA. A total of 110,592 clones, providing 20-fold redundant representation of the genome are available. End sequencing is being conducted with this library with the aim of making contigs for all the chromosomes, which will be ﬁnally used for the multinational genome sequencing project of B. rapa.

6.6.4 Outlook Due to the close phylogenetic relationship of B. rapa with the important model species A. thaliana, for which the entire genome sequence has been available since 2000, it was anticipated that knowledge transfer for B. rapa improvement and map-based cloning would be straightforward by comparative mapping with A. thaliana and Brassica. Development of ampliﬁed consensus genetic marker in Brassica species from A. thaliana sequences of known biological function will inevitably accelerate the procedure (Lisitsyn and Wigler 1993; Lin et al. 1999; Liu et al. 1999; Mayer et al. 1999). And owing to the fact that the physiology and developmental biology of Arabidopsis and Brassica are very similar, the genome sequence information of Arabidopsis and Brassica species will make great contribution to the map-based or position cloning in B. rapa sooner rather than later. And as the development of The Multinational Brassica Genome Project, there are more and more sequence information on the public domain. So according to the conservation within Brassica species, we can also make use of the information from that to accelerate the procedure of position cloning in B. rapa.

6.7 Future Scope of Works In the comprehensive and ﬂooding genome database, the comparative genome is unshakably becoming such an approach that efﬁciently manages these genomic data and makes them easily analyzed. To compare a variation between different evolutionary lineages, a referenced genome would be needed as a model system. The publication of the A. thaliana genome (The Arabidopsis Genome Initiative 2000) and the draft sequences for two rice genomes (Goff et al. 2002; Yu et al. 2002) have provided a reference

253

platform for comparative plant genomics. Only based on the gene content for model plants, the comparative assessments could be a functional resolution of unknown plant genomes. Special emphasis has been on Arabidopsis and Brassica, which are closely related. Arabidopsis represents one copy of the ancestral Brassica genome and three copies of Arabidopsis genes in Brassica (Lagercrantz et al. 1996; Tien-Hung et al. 2000). To understand the biology and the improvement of the important Brassica crops, the comparative assessment between the small genome of A. thaliana and its highly replicated close relatives in the Brassica genus could have a great impact on understanding of the complex relationship. Comparative genomics can be of great value in many different aspects in an assessment of the B. rapa genome. It can be used to assess rapa genome structure and evolution and to clone genes from the complex species. Previous research revealed that comparative mapping between Arabidopsis and Brassica showed extensive chromosomal rearrangements (Lagercrantz et al. 1996; Tien-Hung et al. 2000), and a high level of genome colinearity between them was revealed. As highly conserved gene sequences are described by comparative mapping, evidence for a small inversion, translocations, and gene deletion/insertions was detected (Sadowski et al. 1996). In addition, comparative assessment makes cloning of genes much easier. The A FA desaturation gene (FAD2) in B. rapa was mapped and cloned with a comparison of the QTL corresponding to the Arabidopsis fad2 gene (Tanhuanpää et al. 1998). Through comparative mapping, many powerful tools already created for Arabidopsis can now be applied to Brassica. Arabidopsis cDNA sequences may be used to isolate homologous genes in Brassica, Arabidopsis BAC/YAC contigs may be used in Brassica for map-based cloning, and Arabidopsis high-resolution maps may help to resolve clustered markers in Brassica (Liu et al. 1996). Arabidopsis whole genome sequences can be used to accelerate Brassica genomics by comparative genomics. JBr is a B. rapa BAC library created at John Innes Centre in the UK. High-density gridded ﬁlters of the entire library (comprising more than 30,000 clones each) were hybridized with a set of deﬁned and sequence-authenticated Arabidopsis gene sequence tag (GST) probes. Thousands of Brassica minicontigs will be generated after hybridization. These minicontigs will ultimately be used for large contigs assembling by combining other techniques. Moreover,

254

P. Quijada et al.

a cross-genome comparative map based on a common set of ESTs may eventually provide a direct comparison of macro- and microcolinearity across various species. The combination of ESTs and DNA microarray technology (Winzeler et al. 1998) could accelerate this process. Arabidopsis genomic tools can guide the isolation of Brassica alleles as well. Putative candidate genes from Arabidopsis can serve as molecular markers on suitable segregating populations of Brassica. The control of ﬂowering time in B. rapa, for example, was studied by using information about Arabidopsis genes that had been implicated in this mechanism (Lagercrantz et al. 1996; Osborn et al. 1997). With the help of RFLP markers from Arabidopsis highresolution maps and cDNA tools, the ﬂowering-time genes VFR2 from B. rapa appear to be homologous to FLC from Arabidopsis and may control ﬂowering time through a mechanism similar to that in Arabidopsis (Kole et al. 2001; Schranz et al. 2002). The prospects for comparing model Arabidopsis and Brassica plants would focus on assessments of distantly related species, more genomic data input, and comparative bioinformatics. Comparative assessment between distantly related species is required. Because even comparative assessment between Brassica and Arabidopsis chromosomes shows large orthologous regions, it cannot adequately address the evolutionary changes that drive the formation of new plant species. Previous research reported the comparative analysis of genomic regions in rice and Arabidopsis. Comparative studies between them assessed the degree of colinearity between monotyledonous and dicotyledonous species (Schmidt 2002). Identifying the unique and rapidly evolving genes within model plants and their related species will provide insight into what makes each species individual. To achieve this aim, exploiting new information on comparative genomics entails requiring more input. For example, the large-scale chromosomal duplications are complex. Thus, obtaining more comparative data especially between more distantly related species will shed light on this obstacle. Also, comparative bioinformatics is beginning to play a major role for meeting such large-scale comparative data. Now, we know that how much we can borrow through comparative genomics could determine the volume of research conducted. Comparative assessment has revealed duplication, rearrangement, translocation, inversions, etc. between Arabidopsis and Brassica. All these results taken together indicate that the alternation of sequencing the Brassica genome may play a much more

prominent role in the evolutionary study of Arabidopsis and Brassica (Schmidt 2002). In 2003, the Multinational Brassica Genome Sequencing Project produced a detailed blueprint and a multinational research consortium was organized. A BBSRC-appointed steering committee executed a Brassica genomics sequencing project. The B. rapa A genome project is successfully up and running, and, due to the availability of the annotated genome sequence of the related species A. thaliana, the whole genome sequence of the A genome will take place before our eyes step by step.

References Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoluos MH, Xiao H, Merril CR, Wu A, Olde B, Moreno RF, Kerlavage AR, McCombie WR, Venter JC (1991) Complimentary DNA sequencing: sequence tags and human genome project. Science 252:1651–1656 Ahmed SU, Zuberi MI (1971) Inheritance of seed coat color in Brassica campestris-variety Toria. Crop Sci 11:309–310 Ajisaka H, Kuginuki Y, Hida K, Enomoto S, Hirai M (1995) A Linkage map of DNA markers in Brassica campestris. Breed Sci 45 (Suppl):195 Ajisaka H, Kuginuki Y, Shiratori M, Ishiguro K, Enomoto S, Hirai M (1999) Mapping of loci affecting the cultural efﬁciency of microspore culture of Brassica rapa syn. campestris L. using DNA polymorphism. Breed Sci 49:187–192 Ajisaka H, Kuginuki Y, Yui S, Yui S, Enomoto S, Hirai M (2001) Identiﬁcation and mapping of a quantitative trait locus controlling extreme late bolting in Chinese cabbage (Brassica rapa L. ssp pekinensis syn. campestris L.) using bulked segregant analysis – a QTL controlling extreme late bolting in Chinese cabbage. Euphytica 118:75–81 Anand IJ (1987) Inheritance of seed coat color in yellow seed turnip rapes. J Oilseeds Res 4:130–131 Anonymous (1977) Description of a cultivar: candle. License No. 1754 issued 29 March 1977. Agriculture Canada Production and Marketing Branch, Plant Products Division, Ottawa, Ontario Appelqvist LA (1972) Chemical constituents of rapeseed. In: Appelqvist LA, Ohlson R (eds) Rapeseed: Cultivation, Composition, Processing and Utilization. Elsevier, Amsterdam, pp 123–131 Arumuganathan K, Earle ED (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9:208–219 Aslam MY, Bechyne M (1983) Genetics of the number of rows of seeds in the silique in amphidiploid from three dipoid ancestors. Can J Genet Cytol 28:323–329 Axelsson T, Bowman CM, Sharpe AG, Lydiate DJ, Lagercrantz U (2000) Amphidiploid Brassica juncea contains conserved progenitor genomes. Genome 43:679–688

Chapter 6 Brassica Rapa Axelsson T, Shavorskaya O, Lagercrantz U (2001) Multiple ﬂowering time QTLs within several Brassica species could be the result of duplicated copies of one ancestral gene. Genome 44:856–864 Basten CJ, Weir BS, Zeng ZB (2001) QTL Cartographer, Version 1.15. Department of Statistics, North Carolina State University, Raleigh, NC Beckmann JS, Soller M (1986) Restriction fragment length polymorphisms and genetic improvement of agriculture species. Euphytica 35:111–124 Bernacchi D, Beck-Bunn T, Eshed Y, Lopez J, Petiard V, Uhlig J, Zamir D, Tanksley SD (1998) Advanced backcross QTL analysis in tomato. I. Identiﬁcation of QTLs for traits of agronomic importance from Lycopersicon hirsutum. Theor Appl Genet 97:381–397 Bouchez A, Hospital F, Causse M, Gallais A, Charcosset A (2002) Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite lines. Genetics 162:1945–1959 Brace J, Ockendon DJ, King GJ (1994) A molecular approach to the identiﬁcation of S-alleles in Brassica oleracea. Sex Plant Reprod 7:203–208 Browse J, McConn M, James D Jr, Miquel M (1993) Mutants of Arabidopsis deﬁcient in the synthesis of α-linolenate. J Biol Chem 22:16345–16351 Brunklaus-Jung G, Röbbelen E (1987) Genetical and physiological investigations on mutants for polyenoic fatty acid in rapeseed (Brassica napus L.). Plant Breeding 98:9–16 Buczacki ST, Toxopeus H, Mattusch P, Johnston TD, Dixon GR, Hobolth LA (1975) Study of physiologic specialization in Plasmodiophora brassicae: proposals for rationalization through an international approach. Trans Br Mycol Soc 65:295–303 Burkill IH (1930) The Chinese mustards in the Malay Peninsula. Gad’s Bull 5 Burr B, Burr FA, Thompson KH, Albertson MC, Stuber CW (1988) Gene mapping with recombination inbreds in maize. Genetics 118:519–526 Butruille DV, Guries RP, Osborn TC (1999) Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of Brassica napus L. Genetics 153:949–964 Byrum JR, Kinney AJ, Stecca KL, Grace DJ, Diers BW (1997) Alteration of the omega-3 fatty acid desaturase gene is associated with reduced linolenic acid in the A5 soybean genotype. Theor Appl Genet 94:356–359 Chen BY, Heneen WK, Jönsson R (1988) Resynthesis of Brassica napus L. through interspeciﬁc hybridization between B. alboglabra Bailey and B. campestris L. with special emphasis on seed color. Plant Breed 101:52–59 Chen BY, Jorgensen RB, Chen BF, Heneen WK (1997) Identiﬁcation and chromosomal assignment of RAPD marker linked with a gene for seed coat color in a Brassica campestrisalboglabra addition line. Hereditas 126:133–138

255

Chen YP, Cao JS, Miao Y, Ye WZ (2000) Analysis of genetic polymorphism in vegetable crops of Brassica campestris by RAPD markers. J Zhejiang Univ (Agric Life Sci) 26:131–136 Chevre AM, Delourme R, Eber F, Margale E, Quiros CF, Arus P (1995) Genetic analysis and nomenclature for seven isozyme systems in Brassica nigra, B. oleracea and B. campestris. Plant Breed 114:473–480 Choi SR, Kim JH, Han TH, Piao ZY, Plaha P, Koo DH, Bang JW, King GJ, Teakle GR, Allender CJ, Lydiate DJ, Beynon E, Osborn TC, Lim YP (2004) Joint Meeting of the 14th Crucifer Genetics Workshop and the 4th ISHS Symposium on Brassicas, 24–28 Oct 2004, Chungnam National University Daejeon, Korea, 151 p Chyi YS, Hoenecke ME, Sernyk JL (1992) A genetic map of restriction fragment length polymorism loci for Brassica rapa (syn. campestris). Genome 25:746–757 Conner JA, Conner P, Nasrallah ME, Nasrallah JB (1998) Comparative mapping of the Brassica S locus region and its homeolog in Arabidopsis: implications for the evolution of mating systems in the Brassicaceae. Plant Cell 10:801–812 Cooke RJ (1999) Modern methods for the cultivar identiﬁcation and the transgenic plant challenge. Seed Sci Technol 27:669–680 Coulthart M, Denford KE (1982) Isozyme studies in Brassica. 1. Electrophoretic techniques for leaf enzymes and comparison of B. napus, B. campestris andB. oleracea using phosphoglucoisomerase. Can J Plant Sci 62:621–630 Crute IR, Gray AR, Crips P, Buczacki ST (1980) Variation in Plasmodiophora brassicae and resistance to clubroot disease in brassicas and allied crops – a critical review. Plant Breed Abstr 50:91–104 Dan B, Tu JX, Hu SY, He YT, Wang JL, Chen BY, Luan YF, Nimazhuoma, Meng X, Zhuo G (2003) Analysis of rapeseed germplasm resources in Tibet by RAPD. Acta Agron Sin 29:1–7 Das S, Rajagopal J, Bhatia S, Srivastava PS, Lakshmikumaran M (1999) Assessment of genetic variation within Brassica campestris cultivars using ampliﬁed fragment length polymorphism and random ampliﬁcation of polymorphic DNA markers. J Biosci 24:433–440 Dass H, Nybom H (1967) The relationships between B. nigra, B. campestris, B. oleracea, and their amphidiploid hybrids studied by means of numerical chemotaxonomy. Can J Genet Cytol 9:880–890 Daun JK, DeClercq DR (1988) Quality of yellow and dark seeds in Brassica campestris canola varieties Candle and Tobin. J Am Oil Chem Soc 65:122–126 Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet 3:22–32 Delwiche PA, Williams PH (1974) Resistance to Albugo candida race 2 in Brassica sp. Proc Am Phytopathol Soc 1:66 Delwiche PA, Williams PH (1981) Thirteen marker genes in Brassica nigra. J Hered 72:289–290

256

P. Quijada et al.

Demeke T, Adams RP, Chibbar R (1992) Potential taxonomic use of random ampliﬁed polymorphic DNA (RAPD): a case study in Brassica. Theor Appl Genet 84:990–994 Denford KE, Vaughan JG (1977) A comparative study of certain seed isoenzymes in the ten chromosome complex of Brassica campestris and its allies. Ann Bot 41:411–418 Diepenbrock W, Wilson RF (1987) Genetic regulation of linolenic acid concentration in rapeseed. Crop Sci. 27:75–77 Diers BW, Osborn TC (1994) Genetic diversity of oilseed Brassica napus germplasm based on restriction fragment length polymorphisms. Theor Appl Genet 88:662–668 Downey RK, Stringam GR, McGregor DI, Stefansson BR (1975) Breeding rapeseed and mustard crops. In: Harapiak JT (ed) Oilseed and Pulse Crops in Western Canada – A Symposium, Western Coop Fertilizers, Calgary, Alberta, Canada, pp 157–183 Dweikat I, Ohm H, Patterson F, Cambron S (1997) Identiﬁcation of RAPD markers for 11 Hessian ﬂy resistance genes in wheat. Theor Appl Genet 94:419–423 Ebrahimi AG, Delwiche PA, Williams PH (1976) Resistance in Brassica juncea to Peronospora parasitica and Albugo candida race 2. Proc Am Phytopathol Soc 3:273 Edwards MD, Williams PH (1987) Selection for minor gene resistance to Albugo candida in a rapid-cycling population of Brassica campestris. Phytopathology 77:527–532 El-Assal SED, Alonso-Blanco C, Peeters AJ, Raz V, Koornneef M (2001) A QTL for ﬂowering time in Arabidopsis reveals a novel allele of CRY2. Nat Genet 29:435–440 Elers B, Wiebe HJ (1984) Flower formation of Chinese cabbage. I. Response to vernalization and photoperiods. Sci Hort 22:219–231 Erickson LR, Straus NA, Beversdorf WD (1983) Restriction patterns reveal origins of chloroplast genomes in Brassica amphidiploids. Theor Appl Genet 65:201–206 Fan Z, Rimmer SR, Stefansson BR (1983) Inheritance of resistance to Albugo candida in rape (Brassica napus L.). Can J Genet Cytol 25:420–424 Ferreira ME, Williams PH, Osborn TC (1994) RFLP mapping of Brassica napus using doubled haploid lines. Theor Appl Genet 89:615–621 Figdore SS, Kennard WC, Song KM, Slocum MK, Osborn TC (1988) Assessment of the degree of restriction fragment length polymorphism in Brassica. Theor Appl Genet 75:833–840 Frary A, Nesbitt TC, Grandillo S, Knaap E, Cong B, Liu J, Meller J, Elber R, Alpert KB, Tanksley SD (2000) Fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science 289:85–88 Freyssinet M, Dumas B, Sailland A, Pepin R, Freyssinet G, Pallet K (1995) Transgenetic crops expressing oxalate oxidase as a way to increase resistance to oxalate-producing pathygens. In: Proc 9th Int Rapeseed Congr, Cambridge, UK, 4:1278–1279 Fuchs H, Sacristán MD (1996) Identiﬁcation of a gene in Arabidopsis thaliana controlling resistance to clubroot (Plas-

modiophora brassicae) and characterization of the resistance response. Mol Plant-Micr Interact 9:91–97 Fukui K (2003) Brassica and Legume Chromosomes. In: Nagata T, Tabata S (eds) Brassicas and Legumes: From Genome Structure to Breeding. Springer, Berlin Heidelberg New York, pp 114–138 Fukui K, Nakayama S, Ohmido N, Yoshiaki H, Yamabe M (1998) Quantitative karyotyping of three diploid Brassica species by imaging methods and localization of 45S rDNA loci on the identiﬁed chromosomes. Theor Appl Genet 96:325–330 Gama LT, Smith C, Gibson JP (1992) Transgene effects, introgression strategies and testing schemes in pigs. Animal Prod 54:427–440 Gan Y, Zeng FY, Zhao Y, Wang ML (2001) Development of SCAR markers to authenticate the hybrid seed lots of Shuza No. 6 (Brassica napus L.). Acta Agron Sin 27:722–728 Gianfranceschi L, Koller B, Seglias N, Kellerhals M, Gessler C (1996) Molecular selection in apple for resistance to scab caused by Venturia inaequalis. Theor Appl Genet 93:199–204 Goff SA, Ricke D, Lan, TH, Presting G, Wang R, Dunn M, Glazebrook J, Sessions A, Oeller P, Varma H (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296:92–100 Gomez-Campo C, Prakash S (1999) Origin and domestication. In: Gómez-Campo C (ed) Biology of Brassica Coenospecies. Developments in Plant Genetics and Breeding 4. Elsevier, Amsterdam Guo JX, Zhou NY, Ma RC, Cao MQ (2002) Genetic diversity in Brassica rapa revealed by AFLP molecular markers. J Agric Biotechnol 10:138–143 Gupta PK, Balyan HS, Sharma PC, Ramesh B (1996) Microsatellites in plants: A new class of molecular markers. Curr Sci 70:45–54 Gygax M, Gianfranceschi L, Liebhard R, Kellerhals M, Gessler C, Patocchi A (2004) Molecular markers linked to the apple scab resistance gene Vbj derived from Malus baccata jackii. Theor Appl Genet 109:1702–1709 Han HP, Sun RF, Shu J, Li F, Zhang SF, Niu XK (2004) AFLP markers linked to TuMV-resistance gene in Chinese cabbage (Brassica rapa L. ssp. pekinensis). Sci Agri Sin 37:539–544 Hawk JA (1982a) Single gene control of seed color and hypocotyls color in turnip rape. Can J Plant Sci 62:331–334 Hawk JA (1982b) Tight linkage of two seedling mutants in early ﬂowering turnip rape (Brassica campestris). Can J Genet Cytol 24:475–478 He YT, Tu JX, Fu TD, Li DR, Chen BY (2002) Genetic diversity of germplasm resources of Brassica campestris L. in China by RAPD. Acta Agron Sin 28:697–703 He YT, Chen BY, Fu TD, Li DR, Tu JX (2003) Origins and evolution of Brassica campestris L. in China. Acta Genet Sin 30:1003–1012 Hirai M, Harada T, Kubo N, Tsukada M (2004) A novel locus for clubroot resistance in Brassica rapa and its linkage markers. Theor Appl Genet 108:639–643

Chapter 6 Brassica Rapa Hittalmani S, Parco A, Mew TV, Zeigler RS, Huang N (2000) Fine mapping and DNA marker-assisted pyramiding of three major genes for blast resistance in rice. Theor Appl Genet 100:1121–1228 Hoenecke M, Chyi YS (1991) Comparison of Brassica napus and B. rapa genomes based on restriction fragment length polymorphism mapping. In: McGergor DI (ed) Rapeseed in a Changing World. Proc 8th Int Rapeseed Congr, Saskatchewan, Canada, 4:221–226 Hospital F, Moreau L, Lacoudre F, Charcosset A, Gallais A (1997) More on the efﬁciency of marker assisted selection. Theor Appl Genet 95:1181–1189 Hu J, Quiros CF (1991) Molecular and cytological evidence of deletions in alien chromosomes for two monosomic addition lines of Brassica campestris-oleracea. Theor Appl Genet 81:221–226 Hu J, Quiros C, Arus P, Struss D, Röbbelen G (1995) The mapping of a gene determining linolenic acid concentration in rapeseed with DNA-based markers. Theor Appl Genet 90:258–262 Hu J, Li G, Struss D, Quiros CF (1999) SCAR and RAPD markers associated with 18-carbon fatty acids in rapeseed, Brassica napus. Plant Breed 118:145–150 Huang N, Angeles ER, Domingo J, Magpantay G, Singh S, Zhang G, Kumaravadivel N, Bennett J, Khush GS (1997) Pyramiding of bacterial blight resistance genes in rice: markerassisted selection using RFLP and PCR. Theor Appl Genet 95:313–320 Hunter RL, Merkert CL (1957) Histochemical demonstration of enzymes separated by zone electrophoresis in starch gels. Science 125: 1294–1295 Hutcheson DS, Falk KC, Rakow GFW (1999) The development of low aliphatic, low indole glucosinolate Brassica rapa. In: Proc 10th Int Rapeseed Congr, Canberra, Australia (www.regional.org.au/au/gcirc/4/64.htm) Jackson SA, Cheng ZK, Wang ML, Goodman HM, Jiang J (2000) Comparative ﬂuorescence in situ hybridization mapping of a 431-kb Arabidopsis thaliana bacterial artiﬁcial chromosome contig reveals the role of chromosomal duplications in the expansion of the Brassica rapa genome. Genetics 156:833–838 Janick J (2001) Plant Breeding Reviews, vol 21. Wiley, New York Jönsson R (1975) Yellow-seeded rape and turnip rape. II. Breeding for improved quality of oil and meal in yellow-seeded materials. J Swed Seed Assoc 8(5):271–275 Jönsson R (1977) Breeding for improved oil and meal quality in rape (Brassica napus L.) and turnip rape (Brassica campestris L.). Hereditas 87:205–218 Jourdren C, Barret P, Horvais R, Foisset N, Delourme R, Renard M (1996) Identiﬁcation of RAPD markers linked to loci controlling erucic acid acid level in rapeseed. Mol Breed 2:61–71 Katiyar S, Verulkar S, Chandel G, Zhang Y, Huang BC, Bennett J (2001)Genetic analysis and pyramiding of two gall midge

257

resistance genes (Gm-2 and Gm-6t) in rice (Oryza sativa L.). Euphytica 122:327–334 Kikuchi M, Ajisada H, Kuginuki Y, Hirai M (1999) Conversion of RAPD markers for a clubroot-resistance gene of Brassica rapa into sequence-tagged sites (STSs). Breed Sci 49:83–89 Kirk JTO, Hurlstone CJ (1983) Variation and inheritance of Brassica juncea. Z Pﬂanzenzucht 90:331–338 Kole C, Teutonico R, Mengistu A, Williams PH, Osborn TC (1996a) Molecular mapping of a locus controlling resistance to Albugo candida in Brassica rapa. Phytopathology 86:367–369 Kole C, Kole P, Osborn TC (1996b) RFLP mapping of Brassica rapa using recombinant inbred lines. In: Plant and Animal Genome Conf, San Diego, p 73 Kole C, Kole P, Vogelzang R, Osborn TC (1997a) Genetic linkage map of a Brassica rapa recombinant inbred population. J Hered 88:553–557 Kole C, Kole P, Osborn TC (1997b) A high resolution map around VFR2, a vernalization responsive ﬂowering time gene in Brassica rapa. In: Plant and Animal Genome Conf, San Diego, p 107 Kole C, Quijada P, Michaels SD, Amasino RM, Osborn TC (2001) Evidence for homology of ﬂowering-time gene VFR2 from Brassica rapa and FLC from Arabidopsis thaliana. Theor Appl Genet 102:425–430 Kole C, Williams PH, Rimmer SR, Osborn TC (2002a) Linkage mapping of genes controlling resistance to white rust (Albugo candida) in Bassica rapa (syn. campestris) and comparative mapping to Brassica napus and Arabidopsis thaliana. Genome 45:22–27 Kole C, Thorman CE, Karlsson BH, Palta JP, Gaffney P, Yandell B, Osborn TC (2002b) Comparative mapping of loci controlling winter survival and related traits in oilseed Brassica rapa and B. napus. Mol Breed 9:201–210 Kolte SJ (1985) Disease of annual edible oilseed crops. In: Rapeseed, Mustard and Sesame Disease. CRC, Boca Raton, FL, pp 9–82 Konieckzny A, Ausubel M (1993) A procedure for mapping Arabidopsis mutations using codominant ecotype-speciﬁc PCR-based markers. Plant J 4:403–410 Koudande OD, Iraqi F, Thomsonp CA, Teale J, Arendonk JAV (2000) Strategies to optimize marker-assisted introgression of multiple unlinked QTL. Mam Genome 11:145–150 Kresovich S, Szewc-McFadden AK, Bliek SM, NcFerson JR (1995) Abundance and characterization of simplesequence repeats SSRs isolated from a size-fractionated genomic library of Brassica napus L. rapeseed. Theor Appl Genet 91:206–211 Krzymanski J (1970) Inheritance of thioglucoside content by rapeseed (Brassica napus). Journees Internationales sur le Colza. CETIOM 212–218 Kuginuki Y, Ajisaka H, Yui M, Yoshikawa H, Hida K, Hirai M (1997) RAPD markers linked to a clubroot-resistance locus in Brassica rapa L. Euphytica 98:149–154

258

P. Quijada et al.

Kumar A, Singh DP (1998) Use of physiological indices as a screening technique for oilseed Brassica species. Ann Bot 81:413–420 Laakso I, Hovinen S, Seppänen-Laakso T, Hiltunen R (1999) Selection for low -linolenic acid content in spring turnip rape. In: 10th International Rapeseed Congr, Canberra, Australia Lagercrantz U, Lydiate DJ (1996) Comparative genome mapping in Brassica. Genetics 144:1903–1910 Lagercrantz U, Putterill J, Coupland G, Lydiate D (1996) Comparative mapping in Arabidopsis and Brassica, ﬁne scale genome colinearity and congruence of genes controlling ﬂowering time. Plant J 9:13–20 Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199 Landry BS, Hubert N, Etoh T, Harada JJ, Lincoln SE (1991) A genetic map for Brassica napus based on restriction fragment length polymorphisms detected with expressed DNA sequences. Genome 34:543–552 Landry BS, Hubert N, Crete R, Chiang MS, Lincoln SE, Etoh T (1992) A genetic map for Brassica oleracea based on RFLP markers detected with expressed DNA sequences and mapping of resistance gene to race 2 of Plasmodiophora brassicae. Genome 35:409–420 Lanner C, Bryngelsson T, Gustafson M (1997) Relationship of wild Brassica species with chromosome number 2n = 18, based on RFLP studies. Genome 40:302–308 Lemieux B, Miquel M, Somerville C, Browse J (1990) Mutants of Arabidopsis with alterations in seed lipid fatty acid composition. Theor Appl Genet 80:234–240 Li G, Quiros CF (2001) Sequence-related ampliﬁed polymorphism (SRAP), a new marker system based on a simple PCR reaction: its application to mapping and gene tagging in Brassica. Theor Appl Genet 103:455–461 Li JW (1981) The origins and evolution of vegetable crops in China. Sci Agric Sin 14:90–95 Li RG, Zhu L, Wu NF, Fan YL, Wu XM, Qian XH (1997) Genetic diversity among oilseed cultivars of Brassica juncea Czern. et Cross in China. Biotechnol Bull 5:26–31 Lim YP, Kim JH, Bang JW, Nam HG, Cho KW, Jang CS (1998) Genetic mapping of Chinese cabbage (Brassica rapa L. var. pekinensis). Plant & Animal Genome Conf, San Diego, p 221 Lin X, Kaul S, Rounsley S, Shea TP, Benito M-I, Town CD, Fujii CY, Mason T, Bowman CL, Barnstead M, Feldblyum TV, Buell CR, Ketchum KA, Lee J, Ronning CM, Koo HL, Moffat KS, Cronin LA, Shen M, Pai G, Van Aken S, Umayam L, Tallon LJ, Gill JE, Adams MD, Carrera AJ, Creasy TH, Goodman HM, Somerville CR, Copenhaver GP, Preuss D, Nierman WC, White O, Eisen JA, Salzberg SL, Fraser CM, Venter JC (1999) Sequence and analysis of chromosome 2 of the plant Arabidopsis thaliana. Nature 402:761 Lincoln S, Daly M, Lander E (1992) Mapping genes controlling quantitative traits with MAPMAKER/QTL 1.1. 2nd ed. Technical Report Whitehead Institute, Cambridge, MA

Lisitsyn N, Wigler M (1993) Cloning the differences between two complex genomes. Science 259:946–951 Lister C, Dean C (1993) Recombinant inbred lines for mapping RFLP and phenotypic markers in Arabidopsis thaliana. Plant J 4:745–750 Liu HL (1984) Origin and evolution of genus Brassica oilseed rapes. Acta Agron Sin 10:9–18 Liu SC, Kowalski SP, Lan TH, Feldmann KA, Paterson AH (1996) Genome-wide high-resolution mapping by recurrent intermating using Arabidopsis thaliana as a model. Genetics 142:247–258 Liu YG, Shirano Y, Fukaki H, Yanai Y, Tasaka M, Tabata S, Shibata D (1999) Complementation of plant mutants with large genomic DNA fragments by a transformation competent artiﬁcial chromosome vector accelerates positional cloning. Proc Natl Acad Sci USA 96:6535–6540 Liu CL, Guan CY, Li X, Ruan Y, Liao XL, Xiong XH, Zhou XY, Wang GH, Chen SY (2000) Construction of linkage map and mapping resistance gene of Scterotinia sclerotiorum in Brassica napus. Acta Genet Sin 27:918–924 Lombard V, Baril CP, Dubreuil P, Blouet F, Zhang D (2000) Genetic relationships and ﬁngerprinting of rapeseed cultivars by AFLP: consequences for varietal registration. Crop Sci 40:1417–1425 Lowe AJ, Moule C, Trick M, Edwards KJ (2004) Efﬁcient largescale development of microsatellites for marker and mapping applications in Brassica crop species. Theor Appl Genet 108:1103–1112 Lu G, Cao JS, Chen H (2002a) Genetic linkage map of Brassica campestris L. using AFLP and RAPD markers. J Zhejiang Univ (Sci) 3:600–605 Lu G, Cao J, Chen H, Xian GX (2002b) QTLs mapping of some horticultural traits of Chinese cabbage. Sci Agric Sin 35:969–974 Lukowitz W, Gillmor CS, Scheibel WR (2000) Positional cloning in Arabidopsis. Why it feels good to have a genome initiative working for you. Plant Physiol 123:795–805 MacHardy W, Gadoury DM, Gessler C (2001) Parasitic and biological ﬁtness of Venturia inaequalis: relationship to disease management strategies. Plant Dis 85:1036–1051 Mahmood T, Ekuere U, Yeh F, Good AG (2003) RFLP linkage analysis and mapping genes controlling the fatty acid proﬁle of Brassica juncea using reciprocal DH populations. Theor Appl Genet 107:283–290 Marshall E (1997) The hunting of the SNP. Science 278:2047 Matsumoto E, Yasui C, Ohi M, Tsukada M (1998) Linkage analysis of RFLP markers for clubroot resistance and pigmentation in Chinese cabbage (Brassica rapa ssp. pekinensis). Euphytica 104:79–86 Mayer K, Schuller C, Wambutt R et. al (1999) Sequence and analysis of chromosome 4 of the plant Arabidopsis thaliana. Nature 402:769–777 McDonald BE (1995) Oil properties of importance in human nutrition. In: Brassica Oilseeds Production and Utilization. CAB, Wallingford, UK, pp 291–299

Chapter 6 Brassica Rapa McGrath JM, Quiros CF (1991) Inheritance of isozyme and RFLP markers in Brassica campestris and comparison with B. oleracea. Theor Appl Genet 82:668–673 Mero CE, Honma S (1985) Inheritance of bolting resistance in an intraspeciﬁc Chinese cabbage × Turnip cross. HortScience 20:881–882 Michaels SD, Amasino RM (1998) A robust method for detecting single-nucleotide changes as polymorphic markers by PCR. Plant J 14:381–385 Michelmore RW, Paran I, Kesseli RV (1991) Identiﬁcation of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in speciﬁc genomic region by using segregating populations. Proc Natl Acad Sci USA 88:9828–9832 Miller RW, Van Etten CH, McGrew C (1962) Amino acid composition of seed meals from 41 species of Cruciferae. J Agric Food Chem 10:426–430 Mizushima U, Tsunoda S (1967) A plant exploration in Brassica and allied genera. Tohoku J Agric Res 17:249–277 Moreau L, Lemarié, S, Charcosset, A, Gallais A (2000) Economic efﬁciency of one cycle of marker-assisted selection. Crop Sci 40:329–337 Morinage T (1934) Interspeciﬁc hybridization in Brassica. VI. The cytology of F1 hybrids of B. juncea and B. nigra. Cytologia 6:62–67 Muangprom A, Osborn TC (2004) Characterization of a dwarf gene in Brassica rapa, including the identiﬁcation of a candidate gene. Theor Appl Genet 108:378–384 Murren CJ, Pendleton N, Pigliucci M (2002) Evolution of phenotypic integration in Brassica (Brassicaceae). Am J Bot 89:655–663 Nagata T, Tabata S (2003) Brassicas and Legumes from Genome Structure to Breeding. Springer, Berlin Heidelberg New York Nasrallah ME, Wallace DH (1967) Immunogenetics of selfincompatibility in Brassica oleracea Heredity 22:519–527 Nasrallah JB, Nishio T, Nasrallah ME (1991) The selfincompatibility genes of Brassica – expression and use in genetic ablation of ﬂoral tissues. Ann Rev Plant Phys Plant Mol Biol 42:393–422 Nikiforov TT, Rendle RB, Goelet P, Rogers YH, Kotewicz ML, Anderson S, Trainor GL, Knapp MR (1994) Genetic bit Analysis: a solid phase method for typing single nucleotide polymorphisms. Nucleic Acids Res 22:4167–4175 Nishio T, Sakamoto K, Yamaguchi J (1994) PCR-RFLP of S locus for identiﬁcation of breeding lines in cruciferous vegetables. Plant Cell Rep 13:546–550 Nishio T, Kusaba M, Watanable M, Hinata K (1996) Registration of S alleles in Brassica campestris L. by restriction fragment sizes of SLGs. Theor Appl Genet 92:388–394 Nishio T, Kusaba M, Sakamoto K, Ockendon DJ (1997) Polymorphism of the kinase domain of the S-locus receptor kinase gene (SRK) in Brassica oleracea L. Theor Appl Genet 95:335–342

259

Nou IS, Watanabe M, Isogai A, Hinata K (1993) Comparison of S-alleles and S-glycoproteins between two wild populations of Brassica campestris in Turkey and Japan. Sex Plant Reprod 6:79–86 Novakova B, Salava J, Lydiate D (1996) Construction of a genetic linkage map for Brassica campestris L. (syn. Brassica rapa L.). Genetika Slechteni 32:249–256 Nozaki T, Kumazaki A, Koba T, Ishikawa K, Ikehashi H (1997) Linkage analysis among loci for RAPDs isozyme and some agronomic traits in Brassica campestris L. Euphytica 95:115–123 Okasaki K, Kusuba M, Ockendon DF, Nishio T (1999) Characterization of S tester lines and Brassica oleracea: polymorphism of restriction fragment length of SLG homologues and isoelectric point of S-glycoproteins. Theor Appl Genet 98:1329–1334 Oost EH (1985) A proposal for an infraspeciﬁc classiﬁcation of Brassica rapa L. In: Styles BT (ed) Infraspeciﬁc Classiﬁcation of Wild and Cultivated Plants. Oxford University Press, New York, pp 309–315 Osborn TC, Kole C, Parkin IA, Sharpe AG, Kuiper M, Lydiate DJ, Trick M (1997) Comparison of ﬂowering time genes in Brassica rapa, B. napus and Arabidopsis thaliana. Genetics 146:1129 Palmer JD (1988) Intraspeciﬁc variation and multicircularity in Brassica mitochondrial DNAs. Genetics 118:341–351 Paran I, Michelmore RW (1993) Development of reliable PCRbased markers linked to downy mildew genes in lettuce. Theor Appl Genet 85:985–993 Persson C (1985) High palmitic acid content in summer turnip rape (Brassica campestris var. annua L.) Cruciferae Newslett 10:137 Petrie GA (1988) Races of Albugo candida (white rust and staghead) on cultivated Cruciferae in Saskatchewan. Can J Plant Pathol 10:142–150 Piao ZY, Park YJ, Choi SR, Hong CP, Park JY, Choi YS, Lim YP (2002) Conversion of AFLP marker linked to clubroot resistance gene into SCAR marker. J Kor Soc Hort Sci 43:653–659 Piao ZY, Deng YQ, Choi SR, Park YJ, Lim YP (2004) SCAR and CAPS mapping of CRb, a gene conferring resistance to Plasmodiophora brassicae in Chinese cabbage (Brassica rapa ssp. pekinensis). Theor Appl Genet 108:1458–1465 Pleines S, Friedt W (1988) Breeding for improved C18-fatty acid composition in rapeseed (Brassica napus L.). Fat Sci Technol 5:167–171 Plieske J, Struss D (2001) Microsatellite markers for genome analysis in Brassica. I. development in Brassica napus and abundance in Brassicaceae species. Theor Appl Genet 102:689–694 Ponce MR, Robles P, Micol JL (1999) High throughout mapping in Arabidopsis thaliana. Mol Gen Genet 261:408–415 Pound GS, Williams PH (1963) Biological races of Albugo candida. Phytopathology 53:1146–1149 Pradhan A, Prakash S, Mukhopadhyay A, Pental D (1992) Phylogeny of Brassica and allied genera based on variation in

260

P. Quijada et al.

chloroplast and mitochondrial DNA patterns: molecular and taxonomic classiﬁcation are incongruous. Theor Appl Genet 85:331–340 Prakash O (1961) Food and drinks in ancient India. Munshi Ram Manohar Lal, Delhi, pp 265–266 Prakash S, Hinata K (1980) Taxonomy, cytogenetics and origin of crop brassicas, a review. Opera Bot 55:1–57 Quiros CF (2001) DNA-based marker maps of Brassica. In: Phillips RL, Vasil IK (eds) DNA-Based Markers in Plants, 2nd edn. Kluwer, Dordrecht, pp 201–237 Quiros CF, Ochoa O, Kianian SF, Douches D (1987) Analysis of the Brassica oleracea genome by the generation of B. campestris-oleracea chromosome addition lines, characterization by isozymes and rDNA genes. Theor Appl Genet 74:758–766 Quiros CF, Hu J, Truco MJ (1994) DNA-based markers Brassica maps. In: Phillips RL, Vasil IK (eds) Advances in Cellular and Molecular Biology of Plants Vol. I: DNA Based Markers in Plants. Kluwer, Dordrecht, pp 199–222 Rafalski JA, Tingey SV (1993) Genetic diagnostics in plant breeding RAPDs, microsatellites and machines. Trends Genet 9:275–280 Ragot M (1995) Techniques et Utilsation des Marqueurs moleculaires. Les Colloque INRA 72:45–56 Raman H, Read BJ, Brown AHD, Abbott DC (1999) Molecular markers and pyramiding of multiple genes for resistance to scald in barley. Proc 9th Australian Barley Technical Symposium, Melbourne, Australia, 12–16 Sept 1999, pp 2.30.1–14 Ranade SA, Farooqui N, Bhattacharya E, Verma A (2001) Gene tagging with random ampliﬁed polymorphic DNA (RAPD) markers for molecular breeding in plants. Crit Rev Plant Sci 20:251–275 Reddy MP, Sarla N, Siddiq EA (2002) Inter simple sequence repeat (ISSR) polymorphism and its application in plant breeding. Euphytica 128:9–17 Rimmer SR, van den Berg CGJ (1992) Resistance of oilseed Brassica spp. to blackleg caused by Leptosphaeria maculans. Can J Plant Pathol 14:56–66 Rimmer SR, Mathur S, Wu CR (2000) Virulence of isolates of Albugo candida from western Canada to Brassica species. Can J Plant Pathol 22:229–235 Röbbelen G (1960) Beiträge zur Analysis des Brassica-Genomes. Chromosoma 11:205–228 Rubin LJ, Disosady LL, Tzeng YM (1990) Ultraﬁltration in rapeseed processing. In: Shahidi F (ed) Canola and Rapeseed: Production, Chemistry, Nutrition and Processing Technology. Van Nostrand Reinhold, New York, pp 307–330 Sadowski J, Gaubier P, Delseny M, Quiros CF (1996) Genetic and physical mapping in Brassica diploid species of a gene cluster deﬁned in Arabidopsis thaliana. Mol Gen Genet 251:298–306 Samis K, Bowley S, McKersie B (2002) Pyramiding Mnsuperoxide dismutase transgenes to improve persistence and biomass production in alfalfa. J Exp Bot 53:1343–1350

Sandhu SS, Colombo C, Bastos CR, Siqueira WJ (2003) DNA tagging of blast resistance gene (s) in three Brazilian rice cultivars. Genet Mol Biol 26:473–476 Sauer FD, Kramer JKG (1983) The problem associated with the feeding of high erucic acid rapeseed oils and some ﬁsh oils to experimental animals In: Kramer JKG, Sauer FD, Pigden WJ (eds) High and Low Erucic Acid Rapeseed Oils: Production, Usage, Chemistry and Toxicological Evaluation. Academic, New York, pp 254–292 Schilling A (1991) Development of a molecular genetic linkage map in Brassica rapa (syn. campestris) L. M.Sc. Thesis, University of Massachusetts, USA Schmidt R (2002) Plant genome evolution: lessons from comparative genomics at the DNA level. Plant Mol Biol 48:21–37 Schranz ME, Quijada P, Sung S, Lukens L, Amasino R, Osborn T (2002) Characterization and effects of the replicated ﬂowering time gene FLC in Brassica rapa. Genetics 162:1457–1468 Schuler TJ, Hutcheson DS, Downey RK (1992) Heterosis in intervarietal hybrids of summer turnip rape in western Canada. Can J Plant Sci 72:127–136 Schwetka A (1982) Inheritance of seed color in turnip rape (Brassica campestris L.). Theor Appl Genet 62:161–169 Sedun FS, Seguin-Swartz G, Rakow GFW (1989) Genetic variation in reaction to sclerotinia stem rot in Brassica species. Can J Plant Sci 69:229–232 Servin B, Martin OC, Mezard M, Hospital F (2004) Toward a theory of marker-assisted gene pyramiding. Nature 168:513– 523 Sharpe AG, Parkin IA, Keith DJ, Lydiate DJ (1995) Frequent nonreciprocal translocation in the amphidiploid genome of oilseed rape (Brassica napus). Genome 38:1112–1121 Shen SX, Chen S, Li ZQ, Zhang CH, Liang HF (2000) Cytological basis of obtaining primary trisomics from isolated microspore culture of autotetraploid Chinese cabbage (Brassica campestris L. ssp pekinensis (Lour.) Olsson). Acta Hort Sin 27:145–147 Singh D (1958) Rape and Mustard. Indian Central Oilseeds Committee. Examiner, Bombay, India, p 105 Sinskaia EN (1928) The oleiferous plants and root crops of the family cruciferae. Bull Appl Bot Genet Plant Breed 9:1–648 Somers DJ, Friesen KRD, Rakow G (1998) Identiﬁcation of molecular markers associated with linoleic acid desaturation in Brassica napus. Theor Appl Genet 96:897–903 Song K, Osborn TC (1992) Polyphyletic origins of Brassica napus: new evidence based on organelle and nuclear RFLP analyses. Genome 35:992–1001 Song KM, Osborn TC, Williams PH (1988a) Brassica taxonomy based on nuclear restriction fragment length polymorphism (RFLPs). 1. Genome evolution of diploid and amphidiploid species. Theor Appl Genet 75:784–794 Song KM, Osborn TC, Williams PH (1988b) Brassica taxonomy based on nuclear restriction fragment length polymorphism (RFLPs). 2. Preliminary analysis of sub-species within B. rapa (syn. campestris) and B. oleracea. Theor Appl Genet 76:593–600

Chapter 6 Brassica Rapa Song KM, Osborn TC, Williams PH (1990) Brassica taxonomy based on nuclear restriction fragment length polymorphisms (RFLPs) 3. Genome relationships in Brassica and related genera and the origin of B. oleracea and B. rapa (syn. campestris). Theor Appl Genet 76:497–506 Song KM, Suzuki JY, Slocum MK, Williams PH, Osborn TC (1991) A linkage map of Brassica rapa (syn. campestris) based on restriction fragment length polymorphism loci. Theor Appl Genet 82:296–304 Song K, Slocum MK, Osborn TC (1995) Molecular marker analysis of genes encoding morphological variation in Brassica rapa (syn. campestris). Theor Appl Genet 90:1–10 Sparvoli F, Martin C, Scienza A, Gavazzi G, Tonelli C (1994) Cloning and molecular analysis of structural genes involved and stibene biosynthesis in grape (Vitis vinifera L.). Plant Mol Biol 24:743–755 Springer PS (2000) Gene traps: tools for plant development and genomics. Plant Cell 12: 1007–1020 Stein JC, Howlett B, Boyes DC, Nasrallah ME, Nasarallah JB (1991) Molecular cloing of a putative receptor protein kinase gene encoded at the self-incompatibility locus of Brassica oleracea. Proc Natl Acad Sci USA 88:8816–8820 Steven DT, Martin WG, Gergory BM (1995) Chromosome landing: a paradigm for map-based gene cloning in plants with large genomes. Trends Genet 11:63–68 Stringam GR (1980) Inheritance of seed colors in turnip rape. Can J Plant Sci 60:331–335 Stringam GR, McGregor DI, Pawlowski SH (1974) Chemical and morphological characteristics associated with seed coat color in rapeseed. In: Proc 4th Int Rapeseed Conf, Giessen, Germany, pp 99–108 Suwabe K, Tsukazaki, Hiketani H, Hatakeyama K, Fujimura M, Nunome T, Fukuoka H, Matsumoto S, Hirai M (2003) Identiﬁcation of two loci for resistance to clubroot (Plasmodiophora brassicae Woronin) in Brassica rapa L. Theor Appl Genet 107:997–1002 Suwabe K, Tsukazaki H, Iketani H, Hatakeyama K, Fujimura M, Konodo M, Nunome T, Fukuoka H, Hirai M, Matsumoto S (2004) Joint Meeting of the 14th Crucifer Genetics Workshop and the 4th ISHS Symposium on Brassicas 2004, Chungnam National University Daejeon, Korea, 24–28 Oct 2004, p 143 Suzuki G, Kai N, Hirose T, Fukui K, Nishio T, Takayama S, Isogai A, Watanabe M, Hinata K (1999) Genomic organization of the S locus: identiﬁcation and characterization of genes in SLG/SRK region of S9 haplotype of Brassica campestris (syn. rapa). Genetics 153:391–400 Swarup K, Alonso-Blanco C, Lynn JR, Michaels SD, Amasino RM, Koornneef M, Millar AJ (1999) Natural allelic variation identiﬁes new genes in the Arabidopsis circadian system. J Plant 20:67–77 Szewe-McFadden AK, Kresovich S, Bliek SM, Mitchell SE, McFerson JR (1996) Identiﬁcation of polymorphic, conserved simple sequence repeats SSRS in cultivated Brassica species. Theor Appl Genet 93:534–538

261

Takahata Y, Hinata K (1986) Application of a pattern analysis (quantiﬁcation method ?) for the isozyme relations in crop Brassicas. Cruciferae Newslett 11:12–13 Takayama S, Shiba H, Iwano M, Asano K, Hara M, Che FS, Watanabe M, Hinata K, Isogai A (2000) Isolation and characterization of pollen coat proteins of Brassica campestris that interact with S locus-related glycoprotein 1 involved in pollen-stigma adhesion. Proc Natl Acad Sci USA 28:3765–3770 Tanhuanpää PK, Vilkki JP, Vilkki HJ (1995) Association of a RAPD marker with inolenic acid concentration in the seed oil of rapeseed (Brassica napus L.). Genome 38:414–416 Tanhuanpää P (2004) Identiﬁcation and mapping of resistance gene analogs and a white rust resistance locus in Brassica rapa ssp. oleifera. Theor Appl Genet 108:1039–1046 Tanhuanpää P, Schulman A (2002) Mapping of genes affecting linolenic acid content in Brassica rapa ssp. oleifera Mol Breed 10:51–62 Tanhuanpää PK, Vikki HJ, Vihinen M (1998) Mapping and cloning of FAD2 gene to develop allele-speciﬁc PCR for oleic acid in spring turnip rape (Brassica rapa ssp. oleifera). Mol Breed 4:543–550 Tanhuanpää PK, Vilkki JP, Vilkki HJ (1996a) A linkage map of spring turnip rape based on RFLP and RAPD markers. Agri Food Sci Finland 5:209–217 Tanhuanpää PK, Vilkki JP, Vikki HJ (1996b) Mapping a QTL for oleic acid concentration in spring turnip rape (Brassica rapa ssp. oleifera). Theor Appl Genet 92:952–956 Tanksley SD, Young ND, Paterson AH, Bonierbale MW (1989) RFLP mapping in plant breeding: new tools for an old science. Bio/Technology 7:257–264 Teutonico RA, Osborn TC (1994) Mapping of RFLP and quantitative trait in Brassica rapa and comparison to the linkage maps of B. napus, B. oleracea and Arabidopsis thaliana. Theor Appl Genet 89:885–894 Teutonico RA, Osborn TC (1995) Mapping loci controlling vernalization requirement in Brassica rapa. Theor Appl Genet 91:1279–1283 Teutonico RA, Palta JP, Osborn TC (1993) In vitro freezing tolerance in relation to winter survival of rapeseed cultivars. Crop Sci 33:103–107 Teutonico RA, Yandell B, Satagopan JM, Ferreira ME, Palta JP and Osborn TC (1995) Genetic analysis and mapping of genes controlling freezing tolerance in oilseed Brassica. Mol Breed 1:329–339 The Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the ﬂowering plant Arabidopsis thaliana. Nature 408:796–815 Theander O, Aman P, Mikscha GE, Yasuda S (1977) Carbohydrates, polyphenols and lignins in seed hulls of different colors from turnip rape. J Agric Food Chem 25:270–273 Thomas MR, Scott NS (1994) Sequence-tagged site markers for microsatellites: simpliﬁed technique for rapidly obtaining ﬂanking sequences. Plant Mol Biol Rep 12:58–64

262

P. Quijada et al.

Thompson C, Dunwell JM, Johnstone CE, Lay V, Ray J, Schmitt M, Watson H, Nisbet G (1995) Degradation of oxalic acid by transgenic oilseed rape plants expressing oxalate oxidase. Euhytica 85:169–172 Thormann CE, Ferreira ME, Camargo LEA, Tivang JG, Osborn TC (1994) Comparison of RFLP and RAPD markers to estimating genetic relationships within and among cruciferous species. Theor Appl Genet 88:973–980 Thormann CE, Romero J, Mantet J, Osborn TC (1996) Mapping loci controlling the concentrations of erucic and linolenic acids in seed oil of Brassica napus L. Theor Appl Genet 93:282–286 Tien-Hung L, Terrye AD, Kim PR, Joel H, Stanley PK, Jim M, Stephen K, Andrew HP (2000) An EST-enriched Comparative Map of Brassica oleracea and Arabidopsis thaliana. Genome Res 10:776–788 Tiwari AS, Petrie GA, Downey RK (1988) Inheritance of resistance to Albugo candida Race 2 in mustard (Brassica juncea (L.) Czern.). Can J Plant Sci 68:297–300 Torssel B (1959) Hardiness and survival of winter rape and winter turnip rape. Vaxtodling Plant Husbandry, Dept Plant Husbandry, Royal School of Agriculture, Uppsala, 15:1–168 Toxopeus H, Janssen AMP (1975) Clubroot resistance in turnip II. The slurry screening method and clubroot races in the Netherlands. Euphytica 24:751–755 Toxopeus H, Oost E, Reuling R (1984) Current aspects of the taxonomy of morphological characters. Pl Sp Biol 1:79–88 Trehane P, Brickell CD, Baum BR, Hetterscheid WLA, Leslie AC, McNeil JSA, Vrugtuman F, Spongberg F (1995) International code of nomenclature for cultivated plants. Quarterjack, Wimborne, UK, pp 1–192 U N (1935) Genome analysis in Brassica with special reference to the experimental formation of B. napus and its peculiar mode of fertilization. Jpn J Bot 7:389–452 Udall JA, Quijada PA, Osborn TC (2005) Detection of chromosomal rearrangements derived from homeologous recombination in four mapping populations of Brassica napus L. Genetics 169:967–979 UPOV (Union for the Protection of new Varieties of Plants) (1978) International convention for the protection of new varieties of plants test guidelines. UPOV, Geneva UPOV (1994) International convention for the protection of new varieties of plants. UPOV, Geneva, C/28/10 UPOV (1996) Guidelines for the conduct of tests for distinctness, uniformity and stability. Rape seed (Brassica napus L. oleifera), UPOV, Geneva, TG/36/6 Uzunova M, Ecke W, Weissleder K, Röbbelen G (1995) Mapping the genome of rapeseed (Brassica napus L.). I. Construction of an RFLP linkage map and localization of QTLs for seed glucosinolate content. Theor Appl Genet 90:194–204 Van Caseele L, Mills JT, Summer M, Gillespie R (1982) Cytological study of the palisade development in the seed coat of Candle canola. Can J Bot 60:2469–2475 Vavilov NI (1949) The origin, variation, immunity and breeding of cultivated plants. Chron Bot 13:1–364

Venkateswarlu J, Kamala T (1973) Meiosis in double trisomic Brassica campestris. Genetica 44:283–287 Verma PR, Harding H, Petrie GA, Williams PH (1975) Infection and temporal development of mycelium of Albugo candida in cotyledons of four Brassica species. Can J Bot 53:1016–1020 Visscher PM, Haley CS, Thompson R (1996) Marker-assisted introgression in backcross breeding programs. Genetics 144:1923–1932 Wang XY, Chen PD, Zhang SZ (2001) Pyramiding and markerassisted selection for powdery mildew resistance genes in common wheat. Acta Genet Sin 28 (7):640–646 Wang M, Zhang FL, Meng XD, Liu XC, Zhao XY, Fan ZC (2004) A linkage map construction for Chinese cabbage based on AFLP markers using DH population. Acta Agric Boreali-Sin 19:28–33 Warwick SI, Black LD (1991) Molecular systematics of Brassica and allied genera (Subtribe Brassicinae Brassicae) – chloroplast genome and cytodeme congruence. Theor Appl Genet 82:81–92 Watt G (1989) Brassica. In: Dictionary of Economic Products of India. I. Calcutta, pp 520–534 Williams PH (1966) A system for the determination of races of Plasmodiophora brassicae that infect cabbage and rutabaga. Phytopathology 56:624–626 Williams PH (1989) Wisconsin fast plants manual. Carolina Biological Supply Company, Burlington, NC Williams PH, Hill CB (1985) Crucifer genetics cooperative: resource book. Crucifer Genetics Cooperative, Madison, WI Williams GK, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV (1990) DNA polymorphisms ampliﬁed by arbitrary primers are useful as genetic markers. Nucleic Acids Res 18:6531–6535 Winzeler EA, Richards DR, Conway AR, Goldstein AL, Kalman S, McCullough MJ, McCusker JH, Stevens DA, Wodicka L, Lockkhart DJ, Davis RW (1998) Direct allelic variation scanning of the yeast genome. Science 281:1194–1197 Wit F (1964) Inheritance of reaction to clubroot in turnips. Hort Res 5:47–49 Wu TL, Tang N, Wu ZP, Gao FL, Yang QK, Cai XZ (2001) A discussion on program for selection methods of multiple resistance of scn3 and smv1 of soybean. Soybean Sci 20(2):120–124 Xu JL, Lin YZ, Weng JP, Zhao XL (1996) Convergence of resistance genes to bacterial blight in rice and its genetic effect. Acta Agron Sin 22:129–134 Yarnell SH (1956) Cytogenetics of the vegetables crops. II. Crucifers. Bot Rev 22:81–166 Yoshikawa H (1981) Breeding for clubroot resistance in Chinese cabbage. In: Talekar NS, Griggs TD (eds) Chinese Cabbage. Proc 1st Int Symp, Tsukuba, Japan, pp 405–413 Young ND (1999) A cautiously optimistic vision for marker assisted breeding. Mol Breed 5: 505–510 Yu J, Hu S, Wang J, Wong GK-S, Li S, Liu B, Deng Y, Dai L, Zhou Y, Zhang X, Cao M, Liu J, Sun J, Tang J, Chen Y, Huang X, Lin

Chapter 6 Brassica Rapa W, Ye C, Tong W, Cong L, Geng J, Han Y, Li L, Li W, Hu G, Huang X, Li W, Li J, Liu Z, Li L, Liu J, Qi Q, Liu J, Li L, Li T, Wang X, Lu H, Wu T, Zhu M, Ni P, Han H, Dong W, Ren X, Feng X, Cui P, Li X, Wang H, Xu X, Zhai W, Xu Z, Zhang J, He S, Zhang J, Xu J, Zhang K, Zheng X, Dong J, Zeng W, Tao L, Ye J, Tan J, Ren X, Chen X, He J, Liu D, Tian W, Tian C, Xia H, Bao Q, Li G, Gao H, Cao T, Wang J, Zhao W, Li P, Chen W, Wang X, Zhang Y, Hu J, Wang J, Liu S, Yang J, Zhang G, Xiong Y, Li Z, Mao L, Zhou C, Zhu Z, Chen R, Hao B, Zheng W, Chen S, Guo W, Li G, Liu S, Tao M, Wang J, Zhu L, Yuan L, Yang H (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296:79–92 Yu SC, Wang YJ, Zheng XY (2003a) A genetic linkage map of Brassica campestris L. ssp. pekinensis (syn. B.rapa L. ssp. pekinensis). Agri Sci in China 2:49–55 Yu SC, Wang YJ, Zheng XY (2003b) Mapping and analysis of QTL controlling some morphological traits in Chinese Cabbage (Brassica campestris L. ssp. pekinensis). Acta Genet Sin 30:1153–1160 Yu SC, Wang YJ, Zheng XY (2003c) Mapping and Analysis QTL Controlling Heat Tolerance in Brassica campestris L. ssp. pekinensis. Acta Hort Sin 30:417–420 Yui S, Yoshikawa H (1991) Bolting resistant breeding of Chinese cabbage. 1. Flower induction of late bolting variety without chilling treatment. Euphytica 52:171–176 Zaman MW (1989) Inheritance of seed color in Brassica campestris. Sver Utsadesforen Tidskr 99:205–207 Zeven AC, Zhukovsky PM (1975) Dictionary of Cultivated Plants and their Centres of Diversity. Centre for Agricultural Publishing & Documentation, Wageningen, The Netherlands

263

Zhang LG (1999) Construction of molecular genetic map and location of QTLs in Brassica campestris by F2 population of turnip × Chinese cabbage. Ph.D. Thesis. Northwest Agricultural University, Yangling, Shanxi Province, China Zhang LG, Wang M, Chen H (2000) Construction of RAPDs molecular genetic map of Chinese cabbage. Acta Bot Sin 42:485–489 Zhang FL, Aoki S, Takahata Y (2003) RAPD markers linked to microspore embryogenic ability in Brassica crops. Euphytica 131:207–213 Zhang XK, Chen L, Yin JM, Tang ZL, Li JN (1993) Genetic diversity of RAPD marker and testa pigments of yellow-seeded rapeseed (Brassica napus L.). Sci Agric Sin 26(7):752–756 Zhao JY, Becker HC (1998) Genetic variation in Chinese and European oilseed rape (B. napus) and Turnip rape (B. compestris) analysed with isozymes on. Acta Agron Sin 24(2):213–220 Zhao JJ, Bonnema G, Wang XW, Xu ZY, Sun RF, Vreugdenhil D, Koornneef M (2004a) Genetic dissection of quality traits in Brassica rapa species for improved human health. In: Joint meeting of the 14th Crucifer Genetics Workshop and the 4th ISHS Symposium on Brassicas 2004, Chungnam National University Daejeon, Korea 24–28 Oct 2004, p112 Zhao JJ, Wang XW, Deng B, Lou P, Wu J, Sun RF, Xu ZY, Vroomans J, Koornneef M, Bonnema G (2004b) Phylogenetic relationships within Brassica rapa inferred from AFLP ﬁngerprints. In: Joint meeting of the 14th Crucifer Genetics Workshop and the 4th ISHS Symposium on Brassicas 2004, Chungnam National University Daejeon, Korea, 24–28 Oct 2004, p 128

CHAPTER 7

7 Black Mustard Sandip Das1,2 , Ulf Lagercrantz3 , and Martin Lascoux3 1

2 3

Max Planck Institute for Developmental Biology, Department of Molecular Biology, Spemannstrasse 37–39, 72076 Tuebingen, Germany e-mail: [email protected] Present Address: Center for Biotechnology, Hamdard University, 110 062 Delhi, India Department of Evolutionary Functional Genomics, Evolutionary Biology Centre, Uppsala University, Norbyv. 18D, SE-752 36, Uppsala, Sweden

7.1 Introduction

Ethiopia. Among these six species, B. rapa, B. nigra, and their amphidiploid B. juncea were the ﬁrst to be domesticated (Gomez-Campo and Prakash 1999). The taxonomic classiﬁcation of B. nigra is as follows: Taxonomic classiﬁcation

7.1.1 Taxonomy The genus Brassica comprises 38 species, of which six are economically important (Warwick 1993; Warwick et al. 2000). These include three diploid species, B. rapa syn. campestris (A genome, n = 10), B. nigra (B genome, n = 8), and B. oleracea (C genome, n = 9). The other three are amphidiploids and are natural hybrids of these diploid species; these are B. carinata (BC genome, n = 17), B. juncea (AB genome, n = 18), and B. napus (AC, n = 19). The genome size of B. nigra has been estimated to be ca. 478 Mbp and the DNA content 0.97 pg/2C (Arumuganathan and Earle 1991). The cytogenetic relationships between the cultivated species were revealed by Morinaga (1928) based on interspeciﬁc hybridization and chromosome pairing and is classically represented by the U-triangle (U 1935). Later cpDNA and mtDNA analyses (Erickson et al. 1983; Palmer et al. 1983; Warwick and Black 1991; Pradhan et al. 1992) established that B. nigra and B. rapa are the cytoplasmic donors for B. carinata and B. juncea, respectively. The six species of the U-triangle are usually referred to as cultivated Brassicas. These can further be divided into vegetable crops, oilseed-yielding, and condiment-yielding Brassica. B. oleracea is important as a vegetable crop, whereas B. juncea, B. napus, and B. rapa are major oilseed-yielding species. B. nigra and B. carinata are, on the other hand, useful as condiments (Table 1). The agricultural utility of B. nigra has been on the decline over the last 50 years and has successively been replaced by B. juncea. Presently, B. nigra is mainly used as a source of condiment in India and

– – – – – – – – – – – – –

Viridiplantae Streptophyta Embryophyta Tracheophyta Euphyllophyta Spermatophyta Magnoliophyta Eudicotyledons core eudicots; eurosids II Brassicales Brassicaceae Brassica nigra

7.1.2 Genetic Relationship and Evolution of Brassica species The phylogeny of Brassica species and the origin of amphidiploids were initially investigated using restriction fragment length polymorphism (RFLP) in chloroplast DNA in a few species (Erickson et al. 1983; Ichikawa and Hirai 1983; Palmer et al. 1983), and the analysis was later extended to more Brassica species and the use of nuclear DNA (Yanagino et al. 1987; Song et al. 1988a,b, 1990; Warwick and Black 1991, 1994; Demeke et al. 1992; Pradhan et al. 1992). These studies uncovered major incongruities with the classiﬁcation of Brassica and allied genera based on mor-

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

266

S. Das, U. Lagercrantz, M. Lascoux

Table 1. Brassica species and their economic importance Species and variety

Common name

Uses

B. B. B. B. B. B. B. B. B. B. B. B. B. B. B. B. B.

Black mustard Kale Chinese kale, kailan Cauliﬂower Cabbage Broccoli Pak choi Turnip rape Turnip Yellow sarson Toria Brown sarson Mizunami Ethiopian mustard Indian mustard Rapeseed Swede, Rutabaga

Condiment Vegetable Vegetable Vegetable Vegetable Vegetable Vegetable Oilseed Vegetable Oilseed Oilseed Oilseed Salad Condiment Oilseed Oilseed Fodder

nigra oleracea var. acephala oleracea var. alboglabra oleracea var. botrytis oleracea var. capitata oleracea var. italica rapa var. chinensis rapa var. oleifera rapa var. rapifera rapa var. yellow sarson rapa var. toria rapa var. brown sarson rapa var. nipposinica carinata juncea var. oleifera napus var. oleifera napus var. rapifera

phological characters. Within the Brassica species one of the major ﬁndings of the initial analysis was that B. rapa and B. oleracea belong to a lineage of cruciferous species distinct from that containing B. nigra. Furthermore, a wide range of species belonging to genera including Moricandia, Eruca, Diplotaxis, Raphanus, and Sinapis are more closely related to either B. nigra or B. oleracea than these species are to each other. Contrary to this, earlier studies had presumed a monophyletic origin of all three basic diploid Brassica species from an ancestral species with n = 6 (Prakash and Hinata 1980). Furthermore, the natural amphidiploids (B. napus and B. juncea) may have had a polyphyletic origin with the involvement of and recombination between different diploids (Song et al. 1988a,b). 7.1.3 Breeding A prerequisite for any successful crop-improvement program is the availability of germplasm with a wide genetic base, and the genetic variation within Brassica species has been estimated to be broad (Demeke et al. 1992; Jain et al. 1994; Bhatia et al. 1995; Das et al. 1999; Divaret et al. 1999). Genetic variation existing at both intra- and interspeciﬁc levels and in species belonging to several other genera

besides Brassica are used in Brassica improvement programs. The genetic variability within the B. nigra accessions is fairly large, at least in some parts of the natural range (e.g., Westman and Kresovich 1999; Negi et al. 2004). Based on variation at simple sequence repeat (SSR) loci (Westman and Kresovich 1999; Alström-Rapaport and Lascoux, unpubl. data), the natural range of B. nigra can be divided into three main regions, Europe, India, and Ethiopia, the latter having diverged more strongly from the other two and showing limited genetic variation. Interestingly, a similar pattern was observed for the BnCOL1 ﬂowering-time gene (Kruskopf-Österberg et al. 2002; Lagercrantz et al. 2002). The importance of B. nigra in crop improvement has been as donor of the B genome for improvement of B. juncea and as donor of desirable agronomic traits like disease resistance for B. napus and B. juncea. For example, the genetic base of B. juncea was broadened by artiﬁcial resynthesis of B. juncea from diverse lines of B. nigra and B. rapa (Srivastava et al. 2004). The B genome of B. nigra is a major donor as a source of resistance to Leptosphaeria maculans (Phoma lingam, causal organism of blackleg). This trait has been transferred to B. napus, a major oilseed crop in the Europe and Americas (Chevre et al. 1996; Struss et al. 1996; Dixelius 1999; Brun et al. 2001). Species belonging to the primary gene pool outside of Brassica genus, collectively referred to

Chapter 7 Black Mustard

as Brassica-coenospecies, have also been used in Brassica improvement programs. These species include B. adpressa, B. fruticulosa, B. oxyrrhina, B. tournefortii, Sinapis spp., Diplotaxis erucoides, Eruca sativa, Erucastrum abyssinicum, and Raphanus sativus, among several others. Traits that are targeted or have been transferred from these allied genera are C3-C4 photosynthesis system (Moricandia arvensis), cytoplasmic male sterility (B. tournefortii), high nervonic acid (Thlapsi arvense), clubroot resistance (Raphanus sativus), Alternaria leaf spot resistance (Sinapis alba and Camellina sativa), and nematode resistance (S. alba). An exhaustive list of wild relatives, the intergeneric crosses obtained, and the targeted desirable traits has been compiled by Warwick (1993) and Warwick et al. (2000). In cases where viable interspeciﬁc or intergeneric hybrids could not be obtained through traditional crossing and plant breeding, somatic fusions have been carried out. For instance, somatic fusion was used between R. sativus, B. oleracea (C genome), and B. nigra (B genome) in order to transfer the heat tolerance from R. sativus into artiﬁcially synthesized B. carinata (BC genome; Arumugam et al. 2002).

7.2 DNA Marker Technology An important step toward the analysis of genomes and linkage map construction has been the development of various molecular markers. Modern DNA markers principally assay the polymorphism existing at the genomic level through hybridization, polymerase chain reaction (PCR) ampliﬁcation, or direct sequencing. Prominent among the hybridization-based tools are RFLP where genomic DNA or cDNA clones are used as probes for hybridization to restricted DNA. An advantage of the RFLP technique is that it can be used to detect orthologous loci between species and duplicated loci within species, which is useful when analyzing the genome evolution of the highly replicated Brassica genomes (Lagercrantz and Lydiate 1996). For other applications such as quantitative trait locus (QTL) mapping and gene diversity studies, more efﬁcient methods such as analysis of single-nucleotide polymorphism (SNP) and SSRs are preferable. SSRs have been developed for several Brassica species including B. nigra (Szewc-McFadden et al. 1996; Lowe et al. 2002).

267

7.3 Genetic Linkage Mapping in Brassica nigra Genetic mapping in Brassica species has served two purposes, namely, to understand genome relationships and evolution and to detect qualitative and QTLs of important agronomical traits. We discuss here the progress that has been made toward map construction in B. nigra and its outcome, in particular its use in comparative genomics.

7.3.1 Genetic Maps Relatively few attempts have been made to map the B. nigra genome. These involved both classical recombinational mapping strategies or the use of addition lines. The ﬁrst map was based on an F2 population consisting of 83 individuals and the segregation of 67 RFLP, random ampliﬁed polymorphic DNA (RAPD), and isozyme markers. It consisted of eight linkage groups (LGs) corresponding to the basic chromosome number of B. nigra. This map covers a recombination distance of 561 cM with an average of 8.4 cM between markers (Truco and Quiros 1994). A second genetic map was obtained from a backcross population and 158 Brassica RFLP probes. About 60% of these probes detected more than one locus (mostly between 2 and 3) deﬁning 288 loci spanning a total of length 778 cM (Lagercrantz and Lydiate 1995, 1996). This was a clear indication of the highly replicated genome structure of B. nigra. Additionally, there were often conserved groups of loci present on different LGs, a pointer toward large-scale polyploidy in the genome. A third map, described by Lagercrantz (1998), was an expansion of the map described earlier (Lagercrantz and Lydiate 1995, 1996; Fig. 1). An additional 160 RFLP markers previously located on the A. thaliana map were included. These markers detected a total of ca. 250 loci. A combination of heterologous probes (derived from A. thaliana) and the large number of markers allowed a detailed comparison of B. nigra and A. thaliana genetic maps and genomes. This map provides further insight into the polyploid nature of the Brassica genomes. Each of the B. nigra linkage groups turned out to be a mosaic of all ﬁve A. thaliana chromosomes, suggesting that the Brassica genome underwent extensive rearrangements during

268

S. Das, U. Lagercrantz, M. Lascoux

Fig. 1. Graphical representation of Brassica nigra linkage map. The eight linkage groups, corresponding to the eight chromosomes, are designated as G1 to G8. The distribution of the RFLP markers on the eight linkage groups reﬂects the extensive triplication that the Brassica genome has undergone during evolution (after Lagercrantz and Lydiate 1996)

the course of evolution. An estimate of nearly 90 re- by Lagercrantz (1998). However, it may be pointed arrangements since the divergence of B. nigra and A. out that even this map with an average 3-cM marker thaliana about 10 to 20 million years ago was reported spacing still had large areas with unevenly distributed

Chapter 7 Black Mustard

markers. Large tracts of repetitive DNA that are difﬁcult to map using recombination mapping strategies may account for some of these regions. As a complement to recombination mapping, alien addition lines of B. nigra generated in various genetic backgrounds have been used to integrate the genetic LGs into the respective chromosomes. Alien-addition lines representing different chromosomes of the B. nigra genome have been obtained in B. oleracea, B. napus, and Diplotaxis erucoides backgrounds (Quiros et al. 1986, 1987; Jahier et al. 1989; This et al. 1990; Chevre et al. 1991; Struss et al. 1991, 1996).

7.3.2 Comparative Mapping The availability of genetic maps of the three diploid genomes of cultivated Brassicas based on a common set of loci allowed for their comparative analysis (Lagercrantz and Lydiate 1996, Fig. 2). This analysis has thrown light on the evolution of their genomes (the A, B, and C genomes). The high degree of duplicated loci and the spatial pattern of duplicated chromosomal segments in the B. nigra genome indicated that B. nigra was derived from an ancestor with n = 6 chromosomes. Based on comparative mapping of the A, B, and C genomes of B. rapa, B. nigra, and B. oleracea it was suggested that all three diploid crop Brassica genomes as well as a number of related species in other genera might all share the same ancestor with basic chromosome number n = 6 (Lagercrantz and Lydiate 1996). The triplication hypothesis is supported by comparisons of speciﬁc chromosomal segments between A. thaliana and Brassica species (Cavell et al. 1998; O’Neill and Bancroft 2000; Parkin et al. 2002). Other whole-genome studies suggest that several regions deviate from the expectation based on the triplication hypothesis. Although triplication is evident for parts of the genome, other parts of the A. thaliana genome were similar to between zero and seven regions within B. oleracea (Lukens et al. 2003). Furthermore, Lan et al. (2000) reported strong evidence for a whole genome duplication but poor evidence for a triplication. There are several difﬁculties when trying to assess the true level of genome replication. One problem is that the level of replication may be underestimated due to lack of polymorphism in mapping experiments. If one out of three replicated loci is monomorphic in the speciﬁc mapping population, the interpretation is often that the locus is duplicated

269

(Lagercrantz and Lydiate 1996). Furthermore, recent studies suggest that the genomes of Brassica species are characterized by an interspersed pattern of gene loss, possibly as a result of small deletions (O’Neill and Bancroft 2000; Rana et al. 2004). Such a process would also obscure the true level of replication. At any rate, more data are needed to elucidate the evolutionary history of diploid Brassica genomes. A comparison of maps of the A, B, and C genomes also revealed that the structure of the Brassica genome is relatively dynamic, with many rearrangements differentiating the three genomes (Lagercrantz and Lydiate 1996). The relative structures of the chromosomes in the different species suggest that chromosome fusions and/or ﬁssions have occurred, explaining the differences in chromosome numbers between the A, B, and C genomes. New genome structures are also likely the result of translocations and inversions of large chromosomal segments. The A, B, and C genomes have also been mapped in the recently formed amphidiploids B. juncea (AB) and B. napus (AC) (Parkin et al. 1995: Bohuon et al. 1996; Cheung et al. 1997; Axelsson et al. 2000). One interesting question is to what extent the genomes of the recently formed amphidiploids have changed since their formation a few thousand years ago. Are rearrangements like those seen between the A, B, and C genomes taking place as an immediate effect of polyploidization, or is it a slow cumulative process? Song et al. (1995) studied selfed progeny of resynthezised B. napus and B. juncea plants and found a high frequency of altered RFLP patterns derived from nuclear DNA. These changes could be partially the result of homoeologous recombination and tetrasomic inheritance in newly formed amphidiploids (Parkin et al. 1995; Song et al. 1995). However, additional mechanisms are needed to explain all the observed RFLP pattern alterations. Surprisingly, mapping of natural B. napus (AC) and B. juncea (AB), conducted so that the individual diploid genome components could be identiﬁed (Parkin et al. 1995; Bohuon et al. 1996; Axelsson et al. 2000), demonstrated that the nuclear genomes had remained essentially unchanged since polyploid formation. It is possible that there are loci, such as the Ph1 locus in wheat (Sears 1977), that affect the level of bivalent pairing in the polyploids. In conclusion, rapid genome changes can occur after polyploidization, and these can also be associated with morphological changes (Osborn et al. 2003; Schranz and Osborn 2004). Still, we need more data to estimate the type

270

S. Das, U. Lagercrantz, M. Lascoux

Fig. 2. Comparative mapping of A. thaliana and B. nigra: conserved synteny between A. thaliana and B. nigra as detected through genetic mapping using heterologous probes. B. nigra linkage groups are denoted by G. The ﬁgure shows the conserved marker order and orientation between A. thaliana chromosome 5 (LG 5) and G2, G5, and G8 from B. nigra in the CONSTANS regions (adapted from Lagercrantz 1998 and Lagercrantz et al. 1996)

and frequency of the genomic changes and to understand the importance of these changes in evolution. Although frequent rearrangements differentiate the genomes of A. thaliana and Brassica species, large regions of homology are also observed. Being aware of the limitations due to the many undetected rearrangements, there are good prospects for using comparative mapping to facilitate the utilization of the rich source of information emanating from A. thaliana research. The Arabidopsis genome may act as an anchor genome, and markers positioned on it can be utilized for reciprocal localization of markers in Brassica species (Kowalaski et al. 1994; Lagercrantz et al. 1996; Sadowski et al. 1996; Lagercrantz 1998; Sillito et al. 2000). Knowledge of the position of genes controlling qualitative traits as well as quantitative trait loci (QTLs) in A. thaliana can be used to predict the location of homologous genes in Brassica species. Conversely, candidate genes for Brassica QTLs

can be sought in the sequenced A. thaliana genome (Lagercrantz et al. 1996). Comparative mapping based on high-throughput sequencing or highly saturated maps would allow genomic rearrangements like inversions, duplications that are less than a few centiMorgans (the level of resolution achieved presently with recombinational mapping), to be identiﬁed.

7.4 Mapping of Flowering-Time Trait in Brassica nigra Due to its obvious importance in agriculture and also because of its high adaptive value, ﬂowering time has been investigated extensively. Mapping of this trait was initiated in B. nigra with a priori knowledge of genes controlling ﬂowering time in A. thaliana. One of the main genes affecting ﬂowering time in the A.

Chapter 7 Black Mustard

thaliana photoperiodic pathway is CONSTANS. CONSTANS was isolated by chromosome walking (Putterill et al. 1995) and has been shown to control ﬂowering in response to photoperiod. Mapping of the loci affecting ﬂowering-time variation in B. nigra was carried out with the help of an already existing map coupled with a candidate gene approach. A genetic map covering the B. nigra LGs with 145 heterologous markers was further saturated with another 11 heterologous markers from A. thaliana derived from in and around the CO locus (Lagercrantz et al. 1996; Fig. 2). A comparative mapping of the CO locus between A.thaliana and B. nigra revealed conserved synteny with respect to marker order. The CO locus was also found to be triplicated in the B. nigra genome, in agreement with various other reports of the triplicated nature of the B. nigra genome (Fig. 2). A QTL mapping with the ﬂowering time data obtained from a segregating backcross population (from a cross between an early- and a late-ﬂowering accession) identiﬁed two major loci on LG 2 and LG 8 that explained 35% and 12% of the total variation in ﬂowering time, respectively. The major locus on LG 2 included a ﬁrst homolog of the A. thaliana CO gene (BnCOa), a duplicate copy (BnCOb) being present also near the QTL on LG 8. As the QTL on LG 2 explained the majority of the variation in ﬂowering time, the functionality of BnCOa was checked through complementation test. The A. thaliana CO mutant was transformed with BnCOa, which restored the wildtype phenotype in the CO mutant. However, no signiﬁcant difference in ﬂowering time was detected between BnCOa alleles from late- and early-ﬂowering B. nigra plants. Further analysis of the region containing the BnCOa revealed the presence of the B. nigra homolog of A. thaliana CONSTANS LIKE 1 (COL 1) located ca. 3.5 kb upstream of CO. The BnCOL 1 exhibited nucleotide sequence and length variation between alleles derived from early and late ﬂowering B. nigra genotypes (Kruskopf-Österberg et al. 2002). An association mapping exercise carried out between the allelic variation at BnCOL1 and ﬂowering-time data from several natural populations of B. nigra (mainly collected from Europe and one from Africa) revealed signiﬁcant association between the two (KruskopfÖsterberg et al. 2002), suggesting that BnCOL1 affects the ﬂowering-time trait. However, classical neutrality tests failed to reject the standard neutral model at BnCOL1 (Lagercrantz et al. 2002). Although no correlation was obtained between BnCOa and ﬂowering

271

time, the role of cis-regulatory elements for BnCOa located farther away cannot be ruled out.

7.5 Future Scope of Work Among the Brassica species, DNA-based markers have been used to construct numerous genetic maps. Attempts are now underway to saturate these maps with high-density positioning of markers to achieve ﬁne-scale mapping. A comparison of maps between species provided information about the genomic relationships and genome evolution. These studies indicated that extensive duplications, deletions, inversions, and translocations were responsible for the evolution of the present-day Brassica species, which are secondary polyploids. Still, colinear chromosomal segments can be identiﬁed not only among Brassica species but also between Brassica and Arabidopsis. This conservation suggests that comparative mapping of Brassica and Arabidopsis can be used to identify candidate genes controlling important traits in Brassica. Molecular markers have been linked to a wide range of traits in various Brassica species. These traits include those controlled by single or a few genes, i.e., mono- or digenomic traits following Mendelian segregation (seed coat color), and those that have a complex inheritance pattern and are governed by multiple genes, termed quantitative trait loci or QTLs (fatty acid content, vernalization requirement, and ﬂowering time). Markers tightly linked to a trait have provided a means to devise strategies for map-based cloning in Brassica or search for candidate genes in Arabidopsis. Brassica ESTs and genome sequencing initiatives in combination with high-throughput SNP genotyping technologies will also contribute to the identiﬁcation of genes affecting various important traits.

Acknowledgement. SD acknowledges ﬁnancial grants from the Department of Biotechnology and Department of Science and Technology (Government of India) and the Indo-French Center for the Promotion of Advanced Research (IFCPAR-CEFIPERA) to his lab and the lab of Dr. Malathi Lakshmikumaran, with which he was formerly associated. ML thanks VR, FORMAS, the Nillsson Ehle, and Philip Sörensen Foundations for funding.

272

S. Das, U. Lagercrantz, M. Lascoux

References

Arumugam N, Mukhopadhyay A, Gupta V, Sodhi YS, Verma JK, Pental D, Pradhan AK (2002) Synthesis of somatic hybrids (RCBB) by fusing heat tolerant Raphanus sativus (RR) and Brassica oleracea (CC) with Brassica nigra (BB). Plant Breed 121:168–170 Arumuganathan K, Earle ED (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9:208–219 Axelsson T, Bowman CM, Sharpe AG, Lydiate DJ, Lagercrantz U (2000) Amphidiploid Brassica juncea contains conserved progenitor genomes. Genome 43:679–688 Bhatia S, Das S, Jain A, Lakshmikumaran M (1995) DNA ﬁngerprinting of the B. juncea cultivars using the microsatellite probes. Electrophoresis 16:1750–1754 Bohuon EJR, Keith DJ, Parkin IAP, Sharpe AG, Lydiate DJ (1996) Alignment of the conserved C genomes of Brassica oleracea and Brassica napus. Theor Appl Genet 93:833–839 Brun H, Ruer D, Levivier S, Somda I, Renard M, Chevre AM (2001) Presence in Leptosphaeria maculans populations of isolates virulent on resistance introgressed into Brassica napus form Brassica nigra B genome. Plant Pathol 50:69–74 Cavell AC, Lydiate DJ, Parkin IA, Dean C, Trick M (1998) Colinearity between a 30-centimorgan segment of Arabidopsis thaliana chromosome 4 and duplicated regions within the Brassica napus genome. Genome 41:6269 Cheung WY, Friesen L, Rakow GFW, Seguin-Swartz G, Landry BS (1997) An RFLP based linkage map of mustard [Brassica juncea (L) Czern and Coss]. Theor Appl Genet 94:841–851 Chevre AM, This P, Eber F, Deschamps M, Renard M, Delseny M, Quiros CF (1991) Characterization of disomic addition lines of Brassica napus-Brassica nigra by isozymes, fatty acids and RFLP markers. Theor Appl Genet 81:43–49 Chevre AM, Eber F, This P, Barret P, Tanguy X, Brun H, Delseny M, Renard M (1996) Characterisation of Brassica nigra chromosome and black leg resistance in B. napus-B. nigra addition lines. Plant Breed 115:113–118 Das S, Rajagopal J, Bhatia S, Srivastava PS, Lakshmikumaran M (1999) Assessment of genetic variation within Brassica campestris cultivars using AFLP and RAPD markers. J Biol Sci 24:433–440 Demeke T, Adams RP, Chibbar R (1992) Potential taxonomic use of random ampliﬁed polymorphic DNA (RAPD): a case study in Brassica. Theor Appl Genet 84:990–994 Divaret I, Margale E, Thomas G (1999) RAPD markers on seed bulks efﬁciently assess the genetic diversity of a Brassica oleracea L. collection. Theor Appl Genet 98:1029–1035 Dixelius C (1999) Inheritance of the resistance to Leptosphaeria maculans of Brassica nigra and B. juncea in near-isogeniclines of B. napus. Plant Breed 118:151–156 Erickson LR, Straus NA, Beversdorf WD (1983) Restriction pattern reveals origin of chloroplast genomes in Brassica amphidiploids. Theor Appl Genet 65:201–206

Gomez-Campo C, Prakash S (1999) Origin and domestication. In: Gomez-Campo C (ed) Biology of Brassica Coenospecies. Elsevier, Amsterdam, pp 33–58 Ichikawa H, Hirai A (1983) Search for the female parent in the genesis of Brassica napus by chloroplast DNA restriction pattern. Jpn J Genet 58:419–424 Jahier J, Chevre AM, Tanguy AM, Eber F (1989) Extraction of disomic addition lines of Brassica napus-B. nigra. Genome 32:408–413 Jain A, Bhatia S, Banga SS, Prakash S, Lakshmikumaran M (1994) Potential use of random ampliﬁed polymorphic DNA (RAPD) technique to study the genetic diversity in Indian mustard (Brassica juncea) and its relationship to heterosis. Theor Appl Genet 88:116–122 Kowalaski SP, Lan T-H, Feldmann KA, Paterson AH (1994) Comparative mapping of Arabidopsis thaliana and Brassica oleracea chromosomes reveals islands of conserved organization. Genetics 138:499–510 Kruskopf-Österberg MK, Shavorskaya O, Lascoux M, Lagercrantz U (2002) Naturally occurring Indel variation in the Brassica nigra COL 1 gene is associated with variation in ﬂowering time. Genetics 161:299–306 Lagercrantz U (1998) Comparative mapping between Arabidopsis thaliana and Brassica nigra indicates that Brassica genome have evolved through extensive genome replication accompanied by chromosome fusions and frequent rearrangements. Genetics 150:1217–1228 Lagercrantz U, Lydiate DJ (1995) RFLP mapping in Brassica nigra indicates differing recombination rates in male and female meioses. Genome 38:255–264 Lagercrantz U, Lydiate D (1996) Comparative genome mapping in Brassica. Genetics 144:1903–1910 Lagercrantz U, Putterill J, Coupland G, Lydiate D (1996) Comparative mapping of Arabidpsis and Brassica, ﬁne scale genome colinearity and congruence of genes controlling ﬂowering time. Plant J 9:13–20 Lagercrantz U, Kruskopf-Österberg M, Lascoux M (2002) Sequence variation and haplotype structure in the ﬂowering time locus COL1 of Brassica nigra. Mol Biol Evol 19:1474–1482 Lan TH, DelMonte TA, Reischmann KP, Hyman J, Kowalski SP, McFerson J, Kresovich SN, Paterson AH (2000) An ESTenriched comparative map of Brassica oleracea and Arabidopsis thaliana. Genome Res 10:776–788 Lowe AJ, Jones AE, Raybould AF, Trick M, Moule CJ, Edwards KJ (2002) Transferability and genome speciﬁcity of a new set of microsatellite primers among Brassica species of the U triangle. Mol Ecol Notes 2:7–11 Lukens L, Zou F, Parkin I, Lydiate D, Osborn TC (2003) Comparison of the Brassica oleracea genetic map with the Arabidopsis thaliana physical map. Genetics 164:359–372 Morinaga T (1928) Preliminary note on interspeciﬁc hybridization in Brassica. Proc Imp Acad 4:620–622 Negi MS, Sabharwal V, Bhat SR, Lakhsmikumaran M (2004) Utility of AFLP markers for the assessment of genetic

Chapter 7 Black Mustard diversity within Brassica nigra germplasm. Plant Breed 123:13–16 O’Neill CM, Bancroft I (2000) Comparative physical mapping of segments of the genome of Brassica oleracea var. alboglabra that are homoeologous to sequenced regions of chromosomes 4 and 5 of Arabidopsis thaliana. Plant J 23:233–243 Osborn TC, Butruille DV, Sharpe AG, Pickering KJ, Parkin IA, Parker JS, Lydiate DJ (2003) Detection and effects of a homeologous reciprocal transposition in Brassica napus. Genetics 165:1569–1577 Palmer JD, Shields CR, Cohen DB, Orton TJ (1983) Chloroplast DNA evolution and origin of amphidiploid Brassica species. Theor Appl Genet 65:181–189 Parkin IAP, Sharpe AG, Keith DJ, Lydiate DJ (1995) Identiﬁcation of the A and C genomes of amphidiploid Brassica napus (oilseed rape). Genome 38:1122–1131 Parkin IA, Lydiate DJ, Trick M (2002) Assessing the level of colinearity between Arabidopsis thaliana and Brassica napus for A. thaliana chromosome 5. Genome 45:356–366 Pradhan AK, Prakash S, Mukhopadhyay A, Pental D (1992) Phylogeny of Brassica and Allied genera based on variation in chloroplast and mitochondrial DNA pattern: molecular and taxonomic classiﬁcation are incongruous. Theor Appl Genet 85:331–340 Prakash S, Hinata K (1980) Taxonomy, cytogenetics and origin of crop brassicas, a review. Opera Bot 55:1–57 Putterill J, Robson F, Lee K, Coupland G (1995) Chromosome walking with YAC clones in Arabidosis: isolation of 1700kb of contiguous DNA on chromosome 5, including a 300kb region containing the ﬂowering time gene CO. Mol Gen Genet 239:145–157 Quiros CF, Ochoa O, Kianian SF, Douches D (1986) Evolutionary trends in Brassica: Gathering evidence from chromosomal addition lines. Cruciferae Newslett 11:22–23 Quiros CF, Ochoa O, Kianian SF, Douches D (1987) Analysis of Brassica oleracea genome by the generation of B. campestris-oleracea addition lines: characterization by isozymes and rDNA genes. Theor Appl Genet 74:758–766 Rana D, Boogaart T, O’Neill CM, Hynes L, Bent E, Macpherson L, Park JY, Lim YP, Bancroft I (2004) Conservation of the microstructure of genome segments in Brassica napus and its diploid relatives. Plant J 40:725–733 Sadowski J, Gaubier P, Delseny M, Quiros CF (1996) Genetic and physical mapping in Brassica diploid species of a gene cluster deﬁned in Arabidopsis thaliana. Mol Gen Genet 251:298–306 Schranz ME, Osborn TC (2004) De novo variation in life-history and responses to growth conditions of resynthesized polyploid Brassica napus (Brassicaceae). Am J Bot 91:174–183 Sears ER (1977) An induced mutant with homoeologous pairing in common wheat. Can J Genet Cytol 19:585–593 Sillito D, Parkin IA, Mayerhofer R, Lydiate DJ, Good AG (2000) Arabidopsis thaliana: a source of candidate diseaseresistance genes for Brassica napus. Genome 43:452–460

273

Song KM, Obsorn TC, Williams PH (1988a) Brassica taxonomy based on nuclear restriction fragment length polymorphism (RFLPs). 1. Genome evolution of diploid and amphidiploid species. Theor Appl Genet 75:784–794 Song KM, Obsorn TC, Williams PH (1988b) Brassica taxonomy based on nuclear restriction fragment length polymorphism (RFLPs). 2. Preliminary analysis of sub-species within B. rapa (syn. campestris) and B. oleracea. Theor Appl Genet 76:593–600 Song KM, Obsorn TC, Williams PH (1990) Brassica taxonomy based on nuclear restriction fragment length polymorphism (RFLPs). 3. Genome relationship in Brassica and related genera and the origin of B. oleracea and B. rapa (syn. campestris). Theor Appl Genet 79:497–506 Song K, Lu P, Tang K, Osborn TC (1995) Rapid genome change in synthetic polyploids of Brassica and its implications for polyploid evolution. Proc Natl Acad Sci USA 92:7719–7723 Srivastava A, Mukhopadhyay A, Arumugam N, Gupta V, Verma JK, Pental D, Pradhan AK (2004) Resynthesis of Brassica juncea through interspeciﬁc crosses betweenB. rapa and B. nigra. Plant Breed 123:204–206 Struss D, Bellin U, Robbelen G (1991) Development of B-genome chromosome addition lines of B. napus using different interspeciﬁc Brassica hybrids. Plant Breed 106:209–214 Struss D, Quiros CF, Plieske J, Robbelen G (1996) Construction of Brassica B genome synteny groups based on chromosomes extracted from three different sources by phenotypic, isozyme and molecular markers. Theor Appl Genet 93:1026–1032 Szewc-McFadden AK, Kresovich S, Bliek SM, Mitchell SE, McFerson JR (1996) Identiﬁcation of polymorphic, conserved simple sequence repeats (SSRs) in cultivated Brassica species. Theor Appl Genet 93:534–538 This P, Ochoa O, Quiros CF (1990) Dissection of Brassica nigra genome by chromosome addition lines. Plant Breed 105:211–220 Truco MJ, Quiros CF (1994) Structure and organization of the B genome based on a linkage map in Brassica nigra. Theor Appl Genet 89:590–598 U N (1935) Genomic analysis in Brassica with special reference to the experimental formation of B. napus and peculiar mode of fertilization. Jpn J Bot 7:389–452 Warwick SI (1993) Guide to wild germplasm of Brassica and allied crops. Part I: Taxonomy and genome status in the tribe Brassicaceae (Cruciferae). Technical Bulletin 199314E, Center for Land and Biological Resources Research, Research Branch, Agriculture Canada Warwick SI, Black LD (1991) Molecular systematics of Brassica and allied genera (subtribe Brassicinae Brassicae) – chloroplast genome and cytodeme congruence. Theor Appl Genet 82:81–92 Warwick SI, Black LD (1994) Evaluation of the subtribes Moricandiinae, Savignyinae, Vellinae, and Zillinae (Brassi-

274

S. Das, U. Lagercrantz, M. Lascoux

caceae, Tribe Brassiceae) using chloroplast DNA restriction site variation. Can J Bot 72:1692–1701 Warwick SI, Francis A, La Fleche J (2000) Guide to wild germplasm of Brassicaceae, 2nd edn. Technical Bulletin 1993-14E, Center for Land and Biological Resources Research, Research Branch, Agriculture Canada

Westman AL, Kresovich S (1999) Simple sequence repeat (SSR)based marker variation in Brassica nigra genebank accessions and weed populations. Euphytica 109:85–92 Yanagino T, Takahata Y, Hinata K (1987) Chloroplast DNA variations among diploid species in Brassica and allied genera. Jpn J Genet 62:119–125

CHAPTER 8

8 Flax Chris A. Cullis Department of Biology, Case Western Reserve University, Cleveland, OH 44106-7080, USA e-mail: [email protected]

8.1 Introduction 8.1.1 Origin and History Flax (Linum usitatissimum L.) is one of the most ancient of useful herbs. The ﬁrst recorded use of ﬂax is in Cayonu in southeastern Turkey about 9000 years ago. Large ﬂax seeds, showing signs of domestication, and a piece of calciﬁed linen wrapped around a curved bone sickle were discovered by Halet Cambel of Istanbul University and Robert Braidwood of the University of Chicago. Small ﬂax seeds have been discovered in Syria dating from about 8000 BC, Iran about 7500 BC, and in Bulgaria in the Mesolithic era (Stitt 1986). The start of the domestication of ﬂax is not clear-cut, but the available archaeological evidence suggests that ﬂax belongs to the ﬁrst group of crops that started agriculture in the Near East since the gradual increase in seed size is an indication that ﬂax cultivation was practiced in the Near East before 6000 BC (Fig. 1). The additional evidence from living plants fully supports a Near East domestication. Domestication of ﬁber ﬂax, to say nothing of seed ﬂax, occurred in India and China before that of cotton more than 5000 years ago (Fuller et al. 2004). The earliest cultivated ﬂax was Linum angus-

tifolium, a smaller plant with fewer and narrower leaves than L. usitatissimum, and usually perennial. This is found wild in southern and western Europe (including England), North Africa, and western Asia. The annual ﬂax (L. usitatissimum) is found wild in the districts included between the Persian Gulf, the Caspian Sea, and the Black Sea and is probably descended from the earlier cultivation in these areas. The usefulness of the plant resulted in a wide dissemination. The Greeks and Romans probably ﬁrst brought ﬂax from Egypt, from where it spread to western Europe from Rome and eastern Europe from Greece. This annual ﬂax appears to have been introduced into the north of Europe by the Finns (de Candolle 1890). Flax on the North American continent dates back almost 400 years to 1617 when Lois Hébert, the ﬁrst farmer in Canada, brought it to New France. With time, ﬂax production expanded and moved westward across the continent. By 1875, European settlers were seeding the unbroken western prairie with ﬂax brought from their homelands. Flax ﬂourished in the clean environment, and production in the new land advanced. The evidence is that ﬂax was always a multifunctional crop with the ﬁber used for cloth and the seed for many purposes, including oil for lamps, as a source of food for human and animal nutrition, and for medicinal purposes.

8.1.2 Biological Descriptions Taxonomic Position Flax is an annual of the family Linaceae. The overall place of ﬂax in the plant classiﬁcation is as follows: Fig. 1. Seeds of ﬂax varieties and progenitors. a Bolley Golden Sel. b Bison. c L. angustifolium. d L. bienne

Division: Magnoliophyta Order: Malpighiales Family: Linaceae

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

276

C.A. Cullis

Genus: Linum Species: Linum usitatissimum The progenitor of cultivated ﬂax is proposed to be L. angustifolium (the narrow leaved ﬂax) or L. bienne (pale ﬂax), and this relationship is supported by the evidence from the ribosomal RNA gene organization within the chromosomes as well as conservation of the intergenic spacer region (Muravenko et al. 2001a, 2003, 2004; Cullis, unpubl.).

There are 12 distinct growth stages in the development of a ﬂax plant. 1. 2. 3. 4. 5. 6. 7. 8.

Habitat Commercially grown ﬂax crops are grouped into two 9. main types: ﬁber ﬂax and seed ﬂax, alternatively referred to as long-stalked ﬂax and crown ﬂax, respec- 10. tively. Long-stalked ﬂax is grown for ﬁber and cultivated as a spring crop on primarily silt or clay 11. loams in a moist and warm climate. It is traditionally grown in no more than 20 countries worldwide, mainly in central Europe but also in Egypt, Turkey, China, Argentina, and Chile. Long-stalked ﬂax supposedly spread from Russia. Compared to ﬁber ﬂax, crown ﬂax tends to require more sunlight and less 12. moisture and is mainly cultivated for linseed oil. Modiﬁed varieties tailored for human nutrition are also being developed. Habit The ﬂax collections cover more than 25,000 accessions, and there is a need to identify duplicates to determine the real base of the worldwide collections. The cultivated varieties, depending on the regional conditions and climate, range in length (from 25 to 125 cm), shape (sparsely and heavily branched varieties), and maturity periods (from fast-growing varieties in the northern latitudes and mountainous regions to slower-growing varieties cultivated on irrigated soils in Asia). Life Cycle The life cycle of the ﬂax plant consists of a 45- to 60-d vegetative period, a 15- to 25-d ﬂowering period, and a maturation period of 30 to 40 d. Water stress, high temperature, and disease can shorten any of these growth periods. The majority of ﬂowering takes place during a period of intense ﬂowering, but ﬂowers may continue to appear until maturity. Under conditions of high soil moisture and fertility, the stems can remain green with the result that new growth can occur with a second period of intense ﬂowering.

Cotyledon Growing point emerges First pair of true leaves unfolds Third pair of leaves unfolds with start of leaf spiral Stem extension Buds visible First ﬂower with early branching Full ﬂower, when capsules start forming and further branching, occurs Late ﬂower with most branches and capsules formed Green capsules; seeds are still white and lower leaves become yellow Brown capsule where the seeds are light brown (or other wise pigmented), plump and pliable with maximum dry matter. The branches, stem, and upper leaves are green or yellowing, the middle leaves are partly senescent, and the lower leaves have shriveled or dropped Seed ripe and brown (or other wise pigmented) and rattle in capsules. The branches and upper leaves are senescent but stem still green or yellow

Architecture The ﬂax plant has one main stem, but two or more branches (tillers) may develop from the base of the plant when plant density is low and soil nitrogen is high. The main stem and branches give rise to a multibranched, irregular arrangement of ﬂowers. The plant has a short, branched taproot that may extend to a depth of more than 1 m, with side branches stretching ca. 30 cm. Flowering The ﬂower parts (petals, sepals, anthers) all occur in units of ﬁve (Fig. 2). Flax blooms in clusters and the ﬂower opens up at dawn and the petals are shed in the early afternoon so each ﬂower only blooms for a few hours. Flax varieties can be distinguished by the color of the ﬂower parts, which can range from a dark to a very light blue, white, or pale pink. The anthers can be either blue or yellow. The style and ﬁlaments that bear the anthers are blue or colorless. Flax is generally self-pollinating. However, outcrossing may occur in up to 2% normal ﬁeld conditions and depends on the spacing of the plants. Insects are the main pollinators;

Chapter 8 Flax

Fig. 2. Flax ﬂower

for example, bees collect close to 15 kg of honey from 1 ha of ﬂax ﬁeld (Gurbuz 1999). Ripening The mature fruit of the ﬂax plant is a dry boll or capsule. Ripening of the boll begins 20 to 25 d after ﬂowering. The boll has ﬁve segments that are divided by a wall (septum). Each segment produces two seeds separated by a low partition called a “false septum”, whose margin may be hairy or smooth, depending on the variety. With a complete seed set, the boll contains ten seeds, though a lower number if seeds per boll are normal. The bolls do not usually open, so the seeds are retained. Seeds Flax seeds are ﬂat, oval, and pointed at one end. A thousand seeds weigh from about 5 to 7 g, depending on variety and growing conditions. Seeds can range in color from light to dark reddish brown or yellow. Mottled seed, a combination of yellow and brown on the same seed, is the result of external, environmental conditions and is not an inherited characteristic. The seed is covered with a coating of mucilage that gives it a high shine and causes the seed to become sticky when wet.

8.1.3 Karyotype Flax has 30 relatively equal-sized small chromosomes. Early there was some confusion concerning the chromosome number since the interphase nucleus has visible blocks of heterochromatin. These blocks are

277

probably the sites of the highly repetitive tandemly arrayed sequence families in ﬂax. Within the Linaceae the chromosome number varies between 16 and 32. Therefore, it is likely that L. usitatissimum is an ancient tetraploid with one pair of chromosomes being lost or fused following polyploidization. This conclusion is further strengthened by the observation that the majority of the ribosomal RNA genes are located on a single pair of chromosomes. The evolutionary relationships between the Linum species were investigated by intercrossisng a series of species with a chromosome number n = 15 (Yermanos 1966). On the basis of the meiotic behavior of the hybrids and an assumption that L. angustifolium was the oldest species, then the chromosomal differentiation between L. usitatissimum and L. angustifolium was one translocation. L. pillescens differs from L. usitatissimum by two translocations, while L. usitatissimum and L. nervosum are homologous as determined by the normal bivalent formation of all their chromosomes in the hybrid. A comparison of the C-banding patterns of the karyotypes of three closely related species, two wild species L. austriacum L. (2n = 18) and Linum grandiﬂorum Desf. (2n = 16), and cultivated ﬂax L. usitatissimum L. (2n = 30) were studied (Muravenko et al. 2001b 2003). The karyotypes of L. austriacum L. and L. grandiﬂorum Desf. were similar for both the chromosome morphology and size. The chromosome size varied from 1.7 to 4.3 μm, while for ﬂax the chromosomes varied from 1.5 to 3.5 μm. In each species, metacentric and acrocentric chromosomes and one satellite chromosome were observed. All the homologous chromosome pairs were identiﬁed, and quantitative idiograms were constructed. Eight chromosome pairs in the two species L. austriacum L. and L. grandiﬂorum Desf. had similar Cbanding patterns, but a low level of intraspeciﬁc polymorphism in the intercalary and telomeric C-bands was observed in both species. The C-banding patterns of three ﬂax samples, L. usitatissimum L., accession K603 (L. usitatissimum var. usitatissimum), and accession K-594 [L. usitatissimum var. humile (Mill.)], were compared. Heterochromatic bands were located predominantly in pericentromeric regions with intraspeciﬁc size variation of the heterochromatic regions. In general, most crown ﬂax chromosomes had pronounced telomeric C-bands allowing the C-banding markers to be used to investigate intraspeciﬁc genetic polymorphism of L. usitatissimum L. The results indicate that the genomes of the ﬂax species originated from one ancestral genome with a basic chromosome

278

C.A. Cullis

number of 8 or 9. Apparently, the duplication or loss of one chromosome with subsequent redistribution of the chromosome material in the ancestral form resulted in the divergence into three species, L. austriacum L., L. grandiﬂorum Desf., and L. usitatissimum L. An extension of this karyotype analysis has been reported using ﬂuorescence in situ hybridization (FISH) to study the chromosomal location of the 45S (18S–5.8S–26S) and 5S ribosomal genes in the genomes of ﬁve ﬂax species of the section Linum (syn. ProtoLinum and AdenoLinum) (Muravenko et al. 2004). In all ﬁve species a major hybridization site of 45S rDNA was observed in the pericentric region of a large metacentric chromosome. Polymorphic loci of 45S rDNA were found on two of the small chromosomes. Some of the sites of 5S rDNA were colocalized with those of 45S rDNA, with other 5S rDNA sites mapped to additional chromosomal sites. Although the similar location of the ribosomal genes in the pericentric region of the pair of satellite-bearing metacentrics is indicative of a close relationship of the species examined, the difference in chromosomal location of the ribosomal genes between ﬂax species with 2n = 30 and those with 2n = 16 or 18 testiﬁed to their assignment to different sections.

8.1.4 Genome Size The genome size has been estimated both by direct staining of the nuclei and by renaturation kinetics. The values obtained from these two estimates differ by a factor of two, with the staining value being twice the kinetic value (Cullis 1981). The values reported are: 0.7 pg/1C nucleus (Evans et al. 1972) 0.35 × 108 nucleotide pairs/haploid genome from kinetic measurements (Cullis 1981). However, the kinetic complexity of the single-copy sequences (which make up 44% of the total genome) is 3.25 × 108 nucleotide pairs. This kinetic complexity corresponds to a DNA weight of 0.38 pg/1C nucleus, which is very close to the estimate from Feulgen staining (0.31 pg/1C nucleus). Therefore, ﬂax has one of the smaller genomes in plants as far as the complexity is concerned. This is true due to the fact that much of the genome (35%) is comprised of highly repetitive tandemly arrayed sequences. These are likely to be the sites of the heterochromatic regions of the genome.

A light satellite region making up about 15% of the total genome is also present.

8.1.5 Economic Importance Flax is the source of products for existing, highvalue markets in the textile, composites, paper/pulp, and industrial/nutritional oil sectors (Hamilton 1986; Sharma and Van Sumere 1992). Flax is the source of industrial ﬁbers and, as currently processed, results in long-line and short (i.e., tow) ﬁbers (Van Sumere 1992). The uses of ﬂax have expanded in recent years and, in one sense, the crop is returning to the original reasons for its domestication. The two major uses are for the oil from the seed (linseed oil) and for the ﬁber from the stem. In addition to these two traditional uses, ﬂax is gaining more interest in human health applications. Flaxseed represents only 1% of the world supply of oilseeds, while ﬂax ﬁber represents about 3% of the world natural ﬁber production. However, ﬂax products are considered to have high potential for increased industrial use, as well as for human food and feed markets. The largest growers of ﬁber ﬂax are Russia, China, Belarus, France, Ukraine, Egypt, and Belgium, with other contributions from many European countries (Mackiewicz-Talarczyk 2000). Climatic conditions in these regions are perfect for growing ﬂax, and increasing worldwide demand for linen makes it an important cash crop. This crop is supported in the European Union by subsidies that lower the costs by about 30%. The majority of the linseed production is concentrated in three countries, Canada, China, and India, with signiﬁcant production in Germany, Argentina, the UK, and the USA (Kozlowski and Manys 2000). Differences exist between ﬂax grown for ﬁber and ﬂax grown for its seed due to varietal differences as well as harvesting methods. Dual-purpose crop varieties that are productive for both ﬁber and oil extraction may increase the viability of the crop. One such variety is the ﬂax line FP944 (named Klasse) developed by the Agriculture Canada Morden Research Station. An example of the problem is that more than one million tons annually of ﬂaxseed straw, as a byproduct of the linseed industry in western Canada, constitutes a major environmental disposal problem (Domier 1997). If this could be efﬁciently processed, then this linseed straw could potentially produce a value-added natu-

Chapter 8 Flax

ral ﬁber while also reducing pollution from burning millions of tons of straw. Although traditional linen in Europe is constructed with long-line ﬁbers, many industry analysts indicate that the largest use for US textiles will be as short staple ﬂax ﬁbers blended with cotton or other ﬁbers. The low load-bearing cost of ﬂax ﬁbers in composites might predict its potential use in composites and other industrial products. Flax ﬁber composites geared particularly to the huge market potential for automotive components (Domier 1997; Lepsch and Horal 1998) present an opportunity to use the vast resources of linseed straw for a value-added use. Composite stiffness values for ﬂax are comparable to E-glass ﬁber composites and are suited for low-cost engineering uses (Foulk et al. 2002). Flax ﬁbers have a lower density and cost, which reduces the load-bearing cost so ﬂax ﬁbers could compete with glass ﬁbers in speciﬁc composite materials. However, the lack of ﬁber quality parameters, ﬁber standards, uncertainties of raw material supply, unpredictability of ﬂax composite processing, possible long production cycles, and processing costs have likely limited further use of ﬂax ﬁbers in composites (Foulk et al. 2002). Flaxseed (linseed) oil is a nonedible drying oil used in manufacturing paints, varnishes, linoleum, printing ink, oilcloth, putty, and plastics. The introduction of petroleum-based ﬂoor coverings and latexbased paints resulted in a worldwide decrease of the industrial use of linseed oil for paint and ﬂoor covering over the last several decades. Nevertheless, industrial use is expected to increase because of the development of new products. The biodegradability and nonallergenic characteristics of linoleum, coupled with quality improvements, have resulted in a resurgence of demand for linoleum in some parts of Europe. There has also been interest in using a linseed-oil-based concrete sealant (Flax Council, Canada).

Flax as a Food Flaxseed and ﬂaxseed oil have been used for food and herbal products for centuries in Asia, Europe, and Africa. It has recently been receiving renewed attention in relation to human health. Flaxseed has three major components making it beneﬁcial in human and animal nutrition: (1) a very high content of alpha linolenic acid (omega-3 fatty acid) essential for humans; (2) a high percentage of dietary ﬁber, both soluble and insoluble; and (3) the highest con-

279

tent of plant “lignans” of all plant or seed products used for human food. Lignans appear to be anticarcinogenic compounds (Power et al. 2004; Thompson et al. 2004). The health beneﬁts of ﬂaxseed and its derivatives are being actively investigated. Flaxseed is generally high in alpha linolenic acid, an omega-3 fatty acid (FA), and has an omega-6/omega-3 ratio of 0.3/1. Omega-3 FAs contain lower levels of triglycerides in the blood, thereby reducing heart disease, and also show promise in the battle against inﬂammatory diseases such as rheumatoid arthritis. The major restrictions on the use of ﬂaxseed in human nutrition are the presence of cyanogenic glycosides and the rapid rancidity. The development of edible oil-type ﬂaxseed or “Linola” as a vegetable oil is likely to increase this market. However, Linola lines lack the high amounts of omega-3 FAs of conventional ﬂaxseed lines, which makes them less nutritional, but they are more stable at high temperatures and less likely to go rancid, and so more competitive in the vegetable oil market. Linola lines such as the variety solin have been developed in Canada and make a signiﬁcant contribution to the total growth area for this crop in Canada. Designer foods may be deﬁned as those foods composed of one or more ingredients that contribute essential nutrients for health but also protect against certain diseases such as cancer and coronary heart disease. Flaxseed has been evaluated by the National Cancer Institute as a component of such designer foods (Stitt 1990). Allied with these studies Kozlowski et al. (2004) reported the applications of ﬂaxseed and its components in pharmaceutical, food, and cosmetic products in Poland.

Flaxseed in Animal and Poultry Feed Once ground or processed, ﬂaxseed can be fed as an ingredient in poultry. Full-fat (whole) ﬂaxseed is in demand by the laying-hen market. Laying hens consuming 10 to 20% ﬂax in their rations produce eggs that are relatively desirable in their balance of polyunsaturated FAs. These so-called “Omega eggs” are being produced in the USA and Canada and contain increased amounts of omega-3 FAs and decreased amounts of saturated FAs. Flax supplementation in ﬁnishing beef cattle results in an improvement in animal performance and carcass value. Incorporation of ﬂax at 8% of the dry matter can increase internal fat deposition and increase yield grades, although it may

280

C.A. Cullis

reduce the shelf life (Drouillard et al. 2002; Maddock the particular industrial requirements, are the seed size, oil content, and composition. New varieties strive et al. 2004). for an optimal combination of both types of use, although the newer industrial uses of the straw from Conclusions the oil crop should improve both the economics and The demand for this multiuse oilseed crop and its environment-friendly aspects of crop production. derivatives such as solin is likely to increase due to The breeding objectives have evolved over the past its utility as a health-food additive, its use for animal century, especially with respect to the development feed, as well as new industrial uses of both the linseed of linseed varieties. Initially, in the early breeding era oil and the ﬁbers of oilseed ﬂax. (1900–1930) the focus was on yield and wilt resistance. Then speciﬁc genetic resistance to wilt and rust was added to high yield. Currently maturation date is also 8.1.6 important along with higher quality and a continued Breeding Objectives emphasis on genetic resistance to diseases. The breeding objectives will relate to both the current uses and limitations of the crop as well as the development of new products. The current uses of the crop center on the oil and ﬁber while the crop has tremendous potential for the creation of new products, to substitute renewable resources for nonrenewable resources, to address environmental problems, and to protect and enhance human health (Marshall 1989). The breeding objective relates to the uses of the crop as well as the need to optimize yield of the economically important products. Therefore, the original primary targets were the improvement of the yield and quality of the oil and ﬁbers and resistance to the most important diseases. Currently the characters that are focused on in Canada, for example, are disease resistance, early maturity, improved yield (the three major yield components in linseed are number of bolls per unit area, number of seeds per boll, and seed weight), lodging resistance, and chlorosis tolerance. Quality factors such as oil content and quality, speciﬁc seed and oil characteristics, and ﬁber length and quality are still important. The agronomic practices under which ﬂax is cultivated also play a part in the selection of improved varieties, especially for varieties that thrive under alternative production systems such as zero-till, which have the potential to reduce soil erosion and reverse declines in soil quality. The differing requirements for oil- and ﬁber-producing varieties make it difﬁcult to develop a dual-purpose crop that will achieve acceptable production of both oil and ﬁber. Varieties with long, thick unbranched stems and high ﬁber content are desirable qualities for ﬁber production. However, oil-producing varieties should be rather low-growing and free-ﬂowering as a prerequisite for having a high number of capsules per plant. Other factors to be taken into account, according to

Breeding Successes Changes in the properties of four characters in linseed varieties over the past century are shown in Table 1. Future Objectives for Flax Breeding 1. Environmental stress a) Drought b) Moisture c) Lodging 2. Decrease time to maturity without loss of yield 3. Efﬁciency of fertilizer utilization 4. New germplasm 5. Hybrid ﬂax 6. Disease resistance a) Flax rust b) Flax wilt c) Pasmo d) Powdery mildew 7. Seed quality a) Oil content b) FA composition c) Protein content d) Amino acids

Table 1. Improvement in agronomic characteristics due to ﬂax breeding. Variety

Yield

Oil (%)

Alanine

Protein (%)

Breeding era

Bison Linott

1510 1650

42.6 42.9

49.9 54

28.3 26.0

Macbeth 1810

44.0

52.5

25.4

<1930 1930– 1980 Present

Chapter 8 Flax

e) Lignans f) Gums g) Seed color 8. Improvement in ﬁber quality The breeding for new ﬁber ﬂax varieties nowadays is very difﬁcult because the maximal level for many quality traits is being approached. What is needed for a continued improvement and production of new varieties that will have improved yield and disease resistance and that will meet the needs of end users and consumers? Primarily sufﬁcient genetic variability must be available to the breeder. This can be obtained from existing germplasm maintained in wild species and breeding collections or developed through mutation or transgenic programs. There has to be efﬁcient testing procedures that are inexpensive, reliable, and nondestructive. The new technologies using biochemical and molecular markers may reduce the need for extensive and variable testing locations. Linseed oil has generally been used for industrial purposes, but since the mid 1960s, the demand for edible oils has risen dramatically. The low oxidative stability of linseed oil has rendered it unsuitable for use as edible oil. The development of varieties with an altered oil proﬁle has permitted increased usage in the edible oil market. Development of edible linseed has used mutations induced with ethyl methanesulphonate (EMS). Genetic modiﬁcation of the activity of desaturase enzymes blocks the conversion of double-unsaturated linoleic acid (C18:2) into triple-unsaturated linolenic acid (C18:3) in the developing seed. This creates low linolenic mutants with very high levels of linoleic acid as described above. To make an edible linseed oil, the FA composition has been changed and linolenic acid (C18:3) has been substantially reduced from 50 to 2% through traditional breeding procedures. These low linolenic acid mutants have greatly elevated levels of linoleic acid, 65 to 76%. This reduction in linolenic acid greatly increases the oxidative stability of the oil – it becomes an edible polyunsaturated oil almost identical to sunﬂower in FA composition. The high linoleic acid content is consistent across a wide range of growing conditions but in sunﬂower-growing conditions can affect the linoleic acid levels. The color of the seed has also been changed, with edible linseed being a pale yellow color so that it can be distinguished from the nonedible linseed, which is brown. An additional consideration is that ﬂax has also become important to the health industry as a nu-

281

triceutical crop due to the presence of linolenic acid (an omega-3 FA) and lignans in the seed. However, these new altered oil-proﬁle edible oil varieties have lost the high levels of linolenic acid. The FA compositions of various ﬂax and solin cultivars are given in Table 2. A comparison of ﬂax/solin values with those of other major oil crops is given in Table 3. Although linola can be regarded as a “new crop”, agronomically it is very similar to linseed. It matures at the same time as ﬂax or oil crops, is compatible with cereal production systems, and can be harvested with the same machinery that is used for cereals and other small grain crops. Grain can be crushed in existing plants in accordance with standard methods. The crop does not lodge or shatter when mature. The straw also has the novel ﬁber applications described for linseed/ﬂax straw. Therefore linola can be grown wherever ﬂax and linseed varieties currently perform well, as well as in many areas where cereals are grown. It clashes with the sunﬂower market but is more adapted to northern Europe than sunﬂower. Characteristics of Some New Varieties – 2090, low linolenic acid ﬂax was developed by Agricore United. 2090 combines very high oil content with high yield. In the longer growing season black soil zones of western Canada, the yield of 2090 is similar to 2047 and higher than 1084. In the shorter growing season Black and grey-wooded soil zones, 2090 is similar yielding to 2047 and 1084. In the brown and dark brown soil zones, the yield of 2090 is higher than 2047 and 1084. It is immune to North American races of rust, is moderately resistant to Fusarium wilt, and is moderately resistant to powdery mildew. 2090, 2047, and 1084 are low linolenic acid, yellow seed coat solin cultivars (Dribnenki et al. 2004).

Table 2. Edible linseed: fatty acid composition Cultivar

Fatty acid % in oil Oleic Linoleic Linolenic

McGregor (High C18.3)/ nonedible linseed E1747 Glenelg cross Croxton cross

20.3

12.8

48.8

17.5 21.0 18.0

70.3 64.0 69.0

2.0 2.0 2.0

282

C.A. Cullis

Table 3. Fatty acid composition of Linola and ﬁve major vegetable oils

Crop

Linola Safﬂower Sunﬂower Maize Soybean Canola

Fatty acid (%) Saturated MonoPolyunsaturated unsaturated Linoleic Linolenic Oleic

P/S ratio Polyunsaturated/ saturated fatty acid ratio

10 10 12 13 15 7

7.3 7.6 6 4.5 4.1 4.6

17 14 16 29 23 61

71 76 71 57 54 21

2 Trace Trace 1 8 11

– Prairie Blue, a medium-late maturing oilseed ﬂax (L. usitatissimum L.), was released in 2003 by Agriculture and Agri-Food Canada, Morden Research Station, Morden, Manitoba. This cultivar has high oil content, high oil quality, small seed size, very good lodging resistance, and high yield in all soil zones of the prairies. It is immune to current North American races of rust [Melamspora lini (Ehrenb.) Desmaz] and moderately resistant to wilt caused by Fusarium oxysporum Schlecht. f. sp. lini (Bolley) Snyder Hansen (Duguid et al. 2004). – Macbeth, a medium-late maturing oilseed ﬂax (Linum usitatissimum L.), was released in 2002 by Agriculture and Agri-Food Canada, Morden Research Station, Morden, Manitoba. This cultivar has high oil content, high oil quality, large seed size, very good lodging resistance, and high yield when seeded in all the soil zones of the Canadian prairies as compared to Flanders. It is immune to current North American races of rust caused by Melamspora lini (Ehrenb.) Desmaz, moderate resistance to wilt by Fusarium oxysporum Schlecht. f. sp. lini (Bolley) Snyder Hansen, and moderate resistance to powdery mildew by Oidium lini Skoric (Duguid et al. 2003). – CDC Mons, a medium-late maturing oilseed ﬂax (L. usitatissimum L.) was released in 2002 by the Crop Development Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. This cultivar has medium oil content, medium oil quality, small seed size, good lodging resistance, and high yield when seeded early in the black soil zones of the prairies. It is immune to North American races of rust caused by Metampsora lini, moderately resistant to wilt caused by Fusarium oxysporum f. sp. lini, and moderately

resistant to powdery mildew caused by Oidium lini (Rowland et al. 2003).

8.2 Construction of Genetic Maps 8.2.1 Classical Mapping The inheritance of different characteristics of ﬂax has been studied for nearly a century. The initial phase involved the traits of petal, anther, and seed color along with other characteristics. Flor’s work on disease resistance genes laid the foundations for a substantial part of modern disease resistance breeding (Beard 1962). Many of these studies identiﬁed characters for which no gene symbols were assigned. Early studies led to the conclusion that eight genes were involved in ﬂower and petal color (Tammes 1922, 1928) when 14 different varieties of ﬂax were used, while 17 genes were postulated to contribute to petal color, anther color, ﬁlament color, stigma color, or seed color in crosses between seven Indian types of ﬂax (Shaw et al. 1931). The gene symbols are given in Tables 4 and 5. The white ﬂower color was controlled by a single gene (McGregor 1937; Myers 1936) (that also affected seed color since there is no seed color in white ﬂowered varieties) with a second gene controlling seed color (Graham and Rao 1924). The rust resistance genes were at ﬁve loci (K, L, M, N, and P) (Flor 1955) while Fusarium wilt resistance was conditioned by multiple loci (Knowles and Houston 1955). The genetic mapping in ﬂax has been relatively limited. Comstock (1963) investigated the linkage relationships between the color determinants (as de-

Chapter 8 Flax

283

Table 4. Symbols proposed for determinants affecting petal color, shape, anther color and seed color (A to K) (Tammes 1922, 1928) Gene symbol

Petal color

A, a

A – intensiﬁes color a – lighter color B1 – allows color b1 – white B2 – allows color b2 – white C – allows color c – white D – blue d – pink E – intensiﬁes color e – weak color F – dilutes color f – changes blue to lilac

B1, b1 B2, b2 C, c D, d E, e F, f

Petal shape

B1 – ﬂat b1 – narrow crimped B2 – ﬂat b2 – narrow crimped C – ﬂat c – ﬂat with b1 or b2 D – ﬂat d – ﬂat with b1 or b2

Anther color

Seed color

b1 – yellow

b1 – greenish tone

b2 – yellow

d – yellow

G – seed coat colored g – seed coat colorless

G, g H, h K, k

d – yellow to brown

h – anthers yellow K – colors entire petal k – concentrates color on outer edge b1 or b2 – crimped c or d – ﬂat

ﬁned by Tammes), rust resistance loci, and six chlorophyll mutants (Yg 1–4, Y1, and St1). The L locus was linked to B2, C, and G. The N and P loci (shown to be linked at 15 map units by Flor) were both linked to G and Y1, while P was 30 map units from St1. Yg4 was loosely linked to A, St1 to B, while Y1 was closely linked to G. The K and M loci were not linked to any of the other genes in this study. However, attempts to construct genetic maps with these data were inconsistent, perhaps because of the ancient polyploid nature of the ﬂax genome with multiple genes present. A small number of genes for economically important traits have been used in breeding populations but generally without the concomitant development of an overall genetic map. The small size of the chromosomes and the only recent identiﬁcation of these through banding patterns have also hindered the development of genetic maps with linkage groups (LGs) assigned to identiﬁed chromosomes. The genes identiﬁed and used include the following.

B1, B2, D, H – anthers blue

B1, D, G – reddish d or d, b1 – yellow to brown, not reddish

Isozymes for the enzymes diaphorase, leucine aminopeptidase, 6-phosphogluconate dehydrogenase, acid phosphatase, phosphoglucomutase, and peroxidase (Gorman et al. 1992). Genes controlling the oil proﬁle of the seeds have been identiﬁed through EMS mutation screens and incorporated into commercial varieties (Green 1986; Rowland et al. 1995; Saeidi and Rowland 1999) along with the cloning of FA biosynthetic genes beta-ketoacyl CoA synthase, FA elongase, stearoyl-ACP desaturase, and FA desaturase (Fofana et al. 2004), the identiﬁcation and isolation of a pectin methylesterase isoform (Al-Qsous et al. 2004), and inheritance of seed color in ﬂax (Mittapalli and Rowland 2004). Seed color is used in Canada to identify the two main market types. The traditional, high linolenic acid ﬂax must have brown seed, while solin or zero linolenic acid ﬂax must be yellow seeded. Therefore it is important to determine the allelic-gene relationship of the dominant yellow gene, the variegated recessive gene, and various spontaneous and unknown reces-

284

C.A. Cullis

Table 5. Gene symbols from Shaw et al. (1931) (in original reference recessives are denoted by -) for crosses between seven Indian ﬂax varieties B, b – acts with C to produce pink petals C, c – acts with B to produce pink petals D, d – modiﬁes pink to lilac in petals, while b or E get faint tinge of blue in white petal E, e – intensiﬁes color I petal F, f – dilutes color in petal, lilac modiﬁed to blue, pink to white K, k – distributes color all over petal, E and F intensify color. K deepens color in upper half of petal, but with e or f no intensiﬁcation due to K N, n – reduces intensity T, t – restricts blue color in ﬁlamentto distal region immediately below anther Z1, z1 – produces blue color in ﬁlaments if B, C, and K plus either E or F is present Z2, z2 – produces blue in ﬁlament. If either Z1 or Z2 is present with B, C, K and either E or F the ﬁlament is blue H, h – produces blue color in anthers with B and D R, r – produces blue color in style with B, C, K, and either E or F P, p – produces pink color in stigma with B and C; with P, B, C, and D stigma is purple I, i – inhibits color in stigma G, g – produces grey color seed coat M, m – with D gives fawn color in seed coat; M, D, G, fawn is changed to brown. With gor with either m or d the color of the seed coat is yellow X, x – intensiﬁes color in seed; changes fawn to dark fawn and yellow to dark yellow

sive yellow genes in ﬂax. The four spontaneous recessive yellow-seed mutants (YSED2, YSED4, S95407, and S96071) were shown to have the same recessive allele of the g-locus-producing yellow seed. The European yellow line, G-1186/ 94, had a recessive allele at the dlocus-producing yellow seed, and a dominant yellow Y1 allele was carried by CPI84495 and YSED18. The variegated seed color was conditioned by a second recessive allele of the b1 locus, designated as b1(vg). All four loci governing seed color were essentially inherited independently (Mittapalli and Rowland 2003). Seed vigor of solin is often lower than that of brownseeded linseed ﬂax, and this lower seed vigor has been associated with both seed color and linolenic acid levels. Near-isogenic populations of ﬂax differing in seed coat color and linolenic acid concentration were compared (Saeidi and Rowland 1999). There was no difference in seed yield, oil concentration, and seed weight between solin and industrial oil types, although the solin seed was slightly later maturing and had greater seed coat damage than industrial seed in some genetic backgrounds. Interestingly, although the two major crop forms differ signiﬁcantly in height and branching pattern, there appears to be little discussion of the inheritance of these two characters. Certainly there is at least one major gene that inﬂuences each of these two characters (Fig. 3). In the cross between CI1030

(short, branched, early ﬂowering) and Stormont cirrus (tall, unbranched, later ﬂowering), the F1 was tall and branched and these two characters segregated independently in the F2 (Cullis, unpubl. obs.). Mutation breeding in ﬂax (L. usitatissimum L.) has led to the development of a new type of edible ﬂaxseed oil that has nearly eliminated linolenic acid and quadrupled the level of palmitic acid. A variegated seed coat color mutant may be used as a phenotypic marker to distinguish varieties with this particular FA proﬁle from those of linseed (high linolenic acid) or solin (low linolenic acid) varieties. This variegated seed coat color was controlled by a single recessive gene. Also it was determined that the palmitic and variegated seed loci segregated independently (Saeidi and Rowland 1997). In addition to these single gene traits, many quantitative trait loci (QTLs) have been identiﬁed that control economically important characters. These can be more easily manipulated using molecular markers. Thus the advent of DNA markers has greatly increased the ease and speed with which the genetic map of a species can be obtained. All of the opportunities afforded by the existence of a genetic map in any species are more readily available. The genetic maps developed using DNA markers have been used to isolate genes, identify QTLs, and aid in marker-assisted breeding. Two sig-

Chapter 8 Flax

285

fragment length polymorphisms (RFLPs), random ampliﬁed polymorphic DNAs (RAPDs) (Williams et al. 1990), ampliﬁed fragment length polymorphisms (AFLPs) (Vos et al. 1995), single-nucleotide polymorphisms (SNPs), and simple sequence repeats (SSR) or microsatellites (Senior and Heun 1993). The usefulness or necessity of each of the marker systems depends largely on the genetic resources available for the species under consideration. Marker Systems – RFLPs These were the ﬁrst generation of DNA markers and are generally detected by the hybridization of a probe to restriction-digested genomic DNA. Many of the probes are derived from single-copy sequences, with EST sequences being one rich source of potential probes. One source of useful probes in ﬂax is genomic fragments generated by the digestion of genomic DNA with the restriction enzyme PstI. These Fig. 3. Inheritance of height and branching. The cross between fragments are enriched in low copy number sequences CI1303 (a), Stormont cirrus (c), and and F1 plant. Note that and frequently show polymorphisms (Oh et al. 2000; F1 is as tall as the tall parent (c) and as branched as the most Cullis unpubl. obs.). branched parent (a)

niﬁcant mapping efforts are available for ﬂax, along with additional molecular data characterizing the ﬂax germplasm. 8.2.2 Molecular Maps There are a number of different types of molecular maps. The genetic map includes the loci ordered with respect to the frequency with which they recombine. A physical map is the genomic sequence arranged in a linear sequence either as a set of ordered bacterial artiﬁcial chromosomes (BACs) or as a complete genome sequence. The most useful type of molecular map will be the result of integrating the genetic and the physical maps. Genetic markers are used to anchor the physical assemblies so that any locus that is genetically mapped can then be placed in a speciﬁc physical region of the genome. The ultimate example in plants of an integrated genetic and physical map is Arabidopsis thaliana, where the complete genome is available. DNA-based markers have revolutionized the whole process of generating genetic maps since, for the ﬁrst time, a large number of loci can be followed in a single segregating population. The range of genetic markers that are available include restriction

– AFLPs These markers are RFLPs detected by PCR ampliﬁcation. The polymorphic fragments are observed against the background of all the possible sized restriction fragments that can be ampliﬁed. Adaptors are added to the ends of restriction fragments, and these adaptors are then used as primers in a PCR reaction. Every possible band should be ampliﬁed and the complex mixture of bands is separated on gels or through automated sequencer sequence readers. The polymorphisms can be cloned and sequenced to generate sequence-tagged sites (STSs). Potential epigenetic effects resulting from hyper- or hypomethylated regions of the genome can be investigated using methylation-sensitive and insensitive restriction enzyme isoschizomers. An AFLP map was generated for ﬂax (Spielmeyer et al. 1998). This AFLP genetic linkage map was used to identify two QTLs on independent LGs with a major effect on resistance to Fusarium wilt. – RAPDs Statistically a sequence of ten base pairs should appear once every 106 nucleotides. PCR ampliﬁcation using genomic DNA as the target and a series of single random 10-base primers has been very successful in generating large numbers of polymorphisms (Williams et al. 1990). The methodology can be used when little other genomic information is known. Unfortunately, the technique appears to suffer from irreproducibility between laboratories and sources of thermostable enzyme, although, within

286

C.A. Cullis

a laboratory, reproducible results can be achieved ucts will allow the identiﬁcation of SNPs that can then (Jones et al. 1997). However, the ﬁrst molecular map be easily added to the genetic map. for ﬂax was that using RAPDs, isozyme markers, and RAPD/RFLP Map RFLPs (Cullis et al. 1995; Oh et al. 2000). A map of ﬂax (L. usitatissimum) using RFLPs RAPDs was constructed. The mapping populations were the – Microsatellites and SSRs Microsatellites or SSRs are F2 populations from two crosses between diverse culgenetic markers derived from short (usually <6 base tivars. From one cross, CI1303 × Stormont Cirrus, 20 pairs) tandemly repeated sequences such as (GA)n , RFLP and 520 RAPD markers were analyzed. Thirteen (AAT)n , (GT)n . The terms are often used interchangeRFLP and 80 RAPD markers were on the 15 LGs, in ably although microsatellites are generally longer than addition to one STS. The 94 markers were assigned the two to three base pair unit of the simple sequence to 15 LGs covering about 1,000 cM (Oh et al. 2000). repeats. This type of sequence is widely dispersed The LGs comprised between four and ten markers. through most animal and plant genomes and polyOnly four of the LGs had RFLP markers that anchored morphisms are due to the variability in the number of those groups and could be used to compare across repeats at a given site. SSRs can be isolated from gevarieties since RAPDs are not useful for additional nomic libraries or enriched genomic libraries (Panaud comparisons. However, a number of the RAPD polyet al. 1995) or generated from an analysis of cDNA morphisms have been cloned and are being tested to sequences. They can be converted into STSs using determine whether or not they can be converted into primers designed in unique regions surrounding the STSs (Cullis, unpubl. data). In addition the PstI clones repeat and have become an important source of gewere hybridized across a series of 25 ﬂax varieties. netic markers for many eukaryotic genomes (Panaud Twenty different inserts from the ﬁrst 100 clones et al. 1995), especially when the primers are designed tested demonstrated polymorphisms with one or in a conserved region of a transcribed sequence, more restriction enzymes across this germplasm. making them applicable over a wide range of taxa. Only 12 of these polymorphisms were segregating Such repeats have been used for the comparison of in the P1 × CI1303 population used in these exgermplasm in ﬂax (Wiesnerova and Wiesner 2004). periments, and only 6 of these were unambiguously mapped. The six unmapped polymorphic bands may – SNPs These are DNA sequence variations that occur be present in multiple copies and therefore cannot be when a single nucleotide (A, T, C, or G) in the genome unambiguously assigned. Since ﬂax appears to be an sequence differs between two individual DNA samancient tetraploid, it is possible that these are loci that ples. Many SNPs have no effect on cell function since have not diverged sufﬁciently to be distinguished and they may not change protein structure (in fact any therefore cannot be mapped at present. If so, then this SNP that occurs at the third position in the amino map will become a more useful tool in ﬂax breeding acid codon will have no effect if they do not change and comparisons. Seven polymorphic RAPD markthe amino acid sequence of the resulting protein). ers were found to have unusual segregation patterns. Their high frequency (perhaps as high as 2 to 3% RAPDs were expressed as dominant markers, but for in plant DNAs) means that they can be particularly these markers a prevalence of the progeny lacked useful in linkage mapping (Kristensen et al. 2001; Lai a band rather than the expected one fourth ratio. 2001). They have to be derived from sequence information, and that information needs to be obtained AFLP Map from two or more individuals. Since there are rel- The linkage map was constructed using a mapping atively few genomic or EST sequence data available population from doubled-haploid (DH) lines derived for ﬂax, these markers are not immediately available. from the haploid component of F2 haploid-diploid However, a combination of the RFLP data and se- twin seed originating from a cross between a polyemquence may provide a method to target SNP identiﬁ- bryonic, low linolenic acid genotype (CRZY8/RA91) cation. A number of the polymorphic PstI fragments and the Australian cultivar Glenelg (Spielmeyer et al. have been sequenced, and they show homology with 1998). Two hundred thirteen marker loci covered ca. known or putative genes from other species (Cullis, 1,400 cM of the ﬂax genome (n = 15), with an average unpubl. data). PCR primers can be designed from spacing of 10 cM and comprising 18 LGs. These LGs these sequences and used to amplify from a range of had 4 to 17 markers placed upon them. Since some ﬂax varieties. The sequencing of these ampliﬁed prod- of the LGs are quite small, it is possible that they

Chapter 8 Flax

might be merged when additional markers are added to the map. As with the RAPD/RFLP map, some 60 AFLP markers (28%) deviated signiﬁcantly (P < 0.05) from the expected segregation ratio. The map also incorporated RFLP markers tightly linked to ﬂax rust (Melamspora lini) resistance genes and markers detected by disease resistance genelike sequences. The study illustrates the potential of the AFLP technique as a robust and rapid method to generate moderately saturated linkage maps, thereby allowing the molecular analysis of traits, such as resistance to Fusarium wilt, that show oligogenic patterns of inheritance. In both mapping projects, PstI linked polymorphisms proved to be useful, and this enzyme should be the one of ﬁrst choice when mapping ﬂax. It may be particularly instructive due to the genome organization in ﬂax where the large runs of tandemly arrayed repetitive sequences are completely missing in the PstI fragments, thereby effectively reducing the genome size.

8.3 Germplasm Identification The continued improvement of the crop depends on the availability of new alleles that condition important traits. These alleles can come from three main sources: the worldwide germplasm collections, induced mutations, and the introduction of genes from other species through transformation and the production of transgenic plants. Each of these three routes has been adopted for ﬂax. 8.3.1 Germplasm Characterization The worldwide collection of ﬂax probably contains more than 25,000 accessions, only a small number of which have been characterized, and many of the accessions are likely to be duplicates (Maggioni et al. 2002). Unwanted duplications within collections are a burden to gene banks because these do not contribute to the genetic diversity present within the collection but do require capacity for storage and maintenance. Therefore the collections need to be screened in order to assess the genetic diversity available in the ﬂax germplasm that can be used to sustain improvement of cultivars and reduce the accessions to a unique set. The germplasm can be described using a set of phenotypic descriptors and/or by the use of a series of DNA polymorphisms. Both of these two identiﬁ-

287

cation methods have been developed and applied to some of the ﬂax collections. The International Flax Data Base (IFDB) (http:// www.ecpgr.cgiar.org/databases/Crops/ﬂax.htm) has been managed and coordinated by the AGRITEC company in the Czech Republic since 1994. IFDB includes data from 8,387 accessions of ﬂax and linseed, stored in 13 contributing gene banks from 11 countries. This is estimated at 33% of the total number of ﬂax accessions (possibly around 25,000) conserved in Europe. The types of accessions within the database are 38% advanced cultivars, 27% genetic resources, 20% breeding material, and 14% landraces, primitive cultivars, and wild forms. These accessions are described using 22 passport descriptors and 24 speciﬁc characterization and evaluation descriptors. Passport data are included in the database for 82% of the accessions, while 16% are described by speciﬁc characterization descriptors. The passport descriptors and the characterization and evaluation traits are given in Tables 6 and 7. The passport information may not be available for all the accessions, making it difﬁcult to decide directly which lines are duplicated in the various collections. For example, the Centre for Genetic Resources in the Netherlands has a collection that consists of 947 accessions of both ﬁber ﬂax and linseed. This Linum collection established by the Department of Plant Breeding (IVP) of the Agricultural University of Wageningen became a working collection of the former Foundation for Plant Breeding (SVP) at Wageningen and was utilized in ﬂax-breeding research programs during the period 1948 to 1996. This germplasm was particularly utilized for breeding ﬂax resistant to the diseases scorch (Pythium megalacanthum), wilt (Fusarium oxysporum f. sp. lini), and rust (Melamspora lini) and was also used for the improvement of the ﬁber content. The collection includes 499 numbers of ﬁber ﬂax, 442 accessions of linseed, and 6 accessions of ﬁve wild species. The collection contains old landraces of known origin, e.g., Fries Landras (NLD, 1816), Crete (TUR, 1914), Bombay (IND, 1917), and Soddo (ETH, 1914), as well as others whose date of origin is not known. Cultivars developed early in this century, often from old landraces, are included in the collection (e.g., Blenda, NLD, 1926; Frontier, USA, 1898; Pioneer, GBR, 1921 and Ottawa White Flower, CAN, 1913). A large number of accessions, described as research material, with limited information are also included. Despite the time spent on the documentation

288

C.A. Cullis

Table 6. International Flax Data Base – passport part • • • • • • • • • • • • • • • • • • • • • •

IFDB accession number Variety name Year of input into IFDB Code of contributing genebank Country of contributing genebank Contributing genebank number Code of country origin Species name Plant type Ploidy Origin Character of material Creating of genetic variability Growth habit Habits Location of collecting site Year of collecting Longitude of collecting site Latitude of collecting site Altitude of collecting site Availability of material Date of last input or alternation

5 characters 30 characters 4 characters 7 characters 3 characters 9 characters 3 characters 25 characters 1 character 1 character 1 character 1 character 1 character 1 character 1 character 30 characters 4 characters 6 characters 6 characters 6 characters 1 character date

of passport data of the collection, it was not possible to document the complete collection for passport data, and several data are missing, including information on the country of origin, year of development, ancestor of cultivar, etc. (http://www.cgn.wageningenur.nl/pgr/collections/crops/ﬂax.htm). The set of descriptors is comprehensive and to be complete would need a large effort in order to characterize the complete collections. Many of the important characters are affected by the growth environment and have to be evaluated for a number of individual plants (or for bulked samples). Therefore, a system that can indicate the relatedness (or identity) of accessions before all the phenotypic characterization is done would be a signiﬁcant advantage. Such a prescreening could be performed using DNA markers and a start has been made on this task. A comparison of the range of diversity in the world collection of ﬂax maintained by Plant Gene Resources of Canada (PGRC) with the diversity observed in 19 Canadian registered ﬂax cultivars was made (Diederichsen 2001). Morphological and seed-oil characters were used to describe the phenotypic diversity in 2331 ﬂax accessions. The comparison between the Canadian cultivars and the world collection was based on single

Table 7. International Flax Data Base – characterization and evaluation part Morphological characters • • • • • • • • • • • • • •

Plant natural height Stem length Flower – size of corolla Sepal – dotting Petal – color Petal – longitudinal folding Anther – color Stamen – ﬁlament color at top Style – color Boll – type Boll – size Boll – cilliation pf septa Weight per 1000 seeds Seed – color

1 character 1 character 1 character 1 character 1 character 1 character 1 character 1 character 1 character 1 character 1 character 1 character 1 character 1 character

Biological characters • • • • •

Resistance to lodging Fusarium resistance Rust resistance Vegetation period (to maturity) Time of beginning of ﬂowering

1 character 1 character 1 character 1 character 1 character

Agronomic characters • • • • • •

ﬁber content in straw ﬁber yield ﬁber quality oil content oil yield linolenic acid content

1 character 1 character 1 character 1 character 1 character 1 character

characters, as well as on character complexes by application of an existing intraspeciﬁc classiﬁcation for the species. Considering single quantitative or qualitative character expressions, the Canadian cultivars represented a wide range in diversity for the species. The variation of characters Canadian plant breeders have selected for (e.g., plant height, seed weight, seed color, petal color, oil content) was reduced further than those characters not focused on by plant breeders (e.g., dotting of the sepals, style color, ciliation of capsule septa, oil quality characters). The variability in oil content, oil yield, and FA composition of 60 linseed cultivars was assessed to identify suitable accessions for use in future breeding and devel-

Chapter 8 Flax

opment endeavors in Ethiopia (Wakjira et al. 2004). Mean oil content ranged from 291 to 359 g kg−1 , while oil yields varied between 1443 and 3,276 g m−2 . Exotic introductions, especially those from Canada such as CDC-VG, had higher oil content than the local cultivars. Although accessions with variable linolenic acid content were identiﬁed, this variability was insufﬁcient to develop genotypes with less than 20 g kg−1 linolenic acid for cooking oil through conventional crossing and selection methods. Thus the introduction of exotic materials should be given more emphasis through germplasm exchange programs. Hence mutation techniques may be necessary in addition to the introduction of exotic lines to obtain linseed genotypes with low linolenic acid content. 8.3.2 Molecular Markers for Germplasm Identification Various marker systems have been used for the identiﬁcation of redundant germplasm, including isozymes (van Hintum and Visser 1995; van Hintum et al. 1996), RAPDs (Waycott and Fort 1994; Virk et al. 2000; Phippen et al. 1997; Zeven et al. 1998), AFLPs (Cervera et al. 1998; van Treuren et al. 2001), and intersimple sequence repeats (ISSRs) (Wiesnerova and Wiesner 2004). The three DNA-based marker systems have been used for germplasm screening. Isozymes A set of 28 ﬁber ﬂax and linseed cultivars differing in plant morphology and technological parameters were analyzed by isozyme markers in ﬁve ontogenetic phases (Krulickova et al. 2002). Eighteen isozyme systems produced 145 different bands, of which 66 (45.52%) were found to be polymorphic. The highest level of polymorphism was found in acid phosphatase and esterase, but polymorphisms were also detected in aconitase, diaphorase, glutamate dehydrogenase, peroxidase, and superoxide dismutase as well. The highest number of unique isozymic spectra (cultivar × enzyme × ontogenetic phase) was detected in the phase of shoot with removed cotyledons. Use of the complete set of polymorphic isozymes resulted in the identiﬁcation of 20 out of the 28 cultivars (71%) that were screened. RAPD Screening RAPDs have been used to screen ﬂax cultivars and wild species (Lemesh et al. 1999; Stegnii et al. 2000; Fu et al. 2003a,b). Twelve ﬂax accessions were screened with 20 randomly selected primers differing in nu-

289

cleotide sequence and Guanine plus Cytosine makeup, and nine primers revealed polymorphic fragments (Lemesh et al. 1999). The ﬂax cultivars and wild species of Linum genus that were hardly distinguishable by morphological traits were readily distinguished. The genetic relationship of 54 North American ﬂax cultivars was assessed by means of RAPD markers (Fu et al. 2003a). Eighty-four polymorphic RAPD loci were identiﬁed. The genetic relationships of the cultivars inferred via RAPD similarity were largely consistent with known, but incomplete, pedigrees of the cultivars. Both Canadian and US cultivars were intermixed in various groups without distinct separation, but several genetically distinct cultivars (i.e., NDR 52, Vimy, Rocket, Norland, Dakota, and Marine) were identiﬁed. The use of single plants for RAPD analysis can lead to greater apparent variation between the samples. Bulking strategies can help eliminate such variation. Several bulking strategies were assessed for detecting RAPD variations and determining genetic relationships of ﬁve ﬂax landrace accessions (Fu et al. 2003b). Bulking ten individuals before and after DNA isolations generated consistent RAPD variations. About 30% of the polymorphic RAPD loci observed in the plant-by-plant (PBP) sample were difﬁcult to score and/or undetected in the bulked samples of the same accession. Heterogeneity among the six bulked samples of the same accession was observed at 5.6% of the loci scored. The frequency of a speciﬁc RAPD band present in those individuals used to form a bulk was at least 1/11 for its detection in the bulked sample. Despite these limitations, bulking still generated compatible genetic relationships of the ﬁve accessions from its PBP sampling. SSRs ISSR markers are based on size polymorphisms of 200 to 2,500 bp long intermicrosatellite spacers that are ampliﬁable by single-primer PCR reaction. This approach was used to generate polymorphisms useful for the assessment of genetic diversity within ﬂax germplasm collections (Wiesnerova and Wiesner 2004). Nine selected anchored ISSR primers for ﬁngerprinting of 53 ﬂax cultivars or genotypes were used and 62 scorable bands were identiﬁed. Of these, 45 bands (72.6%) were polymorphic. The 53 ﬂax accessions were divided into four groups and eight subgroups using using an unweighted pair group method with arithmetic means (UPGMA) clustering procedure based on genetic similarity expressed by the Jaccard’s similarity coefﬁcient (JSC). The clustering

290

C.A. Cullis

procedure within both groups and subgroups successfully produced smaller homogenous clusters, whereas clustering between the four main groups of ﬂax accessions displayed only a continuous decrease of similarity with a weak clustering effect. This ﬁngerprinting method had a second correlation. A one-way analysis of variance (ANOVA) test conﬁrmed statistically signiﬁcant differences in the average thousand seed mass between eight subclusters of ﬂax accessions from an ISSR-PCR-based UPGMA dendrogram (Wiesnerova and Wiesner 2004). 8.3.3 Inducing New Variability or Traits The ﬂax germplasm screening may provide the genetic variation necessary to continue the improvement in crop performance. However, there are three other methods that have proved to be successful in generating new, useful variation, namely, somaclonal variation, induced mutations, and incorporation of foreign genetic material through transformation. Somaclonal Variation The appearance of mutants when plant cells are passaged through tissue culture and regenerated is known a somaclonal variation. Flax certainly does generate variants in tissue culture (McHughen and Swartz 1984; Rowland et al. 1988; Rakousky et al. 1999). Flax can be efﬁciently regenerated from microspores as well as from somatic explants from hypocotyls, cotyledons, leaves, stems, and roots (Rutkowska-Krause et al. 2003). However, anther cultures were less effective in plant regeneration than somatic cell cultures, but the regenerants derived from anther cells showed valuable breeding features, including increased resistance to fungal wilt. The length of in vitro cultivation signiﬁcantly increases the ploidy and affects such features as regenerant morphological characteristics, petal color, and resistance to Fusarium oxysporum-induced fungal wilt. Mutation Induction EMS and radiation mutation has been successfully applied to ﬂax to generate new mutants, especially those with altered oil quality (Rowland et al. 1995; Bhatia et al. 1999). Additional phenotypes that have been selected include curly stem (Tejklová 2002) and mutations that affect cell walls (Chen et al. 1998a). The oil modiﬁcations are the most important and have been induced in three cultivars, Gleneig (Green

1986), Rauloinus (Nichterlein et al. 1988), and McGregor (Rowland 1991; Rowland and Bhatty 1990; Ntiamoah and Rowland 1997; Ntiamoah et al. 1995). These mutations have subsequently been used in breeding programs as well to generate new varieties. Transgenic Flax Flax can be readily transformed both by Agrobacterium tumefaciens and by particle bombardment (Jordan and McHughen 1988; Dong and McHughen 1993). Flax-speciﬁc promoters are available so tissuespeciﬁc transgene expression can be achieved (Jain et al. 1999). Additionally, the initial transgenic developed varieties appear to perform as well as the nontransformed controls (McHughen and Rowland 1991; Lacoux et al. 2003). Therefore, all the components are in place for novel ﬂax products and new genetic material to be integrated into the commercial germplasm. However, as with all transgenic crops, public acceptability is likely to limit their adoption rather than any technical difﬁculties in getting them developed. A new herbicide-resistant ﬂax line, Trifﬁd, was created by Agrobacterium-mediated transformation in which the transfer-DNA (T-DNA) contained the ALS gene from a chlorsulfuron-tolerant line of A. thaliana (McSheffrey et al. 1992). This mutant ALS gene contains a single base-pair substitution resulting in a single amino acid change in the sequence of this protein when compared with the wild-type enzyme. The advantage of this variety was that it could be seeded into soil previously treated with a sulfonylurea herbicide, whereas ordinary ﬂax cannot normally be seeded into such soil for several years. An alternative to modiﬁed ﬂax as a conventional crop, ﬂax could also be used to produce novel chemicals, either as a resource or to improve the quality of products. An example is the synthesis of polyhydroxybutyrate in transgenic ﬂax. The synthesis of both the ﬁbers and the polyhydroxybutyrate for composites in a single plant has been reported (Wrobel et al. 2004). The ﬂax (cv. Nike) plants were transformed using constructs bearing single cDNA, the encoded beta-ketothiolase enzyme (C plants), or all three of the genes necessary for poly-beta-hydroxybutyrate (PHB) synthesis (M plants) and gene expression targeted to the plastids. To prevent growth retardation due to interference with the normal plastid functions, a stem-speciﬁc promoter was used to drive gene expression.

Chapter 8 Flax

8.4 Gene Discovery

291

to circumvent the lack of other genomic resources. Thus in a similar fashion the Lupme5 cDNA was isolated from ﬂax hypocotyls using the RACE-PCR technique and this cDNA corresponds to the very The lack of both a genetic and a physical map has basic pectin methylesterase isoform (Al-Qsous et al. limited the extent to which ﬂax genes can be isolated. 2004). Added to this there have been no large gene-discovery projects using ESTs either (the number of ﬂax ESTs in 8.5 bdEST is only 2237 as of 27 December 2004, with the Quantitative Trait Loci majority of these coming from a ﬂax-ﬁber-enriched library). It is clear that new functions have been idenQTLs are involved in the control of complex charactiﬁed and used from mutation experiments and from ters. Since a large number of loci, each having a relatransformation events. Currently a small number of tively small effect, are involved, it is difﬁcult to identify genes have been isolated from ﬂax. The ﬁrst genes to these loci without a detailed molecular map. Thus one be isolated were those coding for the ribosomal RNAs, of the few characters with which QTLs have been asafter ribosomal RNAs the 45S and the 5S ribosomal sociated is resistance to Fusarium (Spielmeyer et al. RNAs (Goldsbrough and Cullis 1981; Goldsbrough et 1998). These loci were identiﬁed in the process of genal. 1981). Since then the genes in ﬂax have been isoerating an AFLP linkage map in which the two parents lated through their homology with known represendiffered in their susceptibility to the soil pathogen tatives (ubiquitin, Agarwal and Cullis 1991). The L6 Fusarium oxysporum (lini). rust resistance gene from ﬂax was cloned after tagging with the maize transposable element Activator (Lawrence et al. 1995). The molecular data for the L6 8.6 and M rust-resistance genes, representing two of the Future Scope of Works ﬁve rust resistance gene loci in ﬂax, are fully consistent with earlier genetic data: the L locus is a single Flax has some unique properties that make it suitgene with multiple alleles expressing different rust able to be used as a model system. It has clearly resistance speciﬁcities, and the M locus is complex, been important in the elucidation of the structure containing an array of about 15 similar genes. Thus, of disease resistance genes. It is also the best studied while L6 and M resistance genes have 86% nucleotide example of genomic responses to the environment identity, their locus structure is very different. These (Durrant 1962; Cullis 1977, 1986, 1999). In both of genes encode products belonging to the nucleotide these cases, the understanding of the molecular events binding site–leucine-rich repeat class of disease re- would have been facilitated by additional genomic resistance proteins (Ellis et al. 1997). The elucidation sources. of the ﬂax resistance gene structure has proved to be In many ways ﬂax is ideally positioned to become an important underpinning for the search for many a useful model system. It is a multifunctional crop other plant disease resistance genes. and can be effectively used to unravel both ﬁber and The FA biosynthetic genes beta-ketoacyl CoA syn- oil biosynthesis. The small size of the genome and thase, FA elongase, stearoyl-ACP desaturase, and FA its organization also make it relatively easy to isodesaturase have been cloned. This was achieved by late genes. The high concentration of low-copy seﬁrst constructing a cDNA library made from ﬂax bolls quences in the unmethylated fraction of the genome collected at 12 d after anthesis (12 DAA) (Fofana et al. would also make a gene space project very practical 2004). This library was screened using a one-step RT- in ﬂax. PCR ampliﬁcation with Sad1-1and Sad1-2 primers. Essentially the future for ﬂax depends on the deSad1-1 and Sad1-2 primers designed from ﬂax SAD se- veloping markets. If the importance and use for food quences (AJ006957) and degenerated primers (FAD-F: (both health foods/nutraceuticals for humans and anGCAATCCCACCGCACTGT; FAD-R: AGGTAGCCTC- imal feed) and additional use for the ﬁber increase, CATCGCGT) designed from conserved regions He- then there will be a greater importance in underlianthus (AF251843), Brassica (AF124360), and Soy- standing and manipulating ﬂax. A detailed molecular bean (L4392). This use of heterologous information marker map, as well as sharing of molecular resources, has proved to be useful in ﬂax and can be used is a ﬁrst priority. The development of the molecular

292

C.A. Cullis

map will also facilitate the screening of the germplasm Domier K (1997) The current status of the ﬁeld crop. Euroﬂax Newslett 8:8–10 and the detection of the extent of genetic variation present therein. The ease of transformation and re- Dong J-Z, McHughen A (1993) An improved procedure for the production of transgenic ﬂax plants using Agrobacterium generation is an additional asset since constructs can tumefaciens. Plant Sci 88:61–71 be tested in vivo and putative gene functions directly Dribnenki JCP, McEachern SF, Chen Y, Green AG, Rashid tested.

References Agarwal ML, Cullis CA (1991) The ubiquitin genes in ﬂax. Gene 99:69–75 Al-Qsous S, Carpentier E, Klein-Eude D, Burel C, Mareck A, Dauchel H, Gomord V, Balange AP (2004) Identiﬁcation and isolation of a pectin methylesterase isoform that could be involved in ﬂax cell wall stiffening. Planta 219:369–378 Beard BH (1962) Genetic and cytogenetic investigations with Linum. The Flax Institute of the United States, Fargo, ND, pp 3–6 Bhatia CR, Nichterlein K, Maluszynski M (1999) Oil seed cultivars developed from induced mutations and mutations altering fatty acid composition. Mutat Breed 11:1–38 Cervera MT, Cabezas JA, Sancha JC, Martinez de Toda F, Martinez-Zapater JM (1998) Application of AFLPs to the characterization of grapevine Vitis vinifera L. genetic resources. A case study with accessions from Rioja (Spain). Theor Appl Genet 97:51–59 Chen L, Carpita NC, Reiter W-D, Wilson RH, Jeffries C, McCann MC (1998a) A rapid method to screen for cell-wall mutants using discriminant analysis of Fourier transform infrared spectra. Plant J 16:385–389 Chen Y, Hausner G, Kenaschuk E, Procunier D, Dribnenki P, Penner G (1998b) Identiﬁcation of microspore-derived plants in anther culture of ﬂax (Linum usitatissimum L.) using molecular markers. Plant Cell Rep 18:44–48 Comstock VE (1963) Linkage relationships in ﬂax. The Flax Institute of the United States, Fargo, ND, pp 7–8 Cullis CA (1977) Molecular aspects of the environmental induction of heritable changes in ﬂax. Heredity 38:129–154 Cullis CA (1981) DNA sequence organization in the ﬂax genome. Biochim et Biophys Acta. 652:1–15 Cullis CA (1986) Phenotypic consequences of environmentally induced changes in plant DNA. Trends Genet 2:307–310 Cullis CA (1999) Environmental stress – a generator of adaptive variation? In: Lerner HR (ed) Plant Adaptations to Stress Environments. Marcel Dekker, New York, pp 149–160 Cullis CA, Oh TJ, Gorman MB (1995) Genetic mapping in ﬂax (Linum usitatissimum). In: Proc 3rd Meeting Int. Flax Breed Res Group, St Valery-en-caux, France, pp 161–169 de Candolle A (1890) Origin of Cultivated Plants. Appleton, New York Diederichsen A (2001) Comparison of genetic diversity of ﬂax (Linum usitatissimum L.) between Canadian cultivars and a world collection. Plant Breed 120:360–362

KY (2004) 2090 low linolenic acid ﬂax. Can J Plant Sci 84:797–799 Drouillard JS, Good EJ, Gordon CM, Kessen TJ, Sulpizio MJ, Montgomery SP, Sindt JJ (2002) Flaxseed and ﬂaxseed products for cattle: effects on health, growth performance, carcass quality and sensory attributes. In: Proc 59th Flax Institute of the United States, Fargo, ND, pp 72–87 Duguid SD, Kenaschuk EO, Rashid KY (2003) Macbeth ﬂax. Can J Plant Sci 83:803–805 Duguid SD, Kenaschuk EO, Rashid KY (2004) Prairie Blue ﬂax. Can J Plant Sci 84:801–803 Durrant A (1962) The environmental induction of heritable changes in Linum. Heredity 17:27–61 Ellis J, Lawrence G, Ayliffe M, Anderson P, Collins N, Finnegan J, Frost D, Luck J, Pryor T (1997) Advances in the molecular genetic analysis of the ﬂax-ﬂax rust interaction. Annu Rev Phytopathol 35:271–291 Evans GM, Rees H, Snell CL, Sun S (1972) The relationship between nuclear DNA amount and the duration of the mitotic cycle. Chrom Today 3:24–31 Flor HH (1955) Host-parasite interaction in ﬂax rust – Its genetics and other implications. Phytopathology 45:680–685 Fofana B, Duguid S, Cloutier S (2004) Cloning of fatty acid biosynthetic genes beta-ketoacyl CoA synthase, fatty acid elongase, stearoyl-ACP desaturase, and fatty acid desaturase and analysis of expression in the early developmental stages of ﬂax (Linum usitatissimum L.) seeds. Plant Sci 166:1487–1496 Foulk JA, Akin DE, Dodd RB, McAlister III DD (2002) Flax ﬁber: potential for a new crop in the Southeast. In: Janick J, Whipkey A (eds) Trends in New Crops and New Uses. ASHS, Alexandria, VA, pp 361–370 Fu YB, Rowland GG, Duguid SD, Richards KW (2003a) RAPD analysis of 54 North American ﬂax cultivars. Crop Sci 43:1510–1515 Fu YB, Guerin S, Peterson GW, Carlson JE, Richards KW (2003b) Assessment of bulking strategies for RAPD analyses of ﬂax germplasm. Genet Resource Crop Evol 50:743–746 Fuller D, Korisettar R, Venkatasubbaiah PC, Jones MK (2004) Early plant domestications in southern India: some preliminary archaeobotanical results. Veg Hist Archaeobot 13:115–129 Goldsbrough PB, Cullis CA (1981) Characterization of the genes for ribosomal RNA in ﬂax. Nucleic Acid Res 9:1301–1309 Goldsbrough PB, Ellis THN, Cullis CA (1981) Organization of the 5S RNA genes in ﬂax. Nucleic Acids Res 9:5895–5904 Gorman MB, Cullis CA, Alldridge N (1992) Genetic linkage analysis of isozyme polymorphisms in ﬂax. J Hered 84:73–80

Chapter 8 Flax Green AG (1986) A mutant genotype of ﬂax (Linum usitatissimum L.) containing very low levels of linolenic acid in its seed oil. Can J Plant Sci 66:499–503 Gurbuz B (1999) Determination of cross-pollination in ﬂax (Linum usitatissimum) using different experimental designs. J Agric Sci 133:31–35 Hamilton I (1986) Linen. Textiles 15:30–34 Jain RK, Thompson RG, Taylor DC, MacKenzie SL, McHughen A, Rowland GG, Tenaschuk D, Coffey M (1999) Isolation and characterization of two promoters from linseed for genetic engineering. Crop Sci 39:1696–1701 Jones CJ, Edwards KJ, Castaglione S, Winﬁeld MO, Sala F, van de Wiel C, Bredemeijer G, Vosman B, Matthes M, Daly A, Brettschneider R, Bettini P, Buiatti M, Maestri E, Malcevschi A, Marmiroli N, Aert R, Volckaert G, Rueda J, Linacero R, Vazquez A, Karp A (1997) Reproducibility testing of RAPD, AFLP and SSR markers in plants by a network of European laboratories. Mol Breed 3:381–390 Jordan MC, McHughen A (1988) Glyphosate tolerant ﬂax plants from Agrobacterium mediated gene-transfer. Plant Cell Rep 7:281–284 Knowles PF, Houston BR (1955) Inheritance of resistance to Fusarium wilt of ﬂax in Dakota selection. Agron J 47:131–135 Kozlowski R, Manys S (2000) Global trends and newest achievements in ﬂax and hemp processing and applications. In: Proc 58th Flax Institute of the United States, Fargo, ND, pp 141–156 Kozlowski R, Bobbiewicz-Kozlowska T, Forjasz-Gfrus R, Kozlowska J, Grygorowicz Z (2004) Beneﬁcial effects of linseed as a source of pharmaceutical and dietetic products. In: Proc 60th Flax Institute of the United States, Fargo, ND, pp 72–79 Kristensen VN, Keleﬁotis D, Kristensen T, Borresen-Dale A (2001) High-throughput methods for detection of genetic variation. Biotechniques 30:318–332 Krulickova K, Posvec Z, Griga M (2002) Identiﬁcation of ﬂax and linseed cultivars by isozyme markers. Biol Planta 45:327–336 Lacoux J, Duval I, Dupre P, Gutierrez L, Lesueur S, Roger D, Laine E (2003) Activity of a ﬂax pectin methylesterase promoter in transgenic tobacco pollen. J Plant Physiol 160:977–989 Lai E (2001) Application of SNP technologies in medicine: lessons learned and future challenges. Genome Res 11:927–929 Lawrence GJ, Finnegan EJ, Ayliffe MA, Ellis JG (1995) The L6 gene for ﬂax rust resistance is related to the Arabidopsis bacterial resistance gene RPS2 and the tobacco viral resistance gene N. Plant Cell 7:1195–1206 Lemesh VA, Malyshev SV, Khotyleva LV (1999) Application of molecular markers to assess ﬂax genetic diversity. Doklady Akademii Nauk Belarusi 43:70–72 Lepsch D, Horal J (1998) Development of an integrated modular plastic electrical carrier and ﬂax/polypropylene shelf

293

panel for a vehicle rear shelf system. Plastics: components, processes, and technology (SP-1340), SAE Technical Paper Series 980727 Mackiewicz-Talarczyk M (2000) The potential of the world linen market. In: Proc 58th Flax Institute of the United States, Fargo, ND, pp 165–172 Maddock T, Bauer ML, Koch K, Anderson VL, Maddock RJ, Lardy GP (2004) The effect of processing ﬂax in beef feedlot rations on performance, carcass characteristics and trained sensory panel ratings. In: Proc 60th Flax Institute of the United States, Fargo, ND, pp 118–123 Maggioni L, Pavelek M, van Soest LJM, Lipman E (compilers) (2002) ECP/GR Flax Genetic In: Resources in Europe Ad hoc Meeting, 7–8 December 2001, Prague, Czech Republic ECP/GR (European Cooperative Programme for Crop Genetic Resources Networks), ECP/GR-ICN(ECP/GR – Sugar, Starch and Fibre Crops Network), EUR (Regional Ofﬁce for Europe) Marshall G (1989) Flax: Breeding and Utilisation. Kluwer, Dordrecht McGregor WG (1937) Inheritance of quality and quantity of oil in ﬂax in relation to other palnt characters. Can J Res C 15:362–379 McHughen A, Swartz M (1984) A tissue-culture derived salttolerant line of ﬂax (Linum usitatissimum). J Plant Physiol 117:109–117 McHughen A, Rowland GG (1991) The effect of T-DNA on the agronomic performance of transgenic ﬂax plants. Euphytica 55:269–275 McSheffrey S, McHughen A, Devine M (1992) Characterization of transgenic sulfonylurea resistant ﬂax. Theor Appl Genet 84:480–486 Mittapalli O, Rowland G (2003) Inheritance of seed color in ﬂax. Crop Sci 43:1945–1951 Muravenko OV, Samatadze TE, Popov KV, Amosova AV, Zelenin AV (2001a) Polymorphism of heterochromatic regions of ﬂax chromosomes. Membr Cell Biol 14:743–748 Muravenko OV, Samatadze TE, Popov KV, Amosova AV, Zelenin AV (2001b) Comparative genome analysis in two ﬂax species by C-banding patterns. Russ J Genet 37:253–256 Muravenko OV, Lemesh VA, Samatadze TE, Amosova AV, Grushetskaia ZE, Popov KV, Semenova I, Yu O, Khotyleva LV, Zelenin AV (2003) Genome comparisons of three closely related ﬂax species and their hybrids with chromosomal and molecular markers. Genetika 39:510–518 Muravenko OV, Amosova AV, Samatadze TE, Yu O, Semenova I, Nosova V, Popov KV, Shostak NG, Zoschuk SA, Zelenin AV (2004) Chromosome localization of 5S and 45S Ribosomal DNA in the genomes of Linum L. Species of the Section Linum (Syn. Protolinum and Adenolinum). Russ J Genet 40:193–196 Myers WM (1936) A correlated study of the inheritance of seed size and botanical characters in the ﬂax cross Redwing X Ottawa770B. Agron J 28:623–635

294

C.A. Cullis

Nichterlein K, Marquard R, Friedt W (1988) Breeding for modiﬁed fatty acid composition by induced mutations in linseed (Linum usitatissimum L.). Plant Breed 101:190–199 Ntiamoah C, Rowland GG (1997) Inheritance and characterization of two linolenic acid EMS induced McGregor mutant ﬂax (Linum usitatissimum L.). Can J Plant Sci 77:353–358 Ntiamoah C, Rowland GG, Taylor DC (1995) Inheritance of elevated palmitic acid in ﬂax and its relationship to the low linolenic acid. Crop Sci 35:148–152 Oh T, Gorman M, Cullis CA (2000) RFLP and RAPD mapping in ﬂax (Linum usitatissimum). Theoret Appl Genet 101:590–593 Panaud O, Chen XL, McCouch SR (1995) Frequency of microsatellite sequences in rice (Oryza sativa L). Genome 38:1170–1176 Phippen WB, Kresovich S, Candelas FG, McFerson JR (1997) Molecular characterization can quantify and partition variation among genebank holdings: a case study with phenotypically similar accessions of Brassica oleracea var. capitata L. (cabbage) ‘Golden Acre’. Theor Appl Genet 94:227–234 Power K, Saarinen N, Chen J, Thompson L (2004) Interactive effects of ﬂaxseed and soy in human breast cancer. In: Proc 60th Flax Institute of the United States, Fargo, ND, pp 80–85 Rakousky S, Tejklova E, Wiesner I, Wiesnerova D, Kocabek T, Ondrej M (1999) Hygromycin B – an alternative in ﬂax transformant selection. Biol Planta 42:361–369 Rowland GG (1991) An EMS-induced low-linolenic-acid mutant in McGregor ﬂax (Linum usitatissimum L.). Can J Plant Sci 71:93–396 Rowland GG, Bhatty RS (1990) Ethyl methanesulphonate induced fatty acid mutations in ﬂax. J Am Oil Chem Soc 67:213–214 Rowland GG, McHughen A, Mconie C (1988) Field-evaluation on nonsaline soils of a somaclonal variant of Mcgregor ﬂax selected for salt tolerance in vitro. Can J Plant Sci 68:345–349 Rowland GG, McHughen A, Bhatty RS, Mackenzie SL, Taylor DC (1995) The application of chemical mutagenesis and biotechnology to the modiﬁcation of linseed (Linum usitatissimum L). Euphytica 85:317–321 Rowland GG, Hormis YA, Rashid KY (2003) CDC Mons ﬂax. Can J Plant Sci 83:801–802 Rutkowska-Krause I, Mankowska G, Lukaszewicz M, Szopa J (2003) Regeneration of ﬂax (Linum usitatissimum L.) plants from anther culture and somatic tissue with increased resistance to Fusarium oxysporum. Plant Cell Rep 22:110–116 Saeidi G, Rowland GG (1997) The inheritance of variegated seed color and palmitic acid in ﬂax. J Hered 88:466–468 Saeidi G, Rowland GG (1999) Seed color and linolenic acid effects on agronomic traits in ﬂax. Can J Plant Sci 79:521–526 Senior ML, Heun M (1993) Mapping maize microsatellites and polymerase chain-reaction conﬁrmation of the targeted repeats using a CT primer. Genome 36:884–889

Sharma H, Van Sumere C (eds) (1992) The Biology and Processing of Flax. M Publications, Belfast, UK Shaw FJK, Khan AR, Alam M (1931) Studies in Indian oilseeds. V The inheritance of characters in Indian linseed. Ind J Agric Sci 1:1–57 Spielmeyer W, Green AG, Bittisnich D, Mendham N, Lagudah ES (1998) Identiﬁcation of quantitative trait loci contributing to Fusarium wilt resistance on an AFLP linkage map of ﬂax (Linum usitatissimum). Theor Appl Genet 97:633–641 Stegnii VN, Chudinova YV, Salina EA (2000) RAPD Analysis of Flax (Linum usitatissimum L.) varieties and hybrids of various productivity. Russ J Genet 36:1149–1156 Stitt P (1986) History of ﬂax: 9000 years ago to 1986. In: Proc 51st Flax Institute of the United States, Fargo, ND, pp 152–153 Stitt P (1990) Flax, the ideal designer food ingredient. In: Proc 53rd Flax Institute of the United States, Fargo, ND, pp 20–25 Tammes T (1922) Genetic analysis, schemes of cooperation and multiple allemorphs of Linum usitatissimum. J Genet 12:19–46 Tammes T (1928) The genetics of the genus Linum Bibliographica. Genetica 4:1–36 Tejklová E (2002) Curly Stem – an induced mutation in ﬂax (Linum usitatissimum L.). Czech J Genet Plant Breed 38:125–128 Thompson L, Chen J, Hui E, Mann J, Ip T (2004) Interactive effects of ﬂaxseed and tamoxifen in human breast cancer. In: Proc 60th Flax Institute of the United States, Fargo, ND, pp 86–90 van Hintum ThJL, Visser DL (1995) Duplication within and between germplasm collections. II. Duplication in four European barley collections. Genet Resource Crop Evol 42:135–145 van Hintum ThJL, Boukema IW, Visser DL (1996) Reduction of duplication in a Brassica oleracea germplasm collection. Genet Resource Crop Evol 43:343–349 Van Sumere C (1992) Retting of ﬂax with special reference to enzyme-retting. In: Sharma H, Van Sumere C (eds) The Biology and Processing of Flax. M Publications, Belfast, UK, pp 157–198 van Treuren R, van Soest LJM, van Hintum ThJL (2001) Marker-assisted rationalisation of genetic resource collections. A case study in ﬂax using AFLPs. Theor Appl Genet 103:144–152 Virk PS, Zhu J, Newbury HJ, Bryan GJ, Jackson MT, Ford-Lloyd BV (2000) Effectiveness of different classes of molecular marker for classifying and revealing variation in rice (Oryza sativa) germplasm. Euphytica 112:275–284 Vos P, Hogers R, Bleeker M, Rijans M, Van de Lee T, Hornes M, Frijters A, Pot J, Peleman J, Kuiper M, Zabeau M (1995) AFLP – A new technique for DNA-ﬁngerprinting Nucleic Acids Res 23:4407–4414 Wakjira A, Labuschagne MT, Hugo A (2004) Variability in oil content and fatty acid composition of Ethiopian and introduced cultivars of linseed. J Sci Food Agric 84:601–607

Chapter 8 Flax Waycott W, Fort SB (1994) Differentiation of nearly identical germplasm accessions by a combination of molecular and morphologic analyses. Genome 37:577–583 Wiesnerova D, Wiesner I (2004) ISSR-based clustering of cultivated ﬂax germplasm is statistically correlated to thousand seed mass. Mol Biotechnol 26:207–214 Williams JGK, Kubelik AR, Livak J, Rafalski AJA, Tingey SV (1990) DNA polymorphisms ampliﬁed by arbitrary primers are useful as genetic-markers. Nucleic Acids Res 18:6531–6535

295

Wrobel M, Zebrowski J, Szopa J (2004) Polyhydroxybutyrate synthesis in transgenic ﬂax. J Biotechnol 107:41–54 Yermanos DM (1966) Cytogenetic relationships among Linum species. The Flax Institute of the United States, Fargo, ND, pp 1–2 Zeven AC, Dehmer KJ, Gladis T, Hammer K, Lux H (1998) Are the duplicates of perennial kale (Brassica oleracea L. var. ramosa DC.) true duplicates as determined by RAPD analysis? Genet Resource Crop Evol 45:105–111

Subject Index

Abiotic stress 26, 36, 92, 121, 157, 215, 239 – Al tolerance 27 – Al toxicity 37 – alkalinity 215 – chilling tolerance 27 – cold stress 92 – drought 36, 119, 120, 123, 125, 134, 137, 185, 215, 244, 280 – – early-season drought 125 – – end-of-season (terminal) drought 125 – – midseason drought 125 – freezing 92, 240 – frost 215 – heat 236 – iron deﬁciency 27, 37 – lodging 280 – mineral deﬁciency 22 – mineral toxicity 22 – moisture 280 – salinity 215 – salt stress 37 – salt tolerance 27 – water use efﬁciency 26, 36, 126 – waterlogging 27, 36 – winter hardiness 215 – winter survival 92, 240 Agrobacterium 43, 290 – A. tumefaciens 72, 170 Agronomic trait 7, 168, 266, 267, 288 – bolls per unit area 280 – dwarﬁsm 232 – ﬁber content in straw 288 – ﬁber length 280 – ﬁber quality 288 – ﬁber yield 288 – ﬂowering date 168 – ﬂowering time 89, 223, 239, 270 – lodging 39 – lodging resistance 280 – oil content 126, 288 – oil yield 288 – pod dehiscence 39 – seed coat hardiness (SCH) 39 – seed color 91 – seed weight 39, 280 – seed yield 214, 215 Allergens 123 Antixenosis 25 Antobiosis 25

Arabidopsis 3, 63, 94–96, 140, 204, 229 – A. thaliana 63, 68, 88, 95, 219, 230, 253, 267, 285 Arachis 115 – A. batizocoi 132 – A. cardenasii 132 – A. digoi 132 – A. duranensis 115, 131, 132 – A. hypogaea 115 – A. ipaensis 115, 132 – A. magna 115, 132 – A. monticola 115 – A. stenosperma 131, 132 Association mapping 197 Bacillus thuringiensis 170 Bacterial artiﬁcial chromosome (BAC) 40, 63, 100, 133, 157, 205, 252 – BAC clones 134 – BAC contigs 253 – BAC end sequences 101 – BAC library 40, 133, 134, 157, 164, 252, 253 Banding – C-banding 277 Barbarea vulgaris 68 Biotic stress 43, 157 Brassica 55, 179, 211, 265 – B. campestris 211 – – B. campestris ssp. nipposinica 245 – B. carinata 55, 68, 179, 212, 265 – B. chinensis 68, 211 – B. cossoniana 68 – B. elongata 68 – B. gravinae 68 – B. japonica 211 – B. juncea 55, 68, 179, 212, 265 – – B. juncea var. oleifera 265 – B. napus 55, 179, 212 – – B. napus ssp. napobrassica 55 – – B. napus ssp. napus 55 – – B. napus ssp. napus var. pabularia 55 – – B. napus var. oleifera 265 – – B. napus var. rapifera 265 – B. nigra 55, 68, 179, 212, 265 – B. oleracea 55, 179, 212, 231, 265 – – B. oleracea var. acephala 265 – – B. oleracea var. alboglabra 265 – – B. oleracea var. botrytis 265 – – B. oleracea var. capitata 265 – – B. oleracea var. italica 265

298

Subject Index

– B. pekinensis 211 – B. rapa 55, 68, 179, 211, 265 – – B. rapa ssp. chinensis 245 – – B. rapa ssp. chinensis var. pekinensis 245 – – B. rapa ssp. nipposinica 245 – – B. rapa ssp. oleifera 216 – – B. rapa ssp. pekinensis 101, 212, 219 – – B. rapa ssp. rapifera 245 – – B. rapa subsp. rapifera 212 – – B. rapa var. brown sarson 265 – – B. rapa var. chinensis 265 – – B. rapa var. nipposinica 265 – – B. rapa var. oleifera 265 – – B. rapa var. rapifera 265 – – B. rapa var. toria 265 – – B. rapa var. yellow sarson 265 – – B. rapa-alboglabra 227 Candidate gene 44, 88, 167, 205, 239, 252, 271 Carbon isotope discrimination 126 Chromosomal duplications 254 Chromosomal rearrangements 253 Cluster analysis 44, 71 Coincya monensis 68 Colinearity 94, 96, 164, 224, 253 Contig 44 Crambe abyssinica 68 Cytogenetic stock 218 – alien addition line 269 – – primarily alien addition line 218 – primary trisomics 218 Database 101 – Flax Data Base (IFDB) 287 – GenBank dbEST 163 – GenBank ICCARE 163 – GenBank SPUTNIK EST database 163 – GenBank UniGene 163 – SoyBase 6, 38 Diploid 55, 115, 154, 179, 221, 265, 269 Diplotaxis 67, 266 – D. erucoides 68 – D. tenuifolia 67 Disease 10, 15, 91, 227, 266, 280 – A. zinniae 164 – aﬂatoxins 122 – Albugo candida 60, 92, 183, 215, 227, 229, 236 – Alternaria blight 183 – Alternaria brassicae 60, 183 – Alternaria helianthi 164 – Alternaria leaf spot 164 – Alternaria stem spot 164 – Aspergillus ﬂavus 122, 134 – bacterial blight 10, 15 – bacterial pustule 10, 15

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

black stem 168 blackleg 91, 183, 266 Botrytis cinerea 164 brown stem rot 10, 15, 26 bud blight 26 Cercospora sojina 19 charcoal rot 164 clubroot 60, 227, 236, 246 Cylindrocladeum crotalariae 121 cylindrocladium black rot 121 Diaporthe helianthe 164 Diaporthe phaseolorum 19 downy mildew 15, 19, 60, 164, 167, 183 early leaf spot (ELS) 120, 122 Erysiphe cichoracearum 164 ﬂax rust 280, 287 ﬂax wilt 280 frog leaf spot 15 frogeye leafspot 19 Fusarium oxysporum 282, 287 Fusarium solanii 19 groundnut rosette assistor virus (GRAV) 124 groundnut rosette virus (GRV) 120, 124 head rot 164 Heterodera glycines 19 Indian peanut clump virus (IPCV) 125 late leaf spot (LLS) 120, 122 leaf blight 181 Leptosphaeria maculans 92, 183, 215, 266 Leptosphaerulina crassiasca 121 light leaf spot 60 Macrophomina phasiolina 164 Melamspora lini 282, 287 Meloidogyne 20 Meloidogyne arenaria 119, 121, 123 Microsphaera manshurica 19 middle stalk rot 164 mildew 156 nematode 120 Oidium lini 282 P. meibomiae 19 pasmo 280 peanut clump disease 125 peanut clump virus (PCV) 125 peanut mottle potyvirus (PMV) 19 peanut mottle virus 15 peanut root-knot nematode 119, 121, 123 peanut stem necrosis disease (PSND) 117 pepper spot 121 Perenospora parasitica 60, 183 Peronospora manshurica 19 Phakopsora pachyrhizi 19 Phialophora gregata 10 Phoma lingam 266 Phoma macdonaldii 164, 168

Subject Index

– Phomopsis 156 – phomopsis black stem 164 – Phomopsis helianthi 164 – phytophthora root rot 15 – Phytophthora sojae 19 – phytophthora stem rot 15 – Plasmodiophora brassicae 60, 227, 236, 246 – Plasmopara halstedii 164, 167 – powdery mildew 15, 19, 164, 280, 282 – Pseudomonas syringae 181 – – Pseudomonas syringae pv. glycinea 10 – – Pseudomonas syringae pv. tagetis 164 – Puccinia helianthi 164 – Pyrenopeziza brassicae 60 – Pythium megalacanthum 287 – reniform nematode 16 – rhizoctomia limb rot 121 – Rhizoctonia solanii 121 – Rhizoctonia stolonifera 164 – Rhizopus arrhizus 164 – root-knot nematode 16, 20 – Rotylenchulus reniformis 20 – rust 120, 164, 287 – Sclerotina sclerotiorum 215 – Sclerotinia 156, 183 – – sclerotinia blight 119–121 – – Sclerotinia minor 119, 121 – – Sclerotinia sclerotiorum 60, 92, 164, 167, 183 – – Sclerotinia stem rot 60 – scorch 287 – Septoria helianthi 164 – Septoria leaf spot 164 – soybean cyst nematode (SCN) 15, 19, 36 – soybean mosaic virus (SMV) 15, 19 – soybean rust 15, 19 – stem canker 15, 19, 164, 215 – stem rot 19, 215 – sudden death syndrome 15, 19, 26, 36 – tobacco streak virus (TSV) 117 – tomato spotted wilt virus (TSWV) 134 – turnip mosaic virus (TuMV) 92 – Verticillium dahliae 164 – Verticillium longisporum 60 – verticillium wilt 60, 164 – white rust 60, 92, 183, 195, 215, 227, 236 – wilt 164, 287 – Xanthomonas axonopodis pv. glycines 10 DNA ﬁngerprinting 135 Domestication 153, 275 Embryo rescue 59, 169, 185 Embryogenic ability 233 Enzyme-linked immunosorbent assay (ELISA) Epistasis 126 Eruca 266

123

299

– Eruca sativa 68 Evolution 265 Evolutionary conserved sequences (ECSs) 133 Expressed sequence tag (EST) 99, 204, 291 Fatty acid 18, 23, 57, 73, 126, 182, 198, 200, 231, 271, 279, 280 – arachidic acid 126 – behenic acid 126 – erucic acid 57, 181, 198, 213, 214 – lauric acid 76 – lignoceric acid 126 – linoleic acid 281 – linolenic acid 18, 23, 57, 58, 90, 126, 135, 154, 213, 214, 231, 237, 279 – oleic acid 18, 23, 90, 126, 154, 182, 200, 231 – palmitic acid 18, 23, 126 – stearic acid 18, 23, 126 Fiber quality 280 Gas chromatography (GC) 56, 186 GenBank 203 Gene discovery 203, 291 Gene expression 203 Gene ﬂow 171 Gene introgression 249 Gene mapping 10, 195, 224 Gene pool 65, 67, 119, 266 – primary gene pool 119, 266 – secondary gene pool 119 – tertiary gene pool 119 Gene pyramiding 42, 250 Gene tagging 243 – bulked segregant analysis (BSA) 62, 87, 91, 195, 224 GeneChip 43 Genetic (linkage) map 7, 132, 156, 186, 188, 215, 218, 267, 282 – comparative map 164, 223 – comparative mapping 9, 133, 193, 221, 253, 269 – composite (genetic) linkage map 97, 158 – consensus (genetic) map 11, 77, 88, 157 – ﬁrst-generation (genetic map) 7, 186 – functional map 157 – high-density (genetic) map 94, 221 – high-resolution map 44 – integrated (genetic) map 9, 86, 93 – saturated linkage map 287 – second generation map 188 Genetic complementation 252 Genetic diversity 64, 131, 137, 202, 244 Genetic engineering 43, 59, 72, 75, 183 Genetic mapping 77, 186, 243, 267 – ﬁne-scale mapping 271 Genetic modiﬁcation 72 Genetic relationship 265 Genetic resources 119, 120 – core collection 119

300

Subject Index

– minicore collection 120 Genetic transformation 44, 74, 170 Genome 157 – genome coverage 157 – genome diversity 164 – genome duplication 193, 269 – genome evolution 164, 221 – genome length 188 – genome rearrangement 96, 221 – genome relationships 215 – genome sequencing 205 – genome size 179, 213, 252, 278 – genome structure 267 Genomic rearrangements 270 Genomic resources 128 Genomic sequence 100 Genomics 43, 44, 98, 157, 253 – comparative genomics 44, 253, 254 – functional genomics 43, 99, 103 Germplasm 119, 126, 131, 203, 243, 266, 287 – Germplasm characterization 287 – Germplasm identiﬁcation 289 Glycine 1 – G. soja 3 – Glycine max 1 Graphical genotypes 72 Helianthus 291 – H. anomalus 169 – H. argophyllus 167 – H. deserticola 169 – H. paradoxus 169 – H. petiolaris 163, 169 – H. praecox 167 – Helianthus annuus 154 – – H. annuus ssp. annuus 154 – – H. annuus ssp. lenticularis 154 – – H. annuus ssp. macrocarpus 154 – Helianthus tuberosa 154 Herbicide 16, 20, 72, 169 – herbicide resistance 169 Heterosis 64 Hirschfeldia incarna 68 Hybrid – asymmetric hybrid 185 – cybrids 185 – heterosis 202, 214 – hybrid seed 153 – intergeneric hybrids 267 – somatic hybrids 185 – symmetric hybrid 185 Hybridization 115, 119 – somatic hybridization 184 – wide hybridization 59 In silico analysis

44

In situ hybridization 70 In situ hybridization 163, 165 – ﬂuorescence in situ hybridization (FISH) 67, 163, 224 – genomic in situ hybridization (GISH) 69, 163 Insect 24, 215 – aphid 120, 124, 215 – Aproaerema modicella 124 – Bagrada hilaris 183 – common cutworm 25 – corn earworm 24 – leaf miner 120, 124 – Lipaphis erysimi 183, 215 – Liriomyza spancerella 156 – mustard aphid 183 – painted bug 183 – Rachiplusia nu 156 – Spilosoma virginica 156 – Spodoptera litura 124 – stem weevil 168 – tobacco armyworm 124 Karyotype 169, 277 Karyotype map 100 Lactuca sativa 163 Lesquerella fendleri 68 Linkage disequilibrium 169 Linum – L. angustifolium 275 – L. austriacum 277 – L. bienne 276 – L. grandiﬂorum 277 – L. nervosum 277 – L. pillescens 277 – L. usitatissimum 275 Male sterility 17, 22, 56, 62, 214 – complete sterility 17 – cytoplasmic male sterility (CMS) 62, 153, 155 – genetic male sterility 153 – Ogura CMS 63 – partial male sterility 22 – partial sterility 17 – Polima (pol) system 64 – structural sterility 17 – synaptic sterility 17 Map-based cloning 42, 251 Mapping population 131, 188, 216, 269 – backcross (BC) population 216 – doubled-haploid (DH) lines 78, 186, 188, 286 – doubled-haploid (DH) population 86, 89, 131, 216 – F2 population 7, 186, 216 – immortal mapping population 216 – inbred backcross lines 88 – inbred BC population 216

Subject Index

– near-isogenic lines (NILs) 42, 87, 131, 242, 284 – recombinant inbred lines (RILs) 9, 131, 163, 188, 216 – recombinant substitution lines 24 Mapping program – Mapmaker 186 – Mapmaker v2.0 237 – Mapmaker/QTL 1.1 233, 237 – MapQTL 234 – QTL Cartographer 233 Marker conversion 243 Marker-assisted breeding 40, 195, 202, 243 Marker-assisted introgression 41, 248 Marker-assisted selection (MAS) 121, 132, 195, 224, 243 Medicago truncatula 133 Mendelization 242 Metabolomics 103 Microarray 43, 251, 254 – cDNA array 204 – cDNA microarray 43 – oilgunocleotide array 43 Molecular breeding 131 Molecular diversity 131 Moricandia 266 Morphological trait 235 – days to bud 235 – days to ﬂower 235 – lamina index 235 – lamina length 235 – leaf length 235 – number of leaf lobe 235 – petiole index 235 – petiole length 235 – petiole thickness 235 – petiole width 235 – plant height 235 – pubescence 235 – stem length 235 Multiple alleles 291 Near-infrared (NIR) transmittance spectroscopy 126 Nodulation 16, 20, 27 – Bradyrhizobium japonicum 3 Nuclear magnetic resonance (NMR) spectrometer 126 Open reading frame (ORF) 88 Organogenesis 169 Origin 115, 211, 265 – centers of origin 211 – polyphyletic origin 266 Orychophragmus violaceus 68 Phylogenetic relationship 94, 253 Physical map 44, 93, 100, 133, 232, 285, 291 Physical mapping 98, 163, 164 Physiological trait 126

301

– harvest index 126 – speciﬁc leaf area 126 – transpiration efﬁciency 126 Pigmentation 23 Pleiotropy 38 Pollination – cross-pollination 56 – self-pollination 56 Polymerase chain reaction (PCR) 87, 121, 133, 217, 267 – differential display reverse transcription PCR (DD-RT-PCR) 134 Polyploid 132, 163, 193, 212, 267 – allotetraploid 193 – amphidiploid 55, 61, 132, 179, 265 – – synthetic amphidiploid 140 – hexaploid 164 – tetraploid 119, 164 Positional cloning 251 Proteome 44, 101, 205 Proteomics 44, 103 Protoplast fusion 59, 169, 183, 185 Pure line 59 Quantitative trait loci (QTL) 271, 291 – beavis effect 132 – interval mapping 234

6, 24, 88, 132, 163, 167, 168, 233,

Raphanobrassica 67 Raphanus 62, 266 – R. raphinistrum 68 – R. sativus 62, 68 RNAi – RNAi silencing 44 – RNAi suppression 44 Sarson 211 – brown sarson 211 – toria 211 – yellow sarson 211 Seed quality 126, 214, 280 – amino acids 280 – glucosinolate 90, 181, 201, 214 – gums 280 – lignans 279, 280 – oil 154 – – oil content 280 – oil quality 135 – – canola quality 182, 186, 202 – – linola 279 – protein 117, 154, 213, 214, 280 – seed coat color 183, 197, 224 – seed color 280, 283 Segregation 4 – segregation distortion 189 – transgressive 4

302

Subject Index

Self-compatibility 211 Self-incompatibility (SI) 231, 246 – NS-glycoprotein 232 – S-glycoprotein 232 Sinapis 67, 266 – S. alba 68, 180 – S. arvensis 68 Single nucleotide polymorphism (SNP) Single-pod-descent (SPD) 5 Single-seed-descent (SSD) 5 Somaclonal variation 290 Somatic fusion 267 Southern hybridization 89 SPAD chlorophyll meter 126 Subtractive hybridization 157 Synteny 156, 270 Thlaspi perfoliatum

68

135

Transcript 44, 134, 157 Transcription factor 193 Transcriptome 44 Transcriptomics 44, 157 Transgene 164, 171, 203 Transgenic 72, 133, 140, 170, 290 – transgenic plants 170 Transposable element 23, 291 Transposon tagging 251 Trichome 22 Vernalization requirement Whole-genome sequencing

229, 271 103

Yeast artiﬁcial chromosome (YAC) – YAC contigs 253

253