Genomic and Personalized Medicine
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Genomic and Personalized Medicine Volume 1 Edited by Huntington F. Willard, Ph.D. Director Duke Institute for Genome Sciences & Policy Nanaline H. Duke Professor of Genome Sciences Howard Hughes Medical Institute Professor Duke University Durham, North Carolina 27708
and Geoffrey S. Ginsburg, M.D., Ph.D. Center Director, Center for Genomic Medicine Duke Institute for Genome Sciences & Policy Professor of Medicine Duke University Durham, North Carolina 27708
AMSTERDAM • BOSTON • HEIDELBERG • LONDON • NEW YORK • OXFORD • PARIS SAN DIEGO • SAN FRANCISCO • SYDNEY • TOKYO
Academic Press is an imprint of Elsevier
Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 32 Jamestown Road, London NW1 7BY, UK First edition 2009 Copyright © 2009 Elsevier Inc. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (44) (0) 1865 843830; fax (44) (0) 1865 853333; email:
[email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-369420-1 (set) ISBN: 978-0-12-370888-5 (vol. 1) ISBN: 978-0-12-370889-2 (vol. 2) For information on all Academic Press publications visit our web site at elsevierdirect.com Typeset by Charon Tec Ltd., A Macmillan Company. (www.macmillansolutions.com) Printed and bound in China 09 10 11 12 13 10 9 8 7 6 5 4 3 2 1
Contents in Brief Foreword
xxv
Preface
xxvii
Acknowledgements
xxix
Abbreviations
xxxi
Advisory Board
xxxix
Contributors
xli
PART I GENOMIC APPROACHES TO BIOLOGY AND MEDICINE Section 1 Principles of Human Genomics 1. Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine 2. Concepts of Population Genomics 3. Genomic Approaches to Complex Disease 4. Human Health and Disease: Interaction Between the Genome and the Environment 5. Epigenomics and its Implications for Medicine 6. Systems Biology and the Emergence of Systems Medicine Section 2 Technology Platforms for Genomic Medicine 7. DNA Sequencing for the Detection of Human Genome Variation and Polymorphism 8. Genome-Wide Association Studies and Genotyping Technologies 9. Copy Number Variation and Human Health 10. Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine 11. DNA Methylation Analysis: Providing New Insight into Human Disease 12. Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine 13. DNA Microarrays in Biological Discovery and Patient Care 14. Proteomics: The Deciphering of the Functional Genome
3
4 22 33 47 60 74
87 88 101 108 120 131 143 157 173
15. Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms 16. Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics
180 193
Section 3 Informatic and Computational Platforms for Genomic Medicine 205 17. Bioinformatic and Computational Analysis for Genomic Medicine 206 18. Fundamentals and History of Informatics for Genomic and Personalized Medicine 226 19. Electronic Medical Records in Genomic Medicine Practice and Research 233 20. Clinical Decision Support in Genomic and Personalized Medicine 242 21. Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics 252
PART II TRANSLATIONAL APPROACHES IN GENOMIC AND PERSONALIZED MEDICINE Section 4 Enabling Strategies in the Translation of Genomics into Medicine 22. Translational Genomics: From Discovery to Clinical Practice 23. Principles of Study Design 24. Biobanking in the Post-Genome Era 25. Application of Biomarkers in Human Population Studies 26. Validation of Candidate Protein Biomarkers 27. Pharmacogenetics and Pharmacogenomics 28. The Role of Genomics and Genetics in Drug Discovery and Development 29. Role of Pharmacogenomics in Drug Development 30. Clinical Implementation of Translational Genomics 31. Translating Innovation in Diagnostics: Challenges and Opportunities 32. The Role of Genomics in Enabling Prospective Health Care
261 262 275 284 299 308 321 335 343 357 367 378
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Contents in Brief
Section 5 Policy Challenges in Genomic and Personalized Medicine 33. From Sequence to Genomic Medicine: Genome Policy Considerations 34. Educational Strategies in Genomic Medicine 35. Federal Regulation of Genomic Medicine 36. Economic Issues and Genomic Medicine 37. Public–Private Interactions in Genomic Medicine: Research and Development
387 388 401 414 424 434
Section 6 Genomic Medicine and Public Health 445 38. What Is Public Health Genomics? 446 39. Why Do We Need Public Health in the Era of Genomic Medicine? 454 40. Principles of Human Genome Epidemiology 461 41. Genomics and Population Screening: Example of Newborn Screening 470 42. Family History: A Bridge Between Genomic Medicine and Disease Prevention 481 Section 7 Clinical Technologies Supporting Personalized Medicine 43. Molecular Imaging as a Paradigm for Genomic and Personalized Medicine 44. PET Imaging in Genomic Medicine 45. MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges 46. Fluorescence Imaging: Overview and Applications in Biomedical Research 47. Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers 48. Viral Chip Technology in Genomic Medicine 49. Vaccines Against Infectious Diseases: A BiotechnologyDriven Evolution 50. Cancer Vaccines: Some Basic Considerations 51. Biosensors for the Genomic Age 52. Stem Cells 53. Gene Therapy
493 494 500 512 524 532 538 562 573 590 599 610
PART III DISEASE-BASED GENOMIC AND PERSONALIZED MEDICINE: GENOME DISCOVERIES AND CLINICAL APPLICATIONS Section 8 Cardiovascular Genomic Medicine 623 54. The Genomics of Hypertension 624 55. Lipoprotein Disorders 634 56. Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis 652 57. Genomics of Myocardial Infarction 665 58. Acute Coronary Syndromes 680
59. Heart Failure in the Era of Genomic Medicine 60. Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection 61. Hypertrophic Cardiomyopathy in the Era of Genomic Medicine 62. Genetics and Genomics of Arrhythmias 63. Hemostasis and Thrombosis 64. Peripheral Arterial Disease 65. Genomics of Congenital Heart Disease 66. Genomics of Perioperative and Procedural Medicine
692
Section 9 Oncology Genomic Medicine 67. Cancer Genes, Genomes, and the Environment 68. Immune Cells and the Tumor Microenvironment 69. Lymphomas 70. Genomics in Leukemias 71. Genomics of Lung Cancer 72. Breast Cancer and Genomic Medicine 73. Colorectal Cancer 74. Prostate Cancer 75. Molecular Biology of Ovarian Cancer 76. Pancreatic Neoplasms 77. The Multiple Endocrine Neoplasia Syndromes 78. Genomics of Head and Neck Cancer 79. Genomic Medicine, Brain Tumors and Gliomas 80. Molecular Therapeutics of Melanoma 81. Emerging Concepts in Metastasis 82. Diagnostic-Therapeutic Combinations in the Treatment of Cancer
807 808 818 830 844 856 869 879 898 913 921 931 945 956 967 977
705 716 729 755 773 781 794
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Section 10 Inflammatory Disease Genomic Medicine 1009 83. Environmental Exposures and the Emerging Field of Environmental Genomics 1010 84. Molecular Basis of Rheumatoid Arthritis 1017 85. “Omics” in the Study of Multiple Sclerosis 1032 86. Inflammatory Bowel Disease 1040 87. Glomerular Disorders 1056 88. Spondyloarthropathies 1067 89. Asthma Genomics 1084 90. Genomic Aspects of Chronic Obstructive Pulmonary Disease 1098 91. Genomic Determinants of Interstitial Lung Disease 1110 92. Peptic Ulcer Disease 1122 93. Cirrhosis in the Era of Genomic Medicine 1138 94. Systemic Sclerosis 1155 Section 11 Metabolic Disease Genomic Medicine 95. Genomic Medicine of Obesity 96. Diabetes 97. Metabolic Syndrome 98. Nutrition and Diet in the Era of Genomics
1169 1170 1187 1194 1204
Contents in Brief
Section 12 Neuropsychiatric Disease Genomic Medicine 99. The Genetic Approach to Dementia 100. Parkinson’s Disease: Genomic Perspectives 101. Epilepsy Predisposition and Pharmacogenetics 102. Ophthalmology 103. Genomic Basis of Neuromuscular Disorders 104. Psychiatric Disorders 105. Genomics and Depression 106. Bipolar Disorder in the Era of Genomic Psychiatry Section 13 Infectious Disease Genomic Medicine 107. Genomic Approaches to the Host Response to Pathogens
1221 1222 1233 1243 1256 1265 1282 1289 1299
1313 1314
108. 109. 110. 111. 112.
Genomic Medicine and Aids Viral Genomics and Antiviral Drugs Host Genomics and Bacterial Infections Sepsis and the Genomic Revolution Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine
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1324 1340 1347 1362 1375
Glossary
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Index
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Table of Contents Foreword
xxv
Preface
xxvii
Acknowledgements
xxix
Abbreviations
xxxi
Advisory Board
xxxix
Contributors
xli
PART I GENOMIC APPROACHES TO BIOLOGY AND MEDICINE Section 1 Principles of Human Genomics 1. Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine Huntington F. Willard Introduction The Human Genome Variation in the Human Genome Expression of the Human Genome Genes, Genomes and Disease From Genome to Personalized Medicine Conclusion References Recommended Resources 2. Concepts of Population Genomics Mike E.Weale and David B. Goldstein Introduction Important Concepts in Population Genomics Human Population Genomics Application of Population Genomics to Genomic Medicine Conclusions References Recommended Resources 3. Genomic Approaches to Complex Disease Desmond J. Smith and Aldons J. Lusis Introduction Identifying Common and Rare Genomic Variations in the Population Relating DNA Variation to Phenotypes Integration of “Omic” Technologies with Genetics
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4 4 6 9 11 13 15 18 18 21 22 22 22 26 28 29 30 32 33 33 33 36 40
Conclusions and Prospects Acknowledgements References Recommended Resources 4. Human Health and Disease: Interaction Between the Genome and the Environment Kenneth Olden Introduction Importance of the Environment The Environmental Genome Project Problematic Nature of Gene–environment Interaction Studies Polymorphism and Disease Susceptibility: Case–control Studies Epigenetics and the Environment Conclusion Acknowledgements References 5. Epigenomics and its Implications for Medicine Moshe Szyf Introduction DNA Methylation Patterns Chromatin Modification DNA Methylation and Chromatin States Co-operatively Determine the State of Activity of Genes Epigenetics and Human Disease Conclusions Acknowledgements References 6. Systems Biology and the Emergence of Systems Medicine Nathan D. Price, Lucas B. Edelman, Inyoul Lee, Hyuntae Yoo, Daehee Hwang, George Carlson, David J. Galas, James R. Heath and Leroy Hood Introduction Systems Science in Biology And Medicine Multi-parameter Blood-bourne Biomarkers Emerging in vivo and in vitro Technologies Computational and Mathematical Challenges in Systems Medicine Conclusions and Perspectives References Recommended Resources
43 43 43 46
47 47 48 50 52 53 56 57 57 57 60 60 61 63
65 67 69 70 70 74
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Section 2 Technology Platforms for Genomic Medicine 7. DNA Sequencing for the Detection of Human Genome Variation and Polymorphism Samuel Levy and Yu-Hui Rogers Introduction DNA Sequencing Other Methodologies for Polymorphism Detection Future Directions Acknowledgements References 8. Genome-Wide Association Studies and Genotyping Technologies Kevin V. Shianna Introduction Principles of Genome-wide Association Studies Platform Overview Conclusion References 9. Copy Number Variation and Human Health Charles Lee, Courtney Hyland, Arthur S. Lee, Shona Hislop and Chunhwa Ihm Introduction Basic Principles of CNVs Detecting CNVs in a Genome-wide Manner Association of CNVs to Disease and Disease Susceptibility Implications of CNVs Conclusions Acknowledgements References Recommended Resources 10. Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine Anthony Antonellis and Eric D. Green Introduction Performing Comparative Sequence Analysis: Resources and Methods Comparative Sequence Analysis and Human Genetic Disease CSA and the Future of Human Genetics and Genomic Medicine References Recommended Resources
87 88 88 89 95 96 97 97
101 101 101 103 106 106 108
108 108 112 114 116 118 118 118 119
120 120 121 124 128 128 130
11. DNA Methylation Analysis: Providing New Insight into Human Disease 131 Susan Cottrell,Theo deVos, Juergen Distler, Carolina Haefliger, Ralf Lesche, Achim Plum and Matthias Schuster Introduction Technology to Assess DNA Methylation
131 132
Clinical Impact of DNA Methylation Analysis Conclusion References Recommended Resources 12. Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine Michelle M. Kittleson, Rafael Irizarry, Bettina Heidecker and Joshua M. Hare Introduction Gene Expression Technology Gene Discovery Molecular Signature Analysis Gene Discovery Versus Molecular Signature Analysis Current Issues in Gene Expression Analysis Alternative Technologies for Analysis of the Transcriptome Conclusion Acknowledgements References Recommended Resources 13. DNA Microarrays in Biological Discovery and Patient Care Andrew J.Yee and Sridhar Ramaswamy Introduction Microarray Technology Data Analysis Applications Limitations and Challenges Future Directions Conclusions References Recommended Resources
136 139 139 142
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143 143 146 148 151 151 153 153 154 154 156 157 157 157 160 161 165 165 168 168 172
14. Proteomics: The Deciphering of the Functional Genome 173 Li-Rong Yu, Nicolas A. Stewart and Timothy D.Veenstra Introduction 173 Gel-based and Solution-based Proteomics 174 Mass Spectrometry 175 Bioinformatics 176 Impact of Proteomics on Understanding Diseases 178 Conclusions 178 Acknowledgements 179 References 179 Recommended Resources 179 15. Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms 180 Christopher B. Newgard, Robert D. Stevens, Brett R.Wenner, Shawn C. Burgess, Olga Ilkayeva, Michael J. Muehlbauer, A. Dean Sherry and James R. Bain Introduction 180 Current Metabolomics Platforms: Basic Tools and General Features 181
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Comparison of NMR and MS Technologies for Unbiased Metabolic Profiling MS Methods for Targeted Metabolic Profiling Examples of NMR-based Metabolic Profiling in Disease Research Examples of Targeted MS-based Metabolic Profiling for Understanding of Disease Mechanisms Integration of Metabolic Profiling with Other “Omics” Technologies Future Directions References 16. Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics Bjorn T. Gjertsen and James B. Lorens Introduction RNA Interference Gene Function Analysis: An Overview Chemical Genomics Gene Function Studies Conclusions Acknowledgements References Recommended Resources Section 3 Informatic and Computational Platforms for Genomic Medicine 17. Bioinformatic and Computational Analysis for Genomic Medicine Atul J. Butte Introduction Vignettes: How Specific Bioinformatics Methods Can Change the Practice Ofmedicine Analytic Methods Where Data for Studies May be Found Bioinformatics Vocabularies and Ontologies Freely Available Bioinformatics Tools New Questions for Genomic Medicine Acknowledgements References Recommend Resources 18. Fundamentals and History of Informatics for Genomic and Personalized Medicine A. Jamie Cuticchia Introduction Databases for Genomic Medicine Conclusion References Recommended Resources 19. Electronic Medical Records in Genomic Medicine Practice and Research Glenn S. Gerhard, Robert D. Langer, David J. Carey and Walter F. Stewart
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193 193 194 194 197 199 200 201 201 203
205 206 206 207 213 214 215 216 220 221 221 225 226 226 227 230 231 232
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Introduction EMRs and Genomic Medicine Clinical Practice EMRs and Genomic Medicine Research Conclusion Acknowledgements References Recommended Resources 20. Clinical Decision Support in Genomic and Personalized Medicine Kensaku Kawamoto and David F. Lobach Introduction CDS Background: History, Examples, Evidence of Effectiveness, and Desirable Attributes Potential Uses of CDS to Support Genomic and Personalized Medicine Limited Deployability: The Potential Achilles’ Heel of CDS Systems for Genomic Medicine Challenges to Widespread Deployment of Effective CDS Systems Conclusions Disclosures References Recommended Resources 21. Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics Mark S. Boguski Introduction Characteristics of Consumer Searches for Health Information What and Where Are Consumers Searching? Personalized Genomics for Consumers Summary and Conclusions References Wikipedia References
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252 252 252 253 255 256 256 257
PART II TRANSLATIONAL APPROACHES IN GENOMIC AND PERSONALIZED MEDICINE Section 4 Enabling Strategies in the Translation of Genomics into Medicine 22. Translational Genomics: From Discovery to Clinical Practice Geoffrey S. Ginsburg Introduction A Roadmap for Translation Where Can Genomics Have Impact in the Continuum of Health and Disease? The Genomics “Gold Rush”
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The Personal Genome: Precious Code or Fool’s Gold? “Grand Challenges” in Translation of Genomics to Human Health Translational Genomics: Enabling Competencies How Are We Going to Do This? Developing Environments That Foster Translational Genomics to Health Applications References 23. Principles of Study Design Peter Grass Introduction Principles of Experimental Design Design Issues in Genomic Medicine References Recommended Resources 24. Biobanking in the Post-Genome Era Theresa Puifun Chow, Chia Kee Seng, Per Hall and Edison T. Liu Introduction The Biobanking Evolution The Past Imperfect Resources The Evolving Face of Biobanking Existing Models: Biobanking in Europe and the USA Singapore’s National Biobank and National Aspirations In Biomedical Research The Future of National Biobanks References Recommended Resources 25. Application of Biomarkers in Human Population Studies Stefano Bonassi and Monica Neri Introduction Biomarkers in Medicine Biomarkers of Exposure Biomarkers of Early Disease Risk Biomarkers of Genetic Susceptibility to Disease Conclusions References 26. Validation of Candidate Protein Biomarkers Ingibjörg Hilmarsdóttir and Nader Rifai Introduction Optimization of the Candidate Protein Research Assay Analytical Evaluation Reference Intervals Pre-analytical Variation Clinical Evaluation Indicators of Diagnostic Accuracy And Predictability Diagnostic Research Studies Design of Diagnostic Studies
266 266 267
270 272 275 275 276 281 283 283 284
284 285 285 285 286 288 294 296 297
299 299 299 302 303 304 306 306 308 308 308 309 311 311 312 313 316 316
Transferability Of Test Performance Assay Transfer to Diagnostic Company Regulatory Requirements References Recommended Resources 27. Pharmacogenetics and Pharmacogenomics Iris Grossman and David B. Goldstein Introduction Pharmacogenetic Studies: From Concept to Practice Marker Selection – Strategy and Application From Bench to Bedside: Integration of Pharmacogenetic Testing into Clinical Practice Examples of PGx Tests: Promising New Developments and Marketed Products Future Developments Required for the Field to Fully Meet its Expectations References Recommended Resources 28. The Role of Genomics and Genetics in Drug Discovery and Development Robert I.Tepper and Ronenn Roubenoff Introduction The Drug Discovery Process Genomics in Target Discovery Genomic Approaches to Drug Identification Pharmacogenomics and Drug Development Pharmacodynamic Markers and their Role in Drug Discovery and Development Toxicogenomics Genetics and Genomics in Clinical Trial Design Genomics in Drug Approval and Regulation Conclusion References Recommended Resources 29. Role of Pharmacogenomics in Drug Development Colin F. Spraggs, Beena T. Koshy, Mark R. Edbrooke and Allen D. Roses Introduction Drug Development Critical Path Drug Development Economics Methods for Identification of Genetic Classifiers Pharmacogenomics in the Drug Development Pipeline Efficacy Pharmacogenetics – here and Now Drug Exposure Pharmacogenetics to Tune Efficacy and Safety Profiles Investigation and Management of Safety in Clinical Trials Other Genomic Methods: RNA Interference to Direct Drug Usage “No Samples, No Science” Conclusions Acknowledgements References
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335 335 335 336 337 337 338 340 340 340 341 341 342 343
343 344 344 344 346 348 349 350 352 352 354 355 355
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30. Clinical Implementation of Translational Genomics Wendy K. Chung Introduction Genetic Stratification Will Allow Medical Care to be Individualized After a Diagnosis is Made Population-based Germline Genomic Screening Newborn Screening Pharmacogenetics Somatic Genomic Variation Novel Sources of Genomic Variation Laboratory Standards to Ensure Analytic Validity Clinical Validation and Clinical Utility Cost Reimbursement Who Will Provide Genomic Medical Care? Genomic Literacy Ethical, Legal, and Social Issues Conclusions Acknowledgements References Recommended Resources 31. Translating Innovation in Diagnostics: Challenges and Opportunities Matthew P. Brown, Myla Lai-Goldman and Paul R. Billings Introduction Novel Diagnostics Conclusions: Translational Challenges for Innovative Diagnostics References 32. The Role of Genomics in Enabling Prospective Health Care Ralph Snyderman Introduction Predictive Models Predictive Factors Risk Assessment for Breast Cancer Pharmacogenomics Conclusion Acknowledgements References Recommended Resources Section 5 Policy Challenges in Genomic and Personalized Medicine 33. From Sequence to Genomic Medicine: Genome Policy Considerations Susanne B. Haga Introduction Genome Research after the Human Genome Project Policy Issues in Large-scale Genetics and Genomics Research
357 357 357 358 359 360 360 361 361 362 362 363 363 364 364 365 365 365 366
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367 368 374 375
378 378 380 380 382 383 383 383 384 385
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Integrating Genomic Medicine Applications in Healthcare Conclusion References
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392 396 397
34. Educational Strategies in Genomic Medicine Charles J. Epstein Introduction Genetic and Genomic Literacy of the Public and Makers of Public Policy Education of the Providers of Health Care Conclusion References
401
35. Federal Regulation of Genomic Medicine Janet Woodcock Introduction Regulation of Genomic Tests Pharmacogenomics in Drug Development And Clinical Medicine: the Role of Regulation Fda Efforts to Advance Genomic Product Development Conclusions References Recommended Resources
414
36. Economic Issues and Genomic Medicine David L.Veenstra, Louis P. Garrison and Scott D. Ramsey Introduction Economic Evaluation and Cost-effectiveness Analysis Evaluating Genomic Technologies Economic Incentives and the Future of Genomic Medicine Establishing Value-based Reimbursement For Genomic Technologies Conclusions References 37. Public–Private Interactions in Genomic Medicine: Research and Development Subhashini Chandrasekharan, Noah C. Perin, Ilse R.Wiechers and Robert Cook-Deegan Introduction Landscape of Private Sector Genomics Future Trends Acknowledgements References
401 402 404 411 411
414 416 418 421 422 422 423 424 424 424 426 428 430 431 432 434
434 435 441 442 442
387 388 388 389 391
Section 6 Genomic Medicine and Public Health 445 38. What Is Public Health Genomics? 446 Alison Stewart and Ron Zimmern Introduction 446 The Emergence of Public Health Genomics 446 The Definition of Public Health Genomics 447 Key Concepts in Public Health Genomics 447
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The “Enterprise” of Public Health Genomics Core Activities in Public Health Genomics Moving Public Health Genomics Forward: Leadership And Networks Conclusion References Recommended Resources
448 449 451 452 452 453
39. Why Do We Need Public Health in the Era of Genomic Medicine? 454 Muin J. Khoury and Marta Gwinn Introduction 454 The Continuum from Genetics to Genomics in Health Practice 454 The Role of Public Health in the Translation of Human Genome Discoveries into Health Applications 455 The Focus on Disease Prevention and Health Promotion 457 The Population Perspective: Crucial Role of Public Health Sciences 457 The Role of Knowledge Integration Across Disciplines 458 The Role of Health Services Research and Population Health Assessment, Assurance, and Evaluation 458 Conclusion 458 References 459 40. Principles of Human Genome Epidemiology Marta Gwinn and Muin J. Khoury Introduction Human Genome Epidemiology Epidemiologic Study Designs Epidemiologic Measures Of Disease Frequency, Association, and Risk Measurement and Bias Gene–environment Interaction Probability and Personalized Medicine Building the Evidence Base Conclusion References Recommended Resources 41. Genomics and Population Screening: Example of Newborn Screening John D.Thompson and Michael Glass Introduction Components of the NBS System Screening Technology: Simple Ideas, Complex Realities Variability Among NBS Programs Influence of Genetics and -omic Technologies on NBS Acknowledgements
461 461 461 462 463 464 465 466 466 467 467 469
470 470 471 471 476 476 477
References Recommended Resources 42. Family History: A Bridge Between Genomic Medicine and Disease Prevention Maren T. Scheuner and Paula W.Yoon Introduction Clinical Approach Conclusion Acknowledgements References Recommended Resources Section 7 Clinical Technologies Supporting Personalized Medicine 43. Molecular Imaging as a Paradigm for Genomic and Personalized Medicine Ralph Weissleder Introduction Molecular Imaging and Cancer Detection Molecular Imaging to Determine Treatment Efficacy Molecular Imaging and Drug Development Near-term Needs and Opportunities Acknowledgement References 44. PET Imaging in Genomic Medicine Vikas Kundra and Osama Mawlawi Introduction Physics Imaging Agents and Methods in Analysis of Biological Samples Conclusion References Recommended Resources 45. MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges Dmitri Artemov Introduction Basics of MRI Contrast MR Contrast Agents for Molecular Imaging Applications Molecular Imaging Applications of MRI Conclusions Acknowledgements References 46. Fluorescence Imaging: Overview and Applications in Biomedical Research Vasilis Ntziachristos Introduction Imaging Technology
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481 481 481 488 488 488 492
493 494 494 494 496 497 497 498 498 500 500 500 503 510 510 511
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Fluorescence Applications in Genomic Medicine References 47. Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers Ahmad R. Hariri Introduction Conceptual Basis of Imaging Genetics Basic Principles of Imaging Genetics Imaging Genetics and the Neurobiology of the 5-httlpr Future Directions References 48. Viral Chip Technology in Genomic Medicine Zeno Földes-Papp Introduction Role of Viruses in Human Infectious Disease Microfabrication Nanofabrication Are there Additional Alternatives to Diagnostic Microarrays? Conclusions Acknowledgements References Recommended Resources 49. Vaccines Against Infectious Diseases: A Biotechnology-Driven Evolution Vega Masignani, Hervé Tettelin and Rino Rappuoli Introduction The Genomic Era: From Microbial Genome to Vaccine Development Impact of Whole Genome Analyses In Vivo Gene Expression: Ivet And Stm Microarray Expression Technology Proteomics From Microbial to Human Genome Sequencing: Genomic Medicine Metagenomics: Deciphering Host–microbe Interactions Conclusions References 50. Cancer Vaccines: Some Basic Considerations Hans-Georg Rammensee, Harpreet Singh-Jasuja, Niels Emmerich and Steve Pascolo Introduction Immune Suppression by Tumors and by Regulatory T-cells The Ideal Therapeuticcancer Vaccine Molecularly Undefinedcancer Vaccines Peptides
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532 532 533 534 535 536 536 538 538 538 541 552 553 554 554 554 560
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Proteins and Carbohydrates Nucleic Acids: Plasmid DNA and Messenger RNA Viral and Bacterial Vectors Adjuvants, Formulations, and Route of Application Immunomonitoring Conclusions References 51. Biosensors for the Genomic Age Meghan B. O’Donoghue, Lin Wang,Yan Chen, Gang Yao and Weihong Tan Introduction Biosensors for Detection of Oligonucleotides for the Detection of Disease Nucleic Acid as Tools for Biosensing Outlook References 52. Stem Cells Rikkert L. Snoeckx, Kris Van Den Bogaert and Catherine M.Verfaillie Introduction Types of Stem Cells: Embryonic and Adult Stem Cells How to Define The Molecular Signature of Stem Cells Future Directions to Identify the Global Integrated Regulatory Network Future Directions in Stemcell Therapies Conclusion References 53. Gene Therapy James M.Wilson and Nelson A.Wivel Introduction Gene Delivery Vehicles Gene Therapy Clinical Trials Conclusion References
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599 599 600 601 604 606 606 610 610 611 614 617 617
568 569 570 570 573
573 574 576 576 577
PART III DISEASE-BASED GENOMIC AND PERSONALIZED MEDICINE: GENOME DISCOVERIES AND CLINICAL APPLICATIONS Section 8 Cardiovascular Genomic Medicine 54. The Genomics of Hypertension Chana Yagil and Yoram Yagil Introduction Predisposition Screening Diagnosis
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Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusion Acknowledgements References Recommended Resources 55. Lipoprotein Disorders Sekar Kathiresan and Daniel J. Rader Introduction Overview of Lipoprotein Metabolism Plasma Lipid and Lipoprotein Levels and Atherosclerotic Cardiovascular Disease Inherited Basis for Blood Lipid Traits Screening for Lipid Disorders Genetics of Ldl-c Genetics of Hdl-c Genetics of Triglycerides Genetic Lipid Disorders Without Current Proven Molecular Etiology Influence of Lipid-modulating Mutations on Risk of Atherosclerotic Cardiovascular Disease Future Directions in Genetics And Genomics of Lipoproteins Pharmacogenetics of Lipid-modulating Therapies Implications of Genomics of Lipoprotein Metabolism For The Development of Novel Therapies Clinical Recommendations for Genetic Testing for Lipid Disorders Acknowledgements References Recommended Resources 56. Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis Nageswara R. Madamanchi, Aleksandr E.Vendrov and Marschall S. Runge Introduction Sources of ROS in vascular cells Vascular Dysfunction and Atherosclerosis ROS-induced inflammatory gene expression in vascular cells Association of ROS modulators with atherosclerosis ROS signaling in atherosclerotic risk factors ROS-regulated signaling pathways Regulation of transcription factors by ROS ROS signaling in advanced atherosclerosis Polymorphisms in ROS production genes and atherosclerosis Inhibitors of ROS signaling and vascular disease Conclusion Acknowledgements References
628 630 631 631 631 632 632 633 634 634 634 636 636 637 637 639 642 643 643 644 646 646 647 648 648 651 652
652 652 653 653 654 655 655 656 658 658 658 659 660 660
57. Genomics of Myocardial Infarction Carlos A. Hubbard and Eric J.Topol Introduction Predisposition Screening Strategies Diagnosis of Acute MI Prognostic Implications of MI Pharmacogenomics of MI Novel and Emerging Therapies Conclusion References Recommended Resources
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58. Acute Coronary Syndromes L. Kristin Newby Introduction Predisposition Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusion Acknowledgements References Recommended Resources
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59. Heart Failure in the Era of Genomic Medicine Ivor J. Benjamin and Jeetendra Patel Introduction Predisposition (Genetic and Non-genetic) Screening Pathophysiology Diagnosis Prognosis Pharmacogenomics Monitoring Novel Therapeutics and Future Directions Conclusions and Recommendations Acknowledgements References
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60. Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection Michael Pham, Mario C. Deng, Jay Wohlgemuth and Thomas Quertermous Introduction Cardiac Allotransplantation as a Definitive Therapy for End-stage Heart Failure The Problem of Allograft Rejection Immunosuppression Strategies to Prevent Rejection Current Strategies for Monitoring Transplant Rejection
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The Cargo Clinical Study Development of a Gene Expression Signature For Cardiac Transplant Rejection Pathways Monitored by the Gep (Allomap™) Test Variability of the Biopsy Gold Standard and Relationship to the Gep (Allomap™) Score Discordance Between Biopsy Grade and Molecular Score Effect of Time Post-transplantation on Performance of the Gep Test Relationship of Gep Score to Corticosteroid Dose Relationship of Gep Scores to Cytomegalovirus Infection Prediction of Future Acr by Molecular Score Clinical Use of the Allomap™ Test Future Directions and Ongoing Research With Gep Testing Further Application of Genomic Science to Transplant Rejection References 61. Hypertrophic Cardiomyopathy in the Era of Genomic Medicine J. Martijn Bos, Steve R. Ommen and Michael J. Ackerman Introduction Definitions, Clinical Presentation, and Diagnosis Molecular Genetics of HCM Screening And Treatment for HCM Conclusions References 62. Genetics and Genomics of Arrhythmias Jeffrey A.Towbin and Matte Vatta Introduction Specific Cardiac Arrhythmias Primary Abnormalities in Cardiac Rhythm: Ventricular Tachyarrhythmias Complex Forms of Lqts Short Qt Interval Syndrome Familial Vt/cpvt Primary Conduction Abnormalities References 63. Hemostasis and Thrombosis Richard C. Becker and Felicita Andreotti Introduction Genetics of Coagulation Human Hemostatic Variability Genotype–phenotype Influences Gene-environment Influences on Hemostasis Circulating Cellular and Protein Influences on Hemostasis And Thrombosis
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Race-related Influences On Hemostasis and Thrombosis Linkage Studies in Thrombosis Association Studies in Thrombosis Heritability And Thrombosis: Existing Complexities A Personalized Approach to Hemostasis and Thrombosis Patient Screening: A Traditional Paradigm Patient Screening: A Comprehensive And Population-based Approach Prognostic Considerations Emerging Platform for Hemostasis and Thrombosis Research References
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64. Peripheral Arterial Disease 773 Ayotunde O. Dokun and Brian H. Annex Introduction 773 Epidemiology and Risk Factors for PAD 773 Clinical Manifestations of PAD 773 Therapeutic Strategies for PAD 774 Ic And Cli Are Distinct Clinical Outcomes of PAD 775 Genetic Background as a Risk Factor for PAD 775 Gene Polymorphisms Contributing to Atherosclerosis and PAD 775 Polymorphisms in Pro-atherothrombotic Genes and PAD 776 Genetic Locus Conferring Susceptibility to PAD 776 Identification of Novel Gene Polymorphisms Involved in PAD 776 Identification of a Quantitative Trait in a Preclinical Model of PAD 777 Refining a QTL Using Haplotype Analysis 777 Identification of Candidate Genes 777 Future Potential Use of Genomic Methodologies in PAD 777 Acknowledgements 778 References 778 65. Genomics of Congenital Heart Disease Jessie H. Conta and Roger E. Breitbart Introduction CHD Gene Discovery by Conventional Genetics Genomic Strategies for CHD Gene Discovery Cytogenetic and Molecular Genetic testing Medical Evaluation and Counseling Recommendations Conclusion Acknowledgements References Recommended Resources
781 781 781 786 787 788 789 790 790 793
66. Genomics of Perioperative and Procedural Medicine 794 Simon C. Body, Mihai V. Podgoreanu and Debra A. Schwinn Introduction 794 Why Perioperative Insults are not Equivalent to Chronic Ambulatory Disease 795
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Perioperative Atrial Fibrillation Perioperative Venous and Arterial Thrombosis Perioperative Stroke and Neurocognitive Dysfunction Hemorrhage and Cardiac Surgery Dynamic Genomic Markers of Perioperative Outcomes Conclusion References
796 796 798 799 799 800 800
Section 9 Oncology Genomic Medicine 67. Cancer Genes, Genomes, and the Environment Robert L. Strausberg Introduction Acquired Functions of Cancer Cells Chromosomal Aberrations and Cancer Cancer Genes and their Functions Inherited Predisposition Cellular Progression Toward Cancer Through Somatic Changes From Genome to the Clinic Comprehensive Sequencing of the Kinome Expanding the Search Multiple Molecular Mechanisms for Oncogene Activation Microarrays and Cancer Genomics Environmental Cancer Genomics Cancer Genomic Databases Expedite Progress Mouse Models of Cancer Future Directions References
807 808
68. Immune Cells and the Tumor Microenvironment David S. Hsu, Michael Morse,Timothy Clay, Gayathri Devi and H. Kim Lyerly Introduction Immune Cells of the Tumor Microenvironment Examples of Tissue or Gene Microarrays used to Study Tumors Studies of Genomic Immune Stimulation within the Microenvironment Genomic Analysis of Tumor Microenvironment in Immunotherapy Studies Proteomics of Immune Cells and the Tumor Microenvironment Conclusion References
818
69. Lymphomas Lisa Rimsza Introduction Diffuse Large B-cell Lymphoma Primary Mediastinal Large B-cell Lymphoma Hodgkin Lymphoma Follicular Lymphoma Mantle Cell Lymphoma
808 808 808 809 809 810 811 811 812 812 813 813 813 814 814 815
818 819 822 823 824 825 825 826 830 830 833 835 835 836 838
Burkitt Lymphoma Miscellaneous Lymphomas Clinical Applications of Molecular Assays in Lymphoma References
839 839 840 840
70. Genomics in Leukemias Lars Bullinger, Hartmut Dohner and Jonathan R. Pollack Introduction Genomics in Leukemias-insights into Leukemia Biology Genomics in Leukemias – Evaluation of Drug Effects Genomics in Leukemias – Clinical Outcome Prediction Conclusions Acknowledgments References Recommended Resources
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71. Genomics of Lung Cancer Hasmeena Kathuria, Avrum Spira and Jerome Brody Introduction Early Diagnosis/screening of Lung Cancer Classification And Prognosis Pathogenesis And Treatment of Lung Cancer Conclusion References Recommended Resource
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72. Breast Cancer and Genomic Medicine Erich S. Huang and Andrew T. Huang Introduction The Promise Genetic Bases Molecular Bases Prognosis and Prediction Molecular Markers Genomic Insights Netherlands Cancer Institute Study Duke-taipei Study Nsabp Study Pathway Prediction The Reality of Clinical Genomics References 73. Colorectal Cancer G.L.Wiesner,T.P. Slavin and J.S. Barnholtz-Sloan Introduction Genomic Model of CRC Predisposition for CRC Risk Assessment, Evaluation, and Genetic Testing Screening and Surveillance Prognosis and Treatment
844 846 847 849 851 852 852 855
856 857 859 862 865 866 868 869 869 869 871 871 872 872 873 873 875 875 876 876 877 879 879 880 882 887 889 889
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Pharmacogenetics/genomics of Chemoprevention and Chemotherapy Novel and Emerging Therapeutics Conclusion References 74. Prostate Cancer Phillip G. Febbo and Philip W. Kantoff Introduction Genetic Predisposition and Alterations in Prostate Cancer Prostate Cancer Detection Genomic Changes Associated with Prostate Cancer Behavior Genomic Changes associated with HormoneRefractory Prostate Cancer Future Prospects of Genomics in Prostate Cancer Care References 75. Molecular Biology of Ovarian Cancer Tanja Pejovic, Matthew L. Anderson and Kunle Odunsi Introduction Inherited Ovarian Cancer Syndromes Options for Screening and Prevention Genomic Instability and Ovarian Cancer Fanconi/anemia Pathway Somatic Mutations in Ovarian Cancer Oncogenes and Growth Factors Tumor Suppressor Genes Epigenetics in Ovarian Carcinogenesis Ovarian Cancer Metastases Angiogenesis Summary References 76. Pancreatic Neoplasms Asif Khalid and Kevin McGrath Introduction Predisposition (Genetic and Non-Genetic) Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapies Conclusion References
890 892 892 893 898 898 898 901 904 906 907 908 913 913 913 914 914 914 915 916 917 917 917 918 919 919 921 921 922 922 923 926 927 927 927 927 928
77. The Multiple Endocrine Neoplasia Syndromes Y. Nancy You,Vipul Lakhani and Samuel A.Wells
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Introduction The Multiple Endocrine Neoplasia Syndromes
931 932
Conclusion Acknowledgements References Recommended Resources
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941 941 942 944
78. Genomics of Head and Neck Cancer Giovana R.Thomas and Yelizaveta Shnayder Introduction Head and Neck Squamous Cell Carcinoma Conclusion References Recommended Resources
945
79. Genomic Medicine, Brain Tumors and Gliomas Sean E. Lawler and E. Antonio Chiocca Introduction Predisposition Screening Diagnosis and Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusions References
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80. Molecular Therapeutics of Melanoma Jiaqi Shi,Yonmei Feng, Robert S. Krouse, Stanely Leong and Mark A. Nelson Introduction Diagnosis Genetics of Melanoma Pharmacogenomics Novel and Emerging Therapeutics Conclusions References 81. Emerging Concepts in Metastasis Nigel P.S. Crawford and Kent W. Hunter Introduction Tools to Investigate the Mechanisms of Metastasis Assessement of Prognosis and New Treatments for Metastasis: the Role of New Technologies Conclusion References Recommended Resources 82. Diagnostic-Therapeutic Combinations in the Treatment of Cancer Jeffrey S. Ross Introduction Targeted Therapies for Cancer The Ideal Target The First Diagnostic-therapeutic Combination in Cancer Therapy: Hormonal Therapy for Breast Cancer
945 945 952 953 955
956 957 958 958 961 962 962 963 964 967
967 967 968 969 969 973 973 977 977 979 986 987 988 989 990 990 990 990
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Diagnostic-therapeutic Combinations for Leukemia and Lymphoma 994 Her-2 Positive Breast Cancer and Trastuzumab 994 (Herceptin®) Other Targeted Anticancer Therapies Using Antibodies 995 Selected Targeted Anticancer Therapies Using Small Molecules 999 Pharmacogenomics 1002 Conclusion 1003 References 1003 Section 10 Inflammatory Disease Genomic Medicine 83. Environmental Exposures and the Emerging Field of Environmental Genomics David A. Schwartz Introduction Importance of Environmental Exposures in Human Health Importance of Environmental Exposures in Studying Disease Processes Comparative Environmental Genomics Exposure Assessment in the Gene-environment Paradigm Challenges and Future of Environmental Genomics Conclusion References
1009 1010 1010 1010 1012 1012 1013 1015 1015 1015
84. Molecular Basis of Rheumatoid Arthritis Robert M. Plenge and Michael E.Weinblatt Introduction Clinical Features Predisposition Screening Diagnosis, Prognosis, and Monitoring Pharmacogenomics Conclusions References
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85. “Omics” in the Study of Multiple Sclerosis Francisco J. Quintana and Howard L.Weiner Introduction Genomics in MS Transcriptomics in MS Immunomics in MS Proteomics in MS Conclusion References
1032
86. Inflammatory Bowel Disease Ad A. van Bodegraven and Cisca Wijmenga
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Introduction Predisposition (Genetic and Non-genetic)
1017 1018 1018 1024 1024 1024 1026 1026
1032 1032 1034 1035 1036 1037 1037
1040 1041
Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusions References 87. Glomerular Disorders Tadashi Yamamoto, Hidehiko Fujinaka and Visith Thongboonkerd Introduction Techniques for Detection, Quantification, and Profiling of mRNA Expression in the Kidney mRNA Expression Profiles of Glomerular Disorders Genome Variations in Glomerular Disorders Genetics of Congenital Glomerular Disorders Genomic Medicine for Glomerular Disorders Conclusions References 88. Spondyloarthropathies Dirk Elewaut, Filip De Keyser, Filip Van den Bosch, Dieter Deforce and Herman Mielants Introduction Characteristics of SPA Role of Bowel Inflammation Histopathology of Synovitis in SPA Gut and Synovium Transcriptomes Proteome Analysis Novel and Emerging Therapeutics and Biomarkers Conclusions References 89. Asthma Genomics Scott T. Weiss, Benjamin A. Raby and Juan C. Celedón Introduction Asthma: Basic Pathobiology Predisposition (Genetic and Non-genetic) to Asthma Genome-wide Linkage Analyses of Asthma and its Intermediate Phenotypes Candidate-gene Association Studies of Asthma Genome-wide Association Studies of Asthma Asthma Genomics Screening Diagnosis Prognosis Pharmacogenetics Monitoring Novel and Emerging Therapeutics Conclusions Acknowledgements References Recommended Resources
1043 1044 1045 1047 1047 1048 1049 1050 1056
1056 1056 1059 1060 1062 1062 1064 1064 1067
1067 1067 1070 1072 1074 1075 1075 1077 1078 1084 1084 1084 1085 1085 1087 1088 1088 1088 1091 1091 1091 1092 1093 1093 1093 1093 1097
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90. Genomic Aspects of Chronic Obstructive Pulmonary Disease Peter J. Barnes Introduction Predisposition Pathophysiology Cellular and Molecular Mechanisms Diagnosis and Screening Prognosis Management New Treatments Conclusions References 91. Genomic Determinants of Interstitial Lung Disease P.W. Noble and M.P. Steele Introduction Genetic Determinants of Dpld in Mouse Strains Genetic Determinants of Sarcoidosis Surfactant Proteins and Dpld Genetic Determinants of Pulmonary Fibrosis Identified in Rare Inherited Disorders Genetic Determinants of Fip Conclusion References 92. Peptic Ulcer Disease J. Holton Introduction Clinical and Physiological Aspects of Pud Pathophysiology of Ulcer Formation The Helicobacter Genome Human Polymorphism and Pud Genomics in the Management of Disease Future Developments in the use of Genomic Techniques in Relation to Pud Conclusions Acknowledgements References Recommended Resources 93. Cirrhosis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison Introduction Liver Structure Fibrosis and Cirrhosis Diagnosis of Cirrhosis Treatment of Cirrhosis Genetics of Cirrhosis The Liver Transcriptome The Liver Proteome Development of Liver Fibrosis Transcriptome Analysis of Liver Disease Proteomic Studies of Liver Disease
1098 1098 1098 1099 1101 1104 1105 1105 1107 1107 1108 1110 1110 1111 1111 1114 1115 1115 1117 1118 1122 1122 1123 1124 1125 1130 1131 1132 1132 1133 1133 1137 1138 1138 1138 1140 1140 1141 1142 1143 1143 1144 1146 1148
Proteomics in Other Liver Disease Future Impact of Genomics Studies Conclusion References 94. Systemic Sclerosis Ulf Müller-Ladner Introduction Predisposition Screening Diagnosis Prognosis Pharmacogenomics Monitoring and Genomic Factors Therapeutic Strategies Novel and Emerging Therapeutics Conclusions Acknowledgements References
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1150 1151 1151 1151 1155 1155 1155 1158 1159 1159 1159 1159 1162 1164 1165 1165 1165
Section 11 Metabolic Disease Genomic Medicine 95. Genomic Medicine of Obesity J. Alfredo Martínez Introduction Obesity: Causes and Genetic Predisposition Search of Genes Involved in Obesity Diagnosis and Characterization of Genes Associated with Obesity Screening and Diagnosis Prognosis and Gene Based-treatments Novel and Emerging Therapeutics Nutrigenomics, Pharmacogenomics and Gene Therapy Conclusions Acknowledgements References
1169 1170
96. Diabetes Maggie Ng and Nancy J. Cox Introduction GWAS in Type 2 Diabetes Future Research in Type 2 Diabetes Genetics GWAS in Type 1 Diabetes Future Studies in Type 1 Diabetes Clinical Utility of Genetic Research in Diabetes Conclusion References
1187
97. Metabolic Syndrome Rebecca L. Pollex and Robert A. Hegele Introduction Diagnosis: Definition of the MetS Phenotype Pathophysiology of MetS in Brief Genetics of MetS
1170 1170 1172 1175 1179 1180 1182 1183 1183 1184
1187 1188 1190 1191 1192 1192 1192 1192 1194 1194 1194 1196 1196
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Conclusion Acknowledgements References Recommended Resources 98. Nutrition and Diet in the Era of Genomics Jose M. Ordovas and Dolores Corella Introduction Methodological Issues Gene–Nutrient Interactions Path Forward Conclusions Acknowledgements References Section 12 Neuropsychiatric Disease Genomic Medicine 99. The Genetic Approach to Dementia Robert L. Nussbaum Introduction Incidence of Dementia Primary Dementias Clinical Approach to the Dementias Future Prospects for Genomic Medicine in the Dementias Conclusion References Recommended Resources 100. Parkinson’s Disease: Genomic Perspectives Shushant Jain and Andrew B. Singleton Introduction Clinical Characteristics of PD Genetics of PD Genetics of Sporadic PD Conclusion References Recommended Resources
1200 1200 1200 1202 1204 1204 1205 1206 1214 1214 1215 1215
1221 1222 1222 1223 1224 1229 1229 1230 1230 1232 1233 1233 1233 1235 1238 1241 1241 1242
101. Epilepsy Predisposition and Pharmacogenetics Nicole M.Walley and David B. Goldstein Introduction Mendelian Epilepsies Common Epilepsies Future Program of Work References
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102. Ophthalmology Janey L.Wiggs Introduction Extraocular Muscles Cornea Lens
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1243 1243 1248 1251 1252
1256 1257 1257 1259
Iris Trabecular Meshwork Optic Nerve Retina Genetic Testing for Ocular Disorders Summary References
1259 1259 1260 1260 1261 1261 1261
103. Genomic Basis of Neuromuscular Disorders Erynn S. Gordon and Eric P. Hoffman Introduction Motor Neuron Disease Disorders of the Neuromuscular Junction Disorders of the Muscle Predisposition Screening Diagnosis Prognosis Monitoring Current, Novel, and Emerging Therapies Advances in Genomics and Proteomics Conclusion References Recommended Resources
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104. Psychiatric Disorders Stephan Züchner and Ranga Krishnan Introduction Great Prospect, But are We There Yet? Classification Reconsidered How Complex Can it Be? The Value of Rare Genetic Variation Converging Methods Personalized Medicine Conclusion References 105. Genomics and Depression Brigitta Bondy Introduction Diagnosis, Prevalence and Course of Depression Pathophysiological Mechanisms Pharmacogenomics of Antidepressants Current Concepts Future Aspects References 106. Bipolar Disorder in the Era of Genomic Psychiatry Ayman H. Fanous, Frank Middleton, Carlos N. Pato and Michele T. Pato Introduction Diagnosis Predisposition Pharmacogenetics
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The Role of New Technologies in Elucidating the Genetics of BPD 1305 Conclusion 1308 References 1308 Section 13 Infectious Disease Genomic Medicine 107. Genomic Approaches to the Host Response to Pathogens M. Frances Shannon Introduction Genetic Susceptibility to Pathogens Exploring the Host Response Through Expression Profiling Genetical Genomics and Systems Biology: the New Frontiers Application to Clinical Practice Acknowledgement References Recommended Resources 108. Genomic Medicine and AIDS Thomas Hirtzig,Yves Lévy and Jean-François Zagury Introduction Context of HIV and AIDS Predisposition: Susceptibility to HIV-1 Infection Diagnosis Prognosis Monitoring Pharmacogenomics Novel and Emerging Therapeutics Conclusion References 109. Viral Genomics and Antiviral Drug Roberto Patarca Introduction Viral Genomics and the Antiviral Drug Revolution Era Conclusion References
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110. Host Genomics and Bacterial Infections Melissa D. Johnson and Mihai Netea Introduction Genomics and the Study of Bacterial Infections Host Genomics and Gram-positive, Gram-negative and Mycobacterial Infections Future Directions Conclusions References
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111. Sepsis and the Genomic Revolution Christopher W.Woods, Robert J. Feezor and Stephen F. Kingsmore Introduction Genetic Polymorphisms Associated with Sepsis Molecular Signatures and Sepsis Therapeutics Conclusion References
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112. Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison Introduction Virology of Hepatitis Viruses Acquisition and Predisposition to Viral Hepatitis Screening and Diagnosis of Viral Hepatitis Pathogenesis of Viral Hepatitis Therapeutics and Pharmacogenomics Future Impact of Genomics Studies Conclusion References
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Glossary
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Foreword In the health care field today, there are high expectations for a paradigm shift in care delivery over the coming years. According to this view, we have the opportunity to achieve fundamental change and improvement in the delivery of care, with better health outcomes across the board, led by advances in genomics and molecular medicine. The prospect is exciting: a continuum of health maintenance and medical care that is truly tailored to the individual, based on his or her individual biology. We could know our own genetic profiles from birth. Prevention could be more individually charted, based on individual genetic factors. Disease could be detected and treated earlier through molecular diagnostics. Health care dollars might be spent to much better effect. And our lives could be healthier, longer. The stepping stones to such a future are now widely known. Building on the work of the Human Genome Project and its offspring, medical care can be expected to acquire powerful new tools, especially in diagnosis, that could render care much more effective by making it more precise, more individually targeted, and more predictive. We should be able to prescribe drugs more safely because genetic or other characteristics would help clinicians identify which patients would respond well to a given therapy. As diseases come to be understood at a new level, we should be better able to achieve the right diagnosis and the right treatment for each person, without the trial-and-error process that has long characterized medical treatment. As biomarkers are identified, we should be able to intervene in disease at much earlier stages. And when we are able to know our personal genomic profiles, we should be able to better pinpoint our individual health susceptibilities. Our physicians could give more individualized prevention advice – and perhaps it will even come to pass that we will be more motivated to follow it. I am a believer in this view of the possible future – and I am confident it is not just wishful thinking. The pace of discovery in the genomic field today is unprecedented. Furthermore, enough successful applications of this paradigm already exist to give us reason to look toward a new era of effectiveness in medical care, with new information and new tools for both the clinician and the consumer. At the same time, we must be realistic in our assessment of this future, especially the extent of the efforts that will be needed to achieve it. However desirable the idea of this “paradigm shift” may be, the realization of such a shift rests on a foundation that is still very much under construction.
The shift will require development of new capacities that will enable us to differentiate among the needs of individual patients. In turn, these new capacities will depend on data to be derived, analyzed, and employed on a new scale. Such information demands will surely need to be supported by sophisticated electronic data networks that are yet to be created, informatics tools that are yet to be invented, and clinical decision-support systems that are yet to be devised or adopted. Finally, at the bedrock, the trust and understanding of the medical community and of society at large must be won even as the edifice is being created. This is the work of a generation. It is work that spans professions, economic sectors, and even nations. It is driven by science – but it makes new demands for the rapid translation of scientific discovery into clinical practice and improved health outcomes. One part of the work before us is continued discovery. Phenomenal achievements have been made in mapping the human genome, and a rush of findings is occurring today in understanding associations between genomic factors and health. Yet vast areas still remain to be explored. That work is underway on a global scale. Another part of our work might be called the engineering. This includes the development of interoperable health information technology, with all the implications of that goal: development of technical and clinical standards, adoption of health IT across the health sector, and security of personal health information. In the long term, it should also include the use of health IT to enable us to make faster progress in medical research – and then to feed back what we’ve learned into clinical practice, using IT decision-support tools that are physician-, nurse-, and consumer-friendly. Another element of our task is less defined but equally challenging: the collaboration and cooperation needed to bring this vision to reality. Personalized medicine means care that is information-based at a new level. Gathering that information and using it successfully will transcend many disciplines and make new demands on a health system that is often characterized as “fractured.” It may seem ironic that delivery of individualized care depends on standardization, partnerships, and networks. But these kinds of collaboration are among the most important element of the work that lies ahead. Finally, in today’s health care environment, the success of new products, services, and models of care will depend more than ever on the value that is delivered. At its core, personalized medicine is about care that can achieve new levels in predicting, preventing, and detecting disease. Medical effectiveness of this
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kind should translate into cost effectiveness as well. Achieving and demonstrating high value expectations will be challenging, but this discipline will serve the success of personalized medicine over the long term. The paradigm shift of personalized medicine depends on an extensive foundation of scientific knowledge, professional leadership, health information technology, and a spirit of cooperation, collaboration, and dialogue. The many perspectives offered by the
contributors to this text help us appreciate the breadth of these issues, challenges, debates, and opportunities that lie ahead.
Michael O. Leavitt Secretary Department of Health and Human Services Washington, DC
Preface It seems just yesterday that we were first getting used to the notion of introducing the seemingly freshly uncovered concepts of genetics into the practice of medicine. And yet, with the completion of the Human Genome Project and the rapid development and application of new advances in our ability to understand and query the human genome and its gene set, it is time already to anticipate and outline the early stages of what must be called a transformation of medicine. We are beginning to see the first signs of a fundamental shift in how we behold human physiology and pathology, how we view the concept of what is “normal,” how we consider individuals and their prospects for lifelong health, and how we design healthcare systems that are equally adaptable to the demands of population-wide epidemics and the opportunities for personalized care that utilizes genome-based information to consider individual susceptibility to disease and therapeutic options. Genome-based data, information, knowledge, and eventually wisdom will make possible the kind of healthcare that has been dreamed of since the advent of disease-based medicine early in the 20th century. A system of healthcare that harnesses the might of the genome and its derivatives, along with imaging, clinical and environmental information, will empower physicians and other healthcare providers to do what they have always aspired to do – make medical care as individualized as possible. But this newfound information and knowledge will also allow each of us as consumers of healthcare to take more control of our futures and to develop a more strategic and a prospective approach to health. We stand at the dawn of a profound change in science and medicine’s predictive nature and in our understanding of the biological underpinnings of health and disease. Even in this early light, we can see the outlines of a coming ability to: ●
●
●
● ●
predict individual susceptibility to disease, based on genetic, genomic and other factors; provide more useful tools and individualized programs for disease prevention, based on knowledge of one’s susceptibility; detect the onset of disease earlier and before it is clinically evident, based on newly discovered biological markers that arise from changes at the molecular level; preempt disease progression, as a result of early detection; target medicines and their dose more precisely and safely to each patient, on the basis of a deep understanding of disease
mechanism and the role that genetic and genomic factors play in the individual response to drugs. This revolution in genomic and personalized medicine was anticipated nearly three decades ago by Nobel laureate Paul Berg, who stated so presciently: Just as our present knowledge and practice of medicine relies on a sophisticated knowledge of human anatomy, physiology, and biochemistry, so will dealing with disease in the future demand a detailed understanding of the molecular anatomy, physiology, and biochemistry of the human genome. . . . We shall need a more detailed knowledge of how human genes are organized and how they function and are regulated. We shall also have to have physicians who are as conversant with the molecular anatomy and physiology of chromosomes and genes as the cardiac surgeon is with the structure and workings of the heart.
That time has come. This book is intended to lay out the foundations of this new science, to outline the early opportunities for the practice of medicine to incorporate genome-based analysis into healthcare, and to anticipate the many conditions to which genomic and personalized medicine will apply in the years ahead. The chapters in these volumes are designed to be read either sequentially – introducing the scientific underpinnings of this revolution, exploring aspects of translational medicine and genomics that will be critical for bringing about this revolution, and presenting practical aspects of the first applications of genomic and personalized medicine in the context of specific medical conditions – or one-at-a-time for those interested in particular disorders or approaches. These volumes also describe a field in its infancy, with many challenges for society at large, in addition to those associated with healthcare systems strife with inefficiencies and heterogeneity in their ability to deliver the basics of healthcare. There are “grand challenges” for the visionary science and the clinical care highlighted in these pages. Such challenges include the potential for these innovations to exaggerate existing health disparities, information technology systems that have been described as a “tower of Babel,” an unprepared healthcare work force, and economic incentives that are inadequately aligned for the various stakeholders to fully embrace genomic and personalized medicine. Nonetheless, we are optimistic that the appropriate delivery models and economic incentives will be developed
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in a trustworthy framework that will be embraced by societies around the globe. As an indicator of the importance of personalized medicine, international communities are working together on strategies to overcome these obstacles. In the United States, the Department of Health and Human Services has played a leadership role in this area and a plan was published in 2007 (Personalized Healthcare: Opportunities, Pathways, Resources; see http://www. hhs.gov/myhealthcare/). Other governments (in the United Kingdom, Iceland, Estonia, Luxembourg, and Singapore – to name just a few) have funded initiatives that will secure their place in developing genome-based knowledge and its translation into day-to-day patient care. A collective and global approach to what might be arguably one of the most complex scientific and clinical undertakings in the history of healthcare is undoubtedly what is required. Our international collective of contributors to this work reflects the early adopters and members of a global community of physicians, scientists, and policy makers who will make this happen.
Our intended audience is broad, ranging from medical students (and even the intrepid undergraduate eager to explore this new era of personalized and prospective medicine) to residents and fellows to practitioners in any of the healthcare professions – physicians in any of the medical specialties, surgeons, nurses, genetic (and genomic) counselors, and laboratory directors – and, finally, to members of the genomic and personalized medicine research communities who will, we trust, help write future editions of this text. In times of transformation, we are all students. We hope that this book will help usher in this new era of genomic and personalized medicine and will provide a useful and thorough introduction to the science and practice of this new approach to human health.
Huntington F. Willard, Ph.D. Geoffrey S. Ginsburg, M.D., Ph.D.
Terminology Throughout this book, the terms “genetics” and “genomics” are used repeatedly, both as nouns and in their adjectival forms. Although these terms seem similar, they in fact describe quite distinct (though frequently overlapping) approaches in biology and in medicine. Here, we provide operational definitions to distinguish the various terms and the subfields of medicine to which they contribute. The field of genetics is the scientific study of heredity and of the genes that provide the physical, biological, and conceptual bases for heredity and inheritance. To say that something – a trait, a disease, a code or information – is “genetic” refers to its basis in genes and in DNA. Heredity refers to the familial phenomenon whereby traits (including clinical traits) are transmitted from generation to generation, due to the transmission of genes from parent to child. Genomics is the scientific study of a genome, the complete DNA sequence, containing the entire genetic information of a gamete, an individual, a population or a species. The word “genome” was first used as an analogy with the earlier term “chromosome,” referring to the physical entities (visible under the microscope) that carry genes from one cell to its daughter cells or from one generation to the next. Over the past two decades, “genomics” has given birth to a series of other “-omics” that refer to the comprehensive study of the full complement of, for example, proteins (hence, proteomics), transcripts (transcriptomics), or metabolites (metabolomics). The essential feature of the “-omes” is that they refer to the complete collection of genes, proteins, transcripts, or metabolites, not just to the study of individual entities. Medical genetics is the application of genetics to medicine and is one of the 24 medical specialties recognized by The American Board of Medical Specialties, the preeminent medical organization overseeing physician certification in the United States. Genetic medicine is a term sometimes used to refer to the application of genetic principles to the practice of medicine and thus overlaps medical genetics. Both medical genetics and genetic
medicine approach clinical care largely through consideration of individual genes and their effects on patients and their families. Genomic medicine, by contrast, refers to the use of large-scale genomic information and to consideration of the full extent of an individual’s genome, proteome, transcriptome, or metabolome in the practice of medicine and medical decision-making. The principles and approaches of genomic medicine are relevant well beyond the traditional purview of individual medical specialties and include, as examples, gene expression profiling to characterize tumors or to define prognosis in cancer, genotyping variants in the set of genes involved in drug metabolism or action to determine an individual’s correct therapeutic dosage, scanning the entire genome for millions of variants that influence one’s susceptibility to disease, or analyzing multiple protein biomarkers to monitor therapy and to provide predictive information in presymptomatic individuals. Finally, personalized medicine refers to a rapidly advancing field of healthcare that is informed by each person’s unique clinical, genetic, genomic, and environmental information. The goals of personalized medicine are to take advantage of a molecular understanding of disease to optimize preventive healthcare strategies and drug therapies while people are still well or at the earliest stages of disease. Because these factors are different for every person, the nature of disease, its onset, its course, and how it might respond to drug or other interventions are as individual as the people who have them. For personalized medicine to be used by healthcare providers and their patients, these findings must be translated into precision diagnostic tests and targeted therapies. Since the overarching goal is to optimize medical care and outcomes for each individual, treatments, medication types and dosages, and/or prevention strategies may differ from person to person – resulting in unprecedented customization of patient care. The principles underlying genomic and personalized medicine and their applications to the practice of clinical medicine are presented throughout the chapters that comprise this volume.
Acknowledgements We wish to express our appreciation and gratitude to our many colleagues, especially in the Duke Institute for Genome Sciences & Policy, who have shared their knowledge and ideas about genomic and personalized medicine and who, by doing so, inspired this project. We particularly thank our first editor at Academic Press/Elsevier, Luna Han, who encouraged us to develop the concept of a text on genomic and personalized medicine. We are also grateful to Sally Cheney, Kirsten Funk, and Christine Minihane, our Senior Editors at Academic Press/ Elsevier; to Rogue Shindler, our Developmental Editor; and to Ganesan Murugesan, our Production Project Manager, for their patience, advice, and professionalism in all stages of the project.
We acknowledge our Advisory Board for their suggestions and support and especially thank the nearly 300 authors of the 112 chapters that comprise these volumes. Needless to say, without their efforts, this project could never have come to fruition. We also thank Secretary Mike Leavitt for providing a Foreword for this book, as well as for his enthusiastic support of the concept of genomic and personalized medicine. It gives us pleasure to give special thanks to Kathy Hay and to Lynne Skinner, whose tireless efforts kept us on track and saw this project through to completion. Lastly, we thank our families for their patience and understanding for the many hours we spent creating this, the inaugural edition of Genomic and Personalized Medicine.
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Abbreviations 2D-DIGE 2D-PAGE
Positron Neutrino Two-dimensional difference gel electrophoresis Two-dimensional polyacrylamide gel electrophoresis 3-MT 3-Methoxytyramine 5-HT 5-Hydroxytriptamine or serotonin 5-HTT 5-HT transporter 5-HTTLPR 5-HTT gene-linked polymorphic region 6-MMP 6-Methylmercaptopurine 6-MP 6-Mercaptopurine 6-MTIMP 6-Methyl-thioinosinemonophosphate 6-TGN 6-Thioguanine nucleosides 6-TIMP 6-Thioinosinemonophosphate 18 18 F-FLT F-labeled 3-deoxy-3-fluorothymidine 18 18 F-FMAU F-1abeled-2-deoxy-2-fluoro--d-arabinofuranosyl thymine AAU Acute anterior uveitis ABCA1 Adenosine triphosphate-binding cassette protein A1 ABCB1 Adenosine triphosphate-binding cassette, sub-family B ABI Ankle brachial index ACC American College of Cardiology ACE Angiotensin converting enzyme aCGH Array comparative genomic hybridization ACR Acute cellular rejection AD Alzheimer’s disease AD Aortic dilation ADH Autosomal dominant hypercholesterolemia ADHD Attention deficit hyperactivity disorder ADIGE Autosomal dominant idiopathic generalized epilepsy
ADJME ADME ADNFLE ADP ADR AED Ago2 AHA AIDS AIMs AJCC ALL ALOX5AP AMACR AML Amol AMR AOO AOTF Apo(a) ApoE APOE AR AR ARACNe ARE ARH ARJP ARM
Autosomal dominant juvenile myoclonic epilepsy Absorption, distribution, metabolism, and elimination Autosomal dominant nocturnal frontal lobe epilepsy Adenosine 5-diphosphate Adverse drug reaction Antiepileptic drug Argonaute 2 mRNA endonuclease American Heart Association Acquired immune deficiency syndrome Ancestry informative markers American Joint Committee on Cancer Acute lymphoblastic leukemia Arachidonate 5-lipooxygenase-activating protein Alpha methylacyl CoA reductase Acute myeloid leukemia Attomole Antibody mediated rejection Age of onset Acousto-optic tunable filters Apolipoprotein(a) Apolipoprotein E Apolipoprotein E (gene name) Androgen receptor Autosomal recessive Algorithm for the Reconstruction of Accurate Cellular Networks Androgen response element Autosomal recessive hypercholesterolemia Autosomal recessive juvenile Parkinsonism Armadillo
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ARVC AS ASCA ASCO ASCVD ASD ASO ASPE ATM ATP ATP ATR AZT B1-AR BAC BBB BCAA Bcl BCL-2 Bcl-Abl BDNF BER BEST BH BLAST BLAT BMC BMI BMP-7 BOLD BPDE BPH BRCA BRCA1 BRCA2 BRD2 BSML CA CAD CADASIL CAE CALGB CAP CARD CARGO CAV CBD CBP CBZ CCD
Abbreviations
Arrhythmogenic right ventricular cardiomyopathy Ankylosing spondylitis Anti-Saccharomyces cerevisiae antibodies American Society for Clinical Oncology Atherosclerotic cardiovascular disease Atrial septal defect Anti-sense oligonucleotides Allele-specific primer extension Ataxia telangiectasia mutated Adenosine triphosphate Adult Treatment Panel Ataxia-telangiectasia and RAD3-related Azidothymidine 1-adrenergic receptor Bacterial artificial chromosome Blood–brain barrier Branched-chain amino acids B-cell lymphoma protein B-cell lymphoma 2 Breakpoint cluster region – Abelson kinase fusion protein Brain-derived neurotrophic factor Base excision repair -Blocker Evaluation of Survival Trial Bcl-2 homology domain Basic local alignment and search tool BLAST-like alignment tool Bone marrow cell Body mass index Bone morphogenic protein-7 Blood oxygen level-dependent Benzo (a) pyrene diol epoxide Benign prostatic hypertrophy Breast cancer gene Breast cancer 1, early onset, protein Breast cancer type 2 susceptibility protein Bromodomain containing 2 (gene) Bioinformatic sequence markup language Capsid (viral protein) Coronary artery disease Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy Childhood absence epilepsy Cancer and leukemia group B College of American Pathologists Caspase recruitment domain (gene) Cardiac Allograft Rejection Gene Expression Observational study Cardiac allograft vasculopathy Cortical basal degeneration CREB-binding protein Carbamazepine Charge coupled device
CCL CCND1 CCP CD CD CDAI CDC CDDP cDNA CDS CED-4 CETP CF CFTR CGAP CGH CGHa CGP CHD CHD CHF CHMP CHRNA4 CIBEX CIC CICR CKD CLI CLIA CML CMT CMV CNS CNV COPA COR COX COX-2 CPOE CRC CREB CRF CRP CS CSA CSF CSF1 CT CTC CTGF CTL CTLA
Chemokine (C-C motif) ligand Cyclin D1, a proto-oncogene Citrullinated cyclic peptide Cluster of differentiation Crohn’s disease Crohn’s disease activity index Centers for Disease Control Cisplatin (cis-diamminedichloroplatinum Complementary deoxyribonucleic acid Clinical decision support Cell death abnormality gene 4 Cholesteryl ester transfer protein Cystic fibrosis Cystic fibrosis transmembrane conductance regulator Cancer Genome Anatomy Project Comparative genomic hybridization Comparative genomic hybridization array Cancer Genome Project Congenital heart disease Coronary heart disease Congestive heart failure Committee for Medicinal Products for Human Use Cholinergic receptor, nicotinic, alpha 4 (gene) Center for Information Biology Gene Expression Citrate/isocitrate carrier Calcium-induced calcium release Chronic kidney disease Critical limb ischemia Clinical Laboratory Improvement Amendments Chronic myelogenous leukemia Charcot-Marie-Tooth disease Cytomegalovirus or human herpes virus 5 Central nervous system Copy number variant Cancer outlier profile analysis C-terminal of ROC domain Cyclooxygenase Cyclooxygenase-2 Computerized provider order entry Colorectal cancer Cyclic AMP response binding protein Circulating recombined form C-reactive protein Cell signaling factor Comparative sequence analysis Cerebrospinal fluid Colony stimulating factor 1 Computed tomography Circulating tumor cells Connective tissue growth factor Cytotoxic T lymphocytes Connective tissue late antigen
Abbreviations
CTSA CX32 CX37 CYP CYP2C19 CYP2D6
Clinical and Translational Science Awards Connexin-32 Connexin-37 Cytochrome P450 Cytochrome P450 2C19 Cytochrome P450 2D6
D2R DAI DBS dbSNP DCM DGGE DGI DHF DHLPC
Dopamine-2 receptor Disease Activity Score (for Ulcerative Colitis) Deep brain stimulation NCBI SNP database Dilated cardiomyopathy Denaturing gradient gel electrophoresis Diabetes Genetics/Initiative Diastolic heart failure Denaturing high performance liquid chromatography Diffuse Lewy body disease Diffuse Lewy body disease Diagonalized linear discriminant analysis Disc large homologue (gene) Drug-metabolizing enzymes Deoxyribonucleic acid Deoxyribonuclease DNA methyltransferases Dihydroxyphenylacetic acid Double stranded Double-stranded ribonucleic acid Direct-to-consumer Duodenal ulcer Dizygotic
DLB DLBD DLDA DLG DMEs DNA DNase DNMTs DOPAC ds dsRNA DTC DU DZ EAE EASS EBI EBV ECL ECM EDG-1 EGAPP EGF EGFR EGP EHR EIA EL ELISA ELMO1 EM EMB EMBL-EBI EMEA ENCODE EOAD
Experimental autoimmune encephalomyelitis East Asia specific sequence European Bioinformatics Institute Epstein–Barr virus Eenterochromaffin-like cells Extracellular matrix Endothelial differentiation gene-1 Evaluation of Genomic Applications in Practice and Prevention Epidermal growth factor Epidermal growth factor receptor Environmental Genome Project Electronic health record Enzyme immunoassay Endothelial lipase Enzyme-linked immunosorbent assay Engulfment and cell motility 1 Extracellular matrix protein Endomyocardial biopsy European Molecular Biology Laboratory– European Bioinformatics Institute European Medicines Evaluation Agency Encyclopedia of DNA Elements Early-onset Alzheimer’s disease
EPIYA eQTL ERK ERNA ESI ESI-MS/MS ESR ESRD EST FA FACS FANCA FANCD2 FAP FCCS FCHL fCJD FCS FCS FD FDA FDB FDG FFPE FGF FGF-2 FH FHTG FISH FKBP FL FLT-3 Fmol fMRI FMT FNA FOV FRET FSGS FTD FT-ICR-MS FUSION GABBR1 GABRG2 GAIN GAPDH GBM GBS GCKR GC-MS
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Single letter codes for amino acids Expression quantitative trait loci Extracellular signal-regulated kinase Equilibrium radionucleotide angiography Electrospray ionization Electrospray ionization tandem mass spectrometry Erythrocyte sedimentation rate End-stage renal disease Expressed sequence tag Fanconi anemia Fluorescent-activated cell sorting Fanconi anemia proteins A Fanconi anemia protein D2 Familial adenomatous polyposis Two-color fluorescence cross-correlation spectroscopy Familial combined hyperlipidemia Familial Creutzfeldt–Jakob disease Familial hyperchylomicronemia syndrome Fluorescence correlation spectroscopy Familial dysbetalipoproteinemia Food and Drug Administration (USA) Familial defective apoB-100 18 F-labeled 2-deoxy-d-glucose Formalin-fixed, paraffin-embedded Fibroblast growth factor Fibroblast growth factor-2 Familial hypercholesterolemia Familial hypertriglyceridemia Fluorescence in situ hybridization FK506-binding protein Follicular lymphoma FMS-like tyrosine kinase-3 Femtomole Functional MRI Fluorescence molecular tomography Fine needle aspirate Field of view Fluorescence resonance energy transfer Focal segmental glomerulosclerosis Frontotemporal dementia Fourier transform ion cyclotron mass spectrometry Finland–United States Investigation of NIDDM genetics GABA B receptor 1 (gene) GABA A receptor, gamma 2 (gene) Genetic Association Information Network Glyceraldehyde-3-phosphate dehydrogenase Glomerular basement membrane Guillain–Barre Syndrome Glucokinase regulatory protein Gas chromatography–mass spectrometry
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GCSF GDNF GEFS GEO GEP GFAP GFP GLEPP1 GLP GM-CSF GO GPI GR GSEA GSIS GSTT GU GWAS GZM H HAART HAV HBV HCC HCM HCV HDAC HDL HDM2 HDV HeLa HEPS HER2 HEV HF HFPEF HGF HGPRT HHS HHV-6 HITSP HIV HKMT HL HL7 HLA/HLA HLI HMT HNPCC HNSCC
Abbreviations
Granulocyte colony-stimulating factor Glial cell line-derived neurotrophic factor Generalized epilepsy with febrile seizures plus Gene Expression Omnibus Gene expression profiling Glial fibrillary acidic protein Green fluorescent protein Glomerular epithelial protein 1 Good laboratory practice Granulocye macrophage–colony stimulating factor Gene ontology Globus pallidus interna Glucocorticoid receptor gene Gene set enrichment analysis Glucose-stimulated insulin secretion Glutathione S-transferase Gastric ulcer Genome-wide association study Granzyme Heterozygosity Highly active anti-retroviral therapy Hepatitis A Hepatitis B Hepatocellular carcinoma Hypertrophic cardiomyopathy Hepatitis C Histone deacetylase High-density lipoproteins Human double minute 2 Hepatitis D cervical cancer cell line, also the first cancer cell line established Highly exposed persistently seronegative individuals Human epidermal growth factor receptor 2 Hepatitis E Heart failure Heart failure with preserved ejection fraction Hepatocyte growth factor Hypoxanthine phosphoribosyl transferase Health and Human Services (U.S.) Human herpes virus 6 Healthcare Information Technology Standards Panel Human immunodeficiency virus Histone lysine methyltransferases Hepatic lipase Health Level 7 Human leukocyte antigen/(gene) Hind limb ischemia Histone methyltransferase Hereditary nonpolyposis colorectal cancer Head and neck squamous cell carcinoma
HP-1 HPV HPV-16 HR HRPC HS HSC HSP HSV-1 HSV1-TK HSV-2 hTERT HTLV-I HTLV-III HTS HVJ
Heterochromatin-associated protein-1 Human papilloma virus Human papilloma virus-16 Homologous recombination Hormone refractory prostate cancer Hyper-spectral Hepatic stellate cell Heat shock protein Human herpes simplex virus type 1 Herpes simplex virus type 1-thymidine kinase Human herpes simplex virus type 2 Human telomerase reverse transcriptase Human T-cell leukemia/lymphoma virus type I Human T-cells lymphotropic virus type 3 High-throughput screening Hemagglutinating virus of Japan
IBD IBDQ IC IC ICAM ICAT ICD ICD ICDc
Inflammatory bowel disease Inflammatory bowel disease questionnaire Intermittent claudication Interstitial cystitis Intracellular adhesion molecules Isotope-coded affinity tags Implantable cardioverter defibrillator International Classification of Diseases Cytosolic, NADP-dependent isocitrate dehydrogenase Immature dendritic cells Idiopathic dilated cardiomyopathy Insulin-dependent diabetes mellitus Intermediate-density lipoproteins Indolemaine 2,3-dioxygenase Interferon Interferon-beta Interferon-gamma Immunoglobulin Immunoglobulin-A Insulin-like growth factor-I Immunohistochemistry Integrating the Healthcare Enterprise Inhibitor of B-like (gene) Interleukin Interleukin (4/13) Interleukin-1 beta (gene) Interleukin-1 receptor antagonist Interleukin-6 Interleukin-10 Invasive Monitoring Attenuation through Gene Expression study Immature myeloid cells Integrase (viral protein) Institute of Medicine (U.S.) Insertion sequence Institute for Systems Biology Intreferon stimulated gene
iDC IDCM IDDM IDL IDO IFN IFN- IFN- Ig IgA IGF-I IHC IHE IKBL IL IL (4/13) IL-1 IL-1ra IL-6 IL-10 IMAGE iMC IN IOM IS ISB ISG
Abbreviations
ISHLT IT IVDMIA IVVM JCA JHDM1
International Society for Heart and Lung Transplantation Information technology In vitro diagnostic multivariate index assay In vivo videomicroscopy
JME
Juvenile chronic arthritis JmjC domain-containing histone demethylase-1 Juvenile myoclonic epilepsy
Kb KEGG
Kilobase Kyoto Encyclopedia of Genes and Genomes
LARGO
Lung Allograft Rejection Gene expression Observational study Lymphadenopathy-associated virus Lewy body Lecithin cholesterol acyltransferase Laser capture microdissection Liquid chromatography–mass spectrometry Liquid chromatography/mass spectrometry/ mass spectrometry Liquid crystal tunable filters Linkage disequilibrium Low-density lipoproteins Levodopa Late-onset Alzheimer’s disease Logarithm of the odds Loss of heterozygosity Logical Observation Identifiers, Names, and Codes Line of response Lipoprotein(a) Lysophosphatidic acid Lipoprotein lipase Lipopolysaccharide Leucine-rich repeat Leucine rich repeat kinase 2 Lysine-specific demethylase-1 Lymphotoxin alpha Leukotriene A4 hydrolase Leukotriene B4 Long-term non-progression Left ventricle Left ventricular assist device Left ventricular end-diastolic pressure Left ventricular ejection fraction Left ventricular hypertrophy Left ventricular outflow tract (obstruction)
LAV LB LCAT LCM LC-MS LC-MS/MS LCTF LD LDL l-DOPA LOAD LOD LOH LOINC LOR Lp(a) LPA LPL LPS LRR LRRK2 LSD1 LTA LTA4H LTB4 LTNP LV LVAD LVEDP LVEF LVH LVOT(O) MA mAb MADH6 MAGE-ML MAGUK
Matrix (viral protein) Monoclonal antibodies Mothers against decapentaplegic homolog 6 MicroArray and Gene Expression Markup Language Membrane-associated guanylate kinase
MALDI MALDI-TOF MAM MAO MAOA MAP MAPK MAPK MAQC Mb MBD MCD MCD MCP MCP-1 MCS M-CSF MDM2 MDP MDR MedDRA MEF2A Megsin MEN MeSH MeV MGED MGMT MHC MI MIAME MIM miR-192 miRNA MITF MLEM MLPA MMP MMP-3 MMR MPSS MPTP mQTL MR MRI mRNA mRSS
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Matrix-assisted laser desorption and ionization Matrix-assisted laser desorption ionization time-of-flight Metastasis-averse microenvironment Monoamine oxidase Monoamine oxidate A Mitogen-activated protein Microtubule-associated protein kinase Mitogen-activated protein kinase Microarray quality control Megabase Methylated DNA binding domain protein Malonyl CoA decarboxylase Minimal change disease Monocyte/macrophage chemoattractant protein Monocyte chemoattractant protein 1 Multi-species conserved sequence Macrophage colony-stimulating factor Mouse double minute 2 Muramyl dipeptide Multidrug resistance (gene) Medical Dictionary for Regulatory Activities Terminology Myocyte-enhancing factor 2A Mesangial cell-specific gene with homology to serpin Multiple endocrine neoplasm Medical Subject Headings Multiexperiment viewer Microarray Gene Expression Data society O6-Methylguanine-DNA methyltransferase Major histocompatability complex Myocardial infarction Minimal Information About a Microarray Experiment Metastasis-inclined microenvironment MicroRNA-192 Micro-ribonucleic acid Microphthalmia transcription factor Maximum likelihood expectation maximization Multiplex ligation-dependent probe amplification Matrix metalloproteinase Matrix metalloproteinase-3 Mismatch repair Massively parallel signature sequencing 1-Methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine Metabolite quantitative trait loci Magnetic resonance Magnetic resonance imaging Messenger ribonucleic acid Modified Rodnan Skin Score
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MS MS MS MS/MS MSA MTDNA MTHFR mTOR MTP MTX MudPIT MVP MZ NAAT NAD(P)H
Abbreviations
Mass spectrometry Multiple sclerosis Multispectral Tandem mass spectrometry Multiple system atrophy Mitochondrial DNA Methylenetetrahydrofolate reductase (gene) Mammalian target of rapamycin Microsomal triglyceride transfer protein Methotrexate Multidimensional protein identification technology Mitral valve prolapse Monozygotic
NRTI NSAID nt NUD NYHA
Nucleic acid amplification testing Nicotinamide adenine dinucleotide phosphate reduced form Non-alcoholic fatty liver disease Non-alcoholic steatohepatitis N-Acetyltransferase 2 Normal appearing white matter Nucleocapsid (viral protein) Neural cell adhesion molecule National Center for Biotechnology Information New chemical entity National Cholesterol Education Program Refers to a set of 60 cancer cell lines curated at the NCI (National Cancer Institute) Non-coding RNA NEgative regulatory Factor (viral protein) Nucleotide excision repair Neurofibromatosis1/2 National Human Genome Research Institute Non-homologous recombination National Institute of Environmental Health Sciences (NIH) National Institutes of Health Natural killer Nuclear magnetic resonance Non-nucleosidic reverse transcriptase inhibitor Nucleotide oligomerization domain (gene) Negative predictive value Nicotinamide adenine dinucleotide phosphate oxidoreductase Nucleosidic reverse transcriptase inhibitor Non-steroidal anti-inflammatory drug Nucleotides Non-ulcer dyspepsia New York Heart Association
OBO OCTN OHCA OMIM OMP
Open Biomedical Ontologies Organic cation transporter (gene) Out of hospital cardiac arrest Online Mendelian Inheritance in Man Outer membrane protein
NAFLD NASH NAT2 NAWM NC N-CAM NCBI NCE NCEP NCI60 ncRNA NEF NER NF1/2 NHGRI NHR NIEHS NIH NK NMR NNRTI NOD NPV NQO1
OmpC opn OPN OR ORESTES ORF OSEM
Outer membrane porin C of E. coli Osteopontin (gene) Osteopontin (protein) odds ratio Open reading frame expressed sequence tags Open reading frame Ordered subset expectation maximization
p53BP1 PAD PAGE PAI PAM pANCA
p53 Binding protein 1 Peripheral arterial disease Polyacrylamide gel electrophoresis Pathogenicity island Prediction analysis of microarrays Perinuclear antineutrophil cytoplasmatic antibodies Peripheral arterial occlusive disease Papanicolaou Human parvovirus B-19 Primary biliary cirrhosis Peripheral blood lymphocytes Peripheral blood mononuclear cell Pyruvate carboxylase Principal components analysis Proliferating cell nuclear antigen Polymerase chain reaction Proprotein convertase subtilisin/kexin type 9 Procarbazine/CCNU/vincristine Parkinson disease Pharmacodynamic Patent ductus arteriosus Personal digital assistant Parkinson disease with dementia Platelet-derived growth factor Platelet-derived growth factor receptor Pyruvate dehydrogenase Pyrrolidine dithiocarbamate prodynorphin (gene) Positron emission tomography Pulse field gel electrophoresis Prostaglandin E2 Pharmacogenetics and pharmacogenomics Phenytoin Protease inhibitor Phosphotidylinositol triphosphate kinase Prostatic intraepithelial neoplasia Pharmacokinetics Phospholipid transfer protein Polony Multiplex Analysis of Gene Expression Peripheral blood mononuclear cells Pro-myelocytic leukemia retinoic acid receptor oncoprotein Photomultiplier tube Paraoxonase Percutaneous peripheral intervention Positive predictive value
PAOD1 Pap Parvo B19 PBC PBL PBMC PC PCA PCNA PCR PCSK9 PCV PD PD PDA PDA PDD PDGF PDGFR PDH PDTC PDYN PET PFGE PGE2 PGx PHT PI PI3K PIN PK PLTP PMAGE PMBC PML-RAR PMT PON1 PPI PPV
Abbreviations
PR PrEC PRIDE PRNP PS PSA PsA PSC PSP PTEN PTM PUD PZ
Protease (viral) Prostate epithelial cells Proteomics Identifications Database Prion protein (gene) Pulmonary stenosis Prostate-specific antigen Psoriatic arthritis Primary sclerosing cholangitis Progressive supranuclear palsy Phosphatase and tensin homolog Pre-trans-splicing molecule Peptic ulcer disease Plasticity zone
QPCR qRT qRT-PCR
Quantitative PCR Quantitative real-time PCR Quantitative reverse-transcriptase polymerase chain reaction Quantitative trait locus Quadrupole time-of-flight-mass spectrometry Quantitative real-time polymerase chain reaction Quality value
QTL Q-TOF-MS QT-PCR QV R&D RA RAAS RAPD-PCR RB RB RCM REPAIR-AMI
RET REV RFLP RISC RNA RNAi ROC ROCO ROS RP RPA RPLC RRMS RSV RT RT-PCR RT-PCR
Research and Development Rheumatoid arthritis Renin-angiotensin-aldosterone system Random amplified polymorphic deoxyribonucleic acid–polymerase chain reaction Retinoblastoma Retinoblastoma (gene) Restrictive cardiomyopathy Reinfusion of enriched progenitor cells and infarct remodeling in acute myocardial infarction Ret proto-oncogene REgulator of Virion protein expression (viral protein) Restriction fragment length polymorphism RNA interference silencing complex Ribonucleic acid Ribonucleic acid interference RAS in complex proteins ROC (RAS of complex proteins)/COR (C-terminal of ROC) Reactive oxygen species Rapid progression Ribonuclease protection assay Reversed-phase liquid chromatography Relapsing-remitting MS Respiratory syncytial virus Reverse transcriptase (viral protein) Real-time polymerase chain reaction Reverse transcription – polymerase chain reaction
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RV RVHF
Right ventricle Right ventricular heart failure
SACGHS
Secretary’s Advisory Committee on Genetics, Health and Society (U.S.) Serial Analysis of Gene Expression Scoring algorithm for spectral analysis S-Adenosyl-l-methionine (AdoMet) Significance analysis of microarrays Systolic anterior motion of mitral valve Shrimp alkaline phosphatase/exonuclease I Severe acute respiratory syndrome Single-base extension Sudden cardiac death Severe combined immunodeficiency Voltage-gated sodium channel, type 1, alpha subunit (gene) Selective capture of transcribed sequences Strong cation exchange Standard deviation Standards development organization Shared epitope of HLA (human leukocyte antigen) genes Spongiform encephalopathies System for Evidence-Based Advice through Simultaneous Transaction with an Intelligent Agent across a Network Sinusoidal endothelial cell Surface-enhanced laser desorption and ionization Surface-enhanced laser desorption ionization time-of-flight Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry Serological proteome analysis Systolic heart failure Short hairpin ribonucleic acid Smad-interacting protein 1 Small interfering ribonucleic acid Simian immunodeficiency virus Stevens–Johnson syndrome/toxic epidermal necrolysis Solute carrier family 12 member 3 Systemic lupus erythematosus Sentinel lymph node Stanford Microarray Database Substantia nigra Systematized Nomenclature of Medicine, Clinical Terms Single nucleotide polymorphism Substantia nigra pars compacta Substantia nigra pars reticulata SRY-box containing gene 10 Spondyloarthropathy
SAGE SALSA SAM SAM SAM SAP/Exo I SARS SBE SCD SCID SCN1A SCOTS SCX SD SDO SE SE SEBASTIAN
SEC SELDI SELDI-TOF SELDI-TOF-MS SERPA SHF shRNA SIP1 siRNA SIV SJS/TEN SLC12A3 SLE SLN SMD SN SNOMED CT SNP SNpc SNR SOX10 SpA
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SPARC SPECT SPMS SPR SPSM-FCS
SR-BI ss SSc SSCP SSRP SSTR2 STAT STEMI STK11 gene STN SU SVAS SVR SWOG TAM TAT TBB TCF7L2 TCR TDM TF TFAP2B TGF TGF- TH Th1 Th2 TIGR TIMP TIMP-1 TK TLE TLR TLS TM TNF
Abbreviations
Secreted protein, acidic, and rich in cysteine, osteonectin Single photon emission computed tomography Secondary progressive MS Surface plasmon resonance Solution (single)-Phase Single-Molecule Fluorescence auto- and two color crossCorrelation Spectroscopy Scavenger receptor, class BI Single-stranded Systemic sclerosis Single strand conformation polymorphism Simple sequence repeat polymorphism Somatostatin receptor type 2 Signal transducers and activators of transcription ST segment Elevation Myocardial Infarction Serine/threonine kinase 11 Subthalmic nucleus Surface (viral protein) Supravalvar aortic stenosis Sustained viral response Southwest Oncology Group Tumor associated macrophages TransActivator of Transcription (viral protein) Transcriptomic-based biomarkers Transcription factor 7-like 2 T-cell receptor Therapeutic drug monitoring Transcription factor Transcription factor activating protein 2 Transforming growth factor Transforming growth factor beta Tyrosine hydroxylase T-helper cell, type 1 T-helper cell, type 2 The Institute for Genomic Research Tissue inhibitor of metalloproteinase Tissue inhibitor of metalloproteinase-1 Tyrosine kinase Temporal lobe epilepsy Toll-like receptor Trans-lesion DNA synthesis Trans-Membrane (viral protein) Tumor necrosis factor
TNF- TNF- TNM TOF TOF TPMT/TPMT Treg TSA TSG TSP
Tumor necrosis factor-alpha Tumor necrosis factor-beta Tumor-node-metastasis stage Tetralogy of Fallot Time-of-flight Thiopurine methyl transferase/(gene) Regulatory T-cells Trichostatin A Tumor suppressor gene Thrombospondin
UADT UC UCSC UMLS UNAIDS US US USF1 USpA UTR
Upper aerodigestive tract Ulcerative colitis University of California at Santa Cruz Unified Medical Language System United Nation mission on AIDS Ultrasound United States of America Upstream transcription factor 1 Undifferentiated spondyloarthropathy Untranslated region (three prime or five prime)
V-ATPase V-CAM VEGF VF VIF VLDL VPR VPU VS VT VZV
Vacuolar adenosine triphosphatase Vascular cell adhesion molecule Vascular endothelial growth factor Ventricular fibrillation Virion infectivity factor Very low-density lipoproteins Viral protein R Viral protein U Ventricular septal defect Ventricular tachycardia Varicella zoster virus
WGA WGAS WGS WHO AIDS
Whole-genome amplification Whole Genome Association Studies Whole genome shotgun World Health Organization mission on AIDS World Health Organization West Nile virus Western-specific sequence Welcome Trust Case Control Consortium
WHO WNV WSS WTCCC XMRV
Xenotropic murine leukemia virus-related virus
ZDV ZFP
Zidovudine Zinc finger protein
Advisory Board Paul R. Billings President and Chief Executive Officer, CELLPOINT DIAGNOSTICS, 265 N. Whisman Road, Mountain View, CA 94043
Raju Kucherlapati Center for Genetics and Genomics, Harvard Medical School, 77 Avenue Louis Pasteur, Ste 250, Boston, MA 02115
Robert Cook-Deegan Center for Genome Ethics, Law & Policy, Duke Institute for Genome Sciences and Policy, Box 90141, Durham, NC 27708
Elizabeth G. Nabel National Heart, Lung and Blood Institute, 31 Center Drive MSC 2486, Building 31, Room 5A52, Bethesda, MD 20892
Kay E. Davies Department of Human Anatomy and Genetics, Oxford University, South Parks Road, Oxford, QX1 3QX, UK
Robert L. Strausberg Human Genomic Medicine, J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850
Brian Druker Department of Medicine, Oregon Health & Sciences University, 3181 S.W. Sam Jackson Park Road, Portland, Oregon 97239
Robert I. Tepper Third Rock Ventures, LLC, 29 Newbury St., Boston, MA 02116
Victor Dzau Office of the Chancellor, Duke University Health System, Box 3701, Durham, NC 27710
Janet A. Warrington External RNA Controls Consortium, Affymetrix, 3380 Central Expressway, Santa Clara, CA 95051
Eric Green National Institutes of Health, National Human Genome Research Institute, 50 South Drive, Room 5222, Bethesda, MD 20892-8002
Ralph Weissleder Molecular Imaging Research Center, Massachusetts General Hospital, 149 13th Street, Room 5406, Charlestown MA 02129
Muin J. Khoury Office of Genomics & Disease Prevention, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333
Janet Woodcock Food and Drug Administration, 5600 Fishers Lane, Parklawn Buildling, Rm 14-71, Rockville, MD 20857
Mary-Claire King University of Washington, 1705 Northeast Pacific Street, Box 357720, Seattle, WA 98195-7720
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Contributors Michael J. Ackerman Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA. Matthew L. Anderson Departments of Obstetrics and Gynecology and Pathology, Baylor College of Medicine, Houston, Texas, USA. Felicita Andreotti Institute of Cardiology, Catholic University Medical School, Rome-Italy. Brian H. Annex Division of Cardiovascular Medicine and Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA. Anthony Antonellis Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. Dmitri Artemov Department of Radiology – MR Research, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. James R. Bain Departments of Radiology and Internal Medicine, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA. Peter J. Barnes National Heart and Lung Institute, Section of Airway Disease, London, SW3 6LY, UK. J.S. Barnholtz-Sloan Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine; Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. Richard C. Becker Department of Medicine – Cardiovascular Thrombosis Center, Duke University Medical Center, Durham, NC 27705, USA.
Mark Boguski Harvard Medical School, Center for Biomedical Informatics, 10 Shattuck St., Boston, MA 02115 Stefano Bonassi Unit of Molecular Epidemiology, National Cancer Research Institute, Genova, Italy. Brigitta Bondy Section Psychiatric Genetics and Neurochemistry, Psychiatric Hospital of University Munich, Munich, Germany. J. Martijn Bos Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA. Roger E. Breitbart Department of Cardiology, Children’s Hospital Boston, Boston, MA 02115, USA. Jerome Brody Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA. Matthew P. Brown USA.
Omics Consulting LLC, Clayton, CA,
Lars Bullinger Department of Internal Medicine III, University of Ulm, Ulm, Germany. Shawn C. Burgess Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA. Atul J. Butte Department of Medicine and Department of Pediatrics, Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94305, USA. David J. Carey Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. George Carlson McLaughlin Research Institute, Great Falls, MT, USA
Ivor J. Benjamin Division of Cardiology, Center for Cardiovascular Translational Biomedicine, University of Utah Health Sciences Center, Salt Lake City, UT, USA.
Juan C. Celedón Channing Laboratory, Brigham and Women’s Hospital, Boston, MA; Division of Pulmonary and Critical Care, Brigham & Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA.
Paul R. Billings President and Chief Executive Officer, CELLPOINT DIAGNOSTICS, 265 N. Whisman Road, Mountain View, CA 94043
Subhashini Chandrasekharan Center for Genome Ethics, Law and Policy, Duke Institute for Genome Sciences and Policy, Durham, NC 27708, USA.
Simon C. Body Department of Anesthesiology, Perioperative & Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.
Wing C. (John) Chang Department of Pathology, Center for Lymphoma and Leukemia Research, University of Nebraska Medical Center, Omaha, NE, USA. xli
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Contributors
Yan Chen Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Antonio Chiocca Department of Neurological Surgery, Dardinger Laboratory for Neuro-oncology and Neurosciences, The Ohio State University Medical Center and Comprehensive Cancer Center, Columbus, OH 43210, USA. Theresa Puifun Chow Agency for Science, Technology and Research, Singapore Tissue Network, Singapore. Wendy K. Chung Department of Pediatrics, Division of Molecular Genetics, Columbia University, New York, NY 10032, USA. Robert Cook-Deegan Center for Genome Ethics, Law and Policy, Duke Institute for Genome Sciences and Policy, Durham, NC 27708, USA. Dolores Corella Nutrition and Genomics Laboratory, Jean Mayer–U.S. Department of Agriculture, Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA; Genetic and Molecular Epidemiology Unit, School of Medicine, University of Valencia, Valencia, Spain. Susan Cottrell Amgen, Inc., Seattle, WA 98119, USA. Nancy J. Cox Departments of Medicine and Human Genetics, University of Chicago, Chicago, IL 60637, USA. Nigel P.S. Crawford CCR/NCI/NIH, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD 20892-4264, USA. A. Jamie Cuticchia Bioinformatics Group, Duke Comprehensive Cancer Center, Durham, NC 27708, USA. Dieter Deforce Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium. Mario C. Deng
Columbia University, New York, NY, USA.
Gayathri Devi Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA.
Dirk Elewaut Department of University Hospital, Ghent, Belgium.
Rheumatology, Ghent
Charles J. Epstein Department of Pediatrics and Institute for Human Genetics, University of California, San Francisco, CA 94143, USA. Ayman H. Fanous Washington VA Medical Center, Washington, DC, 20422, USA. Phillip G. Febbo Institute for Genome Sciences and Policy, Department of Medicine – Oncology, Duke University Medical Center, Durham, NC 27710, USA. Robert J. Feezor Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, FL. Yonmei Feng Department of Pathology and the Arizona Cancer Center, University of Arizona, Tucson, AZ, USA. Zeno Földes-Papp ISS, National Center of Fluorescence, Champaign, Illinois 61822, USA. Hidehiko Fujinaka Institute for Clinical Research, Niigata National Hospital, Niigata 951-8585, Japan. David J. Galas Institute for Systems Biology, Seattle, WA, USA; Battelle Memorial Institute, Columbus, OH, USA. Louis, P. Garrison Department of Pharmacy, University of Washington, Seattle, WA 98195, USA. Glenn S. Gerhard Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Geoffrey S. Ginsburg Center for Genomic Medicine, Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA. Bjorn T. Gjertsen Institute of Medicine, Hematology, Haukeland University Hospital, University of Bergen, Bergen, Norway. Michael Glass Newborn Screening Program, Washington State Department of Health, Shoreline,WA 98155, USA.
Juergen Distler Epigenomics, AG, Berlin, Germany.
David B. Goldstein Center for Population Genomics and Pharmacogenetics, Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA.
Harmut Dohner Department of Internal Medicine III, University of Ulm, Germany.
Erynn S. Gordon Division of Pediatric Genetics, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Ayotunde O. Dokun Metabolism and Nutrition.
Endocrinology
Tucker Gosnell Massachussetts General Hospital Cancer Center, Boston, MA, USA.
Mark R. Edbrooke Pharmacogenetics, GlaxoSmithKline, Durham, NC 27709, USA.
Peter Grass Biomarker Development, Novartis Pharma AG, Basel, Switzerland.
Lucas B. Edelman Department of Bioengineering and Institute for Genomic Biology, University of Illinois, Urbana-Champaign.
Eric D. Green Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesa, MD 20892, USA.
Theo deVos
Epigenomics, Inc, Seattle, WA, USA.
Division
of
Contributors
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Iris Grossman Pharmacogenetics, Research and Development, GlaxoSmithKline, Research Triangle Park, NC 27709, USA.
Chunhwa Ihm Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.
Marta Gwinn National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.
Olga Ilkayeva Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.
Carolina Haefliger
Rafael Irizarry Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.
Epigenomics, AG, Berlin, Germany.
Susanne B. Haga Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA. Per Hall Department of Medical Epidemiology Biostatistics, Karolinska Institute, Stockholm, Sweden.
and
Joshua M. Hare Division of Cardiology and Interdisciplinary Stem Cell Institute, University of Miami, Miller School of Medicine, Miami FL 33136, USA. Ahmad Hariri Developmental Imaging Genetics Program, University of Pittsburgh, Pittsburgh, PA 15213, USA. James R. Heath Department of Chemistry, California Institute of Technology, Los Angels, CA, USA.
Shushant Jain Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Reta Lila Weston Institute of Neurological Studies, University College London W1T 4JF, UK; Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK. Melissa D. Johnson Department of Medicine – Infectious Diseases, Duke University Medical Center, Durham, NC 27710, USA. Philip W. Kantoff Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA, USA.
Robert A. Hegele Robarts Research Institute, Blackburn Genetics Laboratory, London, ONT, Canada.
Sekar Kathiresan Massachusetts General Hospital, Cardiovascular Disease Prevention Center, Boston, MA, 02114, USA.
Bettina Heidecker Division of Cardiology and Interdisciplinary Stem Cell Institute, University of Miami, Miller School of Medicine, Miami, FL, USA.
Hasmeena Kathuria Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.
Shona Hislop Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA. Eric P. Hoffman Research Center for Genomic Medicine, Children’s National Medical Center, Washington, DC 20010, USA. John Holton Centre for Infectious Diseases and International Health, Windeyer Institute of Medical Sciences, Royal Free and University College London Medical School, London, W1T 4JF, UK. Leroy Hood The Institute for Systems Biology, Seattle, WA 98103-8904, USA. Andrew T. Huang Koo Foundation Sun Yat-Sen Cancer Center, Taiwan. Erich S. Huang Duke University Medical Center, Durham, NC, USA. Carlos A. Hubbard Southern Medical Group, Tallahassee, FL 32308, USA. Kent Hunter CCR/NCI/NIH, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD 20892-4264, USA. Courtney Hyland Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA.
Kensaku Kawamoto Division of Clinical Informatics, Duke University Medical Center, Durham, NC 27710, USA. Filip De Keyser Department of Rheumatology, Ghent University Hospital, Ghent, Belgium. Asif Khalid Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA 15213, USA. Muin Khoury National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. Stephen F. Kingsmore Resources.
National Center for Genome
Michelle M. Kittleson Division of Cardiology, UCLA School of Medicine, Los Angeles, CA, USA. Beena T. Koshy Pharmacogenetics, GlaxoSmithKline, Durham, NC 27709, USA. Ranga Krishnan Department of Psychiatry and Behavioral Science, Duke University Medical Center, Durham, NC 27710, USA. Robert S. Krouse Southern Arizona Veterans Affairs Health Care System, Tucson, AZ, USA. Vikas Kundra Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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Contributors
Myla Lai-Goldman Laboratory Corporation of America, Holdings, Burlington, NC, USA.
Herman Mielants Department of Rheumatology, Ghent University Hospital, Ghent, Belgium.
Vipul Lakhani Division of Endocrinology, Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA.
Michael J. Muehlbauer Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.
Robert Langer Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Sean E. Lawler Department of Neurological Surgery, The Ohio State University Medical Center, Wiseman Hall Columbus, OH 43210, USA. Inyoul Lee
Institute for Systems Biology, Seattle, WA, USA.
Ulf Müller-Ladner Department of Internal Medicine and Rheumatology, Justus-Liebig University Giessen, Bad Nauheim, Germany. Mark A. Nelson Department of Pathology, University of Airzona, Tucson, AZ 85724, USA.
Charles Lee Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.
Monica Neri Unit of Molecular Epidemiology, National Cancer Research Institute, Genoa, Italy.
Arthur S. Lee Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA.
Mihai Netea Department of Medicine, University Medical Center St. Radboud, Nijmegen, The Netherlands.
Stanely Leong Department of Surgery, UCSF Medical Center at Mount Zion, University of California, San Francisco, CA, USA.
L. Kristin Newby Department of Medicine – Cardiology, Duke University Medical Center, Durham, NC 27710, USA.
Ralf Lesche Epigenomics, AG, Berlin, Germany. Samuel Levy J. Craig Venter Institute, Rockville, MD 20850, USA. Edison T. Liu Agency for Science, Technology and Research, Singapore Tissue Network, Genome Institute of Singapore, Singapore. David F. Lobach Division of Endocrinology and Metabolism, Duke University Medical Center, Durham, NC 27710, USA. James B. Lorens Department of Biomedicine, University of Bergen, Bergen, Norway. Aldons J. Lusis Department of Medicine, Division of Cardiology, University of California, Los Angeles, CA 900951679, USA. H. Kim Lyerly Duke Comprehensive Cancer Center, Duke University Medical Center, Durham, NC 27705, USA. Nageswara R. Madamanchi Department of Medicine, Carolina Cardiovascular Biology Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7126, USA. J. Alfredo Martinez Department of Physiology and Nutrition, University of Navarra, Pamplona, Spain.
Christopher B. Newgard Duke Independence Park Facility, Duke University Medical Center, Durham, NC 27704, USA. Maggie Ng Departments of Medicine and Human Genetics, Chicago, IL 60637, USA. Paul W. Noble Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Box 3171, Durham, NC 27710, USA. Vasilis Ntziachristos Technical University of Munich, Helmholtz Center Munich, Munich, Germany. Robert L. Nussbaum Division of Medical Genetics, Department of Medicine & Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, San Francisco, CA 94143 Meghan B. O’Donoghue Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Kunle Odunsi Departments of Gynecologic Oncology and Immunology, Roswell Park Cancer Institute, Buffalo, NY, USA.
Kevin McGrath Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Kenneth Olden Laboratory of Molecular Carcinogenesis, NIEHS, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC 27709-2233, USA.
John McHutchison Department of Gastroenterology, Duke University Medical Center, Duke University Clinic Research Institute, Durham, NC, USA.
Steve R. Ommen Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA.
Frank Middleton Upstate Medical University, Washington VA Medical Center, Washington, DC, USA.
Jose M. Ordovas Nutrition and Genomics Laboratory, Tufts University, Boston, MA 02111-1524, USA.
Vega Masignani
Novartis Vaccines, Siena, Italy.
Contributors
Roberto Patarca E. M. Papper Lab of Clinical Immunology and Molecular Biology, University of Miami School of Medicine, Sunny Isles Beach, FL 33160, USA. Jeetendra Patel Division of Cardiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. K. Patel Department of Gastroenterology, Duke University Medical Center, Duke University Clinic Research Institute, Durham, NC, USA. Carlos N. Pato Center for Neuropsychiatric Genetics and Department of Psychiatry, Department of Neuroscience and Physiology, Upstate Medical University, Syracuse, NY.
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Sridhar Ramaswamy Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA. Hans-Georg Rammensee Interfakultares Institut fur Zellbiologie, Abt. Immunologie, D-72076 Tubingen, Germany. Scott D. Ramsey Department of Pharmacy, University of Washington, Seattle, WA 98195, USA. Rino Rappuoli Novartis Vaccines, Siena, Italy. Nader Rifai Laboratory Medicine, Children’s Hospital Boston, Boston, MA 02115, USA. Lisa Rimsza Department of Pathology, University of Arizona, Tucson, AZ 85724-5043, USA.
Michele T. Pato Center for Neuropsychiatric Genetics and Department of Psychiatry, Department of Neuroscience and Physiology, Upstate Medical University, Syracuse, NY.
Yu-Hui Rogers J. Craig Venter Institute, Rockville, MD 20851, USA.
Tanja Pejovic Department of Obstetrics and Gynecology, Oregon Health Sciences University, Portland, OR 97239, USA.
Allen D. Roses Deane Drug Discovery Institute, Duke University Medical Center, One Science Drive, Suite 342, Durham, NC 27708.
Noah C. Perin Tularik, Inc., South San Francisco, CA 94080, USA. Michael Pham Division of Cardiovascular Stanford University, Stanford, CA 94305, USA.
Medicine,
Robert M. Plenge Division of Rheumatology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA. Achim Plum Epigenomics, AG, Berlin, Germany. Mihai V. Podgoreanu Department of Anesthesiology, Duke University Medical Center, Durham, NC 27710, USA. Jonathan R. Pollack University, CA, USA.
Department of Pathology, Stanford
Rebecca L. Pollex Robarts Research Institute, London, ONT, Canada. Nathan D. Price Department of Chemical and Biomolecular Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois, UrbanaChampaign, IL USA. Thomas Quertermous Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA. Francisco J. Quintana Center for Neurologic Diseases, Harvard Medical School, Boston, USA. Benjamin A. Raby Channing Laboratory, Brigham and Women’s Hospital, Boston, MA; Division of Pulmonary and Critical Care, Brigham & Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA. Daniel J. Rader University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6160, USA.
Jeffrey S. Ross Department of Pathology and Laboratory Medicine, Albany Medical College MC-80, Albany, NY 12208, USA. Ronenn Roubenoff Biogen Idec, Inc.
Immunology
Medical
Research,
Marschall S. Runge Department of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA. Maren T. Scheuner 90407-2138, USA.
Rand Corporation, Santa Monica, CA
Matthias Schuster Epigenomics, AG, Berlin, Germany. David A. Schwartz Center for Genetics and Therapeutics, National Jewish Medical and Research Center, 1400 Jackson Street, Denver, CO 80206 Debra A. Schwinn Department University of Washington, Seattle, USA.
of
Anesthesiology,
Chia Kee Seng Centre for Molecular Epidemilogy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. Nicholas A. Shackel Department of Gastroenterology, Royal Prince Alfred Hospital, Sydney, Australia. M. Frances Shannon Division of Molecular Bioscience, John Curtin School of Medical Research, Australian National University, Canberra ACT 2601 Australia. A. Dean Sherry Departments of Radiology and Internal Medicine, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas,TX 75235, USA. Jiaqi Shi Department of Pathology and the Arizona Cancer Center, University of Arizona, Tucson, Arizona, USA.
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Contributors
Kevin Shianna Duke Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710, USA. Yelizaveta Shnayder Department of Otolaryngology – Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, KS, USA. Andrew B. Singleton Laboratory of Neurogenetics, Molecular Genetics Unit, NIH, National Institute on Aging, Bethesda, MD 20892, USA. T.P. Slavin Center for Human Genetics, University Hospitals Case Medical Center; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine; Department of Pediatrics, University Hospitals Case Medical Center, Cleveland, Ohio, USA. Desmond J. Smith Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095-1735, USA. Rikkert L. Snoeckx Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium.
Hervé Tettelin
Novartis Vaccines, Siena, Italy.
Giovana R. Thomas Department of Otolaryngology – Head & Neck Surgery, University of Miami School of Medicine, Miami, FL 33136, USA. John D. Thompson Newborn Screening Program,Washington State Department of Health, Shoreline,WA 98155, USA. Visith Thongboonkerd Medical Molecular Biology Unit, Office for Research and Development, Siriraj Hospital, Mahidol University, Bangkoknoi, Bangkok 10700, Thailand. Eric J. Topol Scripps Clinic Division of Cardiovascular Disease, La Jolla, CA 92037, USA. Jeffrey A. Towbin Pediatric Cardiology, Baylor College of Medicine, Houston, TX 77030, USA. Ad A. van Bodegraven Department of Gastroenterology, VU University Medical Centre, Amsterdam, The Netherlands. Kris Van Den Bogaert Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium.
3059,
Filip Van den Bosch Department of Rheumatology, Ghent University Hospital, Ghent, Belgium.
Avrum Spira Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.
Matte Vatta Departments of Pediatrics (Cardiology), Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA.
Colin F. Spraggs Pharmacogenetics, Durham, NC 27709, USA.
GlaxoSmithKline,
Timothy D. Veenstra SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA.
Robert D. Stevens Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.
David L. Veenstra Department of Pharmacy, University of Washington, Seattle, WA 98195, USA.
Ralph Snyderman Duke Durham, NC 27710, USA.
University,
DUMC
Nicolas A. Stewart SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA. Alison Stewart Strangeways Research Laboratory, Public Health Genetics Unit, Worts Causeway, Cambridge, CB1 8RN, UK. F. Stewart Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Robert L. Strausberg J. Craig Venter Institute, Rockville, MD 20850, USA.
Aleksandr E. Vendrov Department of Medicine, Carolina Cardiovascular Biology Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7126, USA. Catherine M. Verfaillie Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium. Nicole M. Walley Center for Population Genomics and Pharmacogenetics, Duke University Medical Center, Durham, NC 27710, USA. Ling Wang Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA.
Moshe Szyf Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec H3G 1Y6, Canada.
Mike Weale Statistical Genetics Unit, Department of Medical and Molecular Genetics, King’s College London, Guy’s Hospital, London SE1 9RT, UK.
Weihong Tan Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA.
Michael E. Weinblatt Division of Rheumatology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA.
Robert I. Tepper Third Rock Ventures, LLC, Boston, MA 02116, USA.
Howard L. Weiner Center for Neurologic Diseases, Harvard Medical School, Boston, MA 02115, USA.
Contributors
Scott T. Weiss Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Ralph Weissleder Center for Systems Massachusetts General Hospital, Boston, MA.
Biology,
Samuel A. Wells Department of General Surgery, Duke University Medical Center, Durham, NC 27705, USA. Brett R. Wenner Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA. Ilse R. Wiechers Massachusetts General/McLean Hospital, Adult Psychiatry Residency Program, Boston, MA 02114, USA. Georgia L. Wiesner School of Medicine – Department of Genetics, Case Western Reserve University, Cleveland, Ohio 44106-4955, USA. Janey L. Wiggs Department of Opthalmology, Harvard Medical School and the Massachusetts Eye and Ear Infirmary, Boston, MA 02114, USA. Cisca Wijmenga Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.
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Christopher W. Woods Department of Medicine – Infectious Diseases, Duke University Medical Center, Durham, NC 27705, USA. Paula W. Woon National Center for Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. Chana Yagil Department of Nephrology and Hypertension and Laboratory for Molecular Medicine, Ben-Gurion University, Barzilai Medical Center Campus, Ashkelon, Israel. Yoram Yagil Department of Nephrology and Hypertension, Ben-Gurion University, Barzilai Medical Center Campus, Ashkelon, Israel. Tadashi Yamamoto Department of Structural Pathology, Institute of Nephrology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan. Gang Yao Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Andrew J. Yee Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA 02115, USA. Hyuntae Yoo Institute for Systems Biology, Seattle, WA, USA.
Huntington F. Willard Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA.
Y. Nancy You Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA.
James M. Wilson Gene Therapy Program, Translational Research Lab, University of Pennsylvania, Philadelphia, PA 19104-3403, USA.
Li-Rong Yu SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA.
Nelson A. Wivel Gene Therapy Program, Translational Research Lab, University of Pennsylvania, Philadelphia, PA 19104-3403, USA.
Jean-François Zagury Conservatoire National des Arts et Metiers, Chaire de Bioinformatique, 292, rue Saint-Martin, 75003 Paris.
Jay Wohlgemuth Via Pharmaceuticals, San Francisco, CA, USA.
Ron Zimmern Strangeways Research Laboratory, Public Health Genetics Unit, Worts Causeway, Cambridge, CB1 8RN, UK.
Janet Woodcock MD 20857, USA.
Stephan Züchner University of Miami School of Medicine, Miami, FL 33136.
Food and Drug Administration, Rockville,
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PART
One
Genomic Approaches to Biology and Medicine
1
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Section
Principles of Human Genomics
1. 2. 3. 4. 5. 6.
1
Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine Concepts of Population Genomics Genomic Approaches to Complex Disease Human Health and Disease: Interaction Between the Genome and the Environment Epigenomics and Its Implications for Medicine Systems Biology and the Emergence of Systems Medicine
CHAPTER
1 Organization, Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine Huntington F. Willard
INTRODUCTION The genetic variation that can influence health and disease has been a central, if not widely practiced, principle of medicine for over a hundred years, since the prescient observations of the British physician Sir Archibald Garrod established the concept of “chemical individuality” over a century ago (Garrod, 1902). What has limited broad application of this principle until now has been the generally presumed rarity or special nature of clinical circumstances or conditions to which genetic variation was relevant – rare disorders such as Garrod’s alkaptonuria, inherited conditions limited to specific populations such as sickle cell anemia, or specialized situations such as the role of ABO incompatibility in blood transfusion. Now, however, with the availability of a “reference sequence” of the human genome, with emerging appreciation of the extent of genome variation among different individuals, and with a growing understanding of the role of common, not just rare, variation in disease, we are poised to begin to exploit the impact of that variation on human health on a broad scale, in the context of genomic and personalized medicine. Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 4
Variation in the human genome has long been the cornerstone of the field of human genetics (see Box 1.1), and its study led to the establishment of the medical specialty of medical genetics (Nussbaum et al., 2007). A crucial set of connections joining Mendel’s principles of heredity, Garrod’s concept of chemical individuality, the practice of medicine and the sequence of the genome came with Pauling’s discovery of the molecular basis of sickle cell anemia and the direct correspondence between an individual’s genetic make-up and the type of hemoglobins present in that individual’s red cells (Pauling et al., 1949). The general nature and frequency of gene variants in the human genome became apparent with the classic work in the 1960s on the incidence of polymorphic protein variants in populations of healthy individuals (Harris and Hopkinson, 1972; Lewontin, 1967; reviewed in Harris, 1980). Calculations based on those protein polymorphism data – now extended in a robust and comprehensive manner with the analysis of variation on a genome scale – lead to the inescapable conclusion that virtually every individual should be found to have his or her own unique constitution of gene products, the implications Copyright © 2009, Elsevier Inc. All rights reserved.
Introduction
BOX 1.1
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5
Genetics and Genomics
Throughout this and the many other chapters in this volume, the terms “genetics” and “genomics” are used repeatedly, both as nouns and in their adjectival forms. While these terms seem similar, they in fact describe quite distinct (though frequently overlapping) approaches in biology and in medicine. Having said that, there are inconsistencies in the way the terms are used, even by those who work in the field. To some, genetics is a subfield of genomics; to others, genomics is a subfield of genetics. Arguably, depending on the perspective one has in mind, both may be right! Here, we provide operational definitions to distinguish the various terms and the subfields of medicine to which they contribute. The field of genetics is the scientific study of heredity and of the genes that provide the physical, biological and conceptual bases for heredity and inheritance. To say that something – a trait, a disease, a code or information – is “genetic” refers to its basis in genes and in DNA. Heredity refers to the familial phenomenon whereby traits (including clinical traits) are transmitted from generation to generation, due to the transmission of genes from parent to child. A disease that is said to be inherited or hereditary is certainly genetic; however, not all genetic diseases are hereditary (witness cancer, which is always a genetic disease, but is only occasionally an inherited disease). Genomics is the scientific study of a genome or genomes. A genome is the complete DNA sequence, containing the entire genetic information of a gamete, an individual, a population or a species. As such, it is a subfield of genetics when describing an approach taken to study genes. The word “genome” originated as an analogy with the earlier term “chromosome,” referring to the physical entities (visible under the microscope) that carry genes from one cell to its daughter cells or from one generation to the next. “Genomics” gave birth to a series of other “-omics” that refer to the comprehensive study of the full complement of genome products – for example, proteins (hence, proteomics), transcripts (transcriptomics) or metabolites (metabolomics). The essential feature of the “omes” is that they refer to the complete collection of genes or their derivative proteins, transcripts or metabolites, not just to the study of individual entities. While formally the field of genomics refers to the study of genomes (and hence, DNA) only, it sometimes takes on the broader meaning of referring to any large-scale approach; the less specific term “genome sciences” is also sometimes used to refer to all of the “-omics” to connote global and comprehensive approaches to the study of biology and medicine. By analogy with genetics and genomics, epigenetics and epigenomics refer to the study of factors that affect gene (or, more globally, genome) function, but without an accompanying change in genes or the genome. As presented later in this chapter and others, some typical epigenetic factors involve changes in DNA methylation or modifications to chromatin that change genome structure and hence influence gene expression even in the absence of changes in the DNA sequence. The epigenome is the comprehensive set of epigenetic changes in a given individual, tissue, tumor or population.
of which provide a conceptual foundation for what today we call “personalized medicine” as a new-age rediscovery of Garrod’s “chemical individuality.” Thus, with the availability of the human genome sequence (International Human Genome Sequencing Consortium, 2001, 2004; Venter et al., 2001) and determination of
It is the paired combination of the genome and the epigenome that best characterize and determine one’s phenotype. Medical genetics is the application of genetics to medicine with a particular emphasis on inherited disease. Medical genetics is a broad and varied field, encompassing many different subfields, including clinical genetics, biochemical genetics, cytogenetics, molecular genetics, the genetics of common diseases and genetic counseling. Medical genetics is one of 24 medical specialties recognized by The American Board of Medical Specialties, the preeminent medical organization overseeing physician certification in the United States. As of 2007, there were approximately 2300 board-certified medical geneticists in the United States. Genetic medicine is a term sometimes used to refer to the application of genetic principles to the practice of medicine and thus overlaps medical genetics. However, genetic medicine is somewhat broader, as it is not limited to the specialty of Medical Genetics but is relevant to health professionals in many, if not all, specialties and subspecialties. Both medical genetics and genetic medicine approach clinical care largely through consideration of individual genes and their effects on patients and their families. By contrast, genomic medicine refers to the use of large-scale genomic information and to consideration of the full extent of an individual’s genome, proteome, transcriptome, metabolome and/or epigenome in the practice of medicine and medical decision-making. The principles and approaches of genomic medicine are relevant well beyond the traditional purview of medical genetics and include, as examples, gene expression profiling to characterize tumors or to define prognosis in cancer, genotyping variants in the set of genes involved in drug metabolism or action to determine an individual’s correct therapeutic dosage, scanning the entire genome for millions of variants that influence one’s susceptibility to disease, or analyzing multiple protein biomarkers to monitor therapy and to provide predictive information in presymptomatic individuals. Finally, personalized medicine refers to a rapidly advancing field of health care that is informed by each person’s unique clinical, genetic, genomic and environmental information. The goals of personalized medicine are to take advantage of a molecular understanding of disease to optimize preventive health care strategies and drug therapies while people are still well or at the earliest stages of disease. Because these factors are different for every person, the nature of disease, its onset, its course, and how it might respond to drug or other interventions are as individual as the people who have them. In order for personalized medicine to be used by health care providers and their patients, these findings must be translated into precision diagnostic tests and targeted therapies. Since the overarching goal is to optimize medical care and outcomes for each individual, treatments, medication types and dosages, and/or prevention strategies may differ from person to person – resulting in unprecedented customization of patient care. The principles underlying genomic and personalized medicine and their applications to the practice of clinical medicine are presented throughout the chapters that comprise this volume.
the extent of human genome variation, both within and among populations (International HapMap Consortium, 2003, 2007) and within individual genomes (Levy et al., 2007; Wheeler et al., 2008), awareness of widespread human variation can begin to be applied generally to the exploration of common human disease.
6
CHAPTER 1
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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
In this chapter, the organization, variation and expression of the human genome is presented as a foundation for the many chapters to follow on human genomics, on genome technology and informatics, on approaches in translational genomics and, finally, on the principles of genomic and personalized medicine as applied to specific diseases.
THE HUMAN GENOME The typical human genome consists of approximately 3 billion (3 109) bp of DNA, divided among the 24 types of nuclear chromosomes (22 autosomes, plus the sex chromosomes, X and Y) and the much smaller mitochondrial chromosome (Tables 1.1, 1.2). The genome can be represented and evaluated in different ways, with different levels of resolution and degrees of sensitivity, depending on the clinical need (Figure 1.1). Individual chromosomes can best be studied at metaphase in dividing cells, and karyotyping of patient chromosomes has been a valuable and routine clinical laboratory procedure for decades (Trask, 2002); various staining or hybridizationbased analytical techniques have the ability to detect chromosome abnormalities ranging from an extra or missing whole chromosome (aneuploidy), to translocations or rearrangements involving just a portion of a chromosome(s), to deletions or duplications involving as little as perhaps a megabase (106 bp; Mb) of DNA. More recent technologies involving overlapping sets (called “tiling paths”) of isolated segments of the genome arrayed on microscope slides have provided vastly improved resolution and precision capable of evaluating in a rapid and comprehensive way the proper dosage (and in some cases the organization) of the corresponding DNA segments within an individual’s genome (Figure 1.1b) (see Chapter 9). The ultimate resolution, of course, comes from direct sequence analysis, and a number of new technologies have reduced the cost and improved the throughput of sequencing individual genomes, facilitating comparisons with the reference human genome sequence and enabling medical resequencing of patient samples (see later section in this chapter) to search for novel variants or mutations that might be of clinical importance (Bentley, 2006) (Figure 1.1c, d) (see Chapter 7). Genes in the Human Genome While the human genome contains a currently estimated 20,000–25,000 genes (Clamp et al., 2007; International Human Genome Sequencing Consortium, 2004), the coding segments of those genes comprise less than 2% of the genome; as represented in Figure 1.1c, most of the genome, therefore, consists of DNA that lies between genes, far from genes or in vast areas spanning several Mb that appear to contain no genes at all (“gene deserts”). A caveat for this statement is that the process of gene identification and genome annotation remains very much a work-in-progress; despite the apparent robustness of recent estimates (Clamp et al., 2007), it is conceivable that
TABLE 1.1
Characteristics of the human genomea
Length of the human genome (basepairs)
3,253,037,807
Number of known protein-coding genes
21,541
b
Average gene density (genes/Mb)
6.6
Number of non-coding RNA genes
4421
Number of SNPsb
13,022,900
a b
From Ensembl v. 48 (accessed February 2008) Mb megabasepairs; SNP single nucleotide polymorphism
TABLE 1.2 Chromosome
Variation among human chromosomesa Mb
Genes/ Mb
miRNA genes
1
247.25
2153
8.7
68
2
242.95
1315
5.4
60
3
199.50
1105
5.5
57
4
191.27
786
4.1
42
5
180.86
894
4.9
46
6
170.90
1109
6.5
36
7
158.82
1008
6.3
43
8
146.27
743
5.1
38
9
140.27
904
6.4
40
10
135.37
819
6.1
35
11
134.45
1368
10.2
37
12
132.35
1069
8.1
43
13
114.14
356
3.1
23
14
106.37
662
6.2
62
15
100.34
634
6.3
21
16
88.83
902
10.2
20
17
78.77
1217
15.5
40
18
76.12
289
3.8
15
19
63.81
1427
22.4
82
20
62.44
603
9.7
28
21
46.94
283
6.0
10
22
49.69
508
10.2
18
X
154.91
874
5.6
97
Y
57.77
80
1.4
3
22
–
–
Mitochondrial a
Proteincoding genes
From Ensembl v. 48
0.016
The Human Genome
(a)
(d)
1
2
3
6
7
8
13
14
15
19
20
4
9
21
22
5
10
11
12
16
17
18
X
Y
(b)
0Mb
1Mb
2Mb
3Mb
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7
>chr1:2040588,2043588 actgcaacctccacctcctgggttcaagtgattctgctgcctcagcctcctgagtagctg ggattacaggtgcccaccaccatgcccaactattttttgtatttttagtagaggcagggt ttcaccatattgaccaggctggtatcgaattcctggcctcaagtgatctgtctgccttgg cctcccaaagtgctggg[t/a]ttacaggcatgagccactgtgcctggcctaattattct tctttccttattgttagtttgtgctattattttatcagtctttgtgctgttattatcatg cctgtaaattctacgtgtatttcagacccacaaaccaagtgttgtcttagacagtggtcc ttcagatttacccccaggttacccttctagtcttcctgcaggacggcgcttacatggaga ccagcttccttctgcctgaagtagtccctttagtattcctttcagcacagacttgtaatt aattctttttatttcttttcttttcttttttttttttttgagatggatttttgctcttgt tgcccaggctggagtgcagtggtgtgattttggctcactgcagcctccacctcccaggtt caagcgattctcctggctcagcctcctgaggagctaggattgcaggtgtgcgccaccacg cccagttgttttttgtttgtgtgggaaatgtctttggcattctttctggagggtgttctc cactctgtgtggagttctaggcaggtagggggtttcccccaacaggtttttgtgttggct tggagtgtt[t/g]gtcatttctgtggtgagggcgccttccagcctcactgccacccctg gaaggcaacatctcttttctctgactcctgttaaaagtgttttcatcacaacagcagcct tgtgaaggacagaggaatcgagaatttctcctaattgagattggtagagcttcttgaatc agggacatgatagcttttgtctcttttggaaaatatcagcccttgacttttcgttttttt ttttttttttttttttttttttgagtctcgctcttgttgcccaggctggagtgcaatggc gcgatctcgactcactgcaatctccacctccccggttcaagtgattctcctgcctcagcg tcccgagtagctgggattacaggcacttgccaccatgaccggctaattttttttgcattt ataggagagacagggtttcaccatgttgaccaggctggtctggaactcctgatcatacat ccaccttggcctcccaaagtgctgggattacaggtgtgagccaccgtgcccggccagccc ttggcttttcaaatagcatcctgttctctctcccctgggacccccacacttcacacctgt gtgtctaatgtgctcttttttctgggtttcttctgcgttggttttttcccgctttgtgct tcaatgtggatttttttctactgttatctcttatttcacccaatctactcttaaatctac cctttaaattattaatttcagtcacttcattttttacttttagaatttccatttgattct ttttttttttttttttgcccaggatggcaatggcacgctctcggctcactgcaacctccg cctcccaggttcaagcaatattcctgccccagcctcccaagcagctgggattacagggtc acactaccacgccccactaatttttatgtttttattagagacggggttttgccatgttgg ccaggctggtctcgaactcctgaccttgggtgatccg[c/t]ttgcctcagcctcccaaa gtgttgggattacaggcgtgagccactgcgcctggcatcgtagttctctcttctggggtg ggaatgtctattctgtgtccttctcacgtgcaaaatactgtcattacatcccaatggccc cagaacccttaactcctcccagtgtggcgggggcagtcttgtctgaacaaggcatggggg agcctggaggcccattcctcctgaggccaagt[t/a]cctccctggctgtgggccagcat aagcgaacaaggcgtgtacttccggaatgctatggactgagtgtgtgtctccccagaatc catatgttgaagccctaaccctccagtgtgatggtgtttggagacgaagcctttgacagg tagttagagtcatggcggtagttagttagagtcatggcggtagttagttagggtcacggt ggtagttaggatcatggtggtacttaaggtcatggcagtagttagggttatatcagtagt tagggctatggctgtagttagggtgatggtggtagttaaggtcacagcagtaattagggt catggtggtggttagggtcacagtggtagttagggtcacggtggtggttagggtcgtggt ggtggttagggtcacggtggtggttagggtcacggtggtagttagggtcacggcggtact tagggtcacggcggtggttagggtcacggcggtggttagggtcacggtggtggttagggt cacggcggtggttagggtcacggtggtggttagggtcgtggtagttaggttcatggtggt ggttagggtcgtggtggttagggtcacggtggtggttagggtcacggtggtagttagggt cacggctgtagttagcgtcatggtggtggttagggtcacggcggtggttagggtcacggt ggtggttagggtcacggcggtggttagggtcacggtggtggttagggtcgtggtagttag gttcatggtggtggttagggtcgtggtggttagggtcacggtggtagttagggtcgtggt ggttagggtcatggtggtggttagggtcacggtggtggttagggtcgtggtggttagggt cgtggtggttagggtcgtggtggttagggttgtggtggttagggtggtggtggttagggt cgtggcggtggttagggtcgtggcggtggttagggttgtggtggttagggtcacggtggt ggttagggtcacggtgg…
4Mb
5Mb
6Mb
Chromosome 6
(c)
p25.2
Figure 1.1 Four views of the human genome. (a) Karyotype of a normal male donor, HuRef, whose genome was the first individual diploid genome to be sequenced (Levy et al., 2007). The 24 types of human chromosome are shown after conventional G-banding – 22 pairs of autosomes and the two sex chromosomes, X and Y. (b) An array of genomic segments, showing 244,000 genomic elements hybridized to DNA from HuRef. (c) Schematic representation of the content of 6 Mb from the short arm of chromosome 6, including the location of various genes and other features from the HuRef genome. (d) DNA sequence from the genome of James D. Watson, showing 3000 bp from chromosome 1. Watson’s sequence is heterozygous at four positions, three (in yellow) that are known polymorphisms in various populations and one (in red) that is a novel variant. Figures in (a) and (b) were provided courtesy of Steve Scherer, Hospital for Sick Children, Toronto, Canada. (c) is part of a large poster representing the complete diploid HuRef genome (Levy et al., 2007).
there are some genes, including clinically relevant genes, that are currently undetected or that display characteristics that we do not currently recognize as being associated with genes. A maximum of 5% of the genome consists of DNA that has been quite
well conserved through evolution, one indication of an important function. These and other considerations have led to the estimate that at most 20% of the genome is of functional importance (Pheasant and Mattick, 2007). Nonetheless, the statement
8
CHAPTER 1
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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
that the vast majority of the genome consists of spans of DNA that are non-genic, of no obvious function, and of uncertain clinical relevance remains true. In addition to being relatively sparse in the genome, genes are distributed quite non-randomly along the different human chromosomes. Some chromosomes are relatively gene-rich, while others are quite gene-poor, ranging from a high of 22 genes/Mb to a low of 3 genes/Mb (excluding the Y chromosome and the mitochrondrial chromosome) (Table 1.2). And even within a chromosome, genes tend to cluster in certain regions or in particular bands, a point of clear clinical significance when evaluating genome integrity, dosage or arrangement in different patient samples. Coding and Non-Coding Genes There are a number of different types of gene in the human genome. Most genes are protein-coding and are transcribed into messenger RNAs (mRNAs) that are ultimately translated into their respective proteins; their products comprise the list of enzymes, structural proteins, receptors and regulatory proteins that are found in various human tissues and cell types. However, there are additional genes whose functional product appears to be the RNA itself. These so-called non-coding RNAs (ncRNAs) have a range of functions in the cell, and some do not as yet have any identified function. But the genes whose transcripts make up the collection of ncRNAs represent about a sixth of all identified human genes (Table 1.1). Some of the types of ncRNA play largely generic roles in cellular infrastructure, including transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs) involved in translation of mRNAs on ribosomes, spliceosomal RNAs involved in control of RNA splicing, and small nucleolar RNAs (snoRNAs) involved in modifying rRNAs (Griffiths-Jones, 2007; Mattick and Makunini, 2006). Other ncRNAs play roles in gene regulation, for example in epigenetic gene silencing (Ogawa and Lee, 2002). A class of small RNAs of growing importance are the microRNAs, ncRNAs of only 22 bases in length that suppress translation of target genes by binding to their respective mRNAs and thus regulate protein production from the target transcript(s) (Filipowicz et al., 2008). Some 255 microRNA genes were identified in the human genome initially (Lim et al., 2003), although the total number of such genes is now thought to be closer to 1000 (Bentwich, 2005; Griffiths-Jones, 2007) (Table 1.2). Some are evolutionarily conserved, while others appear to be of quite recent origin during primate evolution, thus underscoring the difficulty of determining the precise number and identity of human genes (Clamp et al., 2007). MicroRNAs have been shown to downregulate hundreds of mRNAs each, with different combinations of target RNAs in different tissues (Lim et al., 2005); combined, the microRNAs are thus predicted to control the activity of as many as 30% of all protein-coding genes in the genome (Filipowicz et al., 2008). While this is a fast-moving area of genome biology, several microRNAs have already been implicated in various human diseases, including cancer,
developmental disorders and heart disease (Chang and Mendell, 2007; van Rooij et al., 2008). Genome Composition and Landscape As observed earlier, the distribution of genes in the genome is non-random, both within and between chromosomes. This in part is a reflection of the distribution of different types of DNA sequence, as the genome is partitioned into domains spanning hundreds of kilobasepairs to megabases, reflecting large-scale variation in the GC content of DNA. These so-called “isochores” have been known for decades and, at a very gross level, mimic the pattern of light- and dark-staining bands that one observes in metaphase chromosomes (e.g., Figure 1.1a, c) (Eyre-Walker and Hurst, 2001). While the driving force behind the evolution of isochores is not clear, they influence the GC content of genes contained within them (and, by virtue of the genetic code, therefore, the amino acid composition of the encoded proteins), the patterns of mutation and polymorphism detected, and the nature of various families of repeated DNA that reside there. Further – and most strikingly – different isochore domains contain clusters of genes that are highly or weakly expressed in a coordinated manner in different tissues (Caron et al., 2001; Gierman et al., 2007; Hurst et al., 2004). Thus, isochores reflect both the functional as well as structural organization of the genome. (See later section on “Expression of the Human Genome” for further discussion.) Repetitive DNA Overall, only about half of the total linear length of the genome consists of so-called single-copy or unique DNA, whose sequence is represented only once or at most a few times (International Human Genome Sequencing Consortium, 2001, 2004; Venter et al., 2001). The rest of the genome consists of several classes of repetitive DNA and includes DNA whose sequence is repeated, either perfectly or with some variation, hundreds to millions of times in the genome. Several different categories of repetitive DNA are recognized. Clustered repeated sequences constitute an estimated 10–15% of the genome and consist of arrays of various short repeats organized tandemly in a head-to-tail fashion. Such arrays can stretch several Mb or more in length and constitute up to several percent of the DNA content of individual human chromosomes; a notable outlier in this respect is the male-specific Y chromosome, of which more than half consists of such repeated DNA families (Skaletsky et al., 2003). Other tandem repeat families are based on somewhat longer basic repeats. For example, the alpha-satellite family of DNA is composed of tandem arrays of different copies of an 171 bp unit, found at the centromere of each human chromosome, which is critical for proper segregation of chromosomes during cell division (Rudd and Willard, 2004; Schueler and Sullivan, 2006). Another highly significant family of repeats is found at the very ends of chromosomes, the telomeres. While the repeats at the functional telomeres consist of relatively short stretches of perfect (TTAGGG)n repeats, different subtelomeric regions (just proximal to the telomere repeats) share patterns of homology with other subtelomeres around the genome that
Variation in the Human Genome
create clinically relevant hotspots of interchromosomal recombination (Linardopoulou et al., 2005; Riethman et al., 2005). Other major types of repetitive DNA in the genome consist of related sequences that are dispersed throughout the genome rather than localized. Among the best-studied dispersed repetitive elements are short, interspersed nuclear elements (SINEs). The most prominent family of these contains repeats that are about 300 bp in length and are recognizably related to each other although not identical in DNA sequence. In total, members of this family make up at least 10% of human DNA, although they make up a much higher percentage of the DNA in some isochores. A second major dispersed, repetitive DNA family is called the LINE (where the L stands for long) family, whose members range in size up to 6 kp in length and account for about 20% of the genome. Families of repeats dispersed throughout the genome are clearly of medical importance. Both SINE and LINE sequences have been implicated as the cause of mutations in genetic disease. At least a few copies of these families generate copies of themselves that can integrate elsewhere in the genome, occasionally causing insertional inactivation of a medically important gene. The frequency of such events causing genetic disease in humans is largely unknown, but they have been suggested to account for as many as 1 in 500 mutations (Deininger et al., 2003; Kazazian and Moran, 1998). In addition, aberrant recombination events between different LINE or SINE repeats can also be a cause of mutation in some genetic diseases. Segmental Duplications An important subclass of repetitive DNA, distinct from the large families just mentioned, includes blocks of different sequences (hence, not defining a particular family of sequences) that are present in multiple copies, often with extraordinarily high sequence conservation, in many different locations around the genome. Duplications involving substantial segments of a chromosome, called segmental duplications, account for at least 5% of the genome (Bailey and Eichler, 2006). When the duplicated regions contain genes, genomic rearrangements can result in the deletion of the region (and the genes) between the copies and thus give rise to disease (Conrad and Antonarakis, 2007). In addition, rearrangements between duplicated segments are a source of significant variation between individuals in the number of copies of these DNA sequences (Sharp et al., 2005), as will be discussed in the next section.
With completion of the reference human genome sequence, attention turned to the discovery and cataloging of variation in that sequence among different individuals (including both healthy individuals and those with various diseases) and among different populations. It has been estimated that there are some 10–15 million common sequence variants that are of sufficient frequency (minor allele frequency 5%) in one or more
9
populations to be considered polymorphic in our species. In addition, there are countless very rare variants, many of which probably exist in only a single or a few individuals. In fact, given the number of individuals in our species, essentially each and every base pair in the human genome is expected to vary in someone somewhere around the globe. It is for this reason that the original genome sequence is considered a “reference” sequence, derived as a consensus of the limited number of individual genomes whose sequencing was part of the Human Genome Project, but actually identical to no individual’s genome. Types of Variation Early estimates were that any two randomly selected individuals have sequences that are 99.9% identical or, put another way, that an individual genome would be heterozygous at approximately 3–5 million positions, with different bases (i.e., a T or a G) at the maternally and paternally inherited copies of that particular sequence position. The majority of these differences involve simply a single unit in the DNA code and are referred to as single nucleotide polymorphisms (SNPs) (Table 1.1) (see Chapter 7). The remaining variation consists of insertions or deletions (in/dels) of (usually) short sequence stretches, variation in the number of copies of repeated elements or inversions in the order of sequences at a particular locus in the genome (Figure 1.2). The total amount of in/del variation is more than originally anticipated and approaches 0.5%, not 0.1%, between any two randomly selected individuals (Levy et al., 2007). Any and all of these types of variation can influence disease and thus must be accounted for in any attempt to understand the contribution of genetics to human health (Table 1.3).
A
B
C
D
Reference A
B
C
C
D
Segmental duplication – Biallelic CNV (C)2 A
B
C
C
C
D
Multiallelic copy number variant (C)0-n A
B
C
D
D
D
D
C
D
C
D
C
D
Complex CNV (D)4(CD)3 A
VARIATION IN THE HUMAN GENOME
■
C
B
D
Inversion (CB) Chromosome
Figure 1.2 Schematic representation of different types of structural polymorphism in the human genome, leading to deletions, duplications, inversions and CNV changes relative to the reference arrangement. From Estivill and Armengol (2007), with permission.
10
CHAPTER 1
TABLE 1.3
■
Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
Common variation in the human genome
Type of variation
Size range (approx.)a
Effect(s) in biology and medicine
Single nucleotide polymorphisms (SNPs)
1 bp
Non-synonymous → functional change in encoded protein? Others → potential regulatory variants? Most → no effect? (“neutral”)
Copy number variants (CNVs)
10 kb to 1 Mb
Insertion/deletion polymorphisms (in/dels)
1 bp to 1 Mb
Inversions
Few bp to 100 kb
Segmental duplications
10 kb to 1 Mb
a
Gene dosage variation → functional consequences? Most → no effect or uncertain effect
In coding sequence: frameshift mutation? → functional change Most → uncertain effects ? break in gene sequence ? long-range effect on gene expression ? indirect effects on reproductive fitness Most → no effect? (“neutral”)
Hotspots for recombination → polymorphism (CNVs)
Abbreviations: bp basepair; kb kilobasepair; Mb megabasepair
While the overall estimate of SNP heterozygosity is approximately 1 in 1500 bp, there is much more variation in non-coding sequences than in the coding segments of genes, reflecting strong selective pressure during evolution against certain types of change in gene sequences. The combination of particular alleles along chromosomes is also non-random, with particular combinations (haplotypes) being more prevalent over short distances, due to the relative inefficiency of meiotic recombination to separate alleles at sites that are physically close together (International HapMap Consortium, 2007; Nussbaum et al., 2007). The resulting patterns of linkage disequilibrium are relevant for designing strategies to examine genetic variation genome-wide, both as a practical matter (i.e., reducing the number of SNPs that need to be tested to reveal the underlying patterns of variation) and for evaluating the potential functional importance of any particular SNP allele (see Chapters 2, 8 and 27). Copy Number Variation Over the past few years, a number of important studies have identified a previously unanticipated prevalence of structural variants in the genome, which collectively account for more variation in genome sequence than do SNPs (e.g., Levy et al., 2007; Redon et al., 2006; Sebat et al., 2004; Tuzun et al., 2005). The most common type of structural variation involves changes in the local copy number of sequences (including genes) in the genome, and these are generally referred to as copy number variants (CNVs) (Figure 1.2) (see Chapter 9). A number of different technology platforms are now being used to detect CNVs, including arrays and direct genome sequencing (Korbel et al., 2007; Levy et al., 2007; Wong et al., 2007). As many such CNVs encompass genes (including microRNA genes; Wong et al., 2007) and as a significant number of
novel CNVs are uncovered with every new population studied, a dedicated effort is underway to cataloged CNVs in the human genome worldwide and to associate these with clinical phenotype (Feuk et al., 2006; Scherer et al., 2007; Sharp et al., 2006). While most variation of this type is inherited, some CNVs occur de novo or even in somatic cells; in these cases, an individual will have different repeat lengths than do either of his or her parents. Array-based methods (see Chapter 9) have rapidly gained acceptance for evaluating the association of both inherited and de novo CNVs with mental retardation and other developmental disorders (de Vries et al., 2005; Friedman et al., 2006; Lee et al., 2007;Weiss et al., 2008). It is of considerable ongoing interest to evaluate the role of CNVs and other structural variants including deletions (Conrad et al., 2006) and inversions (Korbel et al., 2007; Stefansson et al., 2005; Tuzun et al., 2005) in the etiology of more common, complex diseases or traits of adulthood, including neurological and psychiatric conditions as well as pharmacogenetic traits (Beckmann et al., 2007; Buckland, 2003). Variation in a Single Genome The most extensive current inventory of the amount and type of variation to be expected in any given genome comes from the direct analysis of the diploid genome sequence of a single male individual, HuRef (Levy et al., 2007). Over 4 million variants were described, spanning some 12.3 Mb of DNA. About 20 Mb of “new” sequence was determined that was not previously available as part of the human reference sequence, reflecting in part the still unfinished nature of the human genome sequence and in part the particular patterns of inserted or deleted sequences that distinguish different genomes. Several hundred thousand in/dels were also found in this single genome. In addition, several hundred CNVs were detected, which overlapped at
Expression of the Human Genome
least 95 well-annotated genes. While most of these variants are identical to those found in other individuals in the population, others are likely to be what are termed “private” mutations, specific to HuRef and his family. In the HuRef genome, at least 850 genes known to be involved in inherited disease contained at least one heterozygous variant, and over 300 of them contained at least one nonsynonymous SNP (i.e., a SNP that, by virtue of the genetic code, is predicted to change the encoded amino acid). Of course, additional genes may also impact disease, and, overall, more than 4000 genes in the HuRef genome contained one or more non-synonymous SNP. Thus, at least 17% and perhaps as many as 44% of the genes in the HuRef genome were heterozygous and could encode proteins that differ in their amino acid sequence and/or are produced in different amounts (Levy et al., 2007). These estimates underscore the impact of gene and genome variation on human biology and on medicine. They also provide remarkable validation of the original estimates of Harris and Lewontin decades ago of the proportion of genes that are heterozygous in any given individual (Harris, 1980; Lewontin, 1967). Table 1.3 and Figure 1.2 capture the general types of and characteristics of the most common variation in the human genome and in human genes. However, it is clear that we are still in a mode of discovery, as relatively few genomes or populations have been assessed to date; no doubt millions of additional SNPs remain to be uncovered, as well as many additional in/ dels, inversions and CNVs, a portion of which will be expected to involve genes and other sequences of direct relevance to medicine. The issue of “what is normal?” – an essential concept in clinical medicine – remains very much an open question when it comes to the human genome (Shianna and Willard, 2006). Variation in Populations Most of the heterozygosity in the human genome is believed to be due to variants with a minor allele frequency of at least 1%. Taking advantage of major technological developments that have greatly increased the throughput of genotyping on a genome-wide scale, several large-scale projects have validated these estimates by gathering genotypic information on millions of SNPs worldwide (Hinds et al., 2005; International HapMap Consortium, 2003, 2007). Most of the studies to date, however, have been restricted to a small number of populations of Northern European, African and Asian origin used for SNP detection. From these and a large number of earlier studies that examined more populations but for many fewer variants, it has been concluded that some 85–90% of the variation found in our species is shared among different population groups; a relative minority of variants, therefore, are specific to or highly enriched/depleted in genomes from a particular population. It is possible to use population-specific variants to obtain information on the geographic origin of a genome or of particular segments within a genome. Given the many millions of SNPs now available, there are at least hundreds of thousands of
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11
SNPs that are informative for such studies (so-called “ancestry informative markers,” AIMs) (Kittles and Weiss, 2003; Paschou et al., 2007; Tian et al., 2008). This had led to two related, but distinct applications of such markers. First is the use of admixture mapping, tracing the location of particular SNPs associated with disease in populations of patients whose genomes are a mixture from at least two original populations, for example, African-Americans or Latinos (Price et al., 2007; Smith et al., 2004). Such an approach has already been used to map genes associated with several phenotypes whose frequency differs markedly between different population groups, including prostate cancer (Freedman et al., 2006), hypertension (Deo et al, 2007), skin pigmentation (McEvoy et al., 2006) and white blood cell count (Nalls et al., 2008). The second use of AIMs is for ancestry testing unrelated to disease studies (Kittles and Weiss, 2003; Shriver and Kittles, 2004). While the motivations behind such testing and the potential uses (and, some fear, abuses) of biogeographic information are varied, the commercial availability and interpretation of genetic ancestry testing is controversial (Bolnick et al., 2007). Nonetheless, the availability of such information as an intentional or unwitting by-product of wide-scale genome analysis is inevitable, and both consumers/patients and health professionals need to be aware of this as genetic variation is explored in the context of individual genomes (see Chapter 33).
EXPRESSION OF THE HUMAN GENOME A key question in exploring the origins, structure and function of the human genome is to understand how proper expression of our 20,000–25,000 genes is determined, how it can be influenced by either genetic variation or by environmental exposures or inputs, and by what mechanisms such alterations in gene expression can lead to pathology evident in the practice of clinical medicine. The control of gene activity – in development, in different tissues, during the cell cycle, and during the lifetime of an individual both in sickness and in health – is determined by a complex interplay of genetic and epigenetic features. By “genetic” features, we here refer to those found in the genome sequence (see Box 1.1), which plays a role, of course, in determining the identity of each gene, its particular form (alleles), its level of expression (regulatory elements such as promoters, enhances, splice sites, etc.), and its particular genomic landscape (domains, isochores). By “epigenetic” features, here we mean packaging of the DNA into chromatin, in which it is complexed with a variety of histones as well as innumerable non-histone proteins that influence the accessibility and activity of genes and other genomic sequences. The structure of chromatin – unlike the genome sequence itself – is highly dynamic and underlies the control of gene expression that shapes in a profound way both cellular and organismal function (Felsenfeld and Groudine, 2003).
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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
Genomic and Epigenomic Aspects of Gene Expression Identifying the genomic sequences and features that direct spatial and temporal aspects of gene expression remains a formidable challenge in genome annotation. While several decades of work in molecular biology have defined critical regulatory elements such as promoters and enhancers for many individual genes, it is a tall order to perform such studies on a genomewide scale. At the genetic level, such studies have depended on identifying highly conserved elements, whose demonstrated sequence conservation over, in some cases, hundreds of millions of years provides prima facie evidence of their importance (Bejerano et al., 2004; Margulies et al., 2003) (see Chapter 10). Several such ultraconserved elements have been validated in vitro as gene enhancers, providing some confidence that others identified in this way will also play a role in regulating gene expression (Pennacchio et al., 2006). However, by definition, such approaches will overlook regulatory elements that are not well conserved at the sequence level and/or that are newly evolved on the human lineage. A complementary epigenetic approach has been to explore the characteristics of chromatin that are associated with active or repressed genes as a step towards identifying the transcriptional regulatory code for the human genome (Barrera and Ren, 2006; Bernstein et al., 2007). Such studies, largely employing the method of chromatin immunoprecipitation followed by array or sequence analysis (Hawkins and Ren, 2006), have uncovered predictive chromatin “signatures” for promoters and enhancers in the human genome (Heintzman et al., 2007; Kim et al., 2005). These analyses are part of a broad effort to explore epigenetic patterns in chromatin genome-wide to better understand control of gene expression in different tissues or disease states (ENCODE Project Consortium, 2007; Brena et al., 2006). Increasing evidence points to a role for epigenetic changes in human disease in response to environmental influences (Feinberg, 2007). The dynamic nature of epigenetic regulation – changes in DNA methylation or in histone modification over time, between different tissues, or in various disease states – allows for what has been called ”phenotypic plasticity,” relevant both to the origins and potential treatment of disease (Feinberg, 2007) (see Chapters 5 and 11). That monozygotic twins (who share identical genomes, but are frequently discordant with respect to clinical phenotypes) show epigenetic differences in DNA methylation demonstrates the potential for epigenetic effects to extend or modify information contained in the genome and thus to underlie at least some phenotypic differences (Fraga et al., 2005).
Genetic Control of Gene Expression Levels It has been appreciated for decades that there is high variability in gene expression levels among individuals. A number of groups have begun to examine this variation as a complex quantitative trait, under genetic control (Cheung and Spielman, 2002). Several thousand loci exhibiting such variation have now been studied, and the factor(s) controlling the variation mapped around the genome (Cheung et al., 2005; Morley et al., 2004; Stranger et al., 2007a, b). A significant proportion of such effects map to the genes themselves, a result consistent with local sequence variation influencing the expression of such genes; many such local regulatory variants map to the promoter region of the gene or to the 3 untranslated region (Cheung et al., 2005). Most of the mapped variation could be accounted for by SNP variation, but nearly 20% was due to CNVs (Stranger et al., 2007a, b), underscoring the functional importance of this type of genome variation. It is likely (though unproven in specific instances) that the discovered regulatory variants will correlate with the patterns of epigenetic modifications described earlier (Heintzman et al., 2007; Kim et al., 2005) and with the finding of widespread allelic imbalance around the genome (Serre et al., 2008). Other determinants of gene expression variation map not to the variable locus itself, but to another locus elsewhere in the genome. This implies a network of regulatory interactions among different gene products in the cell, a finding that lends itself to a systems biology approach to begin to better define such networks (see Chapter 6). Some of the differences in gene expression also vary among populations. Where examined, the phenotypes are attributable to common genetic variants that are more common in particular populations than in others (Spielman et al., 2007; Stranger et al., 2007a, b). These findings, therefore, may be relevant to complex genetic diseases whose prevalence differs among populations.
Allelic Imbalance Other than well-known examples of monoallelic gene expression where only one of the two copies of a gene in a diploid cell is expressed (such as genomic imprinting and X chromosome inactivation; Nussbaum et al., 2007), it is commonly assumed that the vast majority of autosomal genes in the genome are expressed
The Genome in Three-Dimensional Space In contrast to the impression one gets when viewing the genome as a linear string of sequence (e.g., Figure 1.1c,d), the genome adopts a highly ordered arrangement within the three-dimensional space of the nucleus (Figure 1.3) (Bolzer et al., 2005; Lanctot et al., 2007). This three-dimensional structure is highly predictive of the transcriptome map (Goetze et al., 2007) and
from both homologs at comparable levels. Recent evidence, however, using expressed SNPs to distinguish transcripts from the two alleles, has demonstrated widespread differential allelic expression for up to 20% of genes in the genome (Serre et al., 2008). This phenomenon, whose epigenetic or genetic basis is unknown, may be related to similar variability observed among X-linked genes in heterozygous females (Carrel and Willard, 2005). Further complexity is suggested by the finding of monoallelic gene expression, random with respect to parental origin, for approximately 5% of genes tested (Gimelbrant et al., 2007). This is an area of ongoing study and further implicates interactions between the genome and epigenetic control mechanisms as a determinant of gene expression in the human genome.
Genes, Genomes and Disease
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13
19
3
7
14
20
8
4
9
10
15
5
11
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22
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Y
Figure 1.3 Organization of the human genome in metaphase and interphase. (a) Individual metaphase chromosomes from a normal male donor, HuRef. Chromosomes are colored differently after hybridization to chromosome-specific DNA. From Levy et al. (2007), with permission. (b) Arrangement of chromosomes in an interphase nucleus. Each chromosome maintains its own territory, with minimal mixing. From Bolzer et al. (2005), with permission.
reflects the megabase-sized domains evident from examining coordinated patterns of gene expression at the chromosome level (Gierman et al., 2007). The biophysical and/or genomic properties that facilitate or specify the orderly and dynamic packaging of each chromosome during each cell cycle without reducing the genome to a tangled mess within the nucleus remain unknown and are the subject of much investigation in genome biology. Overall, then, one should view the gene expression phenotype as the sum of several different (but interrelated) effects, including gene sequence, regulatory sequences and their epigenetic packaging, organization of the genome into domains and isochores, programmed interactions between different parts of the genome, and three-dimensional packaging in the nucleus (Fraser and Bickmore, 2007). All must coordinate in an efficient and hierarchical fashion, and disruption of any one – due either to genetic change or to disease-related processes – would be expected to alter the overall cellular phenotype.
GENES, GENOMES AND DISEASE In the context of genomic and personalized medicine, a key question is to what extent variation in the sequence and/or expression of one’s genome influences the likelihood of disease onset, determines or signals the natural history of disease, and/or provides clues relevant to the management of disease. Variation in one’s constitutional genome can have a number of different direct or indirect effects on gene expression (Table 1.3), thus contributing to the likelihood of disease. It is not, however, just the human genome whose variation is relevant to an individual’s state of health; there are thousands of microorganisms,
both symbiotic and pathogenic, whose genomes are also relevant to human phenotypes, and sequence determination of their genomes is providing new insights and approaches for the diagnosis, study and treatment of infectious disease (see Box 1.2). Contemporary frameworks for considering the impact of variation in the human genome on disease build on decades of success in establishing the role of individual, typically rare, mutations as causal determinants of now more than 2000 simple Mendelian diseases (Online Mendelian Inheritance in Man, 2008). As a result of that success, much of which paralleled the development of technologies in the early stages of the Human Genome Project (Peltonen and McKusick, 2001), attention has now turned to the genes presumed to underlie susceptibility to common complex diseases, which are the subject of many of the subsequent chapters in this book. There are two distinct, but non-exclusive models for thinking about human genetic variation and disease (Altshuler and Clark, 2005; Fearnhead et al., 2005; Florez et al., 2003; Pritchard, 2001). One – the “common allele, common disease” hypothesis – posits that variation common in the population accounts for the relatively higher or lower risk that some individuals (and their families) have for a particular condition. Under this model, the collection of 10–15 million common SNPs, CNVs and other variants in the genome underlies the range of susceptibility that one finds in the general population, modulated by the particular and often variable environmental inputs and factors that are present in that population and that may, in fact, shift or even obscure the relative impact of inherited factors. An alternative model – the rare variant hypothesis – argues that genetic susceptibility to disease is due to the accumulated risk conferred by multiple rare variants in an individual’s genome,
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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
BOX 1.2 The Genomes Within The human genome is not the only genome relevant to the practice of medicine. Both in states of health and disease, our own genome is vastly outnumbered by the genomes of a host of microorganisms, many living peacefully and continuously on various body surfaces, especially throughout the gastrointestinal tract, others wreaking havoc as adventitious viral, bacterial or fungal pathogens. The genomes of thousands of microorganisms have been determined and are being utilized to provide rapid diagnostic tests in clinical settings, to predict antibiotic or antifungal efficacy, to identify the source of airborne, water or soil contaminants, to monitor hospital or community environments, and to better understand the contribution of microbial ecosystems and various environmental exposures to diverse human phenotypes. The human colon contains more than 400 bacterial species comprising some 1013 to 1014 microorganisms. Each adult’s gut provides a unique environment – the microbiome – whose origins and impact on human disease are just being explored. The microbiotic gene set is significantly different from that of the human genome and thus has the capacity to alter the metabolic profile of different individuals or different populations, with clinically meaningful effects on drug metabolism, toxicity and efficacy (Gill et al., 2006; Li et al., 2008; Palmer et al., 2007). The applications to microbiomes of approaches in genomics (as well as proteomics and transcriptomics)
variants therefore not likely to be captured by study of the common variants identified by studies to date. These two hypotheses suggest different approaches that will likely be informative for delineating the genetic contribution to disease, both for designing research studies and for eventual clinical surveillance. It is worth emphasizing that these two hypotheses are not mutually exclusive and are each likely to be correct in some cases; indeed, there is evidence supporting each for different diseases. Genome-wide Association Studies The “common allele, common disease” hypothesis has been explored with notable success in a number of conditions, utilizing large cohorts of well-phenotyped patients and high throughput methods to genotype up to 500,000 or a million SNPs in the genome (Topol et al., 2007) (see Chapter 8). These genome-wide association studies report the statistical association of one or more variants in a narrow genomic region (which may or may not contain an annotated gene) with the presence or absence of the clinical condition. The reported SNPs define immediately accessible risk factors for that condition, at least in the population(s) under study, and can provide novel insights into the biology of the disease. It should be stressed, however, that in most instances, causality of the reported SNP(s) and the increased risk has not been proved; it may be that the actual causal variant is not the SNP itself, but is a currently undetected variant that lies in linkage disequilibrium with the SNP. In the most favorable cases, the associated SNP may be a non-synonymous variant, leading to a pathological amino acid change in the relevant gene (e.g., Thorleifsson et al., 2007), or
are revolutionizing clinical diagnostics, for example to identify unknown viral infections (Delwart, 2007; Long et al., 2004; Wang et al., 2003) or to diagnose antibiotic-resistance infections such as methicillin-resistant Staphylococcus aureus (MRSA) (Francois et al., 2007). The field of metagenomics explores this heterogeneous ecosystem by comprehensive sequence analysis of the collected genomes from biological specimens (such as stool, urine, sputum, water sources and air), followed by both taxonomic and bioinformatic analysis to deconvolute the many genomes contained in such specimens and to define the different organisms, their genes and genome variants. This approach is particularly informative for characterizing organisms that cannot be cultured in standard microbiology labs. A number of diseases have been associated with large-scale imbalances in the gut microbiome, including Crohn’s disease, ulcerative colitis, antibioticresistant diarrhea and obesity (Frank and Pace, 2008; Ley et al., 2006; Turnbaugh et al., 2006). Undoubtedly, the states of health and disease are determined in part by the balance of genomes both within us and external to us. The full complement of genomic information from both of these sources of genomes will provide insights into defining the states of health and disease and the basis for unsurpassed precision in both the prevention of disease and its treatment.
may be a variant in an RNA splice site, leading to a clinically meaningful change in the production of the gene’s transcript(s) (e.g., Heinzen et al., 2007). But in most instances, the functional impact of the associated SNP is obscure, notwithstanding very clear genetic evidence of a role of genome variation in susceptibility to the particular condition. As emphasized throughout this chapter, SNPs are but one of several types of genome variation that can influence gene expression and/or disease (Table 1.3). CNVs have also been associated with some disorders (reviewed in Estivill and Armengol, 2007), and it will require integrated genomic, genetic and functional studies to elucidate the precise basis for the role(s) of genome variation in different diseases. Medical Resequencing in Search of Rare Variants While genome-wide association studies certainly establish that common alleles do indeed underlie susceptibility to common disease in some instances, they do not allow one to conclude that this is always the case. Indeed, in most (but not all) cases to date, the common SNPs found to be associated with disease only explain a small fraction of the total genetic variation, implicating an as yet undiscovered (and presumably rarer) basis for most genetic variation underlying a given condition (Iles, 2008). In other words, while a positive statistical association is sufficient to conclude that a particular variant does indeed contribute to disease in the population under study, such a finding is insufficient to say anything declarative about the cause of or likelihood of disease in a particular case. This conclusion is, of course, highly relevant to the prospects of genomic and personalized medicine.
From Genome to Personalized Medicine
An alternative or complementary approach to genomewide genotyping of common variants is to resequence specific genes in a cohort of affected individuals, in an effort to uncover rare variants responsible for (or at least statistically associated with) the disease in question. To date, most efforts have focused on one or several genes that were believed to be strong candidates for the phenotype under study; the notable exception thus far is the whole-genome resequencing of the HuRef genome and correlation of novel variants detected in that study with his family and personal medical histories (Levy et al., 2007). Rare variants, including non-synonymous variants, in relevant candidate genes have been detected at a statistically significant higher frequency in the genomes of patients with colorectal adenomas (Fearnhead et al., 2004), with low plasma high-density lipoprotein cholesterol (HDL-C) (Cohen et al., 2004), and with triglyceride levels in the lowest quartile (Romeo et al., 2007). These successes point to a strategy of resequencing relevant genes in individuals at the extremes of the population distribution for measurable traits (Topol and Frazer, 2007), in which the only limiting parameters are the cost of sequencing and the quality of the phenotypic or quantitative data. In a proof-of-principle for this case-control approach, a recent study sequenced coding exons and splice junctions of 58 genes in nearly 400 obese and lean individuals, at the 95th or 10th percentile of body mass index (Ahituv et al., 2007). Of the 1000 variants detected, most were rare variants, including over 270 non-synonymous mutations, many of which were found only in the obese cohort and thus become strong functional candidates for a role in obesity. Searching for Somatic Mutations While genome-wide association studies are restricted to inherited variation, medical resequencing studies can target either inherited or somatic variants. In cancer especially, it is of interest to use medical resequencing to search for somatic mutations in tumor tissue in order to identify genes potentially relevant to cancer progression (Greenman et al., 2007; Sjoblom et al., 2006). Two important points emerge from such studies. First, the genes implicated by virtue of discovering rare somatic variants in multiple cases of a particular cancer tend to be different from those identified in previous genetic studies as inherited risk factors. This provides novel insights into the biology of human cancer and suggests candidates for further exploring mechanisms of tumorigenesis or metastasis or for developing therapeutic approaches. Second, however, the large number of mutations uncovered by this approach introduces the need for caution, as many will be “bystander” or “passenger” mutations around the genome that reflect only the increased mutation rate associated with cancer, not genes involved directly in cancer.
FROM GENOME TO PERSONALIZED MEDICINE Of all the promises of the current scientific and social revolution stemming from advances in our understanding of the human
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genome and its variation, genomic and personalized medicine may be the most eagerly awaited. The prospect of examining an individual’s entire genome (or at least a significant fraction of it) in order to make individualized risk predictions and treatment decisions is an attractive, albeit challenging, one (Bentley, 2004; Willard et al., 2005). Having access to the reference human genome sequence has been transformational for the fields of human genetics and genome biology, but by itself is an insufficient prerequisite for genomic medicine. As discussed in subsequent chapters, equally important are the various complementary technologies to reliably capture information on individual genomes, their epigenetic modification, and their derivatives the transcriptome, proteome and metabolome for health and disease status (Table 1.4). Each of these technologies provides information that, in combination with clinical data and evaluation of environmental triggers, will in time contribute to assessment of individual risks and guide clinical management and decision-making (Figure 1.4). Critical enablers of this new approach have been innovations in laboratory technology (to address biologically and medically relevant questions on a scale and with a throughput hardly imaginable just a few decades ago), paired with equally transforming developments in informatics and information systems to handle the onslaught of genomic data (West et al., 2006) (see Chapter 17). Personal Genomics At the heart of the genomic approach to personalized medicine will be information from individual genomes, a fast-moving area of technological development that is spawning a social and information revolution among consumers. Dramatic improvements in sequencing technology (Bentley, 2006) have reduced the cost and time of resequencing projects to a level that invites conjecture about the long awaited “$1000 genome” (Dalton, 2006; Service, 2006; Wolinsky, 2007). The much-ballyhooed release of the genome sequences of Craig Venter (“HuRef ”) (Levy et al., 2007) and James Watson has stirred up concerns about “celebrity genomics” (Check, 2007); however, while the Venter and Watson sequences may have been the first, they will not be alone for long, as numerous additional genomes are already in various sequencing pipelines (Table 1.5). What remains unsettled for now is what degree of genome surveillance will be most useful, either for research or for clinical practice, a topic that is returned to frequently in subsequent chapters in this volume. While whole-genome sequencing is increasingly possible, it is unlikely to provide more information about established disease associations than would highdensity, genome-wide SNP genotyping. Targeted resequencing of, for example, exons and known regulatory regions would allow detection of rare variants in portions of the genome most relevant to disease at a fraction of the cost of whole-genome sequencing (Hodges et al., 2007). The availability of associated clinical data is variable among the studies announced to date, but there exists, at least among some participants, a strong sense of “health-information altruism” to contribute to the much needed large-scale correlation
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TABLE 1.4
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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
Personalized signatures of health or disease
Dataset
“omic” approach
Technology platform or approach
Chapter number(s)
Human genome sequence
Genomics
Single nucleotide polymorphisms (SNPs); Other genome variants
Chapters 7–9
Gene expression profiles
Transcriptomics
Microarrays of 20,000 gene transcripts
Chapters 12, 13
Protein abundance
Proteomics
Protein arrays of specific protein products
Chapter 14
Metabolites
Metabolomics
Analysis of hundreds to thousands of metabolites
Chapter 15
Chromatin
Epigenomics
Array- or sequence-based assessment of chromatin modification
Chapters 5, 11
Gene networks, interactions
Systems biology
Large-scale interactions among genes or proteins
Chapter 6
Microbiome
Metagenomics
Analysis of viral, fungal and bacterial populations in human specimens
Chapter 48
Genomic and clinical data
Informatics
Integrated databases of “omic” data and electronic health records
Chapters 17–21
Individual A
Individual B
• Predisposition to disease
Genome sequence Somatic mutation Epigenetic changes Personal environment
Transcriptome
Proteome
• • • •
Diagnosis Natural history Treatment options Prognosis
Phenotype
Figure 1.4
The promise of personal genomics in the era of genomic and personalized medicine.
of genotype and phenotype (Kohane and Altman, 2005). Notwithstanding individual’s willingness to make genome sequence data (much less medical information) available moreor-less publicly, substantial concerns have been raised about privacy, since a surprisingly (to some) small number of SNPs or other genome variants are sufficient to allow identification of individuals (Lin et al., 2004; McGuire and Gibbs, 2006).
A Consumer Revolution While the genome revolution has without doubt been driven by technological improvements and by an explosion in the availability of genome data, the push for incorporation of genome information into clinical practice may come as much or more from consumers as from professionals. A half dozen or more companies are already offering genome-wide SNP profiles to
From Genome to Personalized Medicine
TABLE 1.5
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Individual sequenced human genomes (as of March 2008)
Individuals
Nature of genome
Method
Year completed/ anticipated
References
“RP11”a
Partial haploid
Sanger sequencing
2003
International Human Genome Sequencing Consortium, (2001); Osoegawa et al. (2001)
J. Craig Venter
Complete diploid
Sanger sequencing
2007
Levy et al. (2007)
James D. Watson
Complete diploid
454 pyrosequencing
2007
Wheeler et al. (2008)
YanHuang researcher
Complete diploid
Illumina sequencing by synthesis
2007
Qiu and Hayden (2008); Xin (2007)
YanHuang paying customer
Complete diploid
Illumina sequencing by synthesis
2008
Qiu and Hayden (2008)
Yoruba HapMap sample
Complete diploid
Illumina sequencing by synthesis
2008
Knome customers
Complete diploid
Various next-generation technologies
2008 and beyond
Personal Genome Project; 10 individuals
“Exomes” (coding exons only)
Polony sequencing
2008
Venter institute; 10–50 genomes
Complete diploid
Various next-generation technologies
2008
ClinSeq 1000
Exons of 200–400 cardiovascular genes
To be determined
2009 or beyond
http://www.genome. gov/25521304
YanHuang 99
Complete diploid
Various next-generation technologies
2010
Xin (2007)
GATC 100
Complete diploid
Various next-generation technologies
2010
http://www.gatc-biotech.com/ en/discover_gatc/research/ Human_Genome_Sequencing_ Service.php
1000 Genomes Project
Variousb
Various next-generation technologies
2010
www.1000genomes.org; http:// www.genome.gov/26524516; Hayden (2008); Kaiser (2008)
Personal Genome – Project; 500–100,000
Exomes
Polony sequencing
?
http://www. personalgeneomes.org
http://www.personalgenomes. org; Church (2006)
Source: Courtesy of Misha Angrist, Duke Institute for Genome Sciences & Policy a An anonymous male from Buffalo, New York, whose genome was overrepresented in the publicly funded Human Genome Project b 6 people fully sequenced at 20x coverage; 180 at 2x; and exons of 1000 genes in 1000 people
the public, some with associated risk estimates for relevant clinical conditions (see Chapter 21). At least a few companies will also sequence individual genomes for a cost that, while high currently, is not out of reach for some individuals. Some in the medical community have called such genome scans premature and ill advised (Hunter et al., 2008), and similar concerns have been raised for more traditional and less comprehensive commercial tests targeted to specific genes or to specific conditions (Janssens et al., 2008). Hesitation on the part of health
professionals aside, however, it seems clear that at least some consumers, whatever their motivation, will choose to test their genomes, regardless of whether any of the findings are (yet) considered actionable by their physicians. This creates both the need and an opportunity for health professionals to stay abreast of the rapidly developing scientific foundations of genomic medicine, to place in context the dynamic and still poorly understood interplays of the genome with the environment and to communicate effectively the complexities of overall genetic risk to their patients.
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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
CONCLUSION In this chapter, we have described the organization, variation and expression of the human genome as a foundation for the many chapters to follow on principles of human genomics, on new technologies in genomics and informatics, on approaches in translational genomics and, finally, on the first applications of genomic and personalized medicine to specific diseases. What is now referred to as the “Genome Revolution” has its roots in a technological revolution over the past two decades that has transformed biology and human genetics as they transition from discovery-based analog sciences to digital systems based on comprehensive information from the genome and its derivatives. Advances in genome technology and the resulting
explosion in knowledge and information stemming from the Human Genome Project are now playing a major and increasingly transformational role in our understanding of human health and in the delivery of health care. While the challenges both for the health care profession and for society are significant, the development of comprehensive, cost-efficient and high-throughput technologies, combined with powerful tools in biomedical and clinical informatics for analyzing and storing vast amounts of data, will enable the growing field of genomic and personalized medicine. As the chapters to follow illustrate, the genome sciences – encompassing large-scale analyses of individual genomes, epigenomes, transcriptomes, proteomes and metabolomes – will drive profound changes in clinical medicine.
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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine
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2 Concepts of Population Genomics Mike E. Weale and David B. Goldstein
INTRODUCTION Over the past decade, population genetics – the study of processes affecting the evolution, distribution and correlation of genetic variants within populations – has truly “gone genomic” (Goldstein and Weale 2001; Jorde et al., 2001). The Human Genome Project provided a blueprint for the “average” human genome. Subsequent projects, and in particular the arrival of affordable high-density genome-wide genotyping, has expanded this picture to provide detailed information on the genomes of many thousands of individuals from different parts of the world, focusing on the genetic differences that distinguish us all. Population genetics has thus been transformed into population genomics. This revolution has resulted in fundamental advances in our understanding of the evolution of the human genome and the history of human populations. But this information is not just of academic interest: it has had a direct bearing on how medical genomic research is conducted today. Indeed, this has been the primary motivation for collecting much of this information in the first place. As medical genomics has moved from the description of simple Mendelian traits to the investigation of complex multifactorial traits, so population genomics has assumed pivotal importance. We need population genomic theory in order to know how to look for variants that affect these traits, how to understand their genealogical history and their geographic distribution, and how to use this information effectively in diagnosis Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 22
and treatment (Box 2.1). Understanding Mendelian traits generally requires only an understanding of variation within families; understanding complex traits requires an understanding of the population within which the complex trait occurs. The first section of this chapter reviews basic concepts in population genomics. The second section reviews what we know about human population genomics, while the third section describes the application of this to medical genomics.
IMPORTANT CONCEPTS IN POPULATION GENOMICS Population genomics is the study, on a genomic scale, of the processes affecting the evolution, distribution and correlation of genetic variants within populations. Population genomics has little interest in the parts of the genome that are the same among all individuals and focuses instead on the genomic differences between us. Roughly 0.1% of our DNA varies appreciably among individuals at the single nucleotide level, and considerably more, we have recently learned (Redon et al., 2006), varies at the larger-scale structural level. The raw engine for variation is mutation (see previous Chapter 1). Once created by a mutation event, two forces – chance (modified by population structure) and selection (only relevant if the variant has an effect on viability or reproductive success) – act on the new variant as it passes from one generation to the next. Many are soon lost from Copyright © 2009, Elsevier Inc. All rights reserved.
Important Concepts in Population Genomics
BOX 2.1
Population Genomics in Action
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Generation t-6 Generation t-5
While many aspects of population genomics can appear technical and somewhat distant from both application and real-world problems and interests in biology and medicine, in fact population genomics is transforming fields as diverse as genetic anthropology and medical genomics. Among the more surprising insights won with our increasing knowledge of the pattern of human genetic variation are a string of observations about relationships among certain populations. Patterns of Y chromosome variation have been used to support the claims of Jewish ancestry of a Bantu-speaking population in South Africa and for a long-term patrilineal pattern of inheritance among Jewish men that consider themselves to be “priests” or Cohanim in Hebrew (see Goldstein, 2008). More recently, Joel Hirschhorn, David Reich and their colleagues have identified a genome-wide signature of Jewish ancestry (Price et al., 2007), using PCA. Similar methodologies have also been developed by Reich and others to study the detailed genomic history of populations that have mixed ancestry. For example, looking at the whole genomes of African Americans with prostate cancer, Reich and his colleagues were able to determine which part of each person’s genome came originally from Africa and which from Europe with a high degree of statistical accuracy (Haiman et al., 2007). Most of the genome reflected the overall proportion of European ancestry in African Americans, but a section of chromosome 8 (within band 8q24) stood out strikingly. African Americans with prostate cancer had a much higher chance of having that region of the genome be of African ancestry compared to the rest of the genome. This result provides overwhelming evidence that there is a major risk factor for prostate cancer in this part of the genome and that this risk factor is likely to contribute to the observed differences in risk of prostate cancer between African Americans and Americans of European ancestry.
the gene pool, but some rise in frequency and may, eventually, come to replace the original ancestral allele. This is how evolution works, and population genomics provides the theory that describes it (Graur and Li, 1999; Hartl and Clark, 2007). Here we briefly review the important population genomic concepts of genealogy, the standard neutral model, population structure and linkage disequilibrium. Our aim is to provide enough information to understand the medically related issues discussed later. For a more thorough coverage of population genetic theory, the reader is referred to one of many standard texts (Gillespie, 2004; Halliburton, 2003; Hartl and Clark, 2007; Hedrick, 2004). Genealogy Imagine you have taken a sample of n individuals and typed them for one single nucleotide polymorphism (SNP) – perhaps a coding polymorphism in a medically relevant gene. The 2n allele copies (two for each diploid individual) are connected by an underlying genealogy, a tree that describes the ancestor– descendent relationships linking all the copies in that population. A simple illustration of how a genealogy might emerge in a population of only four individuals is shown in Figure 2.1. Here the sample size is just n 2 (thus four allele copies, sampled in
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Figure 2.1 A simple example of a genealogy. Here the population in any one generation consists of just 4 individuals (represented by boxes) and 8 allele copies (represented as circles within the boxes). The genealogy connecting a sample of 2 individuals (and 4 allele copies) is shown. A mutation event in Generation t-3 (indicated by a red exclamation mark) has led to two allele types, coded green and blue.
Generation t) and the population from which they are sampled is fixed in each generation at N 4 individuals. For simplicity, we assume discrete non-overlapping generations, so that the set of parents for individuals in Generation t comes exclusively from the previous generation, Generation t-1. The total set of 2N allele copies in any one generation makes up the gene pool. Consider the two allele copies possessed by Individual 1 in Generation t, one inherited from her mother and the other from her father. A separate lineage of ancestor–descendent transmissions can be traced back in time for both copies (note the same thing can be done for any two allele copies sampled from the gene pool, regardless of whether they happen to belong to the same individual or not). Provided that N is finite, eventually one will reach a previous generation where the two lineages coalesce to a common ancestral copy (in Generation t-6 in the example in Figure 2.1). Note that coalescence is inevitable, given enough time, since in each generation there is a non-zero probability that coalescence will occur. However, the timing of coalescence is typically subject to much statistical uncertainty and, in human populations, is usually much further back in time than the six generations depicted here. Superimposed on the genealogical tree are mutation events, here exemplified by a single event in Generation t-3 that results in two distinct allele types (coded as different colors). This overtly genealogical approach to population genomics, focused on an observed sample of allele copies in the present day and tracing a tree backwards in time from these, has given rise to much new work in population genomics over the past 25 years, collectively known as coalescent theory (Donnelly and Tavaré, 1995; Hein et al., 2005; Kingman, 1982; Nordborg, 2001; Rosenberg and Nordborg, 2002; Wakeley, 2007). This contrasts with a more traditional approach that focuses on the entire population and typically arrives at solutions for tracing changes in allele frequencies forward in time. Perhaps the key computational and
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conceptual simplification afforded by coalescent approaches is that they are able to ignore alleles (and the individuals carrying them) that do not contribute in any way to the genetic diversity that is sampled in the current generation. Hence in this example three individuals are irrelevant in Generation t-6, two in Generation t-5, on so on. Note that while these individuals have not contributed to this sample for this locus, they may have contributed to other loci elsewhere in the genome. Thus, for example, the genealogy for a sample of men typed for their mitochondrial DNA is entirely separate from the genealogy for the same sample of men typed for their Y chromosome, and indeed in this case the individuals lying at the root of these genealogies (corresponding to so-called “mitochondrial Eve” and “Y-chromosome Adam”, provided a large enough sample is taken) are thought to have lived at entirely separate times. Both coalescent theory and the more traditional “forward” approach that models all members of a population have their uses, but we have concentrated here on the coalescent approach because it often helps one’s conceptual understanding of population genomics problems if one thinks in terms of the underlying genealogy. The Standard Neutral Model The standard neutral model (or Fisher-Wright model) provides a convenient null case against which other more complex models can be compared. The two basic assumptions are that (1) mating is random, so any allele copy in Generation t has the same probability of descending from any individual in Generation t-1; and (2) reproductive success is random, so a second allele copy picked from Generation t has the same probabilities of descent as the first one. Under these conditions, the probability that two allele copies in Generation t coalesce in Generation t-1 is 1/(2N). This is simply the probability of picking the same parent again (and the same allele copy within this parent) for the second copy, having picked a parent at random for the first allele copy. Much elegant theory flows from this simple rule, and we give two examples here to give a flavor of this. The first is the derivation of the famous Hardy-Weinberg proportions for genotype frequencies based on allele frequencies. Since the two allele copies possessed by an individual represent random samples from the gene pool under the standard neutral model, if Allele Type i has a population frequency of pi then the probability of seeing two Type i copies in the same individual (i.e., a homozygous ii genotype) is pi2 (due to the multiplicative law governing independent statistical events). Expected frequencies for other homozygous genotypes are similarly derived, and the expected frequency of heterozygotes H, known as the heterozygosity, is what is left over after taking away all the other homozym gotes. Thus H 1 ∑ i1 pi2 , where m is the total number of allele types. Note that in the simple case of just two allele types, these relationships reduce to the more familiar p2, q2 and 2pq for the frequencies of the two homozygotes and the heterozygote respectively, where q 1 p. The second example involves the expected value of H under mutation-drift equilibrium, in a model in which mutations
occur with probability to any one allele copy in any one generation, and in which all mutations lead to a new distinguishable allele type (a reasonable assumption for longer DNA sequences where each new point mutation is likely to occur in a different nucleotide). Consider two allele copies chosen at random in Generation t. Three things can happen to their respective lineages in Generation t-1: (1) they coalesce, with probability 1/(2N); (2) a mutation occurs in one or other ancestor, with probability 2 since there are two ancestors; or (3) nothing interesting happens – just two separate ancestors with no mutation events. If Event (3) happens, one simply asks the same question for Generation t-2, with the probabilities of Events (1), (2) and (3) unchanged. Thus the question of heterozygosity boils down to whether (2) happens before (1) in the genealogy connecting the two allele copies, which in turn depends only on the relative probabilities of (2) versus (1). Thus H 2/(2 1/(2N)) 4N/(1 4N). The same relationship can also be derived, somewhat more laboriously, using more traditional forward-in-time population genetics modeling (see, e.g., Crow, 1986). The relationship predicts that heterozygosity (and hence the amount of genetic variation) at neutral loci should be greater in larger populations and with high-mutation rates, and this pattern is indeed generally seen in both human and non-human populations (Graur and Li, 1999). Population Structure The concept of a completely randomly mating population is patently false for humans. Instead, individuals are more likely to have children with some individuals rather than others. One obvious constraint is geographic distance – you are more likely to marry (or otherwise pair with) someone next door than someone on the other side of the world. National boundaries are important barriers to gene flow (the movement of allele copies from one subpopulation to another), but even within national boundaries marriages are more likely to take place within socioeconomic, cultural and ethnic groups than between them (a phenomenon known as endogamy). For humans, however, none of these barriers is absolute; instead there are simply varying probabilities of having children with anyone else. In population genomics, this complexity is often simplified by compartmentalizing people into distinct groups and then by thinking about the different rates of exchange of allele copies between and within these groups. A useful metric that is often used to describe the degree of population differentiation between these groups is the FST statistic. This can be derived in many ways, and, when these result in slightly different answers, there are some differences of opinion on which derivation is best (Balding, 2003; Excoffier, 2001; Nei, 1987). However, a commonly applied derivation of FST is based on contrasting the amounts of genetic variance attributable to within- and between-population differences. For various reasons (Excoffier, 2001; Nei, 1987), the heterozygosity H that we defined earlier is an excellent measure of the genetic variance of a population. Intuitively, one can see that H approaches zero as genetic variability is reduced (i.e., the population becomes dominated by a single allele type) and conversely approaches unity as genetic
Important Concepts in Population Genomics
variability is increased (i.e., the population becomes dominated by many different allele types, such that the probability of picking two of the same type from the gene pool becomes vanishingly small). One interpretation of FST, then, is that it represents the proportion of total heterozygosity, HT (obtained by pooling all populations together), that is attributable to between-population differences. Since it’s easier to calculate the average withinpopulation contribution (simply the mean heterozygosity H calculated over all within-population H values), and since the between- and within-population contributions together sum to HT, the definition of FST is then (H T H )/H T ). For most of the genome, FST values range from a maximum of 15% for intercontinental differences, to typically 1–3% for international differences within a continent, to less than 0.1% for differences within countries in Europe (Cavalli-Sforza et al., 1994; Steffens et al., 2006). Genetic markers with high FST are used for a number of purposes in genomic medicine, including ancestry testing and tracing the likely origin(s) of all or portions of one’s genome (Box 2.1). Linkage Disequilibrium Linkage Disequilibrium, or LD, is a measure of the degree of nonrandom association between allelic forms at two different sites (or loci) in the genome. Two factors influence this: mutation and recombination. Since the chances of recombination increase the further apart two loci are, LD tends to be highest between loci that lie very close to each other in the genome. However, there is a lot of variability around this general rule, partly because the rate of recombination varies throughout the human genome (Myers et al., 2005) and partly because chance factors in the placement of mutations in the underlying genealogy also play a role. These points are illustrated in Figure 2.2. In the absence of recombination (i.e., if the loci are very close to each other), one still sees variation in the degree of LD between two loci. If the polymorphism-generating mutations happen to occur along the same branch of the genealogy (Figure 2.2a), then only two out of the possible four 2-locus haplotypes are formed, and in this case knowledge of the allele type at one locus allows us to perfectly predict the allele type at the other. But if instead the mutations occur at widely separated points on the genealogical tree (Figure 2.2b), then knowledge of the allele type at one locus is only partially predictive of the allele type at the other. This breakdown of association, due simply to mutation, may or may not be of interest to the population geneticist depending on the application. If one is interested in the prediction of allele type at one locus depending on the allele type at the other, then mutation-led breakdown of LD is of interest and a useful LD metric in this case is r 2, which is the amount of variation at locus A that is explained by knowledge of allele states at locus B. If locus A and B are binary SNPs, then r 2 can be found simply by converting the allelic states into 0/1 variables, then calculating the square of the standard Pearson correlation coefficient between them. If instead one is interested only in the supposed amount of recombination between locus A and B, then a metric that
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attempts to factor out the mutation-led breakdown of association is preferable. One such metric is D, which for SNP loci takes its maximum value of 1 if and only if one observes just three out of the possible four haplotypes in one’s sample (for (a) Generation t-6 Generation t-5 Generation t-4
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Figure 2.2 Example genealogies involving 2 linked loci. (a) Both mutations happen on the same genealogical branch, leading to just two haplotypes – green red and blue yellow. Thus knowledge of the allele type at one locus provides perfect prediction of the allele type at the other. LD metrics: r2 1, D 1. (b) The two mutations occur separately on the genealogy tree, leading to three haplotypes and imperfect prediction. LD metrics: r2 0.33, D 1. (c) A recombination event in Generation t-2 (indicated by a red X) has created a recombinant haplotype with contributions from two separate lineages, leading to four different haplotypes and further reducing LD (to zero in the toy example, but this is not a general rule). LD metrics: r2 0, D 0.
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reviews of LD metrics see Devlin and Risch, 1995; Hudson, 2001; Nordborg and Tavaré, 2002; Mueller, 2004). Looking at Figure 2.2b, one should note that wherever one places the two mutations one can never obtain more than three haplotypes in the eventual sample, so D is always 1. Assuming no further mutations, the only way to obtain all four haplotypes in the eventual sample is via one or more recombination events between the two loci, and this is illustrated in Figure 2.2c. Note that recombination events alter the underlying genealogy, which is no longer a bifurcating tree but rather a more complicated web (technically, a “graph”). Linkage disequilibrium is both a boon and a bane for medical genomics, and this is discussed in more detail later.
HUMAN POPULATION GENOMICS We start by describing important sources of genomic data that have emerged recently. Then, we describe what these have told us about the history and evolution of human populations and about how human populations are structured today. Important Sources of Data The HapMap dataset (http://www.hapmap.org/) today constitutes the most important source of information for population genomics (The International HapMap Consortium, 2005, 2007). Instigated in 2002 with funding from the US NIH and other international sources, the project set out to describe and type common SNP variation, evenly spread throughout the human genome, in a collection of 270 individuals sampled from four populations spanning three major continents of origin – Africa, Asia and Europe. This was accomplished in two main phases – an initial Phase I completed in early 2005, featuring around 1.3 million common SNPs at an average density of 0.4 SNPs per kilobase (The International HapMap Consortium, 2005), and a subsequent Phase II completed in late 2005, featuring around 3.1 million common SNPs at an average density of 1 SNP per kilobase (The International HapMap Consortium, 2007). In addition, 10 separate 500 kb regions of the genome (the HapMap-ENCODE regions) were targeted for exhaustive discovery and typing of all but the rarest SNPs, in order to provide a valuable reference source to compare the projected performance of the rest of the HapMap data in representing all common SNP variation. It is estimated that there are some 10 million common SNPs in the human genome (Carlson et al., 2005). HapMap Phase II typed only one third of these; however, comparisons using the HapMap-ENCODE regions show that, thanks to linkage disequilibrium, these 3.1 million SNPs represent or “tag” the other 7 million to a high degree of accuracy (The International HapMap Consortium, 2007). The HapMap project, along with earlier SNP discovery projects such as The SNP Consortium (The International SNP Map Working Group, 2001), played an important role in the development of high-density genome-wide SNP panels by greatly increasing our knowledge of the location of SNPs in the human genome. In addition, the HapMap project provided the
LD data that allowed tag-based SNP panels to be designed (see Chapter 8). Genome-wide SNP panels are now being applied to population samples from around the world, providing valuable extensions of the information available from HapMap in two important ways. Firstly, new datasets have increased the sample sizes for certain populations to thousands of individuals; see, for example, the iControl database (US individuals, http:// www.illumina.com/pages.ilmn?ID231), the WTCCC database (UK individuals, http://www.wtccc.org.uk/) and the dbGaP database (http://www.ncbi.nlm.nih.gov/sites/entrez?dbgap). Secondly, new datasets have expanded the set of populations beyond the four studied for HapMap; see, for example, the Stanford University HGDP-CEPH database (51 distinct worldwide population samples, http://www.cephb.fr/hgdp-cephdb/). A key element in all these datasets is their free availability to the scientific community, reflecting a sea-change in attitudes to data access that has beneficially affected almost all genomic research since the Human Genome Project (see also The GAIN Collaborative Research Group, 2007). Currently, most databases characterize just the SNPs in the human genome. However, much interest is now also focused on Copy Number Variation (CNVs), and this is likely to widen soon to other types of structural variation such as inversions (see previous Chapter 1). The indications are that SNPs have a fair, but not perfect, ability to tag CNVs (Locke, 2006; Redon et al., 2006), and thus the development of genomic databases specifically targeting structural variation remains a priority. A Brief History of Homo sapiens The new genomic data have broadly confirmed the picture of our genetic history that had emerged from previous genetic studies and are also consistent with what we know from paleoanthropological data. This picture is of a relatively small population of ancestral humans living in East Africa between 60–100 thousand years ago, spreading out-of-Africa to central Asia and thence to Europe in one direction and South and East Asia and the Americas in the other, replacing other extant human populations as they went (Cavalli-Sforza et al., 1994; Garrigan and Hammer, 2006; Goldstein and Chikhi, 2002; Jobling et al., 2003). This picture explains why (1) compared to those of other primate species, our genomes are remarkably low in genetic diversity within populations and in differentiation between populations; (2) this diversity steadily decreases in populations further removed from Africa (Prugnolle et al., 2005; Ramachandran et al., 2005); (3) non-African diversity tends to be a subset of African diversity or, to put it in genealogical terms, most gene trees have their root in Africa; and (4) LD in the human genome is high in non-African populations. Not enough time has passed since our recent, small origins to have allowed mutations and recombinations to break down the haplotypic patterns present in the genomes that emerged from Africa. There is still room, and indeed eagerness, for debate regarding this picture. Debate continues over the degree to which there was interbreeding with extant populations either before or after the out-of-Africa expansion (Garrigan and Hammer, 2006;
Human Population Genomics
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2007; Rosenberg et al., 2005; Serre and Pääbo, 2004). Thus, the genome of any one single individual is not composed of a contribution from one single discrete ancestral group, nor even of a series of contributions from different discrete ancestral groups. Just as there are no discrete groups today, so there were none in the past. For these reasons, a more useful paradigm for human population genomic structure is to see it as comprised of a series of quantitative axes, each one reflecting one aspect of a multidimensional structure. We illustrate some of these points in Figure 2.3. The figure displays the principal aspects of the population genomic structure of a sample of approximately 500 individuals collected in North Carolina, USA. Each individual was typed using the Illumina HumanHap 550 k genome-wide SNP panel (see Chapter 8), and these data were then subjected to Principal Components Analysis (PCA), a statistical method that ranks and dissects the correlation structure of multivariate datasets (Patterson et al., 2006; Price et al., 2006). Among the population groups contained within the cohort, some of the allele frequencies change in correlated ways that distinguish between population groups. Thus, in a simple and extreme case, you might have 10,000 SNPs that are all high frequency in group A and low frequency in group B; these would have high FST values, as described earlier.These SNPs would contribute to a single axis (e.g., PC1 in Figure 2.3), and the position of an individual along that axis would be dependent on which of
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Population Genomic Structure and Race The new genomics has allowed us, at last, to separate the population genetic concept of biogeographical shared ancestry from the socio-cultural concept of race. This is because the quantity of genetic data that one can routinely collect on a single individual is enough to provide all one needs to draw up a remarkably accurate picture of the population structure of a sample of individuals. In the past, with less genetic data available, other information such as the individual’s own self-declared racial identity was needed to shore up uncertainties in the underlying population genomic structure, and much debate ensued over the effectiveness of using one to predict the other (Barnholtz-Sloan et al., 2005; Burnett et al., 2006; Liu et al., 2006b; Tang et al., 2005; Wilson et al., 2001). Now that debate is largely irrelevant – we can get all the information we need regarding patterns of ancestral relatedness from the genetic data alone. The fact that confusion still exists over conflating population genomic structure with sociocultural race stems from many reasons, but the research community is at least partly to blame. Recent surveys suggest that the term “race” continues to be used ambiguously both in the scientific literature (Sankar et al., 2007; Shanawani et al., 2006) and in dealings with the press (Condit, 2007; Lynch and Condit, 2006). Genetically, there are no such things as human “races”, in the original strict sense of discrete unequivocal groupings. Because of our recent shared roots and our considerable mobility, we are only marginally more similar to a person sharing common biogeographic ancestry and living locally than we are to a person with different roots living on the other side of the world. Leaving aside sex-linked loci, which one must treat separately, average FST never rises much above 15% even for intercontinental comparisons (Cavalli-Sforza et al., 1994), so at least 85% of all genetic differences are accounted for by “within population” variance. There are no absolute barriers to gene flow between any two parts of the world, and, while partial barriers exist, the location and extent of these are difficult to define unequivocally (Barbujani and Belle, 2006; Handley et al.,
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Harding and McVean, 2004; Liu et al., 2006a; Ray et al., 2005), in particular regarding whether there was interbreeding with Neanderthals in Europe (Currat and Excoffier, 2004; Trinkaus, 2007). This debate revolves not only around the interpretation of genetic data, but also around morphological data such as cranial features, again with contrasting conclusions (Manica et al., 2007; Trinkaus, 2005). Likewise there is debate over the mode of the out-of-Africa expansion, for example over the use of coastal routes as preferred routes for expansion (Amos and Manica, 2006; Macaulay et al., 2005) and over the number and extent of population bottlenecks that may have occurred along the way (Liu et al., 2006a; Ramachandran et al., 2005). While our knowledge of genetic variation in the present day is excellent, the use of this to make inferences about the past is not an exact science. It may be that ancient DNA techniques will help to resolve these issues, by providing more direct information on the population genetics of these ancient populations (Caramelli et al., 2007; Noonan et al., 2006;Vernesi et al., 2004).
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Figure 2.3 Population structure in a sample collected in North Carolina, USA, revealed by Principal Components Analysis applied to genome-wide SNP data (Duke Genetics of Memory Cohort typed on Illumina HumanHap 550 panel). Scores for the first two principal component axes are plotted here. Self-declared racial identity labels are: AA African/AfricanAmerican; C Caucasian; EA East Asian; M Hispanic/ Mixed; SA South Asian. See text for further details. Figure based on data provided by D. Goldstein and A. Need.
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the two groups that individual came from. Different axes would reflect other correlations in the data, involving, for example, a different subset of SNPs that also distinguish different population groups (e.g., PC2 in Figure 2.3). In this way, PCA uses the correlation patterns in the data to reduce the dimensionality of the dataset. Formal methods exist to determine how much information is reflected in each of the axes. In real datasets, the correlation patterns are much less extreme and simple than the group A and group B case described above, but the idea is the same. The first point the figure illustrates is that, even though PCA is based solely on genotype information and is thus blind to non-genetic information, there is a remarkably good correspondence with self-declared racial identity. Most individuals belonging to three population subgroups (Caucasian in red, South Asian in turquoise, and East Asian in black) are clearly identified and separately from each other by analysis of their genotypes (Figure 2.3). However, a second point, and just as important as the first, is that there is additional population structure that is not picked up by the information on racial identity. For example, there is a large spread in the African American group, reflecting individual differences in the degree of European admixture in their genomes. Thirdly, there is more than one axis along which co-ancestry relationships can be defined, and in fact the PCA reveals many more significant axes for this dataset than the two used to plot Figure 2.3. This underlines the multidimensional nature of human population genomic structure. Finally, the degree of resolution in elucidating such structure, as evidenced by the sharpness in the clustering of some of the groups in Figure 2.3, is something altogether new, and is a largely unforeseen benefit of the step-increase in genetic data afforded by the latest genome-wide SNP chips.
APPLICATION OF POPULATION GENOMICS TO GENOMIC MEDICINE Here we show how the concepts we have discussed so far can be applied to genomic medicine. The first two sections deal with the important role that LD and population structure have in the search for medically relevant causal variants, while the last deals with the insights beginning to emerge on the common population genomic patterns describing these variants (Box 2.1). LD in the Search for Medically Relevant Variants Today the hunt for medically relevant variants is taking place in large population-based samples, using genotyping panels that scan the entire genome. This creates both challenges and benefits, and both require an understanding of population genomics. In essence, the reason why association studies are so much more powerful than linkage studies at localizing a causal genetic variant is that linkage studies only make use of recombination events taking place over one or two generations in the pedigree under study, whereas association studies make use of the much larger number of recombination events that have taken place over the entire genealogy linking the individuals in a sample at a given
locus. Thus the breakdown in association with a nearby marker due to LD happens over a much shorter distance than the breakdown in linkage due to observed recombination events within pedigrees. The cost is that you need to cover the genome with a much greater density of markers. Using a simple theoretical model, Kruglyak estimated that this breakdown might happen over a 3 kb distance (Kruglyak, 1999). We now know that breakdown of LD is both region-specific (i.e., varies between different parts of the genome) and population-specific. The former is due at least in part to variation in the rate of recombination throughout the genome as a reflection of genome organization (Myers et al., 2005), while the latter is due to differences in the demographic histories of different populations. For example, levels of LD in European populations are higher than those in African populations and higher than the level estimated by Kruglyak (1999). This is presumed to be due to one or more population bottlenecks that have occurred since the out-of-Africa expansion (bottlenecks generate higher LD by shortening the average genealogical tree linking a sample of individuals). In a practical sense, this means that fewer SNPs are needed to cover the genome in Europeans than in Africans. Still greater efficiency can be achieved, however, by leveraging what we know about the specific patterns of LD to pick out a special set of SNPs that still largely represents all the common variation in a given genomic region – a procedure known as tagging (de Bakker et al., 2005; Weale et al., 2003). This idea is now applicable to the entire genome, with the availability of genomewide genotyping arrays containing hundreds of thousands of SNPs. Thus either through simply the sheer density of SNPs (e.g., Affymetrix platforms) or through explicit use of tagging methods to guide the choice of SNPs in the first place (e.g., Illumina platforms), these modern genome-wide platforms are able to represent all common variation in the human genome to an extremely high level of accuracy (see Chapter 8). Recent studies have shown that the ability to port tagging SNP sets inferred from one population to another related one (e.g., from the same continent) is generally good (Conrad et al., 2006; de Bakker et al., 2006). Since LD varies in extent among different populations, this presents the possibility of conducting different studies at different stages in different populations in order to maximize efficiency. Two types of special populations have been used to capitalize on larger levels of LD in the initial scan for causal variants. One of these is the admixed population, one in which individuals are descendents of a recent period of admixture between two previously separated ancestral populations. The best-studied example of this is the African American population, as on average their genome is comprised of an 80:20 mixture of West African and European DNA, although there is quite a high degree of variation among different individuals who self-identify as African American in terms of the proportion of the genome that is of West African or European origin (Parra et al., 1998). The other type is the isolated population, one in which the individuals descend from a small number of founders and have, through endogamy, remained relatively free from subsequent admixture (Service et al., 2006). One well-studied example is the Dutch “GRIP” population (Liu et al., 2007). Despite their differences
Conclusions
in origin, both admixed and isolated populations have genomes with elevated levels of LD, and this can be used to positive effect in the early stages of the search for new causal variants (Angius et al., 2008; Seldin, 2007; Smith and O’Brien, 2005). Ultimately, though, the very same LD that allows us to economize in the number of SNPs needed to perform effective genome-wide association studies also acts as a barrier, limiting one’s ability to localize a causal variant based on association alone. Instead, a signal pointing to causal involvement of locus A could equally be due to the actual influence of locus B, if locus B is in high LD with locus A. In such cases, other types of information (e.g., based on the location of the respective polymorphisms and their predicted functional influence) are needed to tease apart the causal relationships. Correcting for Population Structure We have already noted that human populations have a complex structure (also known as stratification) that neither conforms to the notion of distinct races nor to that of one single randomly mating gene pool but rather falls somewhere in between. In practice, what this means is that even within a carefully designed study that attempts to match cases and controls for biogeographical shared ancestry, there may be hidden structure within the study. This creates a problem if cases and controls are nonrandomly distributed with respect to this structure. To illustrate this, consider again the structure revealed in Figure 2.3. Imagine a case-control study conducted within the African American group, in which cases are more likely to come from the bottom right and controls from the top left of the cluster. This sets up a situation in which SNPs that differentiate strongly between these two sides would also differentiate cases and controls, resulting in many false signals of association. Luckily, we can use the fact that population structure affects all SNPs in the genome to affect a correction. Older genomic control methods applied a uniform correction to all SNPs (Devlin et al., 2004; Devlin and Roeder, 1999). Newer methods allow a SNP-specific correction to be applied, tailored to the observed degree to which the SNP in question is seen to be affected by population structure (some SNPs vary more simply by evolutionary chance (Balding and Nichols, 1995), others because of presumed selective gradients (Campbell et al., 2005)). Of these newer methods, the EIGENSTRAT method of Price et al. (2006) is particularly convenient and flexible because it (a) makes use of PCA to efficiently dissect the population structure; (b) uses as many co-ancestry related principal component axes as are deemed necessary to effect the correction (guided by theory developed in Patterson et al., 2006); and (c) automatically recognizes, since principal component axes are quantitative, that human population structure is continuous rather than discrete. Understanding the History and Population Distribution of Variants In this final section, we anticipate the forthcoming flood of data on causal variants that will be discovered as a result of genome-wide association studies on common complex diseases
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(Frayling, 2007). These will soon allow the resolution of questions that have been debated for decades regarding the population genomic properties of causal variation relative to other variation in the human genome (Mitchell-Olds et al., 2007). An early example of this is a meta-analysis by Ioannidis et al. (2004) on the question of whether causal variants tend to exert consistent effects in different parts of the world – in other words, does possession of an AA genotype for locus Q always increase your risk of a given disease relative to genotype aa, or is it different for people living in Africa versus people living in Asia? Their study suggested that the general rule was the former and that differences in population risk were more due to differences in allele frequencies among populations than to differences in the effect of possessing a given genotype. The take-home message is that a new causal variant discovered in Population A because it is at high frequency can still lead to new therapies of benefit to people living in Population B, where the causal variant is rare, because the underlying metabolic pathway being affected by that causal variant is the same. As one example of this, a new class of antiretroviral drugs – the HIV entry inhibitors – was developed following the discovery of the importance of the CCR5-32 polymorphism in progression to HIV-AIDS, and these drugs appear to be of universal benefit despite the fact that the CCR5-32 mutation is almost wholly absent in subSaharan Africa (Esté and Telenti, 2007). Looking ahead, large-scale resequencing technologies will help us to build up a picture of the “allelic architecture” of causal variants for common complex diseases (Wang et al., 2005). A null hypothesis would be that the allele frequency distributions of causal variants are no different from those observed for neutral genetic variants in the human genome. Much opinion has been offered on the number of variants open to detection by association studies (which are only powered to detect common variants) (Blangero, 2004; Pritchard, 2001; Pritchard and Cox, 2002; Reich and Lander, 2001; Smith and Lusis, 2002). One way in which this debate has been couched is over the degree to which causal variants have been under selection, and whether this has been large enough to alter their allele frequency and LD properties.Various methods have been proposed recently to look for signals of selection on a genome-wide scale (Kelley et al., 2006; McVean and Spencer, 2006; Sabeti et al., 2006; Sabeti et al., 2007; Voight et al., 2006; Thornton et al., 2007), but so far, while individual examples exist of what appear to be strong signals of selection around genes affecting Mendelian traits (e.g., the lactase gene and adult lactose intolerance (Bersaglieri et al., 2004; Burger et al., 2007)), a systematic treatment of this question for variants affecting common complex disorders awaits further data.
CONCLUSIONS In this chapter we have given what we hope is a concise and accessible account of the main concepts of population genomics and their importance for medical genomics. The field has been
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transformed over the past 5 or so years by the move away from a single “Human Genome” to the diverse human genomes of many thousands of individuals. The new data have allowed us to quantify with unprecedented accuracy the patterns in different human populations of allele frequency and of linkage disequilibrium throughout the genome. This has allowed us to estimate the variable rates of recombination and mutation that apply to
different part of the genome and to reaffirm the remarkably recent and unified origins of our species in Africa. Most importantly, it has provided us with the necessary tools to carry out association-based genome-wide scans to look for novel causal variant of common complex disease, and the necessary framework for interpreting these in the translation of this knowledge into new effective therapies.
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Mueller, J.C. (2004). Linkage disequilibrium for different scales and applications. Brief Bioinform 5, 355–364. Myers, S., Bottolo, L., Freeman, C., McVean, G. and Donnelly, P. (2005). A fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321–324. Nei, M. (1987). Molecular Evolutionary Genetics. Columbia University Press, New York. Noonan, J.P., Coop, G., Kudaravalli, S., Smith, D., Krause, J., Alessi, J., Chen, F., Platt, D., Pääbo, S., Pritchard, J.K. et al. (2006). Sequencing and analysis of Neanderthal genomic DNA. Science 314, 1113–1118. Nordborg, M. (2001). Coalescent theory. In Handbook of Statistical Genetics (D.J. Balding, M. Bishop and C. Cannings, eds), Wiley, Chichester, UK, p. 309. Nordborg, M. and Tavaré, S. (2002). Linkage disequilibrium: What history has to tell us. Trends Genet 18, 83–90. Parra, E.J., Marcini, A., Akey, J., Martinson, J., Batzer, M.A., Cooper, R., Forrester, T., Allison, D.B., Deka, R., Ferrell, R.E. et al. (1998). Estimating African American admixture proportions by use of population-specific alleles. Am J. Hum Genet 63, 1839–1851. Patterson, N., Price, A.L. and Reich, D. (2006). Population structure and eigenanalysis. PLoS Genet 2, e190. Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N. A. and Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904–909. Price, A.L., Butler, J., Patterson, N., Capelli, C., Pascali, V.L., Scarnicci, F., Ruiz-Linares, A., Groop, L., Saetta, A.A., Korkolopoulou, P. et al. (2007). Discerning the ancestry of European Americans in genetic association studies. PLoS Genet. doi:10.1371/journal.pgen.0030236.eor. Pritchard, J.K. (2001). Are rare variants responsible for susceptibility to complex diseases?. Am J Hum Genet 69, 124–137. Pritchard, J.K. and Cox, N.J. (2002). The allelic architecture of human disease genes: Common disease-common variant ... or not?. Hum Mol Genet 11, 2417–2423. Prugnolle, F., Manica, A. and Balloux, F. (2005). Geography predicts neutral genetic diversity of human populations. Curr Biol 15, R159–R160. Ramachandran, S., Deshpande, O., Roseman, C.C., Rosenberg, N.A., Feldman, M.W. and Cavalli-Sforza, L.L. (2005). Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proc Nat Acad Sci USA 102, 15942–15947. Ray, N., Currat, M., Berthier, P. and Excoffier, L. (2005). Recovering the geographic origin of early modern humans by realistic and spatially explicit simulations. Genome Res 15, 1161–1167. Redon, R., Ishikawa, S., Fitch, K.R., Feuk, L., Perry, G.H., Andrews, T.D., Fiegler, H., Shapero, M.H., Carson, A.R., Chen, W. et al. (2006). Global variation in copy number in the human genome. Nature 444, 445–454. Reich, D.E. and Lander, E.S. (2001). On the allelic spectrum of human disease. Trends Genet 17, 502–510. Rosenberg, N.A., Mahajan, S., Ramachandran, S., Zhao, C., Pritchard, J.K. and Feldman, M.W. (2005). Clines, clusters, and the effect of study design on the inference of human population structure. PLoS Genet 6, e70. Rosenberg, N.A. and Nordborg, M. (2002). Genealogical trees, coalescent theory and the analysis of genetic polymorphisms. Nat Rev Genet 3, 380–390.
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Sabeti, P.C., Schaffner, S.F., Fry, B., Lohmueller, J., Varilly, P., Shamovsky, O., Palma, A., Mikkelsen, T.S., Altshuler, D. and Lander, E.S. (2006). Positive natural selection in the human lineage. Science 312, 1614–1620. Sabeti, P.C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E.H., McCarroll, S.A., Gaudet, R. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918. Sankar, P., Cho, M.K. and Mountain, J. (2007). Race and ethnicity in genetic research. Am J Med Genet A 143A, 961–970. Seldin, M.F. (2007). Admixture mapping as a tool in gene discovery. Curr Opin Genet Dev 17, 177–181. Serre, D. and Pääbo, S. (2004). Evidence for gradients of human genetic diversity within and among continents. Genome Res 14, 1679–1685. Service, S., DeYoung, J., Karayiorgou, M., Roos, J.L., Pretorious, H., Bedoya, G., Ospina, J., Ruiz-Linares, A., Macedo, A., Palha, J.A. et al. (2006). Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet 38, 556–560. Shanawani, H., Dame, L., Schwartz, D.A. and Cook-Deegan, R. (2006). Non-reporting and inconsistent reporting of race and ethnicity in articles that claim associations among genotype, outcome, and race or ethnicity. J Med Ethics 32, 724–728. Smith, D.J. and Lusis, A.J. (2002). The allelic structure of common disease. Hum Mol Genet 11, 2455–2461. Smith, M.W. and O’Brien, S.J. (2005). Mapping by admixture linkage disequilibrium: Advances, limitations and guidelines. Nat Rev Genet 6, 623–632. Steffens, M., Lamina, C., Illig, T., Bettecken, T., Vogler, R., Entz, P., Suk, E.-K., Toliat, M.R., Klopp, N., Caliebe, A. et al. (2006). SNPbased analysis of genetic substructure in the German population. Hum Hered 62, 20–29. Tang, H., Quertermous,T., Rodriguez, B., Kardia, S.L., Zhu, X., Brown, A., Pankow, J.S., Province, M.A., Hunt, S.C., Boerwinkle, E. et al. (2005). Genetic structure, self-identified race/ethnicity, and confounding in case-control association studies. Am J Hum Genet 76, 268–275.
The GAIN Collaborative Research Group (2007). New models of collaboration in genomewide association studies: The Genetic Association Information Network. Nat Genet 39, 1045–1051. The International HapMap Consortium (2005). A haplotype map of the human genome. Nature 437, 1299–1320. The International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–862. The International SNP Map Working Group (2001). A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409, 928–933. Thornton, K.R., Jensen, J.D., Becquet, C. and Andolfatto, P. (2007). Progress and prospects in mapping recent selection in the genome. Heredity 98, 340–348. Trinkaus, E. (2005). Early modern humans. Annu Rev Anthropol 34, 207–230. Trinkaus, E. (2007). European early modern humans and the fate of the Neandertals. Proc Nat Acad Sci USA 104, 7367–7372. Vernesi, C., Caramelli, D., Dupanloup, I., Bertorelle, G., Lari, M., Cappellini, E., Moggi-Cecchi, J., Chiarelli, B., Castrì, L., Casoli, A. et al. (2004). The Etruscans: A population-genetic study. Am J Hum Genet 74, 694–704. Voight, B.F., Kudaravalli, S., Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. PLoS Biol 4, e72. Wakeley, J. (2007). Coalescent Theory: An Introduction. Roberts & Co. Wang, W.Y., Barratt, B.J., Clayton, D.G. and Todd, J.A. (2005). Genomewide association studies: Theoretical and practical concerns. Nat Rev Genet 6, 109–118. Weale, M.E., Depondt, C., Macdonald, S.J., Smith, A., Lai, P.S., Shorvon, S.D., Wood, N.W. and Goldstein, D.B. (2003). Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: Implications for linkage-disequilibrium gene mapping. Am J Hum Genet 73, 551–565. Wilson, J.F., Weale, M.E., Smith, A.C., Gratrix, F., Fletcher, B., Thomas, M.G., Bradman, N. and Goldstein, D.B. (2001). Population genetic structure of variable drug response. Nat Genet 29, 265–269.
RECOMMENDED RESOURCES The seminal work by Cavalli-Sforza et al. (1994) is still a standard reference on the use of genetics to infer aspects of human history and demography. A useful, more up-to-date work with an emphasis on human evolution is the textbook by Jobling et al. (2003).
The HapMap website (http://www.hapmap.org/tutorials.html.en) has some nice tutorial material on the rationale and use of this important population genomic resource.
CHAPTER
3 Genomic Approaches to Complex Disease Desmond J. Smith and Aldons J. Lusis
INTRODUCTION Most of the socioeconomic burden of disease in the industrialized nations is due to complex disorders, in which multiple genes interact with the environment to produce the final disease phenotype (Smith and Lusis, 2002). Typical examples include atherosclerosis, diabetes, cancer, multiple sclerosis, autism, alcoholism, and drug abuse (Diabetes Genetics Initiative et al., 2007; Easton et al., 2007; Moffatt et al., 2007; Samani et al., 2007; Scott et al., 2007; Steinthorsdottir et al., 2007; Wellcome Trust Case Control Consortium, 2007). An understanding of complex disease involves: (1) identifying the genetic variations, whether common or rare, that contribute to the disease; (2) determining how the variations influence function and which pathways they perturb; and (3) determining how the variations interact with each other and with the environment to contribute to the disease. Even if the total genetic contribution to a complex trait is substantial, typically each gene contributes only a small amount to the final phenotype, making detection of the responsible genes an arduous process (Wellcome Trust Case Control Consortium, 2007). Further complicating such analyses are allelic heterogeneity and locus heterogeneity, in which a disease can be due to different alleles of the same gene or to alleles in different genes, respectively. Genetic interactions can also occur, in which multiple genes enhance each others’ effects on a disease (synergy) or
Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg
block each others’ effects (epistasis). In addition, there is much variation of the environment in human studies, further contributing to “noise” in an analysis (Smith and Lusis, 2002). The advent of genomics, in which the whole genome is the unit of study rather than individual genes, is revolutionizing the study of complex disease (Olson, 2003) and is rapidly helping establish genomic medicine as a new and exciting field. This chapter focuses on the use of genomic technologies to understand complex disease and is organized as follows: First, we discuss methods for the identification of DNA variations, both common and rare (Figure 3.1). Second, we discuss linkage and association methods for relating DNA variation to phenotypes. And lastly, we discuss how the “omic” technologies can help identify the underlying genes and organize them into pathways and networks (Figure 3.2).
IDENTIFYING COMMON AND RARE GENOMIC VARIATIONS IN THE POPULATION Varieties of Polymorphism Mapping genes became practicable in humans with the realization that polymorphic DNA sequences could be used directly as genetic markers (Botstein et al., 1980). Initially, single nucleotide polymorphisms (SNPs), assayed as restriction fragment length polymorphisms (RFLPs) on gels, were employed as markers in
Copyright © 2009, Elsevier Inc. All rights reserved. 33
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ELUCIDATING COMPLEX DISEASE Phenotype of interest Identify variants Linkage or association Mixed or isolated populations Common Allele Detection Technology (e.g., TaqMan, MS, Affymetrix, Illumina) Rare Allele Discovery Technology (e.g., 454 Life science, polony sequencing, Solexa, MPSS) Gene/allele discovery interaction variants
Proteomics
Systems biology
Genetical genomics
Fxns/pathways Animal models
Molecular subtyping
Transcript profiling
Figure 3.1 Elucidating complex disease. This flow chart parallels the discussion in the text. Genetic dissection of a disease phenotype of interest requires identification of appropriate variants for mapping. These variants will usually be common and chosen from publicly available databases. The strategy for gene identification will require decisions as to whether linkage or association should be used, which type of population should be employed, and the nature of the allele detection technology. Some phenotypes may require the use of recent approaches to rare allele identification. Once a gene responsible for a phenotype has been identified, the methods of systems biology can be used to understand the relation of the causative allele to disease.
combination with Southern blotting. However, this approach is laborious. The strategy became much more feasible with the discovery of microsatellite repeats (Weber and May, 1989). These densely spaced markers (roughly one per every 2 kb) are polymorphic because of differences in lengths of short oligonucleotide repeats. They have the advantage of being easily scored using the polymerase chain reaction (PCR) and gel electrophoresis and also have multiple alleles, which greatly improves their informativeness compared to biallelic markers such as SNPs. The use of microsatellites dominated mammalian genetics until the last several years, at which time the utility of SNPs reemerged with the invention of medium- and high-throughput approaches for analyzing these digital allelotypes. The SNPs also have the advantage that their average spacing is about one every 300 bp in the human genome, denser than microsatellites. SNP Genotyping Technologies A large number of SNP genotyping technologies have emerged over the last few years (Table 3.1). One of the first rapid methods for SNP genotyping, TaqMan technology, uses real-time PCR (Gibson et al., 1996; Heid et al., 1996; Livak, 1999). The SNPs are distinguished by reporter oligonucleotides containing a fluorescent nucleotide at the 5 end and a quenching nucleotide at the 3 end. If the oligonucleotide is a perfect match to a PCR product flanking
the SNP, the Taq polymerase used for the PCR liberates the 5 fluor through the 5 to 3 exonuclease activity of the polymerase. The liberated fluor-substituted nucleotide is no longer inhibited by proximity to the quenching fluor and fluoresces brightly. This fluorescence increases with each cycle of the PCR and is measured in each cycle that is in real time. However, if the oligonucleotide does not bind because of the presence of an SNP, there is no increase in fluorescence. Because a PCR assay has to be set up for each SNP, the method is only of moderate throughput. Another moderate- to high-throughput technology for genotyping biallelic markers employs matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) (Cantor, 2005; Griffin and Smith, 2000). Primer extension reactions are performed resulting in differing product lengths depending on the allele present for a particular SNP. The mass of the reaction products are then quantitated using the MALDI-TOF MS, permitting evaluation of which SNP is present. Throughput can be enhanced by multiplexing different assays in one sample. A dramatic increase in throughput came with the invention of photolithographically synthesized arrays of oligonucleotides for genotyping (Kreiner and Buck, 2005; Lipshutz et al., 1995). Affymetrix arrays consist of very high densities of 25-mers matching major or minor alleles of known SNPs in the human genome. Photolithographic activation of chemical groups is used to synthesize the oligonucleotides in situ. The photolithography permits a dramatic degree of spatial miniaturization with very dense packing of oligonucleotides and high-throughput analysis of SNPs. The presence or absence of a particular SNP in the target genome of interest is revealed by the presence or absence of a corresponding hybridization signal on the array. The arrays are spatially encoded, meaning that the position of an oligonucleotide on the array maps to the SNP it assays. The current feature size on these arrays permits up to 500,000 SNPs to be analyzed. Perlegen, a company that employs Affymetrix technology, has used custom-designed photolithographic arrays to query 1.5 106 SNPs (Hinds et al., 2005). Illumina arrays use sequence-encoding rather than spatialencoding for high-throughput genotyping. Beads with assay oligonucleotides are used to interrogate individual SNPs. A fluorescent extension reaction requiring correct hybridization of the assay oligonucleotide to genomic DNA detects which SNP is present (Shen et al., 2005).The small size of the beads means that hundreds of thousands of SNPs can be analyzed in parallel. Fluorescence readouts from each bead are captured using bundles of optical fibers. In addition to the assay oligonucleotide, the beads also contain decoding oligonucleotides that reveal which SNP the bead is interrogating. The decoding oligonucleotides are revealed using a series of fluorescently labeled probes. This bead-based method has been used to date to analyze up to a million SNPs in one assay. Sequencing Technologies The above genotyping technologies are high throughput but only detect already known SNPs. Most of these SNPs have been identified as a result of resequencing projects in the public domain (Wheeler et al., 2006) and represent reasonably common
Identifying Common and Rare Genomic Variations in the Population
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OMIC SPACE
PC 2 Phenome PC 1
Metabolome
Proteome
Transcriptome
Genome
Figure 3.2 Omic space. Multiple partially orthogonal domains, where various sorts of biological networks, experimental findings, and model-based predictions are represented and integrated (Left). Tools for collection of genome-wide data relevant to omic space are being developed. The phenome (global phenotyping) represents much of classical biology and medicine, supplemented with exciting advances in fields such as molecular imaging (MRS, PET). Repositories for such data are in the early stages. One example is the Mouse Phenome database (http://www.jax.org/phenome). Mass spectrometry (MS) and NMR have been used for high-throughput analysis of the metabolome and for non-invasive diagnosis and screening using non-supervised algorithms such as PCA. An example of PCA used to differentiate disease and non-diseased samples is shown to the right, where PC1 and PC2 represent the first and second principal components, and red and blue points represent metabolites differentiating diseased and non-diseased samples, respectively. The proteome is studied using a number of technologies, including MS. The interactome (study of interactions between proteins) has been investigated using whole-genome yeast two-hybrid screens, literature-based analysis of pathways (e.g., gene ontology, http:// www.geneontology.org/), and co-affinity immunopurification. Protein products of genes mutated in inherited genetic disorders tend to interact with proteins involved in similar disorders (Right). Green, proteins involved in developmental disorders; blue, proteins involved in endocrine disorders. Whole-genome expression arrays have been used for transcriptome analysis, while high-density SNP chips, high-throughput sequencing, and HapMap sequences have been used at the genome level. Some databases, such as the “GenomePhenome Superbrain” (http://www.omicspace.riken.jp/), are designed to integrate data from the various omic domains. The elements (nodes) of each stage appear to have a scale-free topology (right, shown linked to transcriptome). Certain elements (hubs) have many connections while most have relatively few (see also Chapters 1 and 6).
alleles. However, the extent to which complex diseases are due to common or uncommon polymorphisms is unclear (see Common Disease/Common Variant Hypothesis below) (Smith and Lusis, 2002).
In the long term, the evaluation of rare SNPs in common complex disorders will require new sequencing technologies to affordably resequence multiple human genomes. This quest is known as “the $1000 genome” (Shendure et al., 2004). While
36
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TABLE 3.1
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Genomic Approaches to Complex Disease
Genotyping and sequencing technologies
Technology
Applications
Organization
TaqMan
Custom SNPs, 102–103 assays
Applied Biosystems (https://www2. appliedbiosystems. com/)
Mass spectrometry Custom SNPs, 102–103 assays
Sequenom (http: //www.sequenom. com)
Photolithographic arrays
Predesigned SNPs, Affymetrix (http:// 105–106 assays www.affymetrix.com) Perlegen (http:// www.perlegen.com/)
Illumina arrays
Predesigned SNPs, Illumina (http:// 105–106 assays www.illumina.com)
Pyrosequencing
SNP discovery
454 Life Sciences (http://www.454. com)
Solexa sequencing arrays
SNP discovery
Illumina (http:// www.illumina.com)
Massively parallel signature sequencing
SNP discovery
Illumina (http:// www.illumina.com)
Single molecule DNA sequencing
SNP discovery
Helicos Biosciences (http://helicosbio. com)
certainly inspiring, it will likely be many years before this goal is achieved. Nevertheless, recent advances in rapid sequencing may improve our discovery of rare and private alleles (Table 3.1) (see Chapter 7). All of the developments increase speed by avoiding laborious molecular cloning steps in Escherichia coli. In one method developed by 454 Life Sciences, individual genomic DNA molecules are cloned on beads (Margulies et al., 2005). The beads are isolated from one another using oil/water emulsions to create spherical lipid bilayer vesicles about the size of a microorganism. Polymerase-mediated amplification of the DNA molecules on the beads can then occur without cross-contamination (emulsion PCR). Sequence information from each bead is then obtained using pyrosequencing. In this method, release of a pyrophosphate during incorporation of a nucleotide in an extension reaction is detected by light emission from a luciferase reaction. The small size of the beads allows large numbers of sequences to be obtained in parallel in picoliter reactors. Acquisition of the pyrosequencing output uses fiber optics. The throughput is about 25 Mb in an afternoon, although the average sequence reads are less than 100 bp. Polony sequencing is another approach using emulsion PCR, in which the individual beads are immobilized in a thin film of polyacrylamide after DNA amplification (Shendure et al.,
2005). The term “polony” is an abbreviation of “polymerase colony”. The DNA molecules represent self-ligated molecules which permit the acquisition of mate-paired sequence tags, that is, sequences from the two ends of the ligated molecule. Fluorescent sequencing by ligation in combination with a light microscope allows sequence tags of about 13 bp to be obtained from each end of the self-ligated molecule, a total of 26 bp. This method appears to be particularly useful for detecting translocations and inversions. Another approach, used by Solexa, employs PCR amplification of individual DNA molecules (Bennett et al., 2005). The individual PCR products remain localized to a small region of a glass slide because of the presence of capture oligonucleotides. The amplification results in “colonies” of different individual DNA molecule at a high spatial density on the glass slide. Fluorescent dideoxy sequencing reactions are then employed to interrogate each of the colonies in parallel, giving high-throughput sequences. Another bead-based sequencing method is massively parallel signature sequencing, MPSS (Brenner et al., 2000). A population of DNA molecules is ligated en masse to a highly complex mixture of identifying oligonucleotides. Amplification by PCR follows. Purification of multiple copies of a single class of DNA occurs when individual identifying oligonucleotides are captured by their complement on a bead. Sequence from each bead is then obtained by sequential hybridization reactions. The method gives sequence ladders of about 20 bp. A fourth method does not employ any type of DNA amplification, but rather uses fluorescent dideoxy sequencing of single DNA molecules (Braslavsky et al., 2003). The data capture uses a light microscope and the potential degree of parallelism is extremely high. Sequence reads of up to 5 bp have been obtained. At present, there is tremendous activity in developing cheaper, faster, and more accurate sequencing methods. Whether the next generation of sequencing tools uses one of the strategies described above or some completely unexpected technology, it seems clear that the contribution of rare and private polymorphisms to common genetic disease will become better understood in the coming years.
RELATING DNA VARIATION TO PHENOTYPES Candidate Gene Approach In the early days of complex trait genetics, the mid-1980s, the candidate gene approach was widely used. This approach evaluates whether common variations of biochemically defined candidate genes are associated with a particular disease by evaluating the frequency of various alleles in case versus control populations (Table 3.2). One of the first important examples was the gene for apolipoprotein E (APOE) which was found to exhibit three common alleles (E2, E3, and E4). The E2 form was originally found to be strongly associated with a relatively rare
Relating DNA Variation to Phenotypes
TABLE 3.2
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37
Examples of sequence variants implicated in complex disease
Disease phenotype
Gene/sequence varianta
Identification strategy
Technology
References
Hyperlipidemia (type III)
ApoE2
Association/candidate gene
Isoelectric focusing
Wardell et al., 1982
Alzheimer’s disease
ApoE4
Association/candidate gene
PCR/restriction isotyping
Strittmatter et al., 1993
Diabetes mellitus (type II)
Calpain 10
Linkage/association
Sequencing, PCR-RFLP
Horikawa et al., 2000
Crohn’s disease
NOD2
Linkage/LD, Linkage, TDT
Microsatellites/ sequencing, allelespecific PCR
Hugot et al., 2001; Ogura et al., 2001
Familial combined hyperlipidemia
USF1
Linkage/isolated population/association
Sequencing
Pajukanta et al., 2004
Myocardial infarct risk
Lymphotoxin alpha
Association/LD
Sequencing/PCR isotyping
Ozaki et al., 2002
Macular degeneration
Complement factor H
Association
Affymetrix arrays
Klein et al., 2005
Low HDL cholesterol
ABCA1, APOA1, LCAT
Association
Sequencing
Cohen et al., 2004
Low LDL cholesterol
NPC1L1
Association
Sequencing/TaqMan
Cohen et al., 2006
Obesity
SLC6A14
Linkage/LD/isolated population/association
Microsatellites/ sequencing/MS
Suviolahti et al., 2003
Diabetes mellitus (type II)
TCF7L2
Linkage/isolated population/association
Microsatellites
Grant et al., 2006
Myocardial infarct risk
LTA4H
Isolated population/association
Sequencing
Helgadottir et al., 2006
Schizophrenia
NRG1
Linkage/isolated population/TDT
Fluorescence polarization
Stefansson et al., 2002
Crohn’s disease
5q31 cytokine gene cluster
Linkage/LD
Microsatellites/ sequencing
Rioux et al., 2001
Breast cancer
FGFR2, TNRC9/ LOC643714, MAP3K1, rs13281615 (8q), LSP1
Association
Affymetrix arrays/TaqMan/MS
Easton et al., 2007
Diabetes mellitus (type II)
TCF7L2, CDKAL1
Association
Illumina
Steinthorsdottir et al., 2007
Diabetes mellitus (type II)
9p21.3 (CDKN2A/ CDKN2B), IGF2BP2, CDKAL1, HHEX, SLC30A8
Association
Affymetrix
Diabetes Genetics Initiative et al., 2007; Zeggini et al., 2007
Diabetes mellitus (type II)
9p21.3 (CDKN2A/ CDKN2B), IGF2BP2, CDKAL1, HHEX, SLC30A8, TCF7L2, FTO PPARG, KCNJ11
Association/isolated population
Illumina
Scott et al., 2007
Myocardial infarct
9p21.3 (CDKN2A/ CDKN2B), MTHFD1L, PSRC1, MIA3, SMAD3, LPL
Association
Affymetrix
Samani et al., 2007
(continued )
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TABLE 3.2
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Genomic Approaches to Complex Disease
(Continued)
Disease phenotype
Gene/sequence varianta
Identification strategy
Technology
References
Asthma
ORMDL3
Association
Illumina
Moffatt et al., 2007
Bipolar disease
16p12, KCNC2, GABRB1, GRM7, SYN3
Association
Affymetrix
Wellcome Trust Case Control Consortium, 2007
Myocardial infarct
9p21.3 (CDKN2A/ CDKN2B), MTHFD1L, ADAMTS17
Association
Affymetrix
Wellcome Trust Case Control Consortium, 2007
Crohn’s disease
NOD2, 5q31 cytokine gene cluster, IL23R, ATG16L1, 10q21, 5p13.1, IRGM, NKX2-3, PTPN2
Association
Affymetrix
Wellcome Trust Case Control Consortium, 2007
Rheumatoid arthritis
HLA-DRB1, PTPN22, IL2RA, IL2RB, TNFAIP2, GZMB, PRKCQ, KAZALD1, 12q24
Association
Affymetrix
Wellcome Trust Case Control Consortium, 2007
Diabetes mellitus (type I)
MHC, CTLA-4, PTPN22, IL2RA, IFIH1, 12q13, 12q24, 16p13, PTPN2, IL2 receptor
Association
Affymetrix
Wellcome Trust Case Control Consortium, 2007
Diabetes mellitus (type II)
PPARG, KCNJ11, TCF7L2, FTO, CDKAL1, HHEX,
Association
Affymetrix
Wellcome Trust Case Control Consortium, 2007
a
Where the responsible gene is unclear, the relevant chromosome region is given.
hyperlipidemia (Type III) and with low cholesterol levels in the population (Wardell et al., 1982). Later, the E4 allele was found to be strikingly associated with Alzheimer’s disease (Strittmatter et al., 1993). The candidate gene approach continues to be employed extensively, as thousands of association studies are reported each year. However, the strategy has some important problems. First, of course, the gene has to first be linked to the disease through biochemical studies or mechanistic hypotheses. Second, association tests are prone to artifacts due to population stratification and ethnic admixture. One way to overcome this latter problem is to perform family-based association testing such as the transmission disequilibrium test (TDT) (Spielman and Ewens, 1998). This test evaluates the role of a polymorphism in a disorder by assessing its frequency in affected versus non-affected siblings. The idea is that, since the siblings are raised in the same family, most of the differences in susceptibility will be due to genetics rather than to the environment. Linkage In the early 1990s, linkage analysis employing microsatellite markers became a common approach to map loci for complex diseases. In this approach, families with multiple affected members are phenotyped and genotyped. Regions of the genome relevant to the disease can then be detected using allele association or parametric approaches. One advantage of this approach is that a simplified
set of genes may be responsible for the disorder within a particular family compared to the population as a whole. However, this advantage melts away when multiple families are required, as is nearly always the case to obtain adequate statistical power. Another substantial disadvantage of linkage analysis in humans is the cost and difficulty of identifying multiple multigenerational families with the disorder of interest. In addition, mapping power in this design is limited and the responsible genes are frequently poorly localized to regions covering many megabases. A common paradigm since the late 1990s has been to carry out linkage analysis followed by targeted association testing of candidate genes in promising regions or to test for linkage disequilibrium (LD) with genetic markers in the region. Linkage disequilibrium is a situation in which neighboring alleles are linked more often than expected by chance, either because of low recombination rates between the markers or because of expansion of a founder population in which the alleles were originally linked. The first successful example of the twostep strategy was the identification of the calpain 10 gene as a causal gene in type 2 diabetes in a large set of Mexican families, although this finding remains somewhat controversial (Horikawa et al., 2000). A very clear subsequent success was the identification of the NOD2 gene as an important contributor to Crohn’s disease (Hugot et al., 2001; Ogura et al., 2001). Another example is the identification of the USF1 gene as a cause of familial combined hyperlipidemia (Pajukanta et al., 2004).
Relating DNA Variation to Phenotypes
Genome-wide Association Although the combination of linkage and association has been successful in several instances, only a handful of genes have been identified to date. However, with the advent of cheaper methods for the detection of polymorphisms, genome-wide association studies have become feasible (Hirschhorn, 2005; Hirschhorn and Daly, 2005) (see Chapter 8). Thus, association, the strategy first used for genetic dissection of human traits, has come back to center stage. The first example was a study of heart disease by Ozaki et al. (2002), who typed thousands of SNPs in thousands of individuals in Japan (Ozaki et al., 2002). The several genes exhibiting strong evidence of association with heart disease were then typed in a second set of individuals, and one gene, encoding lymphotoxin-, was found to exhibit a consistent association. Another very striking success was the identification of complement factor H in age-related macular degeneration (Klein et al., 2005). In that study, a relatively small group of individuals (96 cases, 50 controls) were typed for 116,204 SNPs, and a common non-synonymous SNP was found to be strongly associated with the disease even after a conservative Bonferroni correction for multiple comparisons. Such whole-genome association studies are rapidly becoming the major source of information on complex disorders in humans as the available technologies become more widely used and are allowing interrogation of 105–106 SNPs. Very recent successful examples include the identification of loci for breast cancer (Easton et al., 2007), type II diabetes (Diabetes Genetics Initiative et al., 2007; Scott et al., 2007; Steinthorsdottir et al., 2007; Zeggini et al., 2007), coronary artery disease (Samani et al., 2007), asthma (Moffatt et al., 2007), and a large study which identified loci contributing to bipolar disorder, coronary artery disease, Crohn’s disease, rheumatoid arthritis, and type 1 and type 2 diabetes (Wellcome Trust Case Control Consortium, 2007). Most gratifying is the high degree of agreement between different studies for identified loci for the same phenotype. Also interesting was the fact that large majority of the loci exerted small effects on the final disease risk (see Chapter 8). Copy Number Variation Another recent realization emerging from the use of microarray genotyping is the prevalence of copy number variants (CNVs) which constitute about 10% of the human genome (e.g., Sebat et al., 2004) (see Chapter 9). The CNVs are type of polymorphism in which segments of DNA with an average length of 500 kb show variation in copy number either due to deletion or duplication. This source of variation is beginning to be explored with respect to common complex disorders, one recent example being autism (Sebat et al., 2007). Common Disease/Common Variant Hypothesis The success of modern genotyping technologies in analyzing common complex disorders depends partly upon the correctness of the common disease/common variant hypothesis. This hypothesis states that common genetic diseases are due
■
39
to common polymorphisms. On the face of it, this hypothesis seems plausible, and there is evidence that it is at least partially true for many common complex diseases (Hirschhorn, 2005). However, it now appears that a significant fraction of common disease will be due to many different rare mutations. In fact, this was observed in a subset of the NOD2 mutations underlying Crohn’s disease (Hugot et al., 2001). More recently, studies of common variations in plasma lipoprotein levels, involving sequencing of candidate genes in many individuals, have clearly demonstrated the importance of rare alleles (Cohen et al., 2004, 2006). Thus, efficient sequencing approaches are likely to be important in understanding at least some forms of complex traits. Although there have been tremendous recent advances in genotyping speed, the throughput is still insufficient to interrogate all possible polymorphisms over the entire genome. In addition, even modern genotyping technologies will not easily identify rearrangements such as inversions, which may be more common than previously thought (Tuzun et al., 2005). Estimates based on mutation rates suggest that nearly every nucleotide in the genome is altered in at least one individual on the planet (see Chapter 1). Isolated Populations As described in a previous section, one of the major stumbling blocks in human genetics is allelic heterogeneity. One potentially helpful approach in analyzing complex human genetic traits is to use isolated populations. These populations are thought to have an extremely small number of founder individuals, often estimated as being in the hundreds. This bottleneck means that a much smaller number of genes and alleles cause the disease phenotype, simplifying the genetic heterogeneity of many complex diseases. Useful isolated populations have been identified in the Hutterites of rural North America, as well as populations in Finland and Iceland. Some theoretical studies suggest that for common complex disorders the advantages of using an isolated population may be blunted compared to that for rare alleles (Hirschhorn and Daly, 2005). This is because common disease alleles will be proportionally represented even in a relatively small founding population. Nevertheless, the promise of isolated populations has been fulfilled in at least a few examples. These include the identification of genes involved in familial combined hyperlipidemia (Pajukanta et al., 2004) and obesity (Suviolahti et al., 2003) in Finland and genes involved in type 2 diabetes (Grant et al., 2006), myocardial infarction (Helgadottir et al., 2006), and schizophrenia in Iceland (Stefansson et al., 2002). HapMap Project Human genetic analysis is currently limited by both genotyping power and ascertainment of sufficiently large study populations for analysis. Strategies are therefore needed to maximize the information obtained from these precious datasets. It was observed that contiguous regions of the mammalian genome tend to remain linked together on the chromosome in which
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Genomic Approaches to Complex Disease
they reside (Rioux et al., 2001). These regions in LD are called haplotype blocks and their existence led to the realization that one SNP, a so-called “tag SNP”, could in principle effectively “stand in” for a larger number of adjacent SNPs. It was conjectured that using carefully chosen tag SNPs could extend the power of present genotyping technologies, thus improving coverage of the genome (see Chapter 2). These observations led to the international HapMap project, an endeavor to comprehensively identify haplotype blocks in the human genome. The main problem in decoding haplotypes is that genotype information represents the admixture of two different linked haplotypes from the parental chromosomes. The admixed “messages” have to be deconvoluted to provide haplotype information. Two distinct approaches have been taken. In the publicly funded approach, genotyping of 269 individuals from four different ethnic human groups was used to identify blocks of SNP alleles that tend appear together more often that would be expected by chance (Altshuler et al., 2005). The other approach taken by the company Perlegen used purification of individual chromosomes by somatic cell genetics, so that the haplotypes could be read out directly (Hinds et al., 2005; Patil et al., 2001). In both approaches, blocks were found to extend for varying lengths in the genome, ranging from 1 kb to 100 kb. Despite this variation, the data should allow more efficient design of genotyping assays. In addition, the uncovered haplotype data will likely be useful in understanding the history and structure of human populations throughout the world. Quantitative Trait Locus Mapping in Model Organisms Animal models, particularly strains of mice and rats, exhibit common variations in traits that are relevant to most common diseases. The mapping of loci contributing to such traits using quantitative trait locus (QTL) analysis is straightforward (Flint and Mott, 2001). At present, thousands of loci for hundreds of complex traits have been mapped, but, apart from positional candidates, only a handful of genes have been identified. The QTL studies in mice suffer from some of the same problems as linkage in humans: the loci identified are usually very large and the underlying genes usually explain only a small fraction of the variance of the trait. Flint et al. (2005) provide an excellent summary of the status of QTL mapping and of new strategies. As discussed below, genome-wide “omics” technologies are expected to greatly facilitate disease gene identification in studies with animal models.
INTEGRATION OF “OMIC” TECHNOLOGIES WITH GENETICS Systems Biology High-throughput assays for DNA sequence variation, transcript levels, protein levels, protein interactions, and metabolites, in combination with data integration methods, are beginning to
transform studies of complex disease and of biology in general. This area of research has become broadly known as systems biology, the scientific discipline that seeks to quantify all of the elements of a biological system and assess their interactions (Hood et al., 2004). An important concept is that the interaction of the parts of a system creates properties or functions that would not be expected from looking at the individual components on their own (Table 3.3) (see Chapter 6). Transcriptomic Analysis The “omic” technology that has had the largest impact on complex disease is the measurement of transcript levels using highdensity microarrays.The original arrays consisted of spotted cDNA capture probes, but the more recent technologies, including Affymetrix and Illumina, utilize synthetic oligonucleotides as capture probes (discussed in Pravenec et al., 2003) (see Chapter 13). In any segregating population, there will be hundreds or thousands of genetic variations that affect gene expression. It seems clear that these variations in expression will make important contributions to complex disease. With the development of microarray technology, it is now possible to quantify transcript levels globally and to integrate such data with information relating to disease. One approach has been to map a critical genomic region for a disease using affected and unaffected individuals and then pinpoint the responsible gene by identifying differentially expressed genes in the critical region. For example, in the rat animal model, a combination of congenic mapping and microarray analysis identified the fatty acid translocase Cd36 gene as a cause of insulin resistance and hypertriglyceridemia (Aitman et al., 1999). In the case of complex, heterogeneous disorders, no one gene would necessarily be expected to exhibit altered expression in cases versus controls, but certain pathways might be perturbed. For example, Mootha and colleagues developed an analytic strategy, termed “Gene Set Enrichment Analysis,” to detect modest but coordinate changes in the expression of functionally related genes (Mootha et al., 2003). They applied the method to muscle biopsies from diabetic versus control individuals. Although no single gene was significantly associated with diabetes, the mitochondrial and oxidative phosphorylation pathways were significantly enriched in genes that were modestly perturbed in diseased individuals. Genetical Genomics A particularly powerful way of integrating expression and clinical data is to apply genetics, an approach that has been called “genetical genomics” (Jansen and Nap, 2001). Thus, if transcript levels are measured in segregating populations, the loci controlling transcript levels can be mapped using the same approaches that have been used to map loci for complex clinical traits. The loci controlling transcript abundance have been termed “expression QTLs,” or eQTLs (Schadt et al., 2003). When an eQTL encompasses the physical location of the gene encoding that transcript, it is likely that the causative genetic variation resides
Integration of “Omic” Technologies with Genetics
TABLE 3.3
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41
Systems biology and genomic medicine
Disease phenotype
Systems biology approach
Insights
Technology
References
Insulin resistance/ hypertriglyceridemia
Transcript analysis
Cd3 gene causative
Microarrays
Aitman et al., 1999
Diabetes mellitus (type II)
Gene set enrichment analysis
Mitochondrial/oxidative phosphorylation plays a role
Microarrays
Mootha et al., 2003
Obesity
Genetical genomics
Molecular subtyping can better define complex phenotypes
Microarrays
Schadt et al., 2003
Hypertension
Genetical genomics
Correlation of biological and expression phenotypes
Microarrays
Hubner et al., 2005
Obesity, diabetes
Genetical genomics
Correlation of biological and expression phenotypes
Microarrays
Lan et al., 2006
Embryonic viability
Interactome mapping
Hub genes more likely to be essential for survival
Yeast 2 hybrid, co-affinity purification, literature curation
Gandhi et al., 2006
Inherited disease
Interactome mapping
Gene products from similar disorders tend to interact
Yeast 2 hybrid, co-affinity purification, literature curation
Gandhi et al., 2006
Cancer
Interactome mapping
Oncogenes can be related to known signal transduction pathways
Yeast 2 hybrid, co-affinity purification, genetic crosses in C. elegans
Tewari et al., 2004
within the gene itself (i.e., the transcript is likely to be regulated in cis). Conversely, when an eQTL does not encompass the physical location of the gene, the transcript must be regulated in trans (i.e., under the control of a different gene). Thousands of such eQTL have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees (Cheung et al., 2005; Doss et al., 2005; Ghazalpour et al., 2005; Hubner et al., 2005; Schadt et al., 2003). One of the initial surprises in these studies has been the extent of variation at the level of transcript levels. In recent studies in mice, where experiments are greatly simplified compared to human studies, a large F2 intercross between two common laboratory strains of mice revealed 6676 significant eQTLs (p 5 105) in liver, over a thousand of which were cis-acting eQTLs (Wang et al., 2006). Similarly, large numbers of common variations are likely to exist in human populations. In the near future, one can envision a genome-wide database relating common DNA variation with common transcript level variations (in other words, a map, such as the HapMap of all common variations in transcript levels). Obviously, such a map would help in prioritizing candidate gene for loci contributing to complex diseases (i.e., genes with cis-eQTLs at the
locus of interest would constitute a restricted set of candidates). Also, it might be expected that transcript levels of the causative genes will be significantly correlated with clinical trait values, so determining Pearson or Spearman correlation coefficients between these may be helpful. Genetical genomic data could also be used in a converse manner to exclude potential candidate genes (Lan et al., 2006). There are two important caveats in considering the relationship of an eQTL and a clinical trait. First, gene expression patterns are cell- and tissue-specific, and certain inaccessible tissues may be required to understand a complex trait (although it has been observed that cis-eQTLs usually tend to affect gene expression in all tissues in which that gene is expressed). The second is that the regulation of biologic processes occurs via control of gene expression in many, but certainly not all, situations. Molecular Subtyping Another use of genomic and other “omic” technologies is to define distinct molecular subtypes for a clinical phenotype. This has been particularly useful in the case of cancers (Bucca et al., 2004), but it should be applicable to most complex disorders. For example, in one study examining liver expression array data
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Genomic Approaches to Complex Disease
in an intercross, distinct subgroups of mice were identified with respect to body fat (Schadt et al., 2003). The array data were consistent with the idea that the classically defined trait of fat pad mass actually consists of at least two molecularly distinct subgroups. Analyzing these two subgroups separately gave much greater sensitivity in detecting loci for obesity. While such an approach has yet to be employed in human complex traits, there is a strong interest in using high-dimensional data methods, such as microarrays and mass spectrometry, to identify biomarkers for disease (Bailey and Ulrich, 2004). It is conceivable that some of these investigations may detect different subclasses of disease that would usually be lumped into one group using classical methods. Separate genetic analysis of these subclasses may well give greater sensitivity to detect the genes responsible for complex human conditions. A particularly powerful application of genetical genomics is to identify genes that are causally related to clinical traits. For example, in a recent study of a cross between two strains of mice differing in adiposity, Schadt and colleagues used liver expression array data to identify genes likely to be causally involved in visceral fat levels (Schadt et al., 2005). It was reasoned that genes controlling adiposity would likely also control expression of genes determining fat levels. However, correlation of gene expression levels with adiposity could represent an effect rather than a cause of obesity. Using mathematical conditioning, it was possible to distinguish between these possibilities. Some of the top candidates were then examined using transgenic mice either overexpressing or underexpressing the causal genes, and it was observed that a large fraction of those genes did indeed influence adiposity. Proteomics The above approaches were made possible by the high-throughput analysis of the transcriptome. The analysis of the full proteome has not yet been achieved, but rapid progress is being made (Domon and Aebersold, 2006). It is clear that the number of distinct proteins in mammals will certainly greatly exceed the number of genes, and proteomes will exhibit large and dynamic complexity. While proteomic and systems approaches have had only a limited role in understanding complex disease to date, this situation is bound to change as the depth and reliability of the databases continues to improve. Interactome The connections between gene products can be mapped using genome-scale surveys of protein-binding partners. The universe of protein–protein interactions is known as the interactome. Two widely used technologies for charting this space are yeast twohybrid screening and co-affinity purification combined with detection by MS (Cusick et al., 2005). Yeast (Gavin et al., 2002; Ho et al., 2002; Ito et al., 2001; Uetz et al., 2000), worms (Li et al., 2004), flies (Formstecher et al., 2005; Giot et al., 2003; Stanyon et al., 2004), and mammalian cells (Rual et al., 2005) have been investigated using these methods. For example, a recent study used yeast two-hybrid screening to examine all pairwise interactions
among 8100 human genes (38% of all genes) and detected 2800 interactions (Rual et al., 2005). A major disadvantage of high-throughput protein interaction surveys is their limited reliability. False-positive and false-negative results are currently estimated at 40–50% for both yeast two-hybrid and MS methods (de Silva and Stumpf, 2005; Rual et al., 2005). Another strategy to map genetic networks employs curation of protein interactions from the literature (Gandhi et al., 2006; Reguly et al., 2006). These datasets are thought to have higher reliability since many interactions have been verified by independent methods from more than one group. The Human Protein Reference Database (HPRD) contains 8800 proteins (42% of all genes) and 25,000 interactions (Gandhi et al., 2006). Nevertheless, curation databases suffer from selection bias and do not randomly sample all protein interactions. Furthermore, comparison of yeast two-hybrid data and literature-derived interactions suggests that the literature-curated data can also suffer from variable quality (Rual et al., 2005). The networks derived from interaction mapping are often scale-free. Such networks show a power law distribution relating the probability of interactions, P(k), of a node on the network with k other nodes: P(k) k, where = 2 (Carter et al., 2004). A feature of scale-free networks is the presence of a few highly connected nodes or hubs, representing genes that are highly connected to many other genes. This network class is robust to random disconnection of individual nodes, but is vulnerable to disruption of highly connected hubs. Correspondingly, genes in hubs are found significantly more likely to be essential for survival (Gandhi et al., 2006). Both scale-free and classical random graphs display “small world” behavior, in which moving between any two nodes on the network takes far fewer steps (edges) than naively expected. Interactome Insights Some interesting insights related to disease have emerged from analysis of these networks. For example, the HPRD revealed that protein products of genes mutated in inherited genetic disorders tend to interact with proteins involved in similar disorders. This observation suggested that certain gene subnetworks are involved in particular diseases and can help guide the search for new genetic culprits. In another example, it was recently shown that combining yeast two-hybrid data, co-affinity purification data, and genetic experiments in worms gave a much sharper outline of the TGF- signaling pathway than using any one dataset alone (Tewari et al., 2004). A total of nine new TGF- signaling related genes were found, of which seven were conserved in humans. One of these had functional homology to the human SNO/SKI oncoprotein. Metabolomics No less promising than proteomics in its relation to early diagnosis and treatment of disease is metabolomics (Claudino et al., 2007) (see Chapter 15). It is estimated that there are approximately 3,000 metabolites, compared with 20,000 genes. The
References
main analytic tool for the metabolome is proton nuclear magnetic resonance spectroscopy (1H NMR). Another useful approach is MS, which has greater processing cost per sample but is thought to have more sensitivity. Metabolomic approaches have been used successfully for non-invasive diagnosis of a number of disorders, including breast cancer, hepatitis, ovarian cysts, and ovarian cancer (Claudino et al., 2007). Many of these studies used unsupervised analytic approaches such as principal components analysis (PCA) to distinguish diseased and nondiseased samples (Griffin, 2006) (Figure 3.2).
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to fruition with the first complete genome sequences of individual members of many species. With the goal of the $1000 genome on the horizon, it is probable that relatively soon we will have complete genome sequences for many individuals within one species. Recent genomic advances in transcript profiling, proteomics, metabolomics, and molecular imaging suggest that comprehensive knowledge in orthogonal descriptive directions is also possible (Figure 3.2). This is systems biology. The challenge now is to integrate these diverse modes of knowledge to a better understanding of complex genetics in both health and disease and make the field of genomic medicine a practical reality.
CONCLUSIONS AND PROSPECTS In his famous textbook Molecular Biology of the Gene (Watson, 1965), James Watson pointed out that the genome is not of infinite size (Olson, 2003). He therefore argued (within physical limits) that in principle we could know everything about the molecular description of a cell or organism. This vision has come
ACKNOWLEDGEMENTS Supported by NIH R01 NS050148, MH71779, NS043562 (D.J. Smith) and NIH HL30568, HL30568, HL28481, DK071673 (A.J. Lusis).
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RECOMMENDED RESOURCES 454 Life Sciences www.454.com Affymetrix www.affymetrix.com Applied Biosystems https://www2.appliedbiosystems.com/ Gene ontology – the gene ontology classification, http://www.geneontology.org Genome-phenome superbrain http://omicspace.riken.jp HapMap http://www.hapmap.org/ Helicos Biosciences http://helicosbio.com Illumina www.illumina.com
Jackson laboratory – a repository for genetic and genomic information on the mouse, http://www.jax.org National Center for Biotechnology Information – repository for SNP information, comprehensive literature database and much more, www.ncbi.nlm.nih.gov Perlegen http://www.perlegen.com/ Sequenom www.sequenom.com UCSC Genome Browser – available genome sequences, http:// genome.ucsc.edu/
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4 Human Health and Disease: Interaction Between the Genome and the Environment Kenneth Olden
INTRODUCTION The importance of inheritance versus the environment in the origin of various diseases has been debated for more than a century. The phrase “nature versus nurture” was coined by Francis Galton (a 19th-century British scientist) to distinguish between characteristics one is born with and those that are acquired as a result of one’s surroundings. Early on, humans looked to the external environment to explain morbidity and mortality as they were cognizant of their exposure to natural agents that were poisonous. Because of investments in environmental health research, we now know that many of the chemical, physical, and biological agents in the environment not only cause acute illness and death, but also increase risk for various chronic diseases such as cancer, asthma, hypertension, stroke, Alzheimer’s, Parkinson’s, infections, and various developmental disorders. However, the era of dominance of external factors as the primary contributor to human disease gradually faded with the development and growth of Mendelian genetics (Dobzhansky, 1941) and the more recent field of genomics (Trask, 1999). In fact, the geocentric view of disease has dominated our concept of the origin of disease for the past 50 years. However, with the completion of the sequencing of the human genome (Lander et al., 2001; Venter et al., 2001), it now seems apparent that complex human Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg
phenotypes cannot be explained based solely on the action of genes or their protein products. Genes account for only a small part of the phenotype, and the environment has a spectacular impact. That is, the onset and clinical course of most chronic diseases are influenced by complex interactions between genes and the environment (Olden, 2007). The concept of gene–gene and gene–environment interactions is not new. Garrod (1902) was one of the first to suggest that the effects of genes could be modified by the environment. He suggested that individual differences in genetics could play a role in variation in response to drugs, and that the effect of genotype could be modified by the diet. Wright (1932) also emphasized the existence of a functional relationship between various biological endpoints and networks of genes and environmental factors in his studies of mutation, selection, and breeding, and Dobzhansky and Levene (1955) reported the existence of genotype–environment interaction with respect to viability of some chromosomes in Drosophila. Whereas concerns about gene–environment interactions have had a long history (reviewed by Haldane, 1946), the importance of understanding these interactions as a means to prevent complex diseases emerged only over the past 15 years (Olden, 1993; Olden and Guthrie, 2000; Olden et al., 2001; Schwartz and Collins, 2007). The geocentric view of disease was virtually unchallenged until the leadership of Copyright © 2009, Elsevier Inc. All rights reserved. 47
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the environmental health community began to emphasize gene– environment interaction as a top research priority in 1992 (Olden, 1993; Washington FAX, January 21, 1992). A few years later, the initiation of the Environmental Genome Project (EGP) (discussed later in this chapter) represented the first large-scale effort to discover susceptibility alleles likely to be important in gene–gene or gene–environment interactions (Brown and Hartwell, 1998; Guengerich, 1998; Kaiser, 1997; Olden and Wilson, 2000). Prior to the initiation of the EGP, most of the momentum for research on gene–environment interactions was occurring in the field of pharmacogenetics to identify genetic variants that influence drug efficacy. As a consequence, many published studies have linked specific polymorphisms with specific drug responses (Frye, 2004; Hulla et al., 1999; Nebert et al., 1996). Studies have shown that interindividual pharmacokinetic variation in rates of drug elimination can vary dramatically (Vessel, 1991). Multinational twin studies were conducted on the kinetics of several drugs to compare relative contribution of genetics and environmental factors with respect to interindividual variations. Results were remarkably similar for the various populations, and pharmacokinetic variation virtually disappeared within monozygotic twins, but was preserved within most dizygotic twins; meaning that variation in drug metabolism is primarily under genetic control. Altering the environment of the monozygotic twin had no effect on kinetics. If reported pharmacogenetic differences of 10- to 200-fold can be extrapolated directly to the risk of human diseases, one could conclude that an individual can be 10- to 200-fold more sensitive to a given drug or environmental chemical because of differences in expression and activity of metabolic enzymes. Recent studies have shown a similar association between specific diseases and various genetic polymorphisms. Some examples of both types of genotype–phenotype association studies will be described later. However, even when all the highly penetrant alleles have been discovered and all the gene–gene and gene–environment interactions are understood, it may still be difficult to relate genotype to phenotype because of the additional layers of complexity associated with post-translational processing and protein–protein interactions. Kouhry and Wagner (1995) suggested that there are at least six types of gene– environment interactions. In the first situation, neither the environment nor the genotype has any effect alone, but can interact to increase risk. In the second situation, the genotype has no effect in the absence of exposure, but can exacerbate the effects of the latter. The third situation is the converse of the second; that is, the environment is without effect alone, but can enhance the effect of the genotype. In the fourth situation, both the environmental exposure and the genotype increase risk for disease, but the combination is interactive or synergistic. The fifth and sixth situations represent cases in which gene–environment interactions are protective. Phenylketonuria is an example of disorder that is caused by both the susceptibility genotype and exposure to phenylalanine in the diet. In contrast, lung cancer can be caused by cigarette smoking even if the susceptibility genotype is not present, and acute hemolytic anemia can be caused by the susceptibility
genotype alone even in the absence of fava bean digestion. Individuals with the susceptibility genotype for a1-antitrypsin deficiency have a high risk for development of emphysema, but the risk is greatly increased by cigarette smoking. The model of gene–environment interaction that has been most studied relates to metabolic susceptibility genes and chemical carcinogenesis. Polymorphisms in carcinogen-metabolizing enzymes (to be discussed later) are relatively common in the population and can play a major role in the development of the common cancers. The phrase gene–environment interaction means that the direction and magnitude of the effect that a genetic variant has on the phenotype can vary as the environment changes; that is, genetic risk is modifiable in an environment-specific manner or that the phenotypic effect of a mutation or environmental exposure depends on the genetic background. Whether a particular variant allele is expressed, the degree to which it is expressed, and when it is expressed can be influenced by the environment or by other genes. However, since the genotype evolves or changes very slowly over hundreds of years, one can assume that most of the increase in disease burden in industrialized nations is the result of a variable environment interacting with a relatively constant genetic substrate. Similar to gene–environment interactions, variants in multiple genes can contribute to the expression of phenotype. For example, multiple genetic loci influence the rate of progression of HIV infection to full blown AIDS (Dean, 2003). The nonsense allele, V641, in the CCR2 gene and heterozygosity for the cytokine receptor/HIV-1 co-receptor CCR5- 32 allele is reported to be associated with the slower progression of AIDS (Dean et al., 1996; Smith et al., 1997); in contrast, a haplotype in the promoter region of CCR5 is associated with the more rapid progression of HIV infection to AIDS (Martin et al., 1998). Also, progression of HIV-infected patients with more heterologous HLA Class I genes is considerably slower (Carrington et al., 1999).
IMPORTANCE OF THE ENVIRONMENT Over the past 50 years, the prevalence of chronic diseases has reached epidemic proportions in industrialized countries, and various governments have expended enormous resources to identify genetic causes. Nevertheless, the expectation that simple direct paths would be found from genes to disease has not been realized. Several explanations have been put forward to explain failure, but gene–gene and gene environment interactions are among the most likely. The merging view is that exposure to environmental toxicants has conspired with genetics to promote the development of these disorders. It is now well established that alterations in high penetrant genes explain only a small fraction of complex diseases, and such genes represent only a small fraction of variations relative to the more common polymorphisms that have a less disruptive effect on protein function. However, it is much less obvious what portion of the disease burden is attributable to interactions among low penetrant
Importance of the Environment
genes versus interactions of low penetrant genes with the environment. Nevertheless, a prominent role for the environment is supported by findings of geographic differences in incidence of disease, by variations in trends over time and by studies of disease patterns in immigrant populations (Doll and Peto, 1981). Also, population-based, twin-cohort studies, the “gold standard” for distinguishing between the contribution of genetics and the environment, suggest that the environment plays a prominent role in the development of human disease. In many cases, concordance among monozygotic twins is very low. For example, such recent studies show that no more than one-third of the cancer burden can be attributed to the action of genes alone (Lichtenstein et al., 2000; Verkasala et al., 1999), only 15% of Parkinson’s disease (Tanner et al.,1999), and about a third of autoimmune diseases (Powell et al., 1999). In a much earlier landmark study, Doll and Peto (1981) estimated that about one-third of all cancers are caused by genes alone based on demographic distribution of the disease in the United States. And, a recent study (Khot et al., 2003) reported that 90% of individuals with severe heart disease have at least one or more of the four classic risk factors captured in the current definition of the environment (Olden et al., 2001). Because of these and other studies, it is now generally accepted that a single risk factor is rarely responsible for the development of most chronic diseases (see Chapter 3). Rather, diseases commonly arise from complex interactions involving genes, environmental factors, behavior, and stochastic events associated with DNA replication and endogenous metabolism, as depicted in the schematic model shown in Figure 4.1. Age and stage of development are also important factors in disease development, as they influence the timing of gene expression and susceptibility to environmental toxicants. Many people with susceptibility genes never actually develop the disease, even for diseases caused by highly penetrant alleles (e.g., BRCA1 and BRCA2 breast cancer genes) (Futreal et al., 1994). There is always a subpopulation of carriers of susceptibility genes who do not progress to the ultimate disease state. Whether one can account for the discordance between genotype and phenotype by lack of exposure to unknown environmental risk factors or to yet undiscovered genomic variations or gene–environment interactions
Intrinsic Genetic Susceptibility
Environmental Exposures
Human Health/Disease
Behavior/Age/Stage of Development
Figure 4.1 Schematic model of interactive factors involved in human health and disease.
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is not known. However, existing evidence for a prominent role for the environment in the development of human disease is so compelling that Rothman et al. (2001) concluded that “the epidemiologic evidence accumulated to date indicates that environmental exposures, broadly defined to include lifestyle factors, are responsible for most cancers.” Others have concluded that the new data now being reported “blow away” the myth that “bad genes” are responsible for the majority of human morbidity and mortality (Olden, 2004a, b). That is, individual life history may be just as important as genetic background in predicting and understanding the development of complex traits. Genetics Loads the Gun, but the Environment Pulls the Trigger Is the relationship between genes and the environment analogous to that of a loaded gun and its trigger, as previously proposed? Olden and Wilson (2000) argued that the inheritance of disease susceptibility alleles creates the potential for adverse health outcomes, but that the adverse health outcome may never be realized provided the carrier can avoid environmental exposures capable of interacting with the susceptibility gene(s) to trigger disease development. If this analogy fully captures the relationship between environmental susceptibility alleles and specific environmental exposures, this can be good news because amelioration of environmental hazards could lead to a dramatic reduction in the incidence of chronic diseases in human populations. From an evolutionary point of view, one could argue that genes and gene variants that were adaptively selected in the past because they endowed the human species with survival or reproductive advantage, now have the potential to increase disease risk because of their incompatibility with the environment. The change in the environment, resulting from human activity and technology innovation, has outpaced evolution of the human genome to the extent that there is now a nature–nurture mismatch. For years, environmental health advocates have had to deal with the question of why more bodies are not piling up on the streets if industrial chemicals, such as benzene, vinyl chloride, and dioxins, are so harmful. Why can some people withstand exposure to nerve gases such as sarin, and organophosphate pesticides such as malathion and parathion? Why do fewer than 25% of smokers develop lung or bladder cancer? How do some individuals who work in the petrochemical industry survive years of exposure to benzene and never develop leukemia? Human population studies to assess gene–environment interactions are beginning to provide answers to these questions. For example, about 25% of Asians and 10% of Caucasians have a protective form of the gene that codes for paraoxonase (PON1, to be discussed later), an enzyme that detoxifies organophosphates, such as sarin and the above-mentioned insecticides, ten times faster than the more common form of the enzyme (Costa and Furlong, 2002). Also, common polymorphisms in genes for the cytochrome P450 enzymes determine how one metabolizes carcinogens (i.e., arylamine) in cigarette smoke or benzene in the environment of petrochemical workers (Lower et al., 1979; Smith et al., 2002). Such studies are beginning to
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explain why a given polymorphism can be highly beneficial in one environment and virtually lethal in another (Boguski, 1999). Furthermore, they suggest that risk reduction can be achieved by re-engineering of genes or by modification of the environment. Now that the Human Genome Project (Lander et al., 2001; Venter et al., 2001) and the haplotype mapping (HapMap) project (Altshuler et al., 2005) have been completed, the major challenge facing biomedical researchers is to elucidate the interactive relationship between genes and between genes and the environment. While descriptive studies by toxicologists and epidemiologists have shed some light on the possible genetic and environmental risk factors, the actual causes and mechanisms of disease development remain poorly understood. Investments in the aforementioned sequencing and mapping projects led to the development of powerful new tools to conduct large-scale, population-based studies necessary to untangle complex interactions between genes and environmental factors. However, the design and conduct of such studies will require the development of even better tools and fundamental restructuring of the biomedical research enterprise to put greater, although not exclusive, emphasis on interdisciplinary research teams. Promise of New Technologies for Unraveling Complex Interactions Between the Human Genome and the Environment The Nobel Prize laureate Sydney Brenner has written that “progress in science depends on new technologies, new discoveries and new ideas, probably in that order” (Brenner, 2002). In no discipline is this more evident than in environmental health research. Environmental health regulation has long been constrained by limitations in our ability to determine the risk presented by specific xenobiotic agents. Integration of genomics and toxicology to develop a new field of toxicogenomics may revolutionize risk assessment and environmental health regulation (Lovett, 2000). Over the past 50 years, advances in molecular biology have dramatically improved our understanding of human biology. Such innovations include the discovery of the DNA double helix (Watson and Crick, 1953), development of recombinant DNA technology (Watson et al., 1983), the polymerase chain reaction (Chamberlin, 1982), siRNA technology (Baulcombe, 2002), gene and protein microarray technology (Brown and Botstein, 1999; Schena et al., 1995), and DNA sequencing methodology (Lander et al., 2001). For example, an important insight gained from gene sequencing studies is that the human genome consists of a large number of polymorphic sites, some of which are associated with altered biological function or phenotype (see Chapter 1). The finding of a large number of variant forms of genes suggested that genetic polymorphisms may play a significant role in susceptibility to disease or toxic exposure. While descriptive studies by toxicologists, molecular geneticists, and epidemiologists, working independently, have shed some light on this topic, the actual role of gene variation in susceptibility to disease development remains poorly understood.
THE ENVIRONMENTAL GENOME PROJECT Although we are beginning to understand how mutations in single genes effect phenotype, we have very limited knowledge of how variations in different genes influence each other and the sensitivity of biological mechanisms to environmental perturbation. But, with the completion of the reference sequence of the human genome, the field of genomics shifted from gene discovery to the study of gene function and elucidation of the genetic basis for differences in susceptibility to common diseases. Although reference is made to the human genome, the concept of a single genome is misleading. Whereas variation in the human genome is not greater than 0.1% among individuals in the population, this represents very significant variation considering that the human genome is composed of approximately three billion nucleotide base pairs. Genetic linkage analysis has been a powerful approach in the discovery of high penetrance allelic variants (e.g., BRCA1/2). However, this approach has not yet been particularly useful in the identification of low penetrance susceptibility alleles associated with the development of complex diseases such as cancer, cardiovascular disease, asthma, diabetes, Alzheimer’s and Parkinson’s disease because the relative low risk associated with a specific allele may be influenced by unknown environmental factors. Of the many types of variations in the human genome, single nucleotide polymorphisms (SNPs) are the most common (Collins et al., 1999). Estimates are that there is on average one SNP per 1300 base pairs, four to five per gene-coding region, or approximately 11 million in total in the human genome (Kruglyak and Nickerson, 2001). The EGP was formally initiated by the US National Institute of Environmental Health Sciences (NIEHS) in 1997 to investigate the role of genetic polymorphisms in susceptibility to environmentally induced disease (Brown and Hartwell, 1998; Kaiser, 1997, 2003). In this regard, NIEHS was way out front in recognizing the importance of genetic variation for understanding human disease. Following their lead, other large-scale public and private initiatives have been developed to identify the millions of SNPs in the human genome. Most SNPs can be expected to be neutral with respect to alteration of environmental sensitivity. To examine the extent of diversity in the human genome, the EGP was organized into five research areas: (1) SNP discovery; (2) functional analysis; (3) disease association studies; (4) development and refinement of technologies to improve sequencing, functional analysis, and population-based studies; and (5) development of policies to address possible ethical, social, and legal issues associated with genetic susceptibility studies. The 544 candidate genes included in the initial survey (see http://www.genome. utah.edu/genesnps and http://www.niehs.nih.gov/research/ supported/programs/egp/) had already been characterized with respect to function and were known to have a significant probability of playing a role in susceptibility to specific environmental exposures. The resequencing analysis of individual DNA samples was based on samples from 90 individuals, representative of the
The Environmental Genome Project
diversity in the US population. This design would allow for the detection of variation that occurs at a frequency of one percent or greater. As of September 2007, approximately 619 environmental response genes have been resequenced with the discovery of 88,016 SNPs. All of the SNPs entered into the database have been validated by multiple sequence analysis and include genotype frequency information. This highly useful SNP database is publicly available (http://www.egp.gs.washington.edu/welcome.html). Decades of studies by environmental health researchers, demonstrating tremendous variation in individual response to environmental exposures, provided the intellectual foundation for the massive resequencing effort undertaking in the EGP. Presumably, natural selection, resulting from exposures to various environmental insults, has operated over the course of human history to promote the evolution of sophisticated metabolic pathways to suppress phenotypic variation or buffer against toxic injury. Buffering is a fundamental aspect of biology, and there are likely to be multiple mechanisms involved, including genetic redundancy, feedback regulation, and cooperative biochemical interactions (Wagner et al., 1997; Gibson and Wagner, 2000; Hartman et al., 2001). Buffering was first inferred from the observation that organisms can acquire a wide range of phenotypes when subjected to varying genetic or environmental stressors (Scheiner et al., 1991). Collectively, the various buffering pathways involved in protecting the organism against environmental stressors have been referred to as the “environmental response machinery” (Olden and Wilson, 2000). According to this model, all human genes, including those that encode protein components of the environmental response machinery, are subject to genetic variability (mutations or polymorphisms) that can alter the metabolic efficiency of protective pathways. Some alleles can strengthen protection and others can reduce it. Another result emerging from the SNP discovery effort is that the variants tend to associate with one another in a nonrandom manner known as linkage disequilibrium (Carlson et al., 2004); this pattern of association is called a haplotype. On average, the EGP candidate genes are organized into 25 haplotypes (Wilson and Olden, 2004). The current thinking is that the generation of haplotype and linkage disequilibrium data will be extremely useful in large-scale genotyping studies to investigate the association between specific genes and disease. Such studies are now underway in several laboratories (see Chapter 8). However, the goal of assigning specific genotypes to phenotypes with respect to environmental sensitivity has proven to be a daunting assignment. For functional analysis of the SNPs discovered in the EGP, both in vitro biochemical and animal model studies have been initiated. For example, Furlong and his colleagues (Costa and Furlong, 2002) investigated the functional consequences of polymorphisms in paraoxonase (PON1) genes. PON1 is a highdensity lipoprotein associated with the plasma membrane that metabolizes oxidized lipids, environmental xenobiotics (e.g., organophosphate insecticides mentioned above) and some pharmaceutical agents (e.g., statins). Studies by Furlong and colleagues suggest that PON1 may play a significant role in the development of cardiovascular disease independent of environmental exposures (Costa and Furlong, 2002). However, their
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studies have shown that the activity of PON1 is significantly altered by genetic variation and that these alterations influence sensitivity to the organophosphate insecticides chlorpyrifos and diazinon. For example, one of the two SNPs in the coding region significantly increased the substrate-specific activity of the enzyme and provides much greater protection against chlorpyrifos oxon than the wild-type PON1. However, the Q192R allele did not correlate with carotid artery disease in a case– control study (Jarvik et al., 2003). Whereas individual SNPs in the promoter region had no effect on enzyme activity, combinations of three of the common haplotypes of the five SNPs in this region of the gene did alter the plasma activity of PON1. While these studies do suggest that polymorphisms in PON1 may play a role in susceptibility to organophosphate toxicity, the exact involvement and possible interactions of the various SNPs in the PON1 gene are yet to be determined. In another project in the EGP, Martyn Smith and colleagues (Smith et al., 2001) identified a gene that codes for NAD(P)H: quinone receptor oxidoreductase1 (NQO1) and demonstrated its potential involvement in the etiology of leukemia. This enzyme plays an important role in protection against oxidative damage from exposure to quinones. And they found that the common C609T polymorphism resulted in an amino acid substitution with a complete loss of enzyme activity in homozygotes. The C609T variant was associated with a 2.5-fold increase in risk for leukemia in case–control studies (Krajinovic et al., 2002; Smith et al., 2001, 2002). In contrast, several polymorphisms in genes involved in folate metabolism reduced the risk of leukemia by two- to threefold in heterozygotes and 3- to 10-fold in homozygotes (Skibola et al., 1999, 2002). Folate is a source of precursors for DNA synthesis, repair and methylation; and dietary folate protects against leukemia (Thompson et al., 2001). Also, there is evidence for a role of gene–environment interactions between polymorphic variants of thymidylate synthetase and methylenetetrahydrofolate reductase (Skibola et al., 2002). Finally, for functional analysis of the various polymorphisms discovered in the EGP, NIEHS developed mouse genome centers in 2000. The mouse is the most widely used mammalian model system for the study of the impact of the environment on human health. The mouse is particularly useful in studies of gene–environment interactions because most human genes have counterparts in the mouse genome. Furthermore, extensive toxicological and pathological databases and molecular genetic techniques for construction of gene-knockout and knock-in strains have been developed using the mouse as a model. The mouse model also offers the advantage of using strain-specific differences in response to environmental exposure to investigate how the genetic background influences susceptibility. First, the mouse genome center resource was used to create genetic variation map databases of laboratory mouse strains for use in studies of gene–environment interactions. This was achieved by using knockout and knock-in technologies to generate polymorphic variations in candidate genes similar to those discovered in the EGP. Exposing such animals to putative environmental risk factors is the most obvious and direct way
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to investigate the relationship between genetic background and susceptibility to environmental exposures. Second, since various mouse strains are known to exhibit significant variation in susceptibility to carcinogens and other environmental toxicants, efforts were undertaken to discover the haplotype maps of the 15 most commonly used mouse strains to identify genetic sequences that underlie differences in response (Olden 2004a; Pearson 2004). Once susceptibility haplotypes have been identified, one can track down the genes involved by crossing a susceptible strain with a resistant strain and identifying the chunks of the genome that are associated with the susceptibility. As the biological and genetic databases expand, the need for such mouse crosses might no longer be necessary.
PROBLEMATIC NATURE OF GENE– ENVIRONMENT INTERACTION STUDIES While there is considerable interest and enthusiasm for research on gene–environment interaction, there is also apprehension and uncertainty about the feasibility of such studies and practical questions about how to conduct them. Current studies underway in different parts of the world are problematic because they do not employ a standard definition of the environment nor standard metrics to assess exposure. But, even more problematic is the fact that methods to accurately measure exposure and to statistically model multiple interactive components that make modest contributions to the phenotype are not yet available. To alter the current state of affairs with respect to technology and database needs will require investments in the following areas of research: (i) identification of disease risk factors; (ii) elucidation of genetic differences and similarities between human and mouse; (iii) development of better tools and statistical models to assess exposure and interactions of multiple components; (iv) development of high-throughput, low-cost and more informative strategies to assess toxicity of drugs and environmental xenobiotics, and (v) elucidation of mechanisms and metabolic pathways influenced by gene–environment interactions. Furthermore, it has become clear that addressing the role of gene–environment interactions in the etiology of complex diseases will require the development of a robust framework to account for “the environment” (see Chapter 83). In spite of current understanding of the multifactorial nature of the etiologic mechanisms of chronic diseases, biomedical researchers still tend to focus on limited or circumscribed components of disease. Epidemiologists tend to focus on exposures; geneticists target susceptibility genes; cell/molecular biologists tend to explore mechanisms; and social scientists study behavior. While the various disciplinary approaches have led to new insights into disease causation, they are unlikely to be able to provide a coherent explanation for disease causation. Given this reality,
the environmental health community moved swiftly to take advantage of new databases and tools, derived from the human genome project, to identify and characterize all the environmental susceptibility alleles in the human genome and all the functional molecules (e.g., RNA, protein, carbohydrates, lipids, and metabolites) encoded by human and mouse genomes (Kaiser, 1997; Lovett, 2000; Olden, 2004a, b; Waters et al., 2003). With approximately 25,000 genes, hundreds of thousands of protein species, and numerous metabolites, the task of identifying and characterizing them with respect to function is an ambitious undertaking, one requiring improvement, standardization, and validation of existing methods to achieve reproducibility, increased sensitivity and specificity, and high throughput. Such studies will also require large-scale, multi-institutional collaboration and interdisciplinary expertise to amass and analyze such large databases. Until the latter part of the 19th century, infectious diseases were the chief causes of death; and their development was characterized by relatively short latency periods between exposure and the onset of illness. The relative ease in identifying the causative agents led to the development of effective prevention measures in the United States and other industrialized nations. Unfortunately, our capacity to prevent infectious diseases tracked with the emergence of chronic diseases, which have latency periods of 10–20 years. The increased prevalence of chronic diseases, the long latency period, and the more insidious nature of their causes necessitated development of new approaches to study their etiology. Epidemiologists responded by developing association studies to analyze the statistical relationship between two or more variables (e.g., genotype, exposure, and disease), based on two fundamental assumptions: first, that human disease does not occur at random; and second, that human disease has causal factors that can be identified by studying different populations. This disciplinary approach has been remarkably successful in the discovery of major risk factors for many common diseases, but has been less successful in discovering risks resulting from gene–gene or gene–environment interactions. Gene–environment interactions have been identified by taking advantage of existing models that take into account various ways that genetic effects can be modified by environmental exposures (Hunter, 2005). Epidemiologists have typically used one of three study designs: retrospective case–control, prospective cohort, and family-based. Family-based association design estimates association between genes and phenotypes based on the assumed Mendelian transmission of alleles from parent to offspring (Kraft and Hunter, 2005). In this study design, it is often difficult to obtain genetic data on parents for late-onset diseases, and exposure data is subject to recall bias as it is collected retrospectively. A further limitation of this approach is that the penetrance of the gene has to be sufficiently strong to cause familial aggregation. However, family-based studies allow one to assess the influence of genes over several generations; if the penetrance of the gene changes over time, this might represent the effect of the environment and lifestyle. In the case–control design, data on exposure is obtained after diagnosis of the disease, so errors in
Polymorphism and Disease Susceptibility: Case–Control Studies
estimating risk from gene–environment interactions can result from exposure recall or from bias in selection of the controls. Even if appropriate controls are found, incentive for participation in the study is often low; so, obtaining high participation is challenging. Also, it is difficult to estimate the effect of the disease on biomarkers used in the study. However, the case–control design is popular among epidemiologists because cases and controls can be enrolled in a short period of time, which leads to reduced cost and delay in reporting out the results. Finally, in the populationbased cohort design, information on exposure is collected prospectively, and tissue for DNA and baseline biomarker analysis is collected before the disease develops. The advantage of this study design is that assessment of exposure is more reliable and it does not suffer from selection bias. Unfortunately, such studies are very costly and time consuming and lack sufficient number of subjects to evaluate risks associated with gene–gene or gene–environment interactions. However, one can increase the power of populationbased cohort studies by pooling data from several studies. Because of the cost and time involved, most of the previous studies of gene–environment interactions have used the case– control design, which relied on family history to infer genetic predisposition and questionnaires to assess environmental and lifestyle exposures. In future studies of gene–environment interactions, it is likely that data from case–control and population-based cohort studies will be combined, and that genotype and exposure will be analyzed with greater precision using more direct and quantitative technologies. For example, the human genome and the various polymorphism discovery projects have opened up a vast new terrain to search for gene–gene or gene–environment interactions. Once these discovery efforts have evolved to the point that genetically susceptible populations have been identified, targeting such populations would increase the magnitude of relative risk, the significance of the observed associations, and the likelihood of discovering important gene–gene or gene– environment interactions. But, because of cost, rigorously designed case–control studies will remain the only option for assessing gene–environment interactions for rare diseases. Exploration of this new vast genomic landscape presents major challenges with respect to design and analysis of meaningful studies, and translation of the findings into public health and clinical practice. In addition to the challenges associated with population studies, the study of gene–environment interactions requires accurate information on exposure to eliminate a major source of uncertainty and the most serious problem in environmental health risk assessment. Not only is it difficult to measure exposure accurately, it is often prohibitively expensive for use in studies of large populations needed to test for association between genes and disease to correlate genetic variation with risk for disease.The current practice of estimating exposure using indirect surrogates, such as toxic release and production inventories and environmental monitoring, is inadequate and limits our understanding of dose–response relationships. Current methods available for measuring exposure include the use of environmental sensors, geographical information systems, biological sensors, direct analysis of body fluids, and more recently, toxicogenomics. Unfortunately,
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all of these exposure assessment tools, with the possible exception of toxicogenomics, have serious limitations. The biological response to environmental exposure is so complex and involves so many interactive factors that the utilization of a combination of exposure assessment tools, capable of integrating all the interactive components, will be required in prospective epidemiologic studies. In my opinion, the development of the field of toxicogenomics will provide powerful and relatively inexpensive tools to identify biomarkers and to relate exposure and biological events during disease progression. In addition to impediments imposed by sample size and exposure assessment tools, population-based studies are fraught with other challenges related to the need for better statistical models. To define complex exposure-disease relationships and interactions between genes and environmental factors in the development of human disease, a comprehensive view of exposure is needed. The achievement of this goal will require improved sophisticated modeling capabilities to explain how components of the disease process respond when perturbed by environmental factors. Even in the simplest case of four combinations of genotype and exposure, where one is comparing risk of carrier versus noncarrier in exposed and nonexposed situations, there are several models for describing interactions between genetic susceptibility and environmental exposures. Of course, modeling of interactions gets more complex as the categories of environmental exposures and the number of variant alleles involved increase (Hunter, 2005). Also, new statistical methods may be needed to extract meaning from large datasets and to integrate information from many sources. Finally, the most critical technological challenge is how to relate exposure-disease association studies to pathways and mechanisms. To address this issue, NIEHS developed the National Center for Toxicogenomics (Lovett, 2000) to promote the development of technologies to monitor gene, protein, and metabolite expression in cells and tissues under various environmental conditions. To understand how genes and environmental factors interact to perturb biological pathways to cause injury or disease, scientists will need tools with the capacity to monitor the global expression of thousands of genes, proteins, and metabolites simultaneously. The generation of such data in multiple species can be used to identify conserved and functionally significant genes and pathways involved in gene–environment interactions.
POLYMORPHISM AND DISEASE SUSCEPTIBILITY: CASE–CONTROL STUDIES An article by Lower et al. (1979) transformed population health research and provided the foundation for the massive sequencing efforts to identify genetic variations involved in modulating human response to drugs and other environmental xenobiotics. This landmark investigation also demonstrated the power of hypothesis-driven, population-based studies for assessing disease risk associated with specific interactions between genes and the
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environment. Furthermore, this pioneering study validated the use of epidemiological tools to investigate the biological significance of human genetic variants in disease risk. Such studies indicate that functional polymorphisms in genes that code for DNA repair or metabolism of environmental xenobiotics can modify the effects of environmental exposures. Gene variants can influence the balance of metabolic intermediates produced by altering the structure, function, and level of enzymes or the catalytic efficiency of the biotransformation process. Several large-scale projects are underway to build databases and to develop tools and infrastructure to investigate gene–gene and gene–environment interactions, and some results are already being reported. An encyclopedic review of the various studies is beyond scope of the present chapter, but a few examples will be presented as illustrative. Bladder Cancer and N-acetyltransferase 2 (NAT2) Polymorphism The seminal study by Lower and colleagues (1979) was prompted by their desire to elucidate the relationship between N-acetyltransferase phenotype and susceptibility to the development of bladder cancer from exposure to arylamines, as a result of exposure to cigarette smoke or chemical dyes. Earlier studies had suggested that (i) a significant portion of bladder cancer can be attributed to cigarette smoking and occupation exposures; (ii) the distribution of N-acetyltransferase activity in the liver is highly variable among individuals, and (iii) individuals with low enzyme activity (the so-called “slow acetylator phenotype”) were most susceptible to the development of bladder cancer (Case and Hasker, 1954; Cole, 1974; Hueper, 1969; Hueper et al., 1938). These observations led Lower et al. (1979) to propose that the slow acetylator phenotype would be over-represented in a population of bladder cancer patients occupationally exposed to arylamine dyes. Their hypothesis was confirmed in a cohort of bladder cancer patients who worked in the dye industry in Denmark. Bladder cancer has been described as a classic illustration of the principle of dose–effect modification of an environmental exposure by genetic polymorphisms (Kelada et al., 2003). Exposure to aromatic amines can cause bladder cancer depending on one’s NAT2 genotype/phenotype. Smokers who are slow acetylators have a higher risk of developing bladder cancer compared to smokers who are rapid acetylators. Marcus et al. (2000) calculated an odds ratio of 1.3–1.7 for smokers who are rapid acetylators, confirming that there is an interaction between NAT2 and smoking. Using nonsmoking rapid acetylators as the reference with estimates of the prevalence of smoking, these investigators were able to demonstrate that the populationattributable risk of the gene–environment interaction was 35% for slow acetylators who had ever smoked and 13% for rapid acetylators who had ever smoked. In laboratory studies designed to investigate plausible mechanisms to explain the results, Hein et al. (1993) and Mattano et al. (1989) demonstrated that NAT2 enzyme can bioactivate procarcinogenic aromatic amines in the bladder. Following N-oxidation of aromatic amines by cytochrome P4501A2 in the liver, O-acetylation of the resulting hydroxylamine by NAT2 can produce unstable acetoxy esters or
proximate carcinogens. The acetoxy ester intermediates can be N-acetylated or N-oxidized to yield N-hydroxy-N-acetyl aromatic amines that can bind to DNA to create point mutations or adducts. Myeloperoxidase Polymorphism and Lung Cancer Myeloperoxidase is a phase I enzyme found in the lysosomes of polymorphonuclear leukocytes and monocytes. It converts lipophilic carcinogens into hydrophilic forms. Exposure to tobacco smoke stimulates the recruitment of neutrophils into lung tissue with local release of myeloperoxidase. This enzyme converts 7,8-diol benzo(a)pyrene to the highly reactive and carcinogenic form called benzo(a)pyrene 7,8-diol epoxide, which can form DNA adducts in lung tissue. Myeloperoxidase can also convert other procarcinogens (e.g., polycyclic aromatic hydrocarbons and aromatic amines) in cigarette smoke to free radicals or reactive oxygen species. A common G to A transition at position 463 of the promoter region of the myeloperoxidase gene, which leads to loss of a SP1 transcription factor binding site in an Alu hormone responsive element has been shown to reduce myeloperoxidase mRNA expression (Kiyohara et al., 2005). Because transcriptional activity is decreased in individuals with the variant A allele, less enzyme is available to convert benzo(a)pyrene intermediate to the highly carcinogenic benzo(a)pyrene 7,8-diol epoxide species. The frequency of the A allele varies between Caucasian (23.4%) and Asian (14.4%) populations. As the myeloperoxidase G-463A variant allele is related to low metabolic activation, individuals with at least one variant allele may be associated with decreased risk of lung cancer. London et al. (1997) reported that Caucasian individuals with the A/A genotype were associated with a significant reduction in risk for lung cancer. Although the biological effects of each genotype (i.e., G/G, G/A and A/A) have not been clarified, lung cancer among the three genotypes decreases from G to A. Similar results have been obtained for Japanese and Hawaiian populations (LeMarchand et al., 2000). The association between myeloperoxidase genotype and lung cancer risk was modified by duration of smoking. In heavy smokers versus light smokers, the combined G/A and A/A genotypes had an odds ratio for squamous cell carcinoma of 6.22 for heavy smokers and 1.72 for light smokers, and for never smokers 1.14. Other studies showed that myeloperoxidase G/A genotype interacted with the glutathione S-transferase T1 (GSTT1) locus to significantly decrease lung cancer among males but not females (Cajas-Salizar et al., 2003; Feyler et al., 2002). NAD(P)H Quinine Oxidoreductose 1 Polymorphism and Lung Cancer Polymorphism in the gene that codes for NAD(P) quinine oxidoreductase 1 (NQO1) is also a lung cancer susceptibility gene that interacts with carcinogens in cigarette smoke (Kiyohara et al., 2005). NQO1 has a dual function in that it can both activate and detoxify carcinogenic quinoid compounds, such as benzo(a)pyrene. The catalytic reduction of benzo(a)pyrene prevents the formation of cancer-causing quinone-DNA adducts. The combined variant genotypes, Pro-Ser and Ser-Ser were
Polymorphism and Disease Susceptibility: Case–Control Studies
significantly associated with decreased risk of lung cancer in Mexican Americans (Wiencke et al., 1997). Similar results were obtained in three Japanese studies (cited in review article by Kiyohara et al., 2005); however, these genotypes were only weakly associated with lung cancer in Chinese, Hawaiians, Caucasians, and African-Americans. In terms of gene–environment interactions, there was an excess small cell lung cancer risk associated with the presence of the Ser allele in heavy smokers, where the odds ratio for the Ser-Ser and Pro-Ser genotypes combined was 12.5; in light smokers the odds ratio was 0.90. In contrast, the frequency of the NQO1 genotypes did not differ significantly between smokers and nonsmokers. The NQO1 Pro187Ser polymorphism was also involved in gene–gene interactions. For example, the combined NQO1 Pro-Pro and GSTT1 null genotype showed a significant association with lung adenocarcinoma risk, with an odds ratio of 4.61 (Sunaga et al., 2002). The odds ratio was calculated using the NQO1 Ser-Ser and GSTT1 nonnull genotypes combined as the reference. Although the risk of developing lung cancer conferred by the various susceptibility alleles is small, lung cancer is such a common malignancy that this small increase in risk translates to a large number of lung cancer cases in the population and is clearly important from a public health point of view. These results also show that lung cancer cannot be explained by variation in a single allele, but rather by multiple polymorphisms within genes involved in the same or different pathways. Discovery of other functional variants may shed some light on the development of this important malignancy, as the burden of this disease surely develops from complex interactions between multiple genetic and environmental factors over time. That is, the effect of polymorphisms may be best represented by their haplotypes. Fibrinogen Polymorphism and Coronary Heart Disease Fibrinogen is an acute phase protein and its plasma level is responsive to trauma, serum cholesterol, hormones, and infections. Increase in genetically determined levels of this glycoprotein is associated with risk of cardiovascular disease. Several epidemiologic studies have shown that gene–environment interactions involving fibrinogen polymorphisms play a role in coronary heart disease. The glycoprotein consists of three different polypeptide chains (a, b, and c) linked by disulfide bonds (Vischetti et al., 2002). Studies show that b-chain synthesis limits the rate of production of mature fibrinogen (Yu et al., 1984). Many polymorphisms have been discovered in the b-chain, two of which show a strong correlation with plasma concentration of the mature protein. The BclI and the G/A-455 polymorphisms have been selected for analysis of gene–environment interaction. Such studies have shown that moderate physical activity decreases the level of fibrinogen; however, these effects are evident only in carriers of the B2 allele of the BclI b-chain polymorphism. Montgomery et al. (1996) have shown that there is an acute rise in fibrinogen levels in response to exercise, and that this response is strongly influenced by the G/A-455 polymorphism in the b-chain gene; in fact, a gradient of increasing
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acute fibrinogen response was seen across the G/G, G/A, and A/A genotypes, respectively. Zito et al. (1999) reported that infection with Helicobacter pylori, the pathogen responsible for most peptic ulcers and gastritis, resulted in a fourfold increase in risk of myocardial infarction; the response to infection was higher in carriers of the BclI polymorphism, with carriers of the B2 allele showing higher fibrinogen levels compared to the B1 homogygous subjects. Carriers of the B2 allele have increased risk for myocardial infarction and have the capacity to amplify the effect of seropositivity for Helicobacter. So patients with both seropositivity for Helicobacter and carriers of the B2 genotype have an additional increased risk. In another study, some subjects with high dietary fat intake developed abnormal high-density lipoprotein cholesterol concentrations, and some did not, depending on their genotype or polymorphisms in the hepatic lipase gene promoter (Ordovas and Corella, 2002; Ordovas 2004). Also, tobacco smokers developed coronary heart disease, or did not, depending on their lipoprotein lipase genotype (Talmud et al., 2000) and their apolipoprotein E4 genotype (Humphries et al., 2001). These results show that genetic polymorphisms in the b-chain gene do not cause myocardial infarction, but influence risk for the disease by rendering individuals more susceptible to environmental risk factors. Mental Disorders and Monoamine Oxidase A Polymorphism A functional polymorphism in the promoter region of the MAOA gene encoding the neurotransmitter metabolizing enzyme monoamine oxidase moderates the effects of child maltreatment on the cycle of violence (Moffitt et al., 2005). Their results show that maltreated children, with the polymorphism in the promoter region, produced low levels of monoamine oxidase A and were more prone to develop conduct disorders, to demonstrate antisocial personality, and to commit crimes as an adult than maltreated children with high-activity levels of monoamine oxidase A. In a related study, Caspi et al. (2003) demonstrated that the serotonin transporter (5-HTT ) gene moderated the effects of stressful life events on depression. Individuals with one or two copies of the 5-HTT polymorphic allele exhibited more depressive symptoms, diagnosable depression, and suicidal tendencies following stressful life events than individuals who were homozygous for the wild-type allele. Asthma and Gene Polymorphisms Asthma is a complex multifactorial disorder with variable phenotypes attributed to interaction of multiple genes with multiple environmental factors (see Chapter 89). The interplay between genes and the environment is best illustrated by the hygiene hypothesis to explain the increase in asthma prevalence. The fundamental tenet of this hypothesis is that the emergence and severity of the disease depends on the interaction between genetic background and environmental exposure. According to the hygiene hypothesis, children in industrialized nations have decreased incidence of bacterial infections, hence reduced exposure to endotoxins. In fact, this is supported by several studies
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which show that exposure to increased endotoxin levels in the domestic environment is associated with decreased occurrence of asthma (for recent review see Kleeberger and Peden, 2005). The risk of developing asthma is influenced by the Western lifestyle, including diet, air pollution, reduced risk of infections, indoor air, and stress (reviewed by Arruda et al., 2005). The length and type of exposure, as well as the time when it occurs, can either promote or suppress the inflammatory process and the subsequent course of the disease. CD14 is an innate immunity gene that has been studied extensively with respect to its genetics, function, and distribution in various populations because of its important role in the development of asthma and other allergic reactions. The CD14 gene product is a component of the endotoxin-recognition receptor complex and is critical for the development of the optimal responses to low concentrations of the environmental stimulant via the Toll-like receptors (TLR4 and TLR2). By some unknown mechanism, both the membrane-bound and soluble forms of CD14 enhance the affinity of the interaction between environmental allergens and the TLRs. In 1999, Baldini et al., as part of the Tucson Children Respiratory Study, demonstrated an association between a SNP in the CD14 promoter (CD14*C-159T), increased serum levels of soluble CD14, and decreased total serum IgE. The discovery of an association between this polymorphism in the CD14 promoter and a reduction in serum IgE levels provided important support for the view that innate immunity genes are key determinants in the development of asthma. Several studies have shown that pro-oxidant environmental exposures are associated with increased incidence and exacerbation of asthma and other lung diseases. For example, a large study conducted in Southern California showed that in utero exposure to tobacco smoke was associated with several indicators of lung morbidity, including increased incidence of asthma diagnosis, increased need for emergency room use for airways symptoms, and increased occurrence of bronchitis (reviewed by Kleeberger and Peden, 2005). Numerous candidate genes have been shown to be associated with asthma and asthma-associated phenotypes, such as elevated IgE levels, bronchial hyperactivity, and SNPs within specific cytokines, IgE regulating enzymes, and phase I and phase II xenobiotic metabolism enzymes. However, only a few genes conferring significant risk have been discovered, and the functional significance of the various gene variants is largely unknown. A glutathione S-transferase M1 (GSTM1) null allele (M1*0), which results in no protein expression and no glutathione antioxidant protection, was shown to be strongly associated with decreased lung growth and function in a cohort of school children in Southern California due to increased oxidant stress (Gilliland et al., 2002a, b, c). These investigators also examined the relationship between the GSTM1 null genotype, the prevalence of asthma, and the in utero exposure to tobacco smoke (Gilliland et al., 2002a, b, c). Children with the GSTM1 null genotype and who were born to mothers who smoked during pregnancy showed increased prevalence of early onset asthma, persistent asthma, wheezing with exercise, and increased need for medical intervention for asthma. Children with the functional GSTM1 genotype did not have asthmatic symptoms; nor did the children
with GSTM1 null genotype if they were born to nonsmoking mothers. Studies have also shown that children with the GSTP1 Val 105/Val 105 genotype had fewer school absences related to asthma, which suggests that this antioxidant protective gene may influence asthma severity (Gilliland et al., 2002a, b, c). Both GSTM1 and GSTP1 appear to modulate the effect of exposure to particulates in diesel exhaust as measured by IgE response following a nasal challenge; both IgE and histamine levels were increased. In a study of the effects of diesel exhaust on response to ragweed challenge, subjects were stratified on the basis of GSTM1 and GSTP1 genotypes and were allowed to inhale diesel exhaust particles followed by ragweed allergen. Subjects with either the GSTM1 null or GSTP1 Ile 105 genotype exhibited a stronger response than did subjects with other GSTM1 and GSTP1 genotypes (Bastain et al., 2003; Gilliland et al., 2004). Studies of the risk for asthma from lifetime exposure to ozone show that GST proteins are important determinants of asthma severity (David et al., 2003). In studies of the combined genotypes of NQO1 Pro187Ser and GSTM1 null or functional sequence, the presence of the Pro-Pro allele of NQO1 significantly reduced asthma risk in subjects homozygous for the GST1 deletion. The studies on lung function and gene variation cited above are representative examples of published and ongoing studies; for a more comprehensive survey, the reader is referred to the excellent review by Kleeberger and Peden (2005) and to other chapters in this book.
EPIGENETICS AND THE ENVIRONMENT A discussion of gene–environment interactions could not be concluded without at least a brief mention of epigenetics, as it represents yet another mechanism by which the environment can influence phenotype or disease development (see Chapter 5). Cells of multicellular organisms are genetically the same, yet structurally and functionally diverse owing to the differential expression of genes. Many of these differences in gene expression arise during development, and most are stable throughout the life of the organism. However, it is important to note that epigenetic changes can occur in the adult. Such stable alterations are referred to as “epigenetic” in that they do not involve mutations in the DNA itself. Most of the research on epigenetic mechanisms has focused on DNA methylation during early development and gene silencing. But studies are now focusing on epigenetic regulation of gene expression response to environmental exposures as a possible pathway for disease development. For example, one could imagine that inactivation of proteins that buffer the cell against environmental stress could lead to disease by allowing the expression of previously suppressed genetic variants. Much attention has been devoted to diet as it represents an abundant source of methyl group donors. Also, diet is known to be a particularly significant determinant in the timing of disease expression. For example, dietary supplements such as folate or vitamins that influence the activity of methyl-transfer enzymes can modulate the rate of disease development and can exert
References
marked effects on the incidence of colon cancer (for review, see Van den Veyver, 2002). Reduced amounts of folate have been associated with genomic instability, neural tube defects, and genomic hypomethylation. In addition, a diet deficient in methylcontaining components has been shown to induce liver cancer. The possibility that classical genetic mechanisms may not be adequate to explain the development of complex diseases looms large on the horizon. Normal physiological responses to certain environmental stimuli may be mediated by epigenetic mechanisms, so progress is likely to require a more intense research effort to fully understand how humans and other organisms respond to environmental cues. For example, epigenetic changes might explain the phenotypic discordance often observed among monozygotic twins, differences in age of onset of diseases, fluctuation in diseases and sporadic cases of disease. Also, since epigenetic-mediated changes in gene expression can occur at a much faster rate than DNA mutation, it represents a more adaptable mechanism for organisms to respond to rapidly changing environmental conditions.
CONCLUSION Although most of the visible environmental problems of the 1950s and 1960s have been ameliorated, massive quantities of toxic agents are still polluting our environment. Whether current levels of exposure to these agents are contributing to the high or increasing incidence of cancer, Parkinson’s and Alzheimer’s disease, asthma, autism, learning disabilities, diabetes, or other complex disorders is a matter of considerable concern. Finding answers to these questions has been a slow and difficult process. The traditional methodologies available to
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medical researchers have not been adequate to elucidate the intricate gene–environment interactions involved in the development of complex diseases. But, thanks to the rare confluence of technology breakthroughs and the rethinking and redirection of the environmental health sciences over the past decade, the link between the environment and human health and disease can now be investigated with more rigor and specificity. For example, the sequencing of the human genome and the development and application of high throughput technologies to monitor the expression of genes and proteins in response to specific environmental exposures has created an unparalleled opportunity to study gene–environment interactions. By using a combination of new “-omic” technologies (genomics, proteomics, and metabolomics), in combination with population studies and better exposure data, one can achieve an integrative view of gene–environment interaction at the level of the whole organism. So, perhaps the most important development in healthcare in the 21st century will be the incorporation of knowledge of gene–environment interactions into public health and the practice of medicine. Many of the expected breakthroughs from use of these technologies will almost definitely lead to new opportunities to prevent, diagnose, and treat human diseases.
ACKNOWLEDGEMENTS I am grateful to my colleague, Dr Samuel Wilson, for his intellectual input and leadership in developing many of the concepts and research initiatives on gene–environment interactions discussed in this chapter. I am also grateful to Ms Betty Mills for editorial and technical assistance.
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5 Epigenomics and Its Implications for Medicine Moshe Szyf
INTRODUCTION The genome holds the entire information required to encode all the proteins expressed in an organism. However, expression of this information is programmed so that only a fraction of the potential repertoire of proteins is expressed in a given cell type. This programming is defined by the epigenome. The epigenome controls gene expression programs by defining the accessibility of transcription machineries, as well as other nuclear machineries, to genes (Razin, 1998). Programming of the epigenome occurs during embryonic development, whereby it establishes the diversity of gene expression patterns amongst organs, tissues, and cell types (Razin, 1998). These programs are stable and maintained through life. The epigenetic machinery is responsive to signaling pathways, which could be activated by different triggers. It has become clear recently that epigenetic programming could potentially change through life, and not just during embryogenesis, in response to environmental exposures such as nutrition, drugs, and social interactions (Meaney and Szyf, 2005). The epigenome thus mediates the effect of the environment on our genes. These effects could result in novel gene expression programs triggering new physiological, behavioral, or pathological states. Improper epigenetic programming might lead to consequences that could be similar to the effects of genetic mutations or deletions. For example, aberrant epigenetic programming could lead to activation of a gene that is normally silenced or
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to inactivation of a gene that is normally active. An example is the aberrant silencing of tumor-suppressor genes in cancer, which could be mediated by either an inactivating mutation or by epigenetic silencing through DNA methylation (Baylin et al., 2001). Epigenetic differences amongst individuals can result in interindividual phenotypic differences with respect to behavior, physiology, and pathology and response to drugs similar to genetic polymorphisms. However, the extent to which epigenetic interindividual differences play a role in generating interindividual phenotypic differences is still unknown. In contrast to the genome, which is identical in different cell types and through life, the epigenome is dynamic and varies from cell type to cell type and from time point to time point in life. This diversity allows the epigenome to regulate the assortment of gene expression programs in a multicellular organism. The epigenome consists of chromatin and its modifications, as well as a covalent modification of cytosine rings found at the dinucleotide sequence CpG (CG) (Razin, 1998). The basic building block of chromatin is the nucleosome, which is formed of an octamer of histone proteins containing an H3-H4 tetramer flanked on either side with a H2A-H2B dimer (Finch et al., 1977). The N-terminal tails of these histones are extensively modified by methylation (Jenuwein, 2001), phosphorylation, acetylation (Wade et al., 1997), and ubiquitination (Shilatifard, 2006). Different histone variants also play a regulatory role (Henikoff et al., 2004). The pattern of histone modifications and
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DNA Methylation Patterns
variants creates a “histone code” that delineates the parts of the genome expressed at a given point in time in a given cell type (Jenuwein and Allis, 2001). Specific transcription factors and transcription repressors recruit histone-modifying enzymes to specific genes and thus define the profile of histone modification around genes (Jenuwein and Allis, 2001). The best-investigated examples are histone acetyltransferases (HAT), which acetylate histone H3 and H4 tails at the lysine-9 (K9) residue, and histone deacetylases (HDAC), which deacetylate histone tails (Kuo and Allis, 1998). Histone acetylation is believed to be a predominant signal for an active chromatin configuration. Deacetylated histones signal inactive chromatin, chromatin associated with inactive genes. Many repressors and repressor complexes recruit HDACs to genes, thus causing their inactivation. Histone tail acetylation is believed to enhance the accessibility of a gene to the transcription machinery whereas deacetylated tails are tightly interacting with DNA and limit its accessibility to transcription factors (Kuo and Allis, 1998). Histone methylation at K9 of H3-histone signals inactivity and is determined by the recruitment of histone methyltransferases (HMTs) such as SUV3–9 to genes (Lachner et al., 2001). The heterochromatin-associated protein HP-1, which binds methylated histones and precipitates an inactive chromatin structure (Lachner et al., 2001), recognizes methylated histones. Recently described histone demethylases remove the methylation mark, causing either activation or repression of gene expression (Shi et al., 2004;Tsukada et al., 2006). Chromatin remodeling complexes, which are ATP dependent, alter the position of nucleosomes around the transcription initiation site and define its accessibility to the transcription machinery (Varga-Weisz and Becker, 2006). It is becoming clear now that there is an interrelationship between chromatin modification and chromatin remodeling. For example, the presence of BRG1 (the catalytic subunit of SWI/ SNF-related chromatin remodeling complexes) is required for histone acetylation and regulation of -globin expression during development (Bultman et al., 2005). The targeting of chromatin modifications and remodeling to discrete genetic loci is established by specific trans-acting factors, which might be responsive to cellular as well as extracellular signaling. Chromatin is dynamic and plastic and could respond by altered recruitment of the different histone modification enzymes in response to a variety of signals. In addition to chromatin, which is associated with DNA, the DNA molecule itself is chemically modified by methyl residues at the 5ⴕ position of the cytosine rings in the sequence CG in vertebrates (Razin, 1998) (see Chapter 11). What distinguishes DNA methylation in vertebrate genomes is the fact that not all CGs are methylated in any given cell type (Razin, 1998). Distinct CGs are methylated in different cell types, generating cell type– specific patterns of methylation. Thus, the DNA methylation pattern confers upon a genome itself its cell type identity (Razin, 1998). Since DNA methylation is part of the chemical structure of the DNA itself, it is more stable than other epigenetic marks and thus has extremely important diagnostic potential (Beck et al., 1999). It was originally believed that the DNA methylation
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pattern is established during development and is then maintained faithfully through life by the maintenance DNA methyltransferase (DNMT) (Razin and Riggs, 1980). The DNA methylation reaction was believed to be irreversible; thus the only way methyl residues could be lost was through replication in the absence of DNMT by passive demethylation (Razin and Riggs, 1980). Recent data support the idea that similar to chromatin modification DNA methylation is also potentially reversible even in post-mitotic tissues (Weaver et al., 2004a, b). A hallmark of DNA methylation patterns is the correlation between chromatin and the DNA methylation pattern. Active chromatin is usually associated with unmethylated DNA while inactive chromatin is associated with methylated DNA (Razin, 1998). Current data suggest a bilateral relation between chromatin structure and DNA methylation. This interrelationship of chromatin modification state and DNA methylation has important implications for understanding of how the epigenome is generated and of its utility as a diagnostic and as a therapeutic target. The gene carries the basic unit of information, but this information has an impact only once it is properly programmed by the epigenome. Thus, the epigenome is a prime target for intervention in human diseases and other conditions that involve altered gene function. Genetic mutations and polymorphisms are irreversible. In difference from the genome, the epigenome is reversible. One could potentially alter epigenetic patterns by either targeting pharmacologically the different chromatin modifications and DNA methylation enzymes or affecting the environmental exposures and the signaling pathways mediating these effects. Epigenetic profiles have therefore important practical implications in prophylaxis, diagnosis, and therapeutics. A true realization of the potential of epigenetics will require a thorough understanding of the mechanisms leading to the formation of specific DNA methylation patterns and a comprehensive mapping of dynamic epigenomes with relation to different environmental exposures. This chapter will review the basic machineries involved in shaping the epigenome and the potential implications of epigenetics on understanding, diagnosing, treating, and preventing human pathologies.
DNA METHYLATION PATTERNS The DNA Methylation Pattern Is Cell Type Specific and Correlates with Chromatin Structure Modification of nucleotides in DNA by addition of a methyl group is widespread but not ubiquitous in biology. Both 5-methylcytosine and 6-methyladenine methylation are found in bacteria and are encoded by sequence-specific DNMTs. The methylated sequence distinguishes one strain from the other and serves for self versus non-self demarcation, protecting bacteria from invading DNA arriving from a different host. For this restriction modification system to function properly, it requires complete methylation of the host DNA in a unique 4–8 base sequence (Bestor, 1990; McClelland et al., 1994).
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DNA methyltransferase (DNMT)
HO HO
S-Adenosylmethionine (SAM)
—
CH N
N
N
CH2SCH2CH2CH(NH2)
—
O— —C
N
—
CH N
COOH
CH — CH3
COOH
—
—
O— —C
C
—
—
CH2SCH2CH2CH(NH2)
—
— N
—
N
CH
N
—
C
CH3
NH2
NH2
—
N
—
N
NH2
—
—
NH2
HO HO
S-Adenosylhomocystein (SAH)
Figure 5.1 The DNA methylation reaction. The DNMTs catalyze the transfer of a methyl group from the methyl donor S-adenosyl methionine onto the 5 in the cytosine ring in DNA.
DNA methylation is not found in all organisms. Certain yeast strains, including Saccharomyces cerevisiae, are devoid of DNA methylation. Since these organisms are eukaryotes with extensive epigenetic control in spite of lack of methylation, it suggests that the primary component of epigenetic control is chromatin, which is highly developed in yeast, and not DNA methylation, which is absent in yeast. Similarly, it was long believed that nematodes and insects are devoid of DNA methylation. However, recent data suggest that Drosophila flies do contain minute amounts of 5-methylcytosine, which is relatively abundant during early development (Lyko et al., 2000). In contrast to bacterial methylation systems, the primary methylated sequence in vertebrates is composed of only two bases, the dinucleotide sequence CG. In plants, CNG (where N is either A, G, or T) sequences are also methylated in addition to CGs (Razin and Riggs, 1980). Another feature that distinguishes bacterial from vertebrate methylation is the fact that only a fraction (80%) of the methylatable CG population is methylated in vertebrate DNA. Different CG sites are methylated in different tissues, creating a pattern of methylation that is gene and tissue specific (Razin and Riggs, 1980). In some cases, such as parentally imprinted genes (Swain et al., 1987) or genes residing on the inactive X chromosome (Mohandas et al., 1981), the two alleles of the same gene are differentially methylated in the same cell. DNA methylation is the only component of the covalent DNA structure that shows cell-, parent of origin- and allelespecific identity. An identical sequence could be either methylated or nonmethylated in different cell types or in different alleles in the same cell type. The DNA methylation pattern is not copied by the DNA replication machinery, but by independent enzymatic machinery the DNMTs (Razin and Cedar, 1977) (Figure 5.1). DNA methylation patterns in vertebrates are distinguished by their tight correlation with chromatin structure. Active regions of the chromatin, which enable gene expression, are associated with hypomethylated DNA whereas hypermethylated DNA is packaged in inactive chromatin (Razin and Cedar, 1977). It is generally accepted that DNA methylation plays an important role in regulating gene expression. Establishing and Maintaining DNA Methylation Patterns Vertebrates establish their DNA methylation patterns during development and these patterns are then maintained in each
cell type during life. Nevertheless, differences in environmental exposure result in differences in DNA methylation patterns that emerge later in life (Weaver et al., 2004a, b), even in monozygotic twins (Fraga et al., 2005). DNA methylation patterns might be affected by changes in the methyl content of diets, maternal behavior (Weaver et al., 2005), and exposures to certain drugs. Understanding the epigenetic drift during life and its environmental causes is now attracting considerable attention and could potentially develop into an important field in medicine. DNA Methyltransferases The methylation of DNA occurs immediately after replication by a transfer of a methyl moiety from the donor S-adenosyl-lmethionine (SAM) (AdoMet) in a reaction catalyzed by DNMTs (Figure 5.1). DNA methylation is only one of the methylation reactions occurring in the cell that uses AdoMet as a cofactor. The N-terminus of most cytosine 5-methyltransferases defines their unique features and regulatory functions which are responsible for specific targeting of DNMTs to their site of action. In mammalian DNMT1, the N-terminus contains a number of functional domains that integrate DNMT activity with chromatin-modifying enzymes such as histone deacetylase 1 (HDAC1) (Fuks et al., 2000) and HDAC2 (Robertson et al., 2000) and coordinate DNMT activity with the replication fork and DNA replication (Milutinovic et al., 2003). This obviously has therapeutic implications, since it might be possible to target these functions independently of DNA methylation and steer clear of the adverse effects associated with loss of DNA methylation (for a review see Szyf [2001]). De Novo and Maintenance Methyltransferase Activities It is critical to accurately replicate the methylation pattern during cell division. Razin and Riggs proposed more than two decades ago that the maintenance DNMT prefers a hemimethylated substrate (Razin and Riggs, 1980). Since hemimethylated sites are generated during DNA replication when a nascent unmethylated C is synthesized across a methylated C in the template parental strand, the DNMT accurately copies the methylation pattern of the template strand (Figure 5.2). Some DNMTs distinguish hemimethylated from nonmethylated sites. In support of the Razin and Riggs model, three distinct phylogenic DNMTs have been identified in mammals.
Chromatin Modification
De novo DNMT (DNMT3a, 3b, and DNMT1?)
Demethylase
DNA replication
Maintenance DNMT (DNMT1)
Figure 5.2 DNA DNMT and demethylases sculpt the methylation pattern. De novo DNMTs introduce new methyl groups into CG sites, which are unmethylated on both strands of DNA. Demethylases remove methyl groups from DNA. Maintenance DNMT copies the DNA methylation pattern from the parental strand into the nascent strand of DNA during DNA replication. Open circles indicate an unmethylated CG site in DNA; red circles indicate a methylated CG site.
DNMT1 shows preference for hemimethylated DNA in vitro, which is consistent with its role as a maintenance DNMT, whereas DNMT3a and DNMT3b methylate unmethylated and methylated DNA at an equal rate, which is consistent with a de novo DNMT role (Okano et al., 1998). Two additional DNMT homologs have been found: DNMT2, whose substrate and methylation activity is unclear (Vilain et al., 1998), and DNMT3L, which belongs to the DNMT3 family of DNMTs by virtue of its sequence and is essential for the establishment of maternal genomic imprints but lacks key methyltransferase motifs, and is possibly a regulator of methylation rather than an enzyme that methylates DNA (Bourc’his et al., 2001). Knockout mouse data indicate that DNMT1 is responsible for a majority of DNA methylation marks in the mouse genome (Li et al., 1992), whereas DNMT3a and DNMT3b are responsible for some but not all de novo methylation during development (Okano et al., 1999). It has recently been demonstrated that an additional factor is required for targeting DNMT1 to newly replicating hemimethylated DNA, the protein UHRF1 (ubiquitin-like, containing PHD and RING finger domains 1), also known as NP95 in mouse and ICBP90 in human (Bostick et al., 2007). Several lines of evidence indicate that DNMTs are targeted to specific sequences by sequence-specific factors, which recognize specific sequences of DNA. For example, the HMT EZH2 or the oncoprotein PML-RAR target DNMTs to specific sequences in DNA (Di Croce et al., 2002;Vire et al., 2006). Thus, in addition to template-directed DNA methylation in replicating DNA, specific factors are responsible for specific methylation in specific genes. Is DNA Methylation a Reversible Reaction? The most controversial issue in the DNA methylation field is the question of whether the DNA methylation reaction is reversible. A passive process through replication in the absence of DNMT
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would result in loss of methylation. This could serve as a mechanism for both site specific and global demethylation (see Figure 5.2 for a scheme of the different DNA methylation reactions). There is a long list of data from both cell culture and early mouse development supporting the hypothesis that active methylation occurs in embryonal and somatic cells. There are now convincing examples of active, replication-independent DNA demethylation during development as well as in somatic tissues. Active demethylation was reported for the myosin gene in differentiating myoblast cells (Lucarelli et al., 2001), the INTERLEUKIN-2 gene upon T-cell activation (Bruniquel and Schwartz, 2003), the INTERFERON- gene upon antigen exposure of memory CD8 T cells (Kersh et al., 2006), and the glucocorticoid receptor gene promoter in adult rat brains upon treatment with the HDAC inhibitor trichostatin A (TSA) (Weaver et al., 2004a, b). Enzymatic Activities Responsible for Demethylation The characteristics of the enzymes responsible for active demethylation are controversial. One proposal has been that a G/T mismatch repair glycosylase also functions as a 5-methylcytosine DNA glycosylase, recognizing methylcytosines and cleaving the bond between the sugar and the base. The abasic site is then repaired and replaced with a nonmethylated cytosine resulting in demethylation (Jost, 1993). An additional protein with a similar activity was recently identified, the methylated DNA binding domain protein 4 (MBD4) (Zhu et al., 2000). Another report has proposed that MBD2 has demethylase activity. MBD2b (a shorter isoform of MBD2) was shown to directly remove the methyl group from methylated cytosine in methylated CGs (Bhattacharya et al., 1999). This enzyme was therefore proposed to reverse the DNA methylation reaction. However, other groups disputed this finding (Ng et al., 1999). Understanding the mechanisms responsible for demethylation is one of the most im
