Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.
Journal: 2007/June - Nature
ISSN: 1476-4687
Abstract:
There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 x 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10(-5) and 5 x 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
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Nature 447(7145): 661-678

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Lists of participants and affiliations appear at the end of the paper.
Correspondence and requests for materials should be addressed to P.D. (ku.ca.xo.stats@yllennod).
Present address: Illumina Cambridge, Chesterford Research Park, Little Chesterford, Nr Saffron Walden, Essex CB10 1XL, UK.

Abstract

There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined ~2,000 individuals for each of 7 major diseases and a shared set of ~3,000 controls. Case-control comparisons identified 24 independent association signals at P<5×10: 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn’s disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10 and 5×10) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

Abstract

Despite extensive research efforts for more than a decade, the genetic basis of common human diseases remains largely unknown. Although there have been some notable successes1, linkage and candidate gene association studies have often failed to deliver definitive results. Yet the identification of the variants, genes and pathways involved in particular diseases offers a potential route to new therapies, improved diagnosis and better disease prevention. For some time it has been hoped that the advent of genome-wide association (GWA) studies would provide a successful new tool for unlocking the genetic basis of many of these common causes of human morbidity and mortality1.

Three recent advances mean that GWA studies that are powered to detect plausible effect sizes are now possible2. First, the International HapMap resource3, which documents patterns of genome-wide variation and linkage disequilibrium in four population samples, greatly facilitates both the design and analysis of association studies. Second, the availability of dense genotyping chips, containing sets of hundreds of thousands of single nucleotide polymorphisms (SNPs) that provide good coverage of much of the human genome, means that for the first time GWA studies for thousands of cases and controls are technically and financially feasible. Third, appropriately large and well-characterized clinical samples have been assembled for many common diseases.

The Wellcome Trust Case Control Consortium (WTCCC) was formed with a view to exploring the utility, design and analyses of GWA studies. It brought together over 50 research groups from the UK that are active in researching the genetics of common human diseases, with expertise ranging from clinical, through genotyping, to informatics and statistical analysis. Here we describe the main experiment of the consortium: GWA studies of 2,000 cases and 3,000 shared controls for 7 complex human diseases of major public health importance—bipolar disorder (BD), coronary artery disease (CAD), Crohn’s disease (CD), hypertension (HT), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). Two further experiments undertaken by the consortium will be reported elsewhere: a GWA study for tuberculosis in 1,500 cases and 1,500 controls, sampled from The Gambia; and an association study of 1,500 common controls with 1,000 cases for each of breast cancer, multiple sclerosis, ankylosing spondylitis and autoimmune thyroid disease, all typed at around 15,000 mainly non-synonymous SNPs. By simultaneously studying seven diseases with differing aetiologies, we hoped to develop insights, not only into the specific genetic contributions to each of the diseases, but also into differences in allelic architecture across the diseases. A further major aim was to address important methodological issues of relevance to all GWA studies, such as quality control, design and analysis. In addition to our main association results, we address several of these issues below, including the choice of controls for genetic studies, the extent of population structure within Great Britain, sample sizes necessary to detect genetic effects of varying sizes, and improvements in genotype-calling algorithms and analytical methods.

Significance levels in genome-wide studies
119. Spitzer RL, Endicott J, Robins E. Research diagnostic criteria: rationale and reliability. Arch. Gen. Psychiatry. 1978;35:773–782. [PubMed] [Google Scholar]
120. Wing JKBT, et al. SCAN. Schedules for Clinical Assessment in Neuropsychiatry. Arch. Gen. Psychiatry. 1990;47:589–593. [PubMed] [Google Scholar]
121. Craddock M, et al. Concurrent validity of the OPCRIT diagnostic system. Comparison of OPCRIT diagnoses with consensus best-estimate lifetime diagnoses. Br. J. Psychiatry. 1996;169:58–63. [PubMed] [Google Scholar]
122. McGuffin P, Farmer A, Harvey I. A polydiagnostic application of operational criteria in studies of psychotic illness. Development and reliability of the OPCRIT system. Arch. Gen. Psychiatry. 1991;48:764–770. [PubMed] [Google Scholar]
123. Green EK, et al. Operation of the schizophrenia susceptibility gene, neuregulin 1, across traditional diagnostic boundaries to increase risk for bipolar disorder. Arch. Gen. Psychiatry. 2005;62:642–648. [PubMed] [Google Scholar]
124. Green EK, et al. Genetic variation of brain-derived neurotrophic factor (BDNF) in bipolar disorder: case-control study of over 3000 individuals from the UK. Br. J. Psychiatry. 2006;188:21–25. [PubMed] [Google Scholar]
125. Samani NJ, et al. A genomewide linkage study of 1,933 families affected by premature coronary artery disease: The British Heart Foundation (BHF) Family Heart Study. Am. J. Hum. Genet. 2005;77:1011–1020.[PMC free article] [PubMed] [Google Scholar]
126. Lennard-Jones JE. Classification of inflammatory bowel disease. Scand. J. Gastroenterol. 1989;170(Suppl.):2–6. discussion 6-9. [PubMed] [Google Scholar]
127. Arnett FC, et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum. 1988;31:315–324. [PubMed] [Google Scholar]
128. MacGregor AJ, Bamber S, Silman AJ. A comparison of the performance of different methods of disease classification for rheumatoid arthritis. Results of an analysis from a nationwide twin study. J. Rheumatol. 1994;21:1420–1426. [PubMed] [Google Scholar]
129. Worthington J, et al. The Arthritis and Rheumatism Council’s National Repository of Family Material: pedigrees from the first 100 rheumatoid arthritis families containing affected sibling pairs. Br. J. Rheumatol. 1994;33:970–976. [PubMed] [Google Scholar]
130. Symmons DP, Barrett EM, Bankhead CR, Scott DG, Silman AJ. The incidence of rheumatoid arthritis in the United Kingdom: results from the Norfolk Arthritis Register. Br. J. Rheumatol. 1994;33:735–739. [PubMed] [Google Scholar]
131. Smyth D, et al. Replication of an association between the lymphoid tyrosine phosphatase locus (LYP/PTPN22) with type 1 diabetes, and evidence for its role as a general autoimmunity locus. Diabetes. 2004;53:3020–3023. [PubMed] [Google Scholar]
132. Wiltshire S, et al. A genomewide scan for loci predisposing to type 2 diabetes in a U.K. population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q. Am. J. Hum. Genet. 2001;69:553–569.[PMC free article] [PubMed] [Google Scholar]
133. Frayling TM, et al. Parent-offspring trios: a resource to facilitate the identification of type 2 diabetes genes. Diabetes. 1999;48:2475–2479. [PubMed] [Google Scholar]
134. Groves CJ, et al. Association analysis of 6,736 U.K. subjects provides replication and confirms TCF7L2 as a type 2 diabetes susceptibility gene with a substantial effect on individual risk. Diabetes. 2006;55:2640–2644. [PubMed] [Google Scholar]
135. Power C, Elliott J. Cohort profile: 1958 British birth cohort (National Child Development Study) Int. J. Epidemiol. 2006;35:34–41. [PubMed] [Google Scholar]
136. Strachan DP, et al. Lifecourse influences on health among British adults: Effects of region of residence in childhood and adulthood. Int. J. Epidemiol. 2007 Jan 25; Advance online publication, doi:10.1093/ije/dyl309. [PubMed] [Google Scholar]
137. Matsuzaki H, et al. Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. 1, 104-105. Nat Methods. 2004;1:104–105. [PubMed] [Google Scholar]
138. Di X, et al. Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays. Bioinformatics. 2005;21:1958–1963. [PubMed] [Google Scholar]
139. Rabbee N, Speed T. A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics. 2006;22:7–12. [PubMed] [Google Scholar]
140. Affymetrix . Technical Report. 2006. [Google Scholar]
141. Stirling WD. Enhancements to Aid Interpretation of Probability Plots. Statistician. 1982;31:211–220.[Google Scholar]
142. Li N, Stephens M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics. 2003;165:2213–2233.[PMC free article] [PubMed] [Google Scholar]
143. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies via imputation of genotypes. Nature Genet. doi:10.1038/ng2088 (in the press) [PubMed] [Google Scholar]

The authors declare no competing financial interests.

Affiliations for participants

Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, UK.

Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department ofMedical Genetics, Cambridge Institute for Medical Research, University of Cambridge,Wellcome Trust/MRC Building, Cambridge CB2 0XY, UK.

Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.

Department of Psychological Medicine, Henry Wellcome Building, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.

The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

The Wellcome Trust, Gibbs Building, 215 Euston Road, London NW1 2BE, UK.

Oxford Centre for Diabetes, Endocrinology and Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK.

Department of Haematology, University of Cambridge, Long Road, Cambridge CB2 2PT, UK.

National Health Service Blood and Transplant, Cambridge Centre, Long Road, Cambridge CB2 2PT, UK.

Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.

Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.

Cancer Research UK Genetic Epidemiology Unit, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK.

National Health Service Blood and Transplant, Sheffield Centre, Longley Lane, Sheffield S5 7JN, UK.

National Health Service Blood and Transplant, Brentwood Centre, Crescent Drive, Brentwood CM15 8DP, UK.

The Welsh Blood Service, Ely Valley Road, Talbot Green, Pontyclun CF72 9WB, UK.

The Scottish National Blood Transfusion Service, Ellen's Glen Road, Edinburgh EH17 7QT, UK.

National Health Service Blood and Transplant, Southampton Centre, Coxford Road, Southampton SO16 5AF, UK.

Avon Longitudinal Study of Parents and Children, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK.

Division of Community Health Services, St George's University of London, Cranmer Terrace, London SW17 0RE, UK.

Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, UK.

University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK.

Department of Psychiatry, Division of Neuroscience, Birmingham University, Birmingham B15 2QZ, UK.

Department of Psychological Medicine, Henry Wellcome Building, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.

SGDP, The Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK.

School ofNeurology, Neurobiology and Psychiatry, RoyalVictoria Infirmary, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP, UK.

LIGHT and LIMM Research Institutes, Faculty of Medicine and Health, University of Leeds, Leeds LS1 3EX, UK.

IBD Research Group, Addenbrooke's Hospital, University of Cambridge, Cambridge CB2 2QQ, UK.

Gastrointestinal Unit, School of Molecular and Clinical Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK.

Department of Medical &amp; Molecular Genetics, King's College London School of Medicine, 8th Floor Guy's Tower, Guy's Hospital, London SE1 9RT, UK.

Institute for Digestive Diseases, University College London Hospitals Trust, London, NW1 2BU, UK.

Department of Gastroenterology, Guy's and St Thomas' NHS Foundation Trust, London SE1 7EH, UK.

Department of Gastroenterology &amp; Hepatology, University of Newcastle upon Tyne, Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK.

Gastroenterology Unit, Radcliffe Infirmary, University of Oxford, Oxford OX2 6HE, UK.

Medicine and Therapeutics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, Grampian AB9 2ZB, UK.

Clinical Pharmacology Unit and the Diabetes and Inflammation Laboratory, University of Cambridge, Addenbrookes Hospital, Hills Road, Cambridge CB2 2QQ, UK.

Centre National de Genotypage, 2, Rue Gaston Cremieux, Evry, Paris 91057, France.

BHF Glasgow Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow G12 8TA, UK.

Clinical Pharmacology and Barts and The London Genome Centre, William Harvey Research Institute, Barts and The London, Queen Mary's School of Medicine, Charterhouse Square, London EC1M 6BQ, UK.

Cardiovascular Medicine, University of Oxford, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK.

arc Epidemiology Research Unit, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK.

Department of Paediatrics, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK.

Genetics ofComplex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road, Exeter EX1 2LU, UK.

Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, Barrack Road, Exeter EX2 5DU, UK.

Centre for Diabetes and Metabolic Medicine, Barts and The London, Royal London Hospital, Whitechapel, London E1 1BB, UK.

Diabetes Research Group, School of Clinical Medical Sciences, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK.

The MRC Centre for Causal Analyses in Translational Epidemiology, Bristol University, Canynge Hall, Whiteladies Rd, Bristol BS2 8PR, UK.

MRC Laboratories, Fajara, The Gambia.

Diamantina Institute for Cancer, Immunology and Metabolic Medicine, Princess Alexandra Hospital, University of Queensland, Woolloongabba, Qld 4102, Australia.

Botnar Research Centre, University of Oxford, Headington, Oxford OX3 7BN, UK.

Department of Medicine, Division of Medical Sciences, Institute of Biomedical Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.

Section of Cancer Genetics, Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK.

Cancer Genome Project, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge CB2 2QQ, UK.

Present address: Illumina Cambridge, Chesterford Research Park, Little Chesterford, Nr Saffron Walden, Essex CB10 1XL, UK.

See Supplementary Information for details.

Affiliations for participants
Affiliations for participants

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