TY - JOUR
T1 - Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
AU - The Wellcome Trust Case Control Consortium
AU - Breast Cancer Susceptibility Collaboration (UK)
AU - The Biologics in RA Genetics and Genomics Study Syndicate (BRAGGS) Steering Committee
AU - Burton, Paul R.
AU - Clayton, David G.
AU - Cardon, Lon R.
AU - Craddock, Nick
AU - Deloukas, Panos
AU - Duncanson, Audrey
AU - Kwiatkowski, Dominic P.
AU - McCarthy, Mark I.
AU - Ouwehand, Willem H.
AU - Samani, Nilesh J.
AU - Todd, John A.
AU - Donnelly, Peter
AU - Barrett, Jeffrey C.
AU - Davison, Dan
AU - Easton, Doug
AU - Evans, David
AU - Leung, Hin Tak
AU - Marchini, Jonathan L.
AU - Morris, Andrew P.
AU - Spencer, Chris C.A.
AU - Tobin, Martin D.
AU - Attwood, Antony P.
AU - Boorman, James P.
AU - Cant, Barbara
AU - Everson, Ursula
AU - Hussey, Judith M.
AU - Jolley, Jennifer D.
AU - Knight, Alexandra S.
AU - Koch, Kerstin
AU - Meech, Elizabeth
AU - Nutland, Sarah
AU - Prowse, Christopher V.
AU - Stevens, Helen E.
AU - Taylor, Niall C.
AU - Walters, Graham R.
AU - Walker, Neil M.
AU - Watkins, Nicholas A.
AU - Winzer, Thilo
AU - Jones, Richard W.
AU - McArdle, Wendy L.
AU - Ring, Susan M.
AU - Strachan, David P.
AU - Pembrey, Marcus
AU - Breen, Gerome
AU - St. Clair, David
AU - Caesar, Sian
AU - Gordon-Smith, Katherine
AU - Jones, Lisa
AU - Fraser, Christine
AU - Zeggini, Eleftheria
N1 - Publisher Copyright:
© 2007 Nature Publishing Group.
PY - 2007/6/7
Y1 - 2007/6/7
N2 - There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. Wedescribeajoint 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 × 10-7: 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 5×10-7) 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 generateda genome-wide genotype database for future studiesofcommon diseasesinthe 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.
AB - There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. Wedescribeajoint 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 × 10-7: 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 5×10-7) 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 generateda genome-wide genotype database for future studiesofcommon diseasesinthe 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.
UR - https://www.scopus.com/pages/publications/84969213492
U2 - 10.1038/nature05911
DO - 10.1038/nature05911
M3 - Article
C2 - 17554300
AN - SCOPUS:84969213492
SN - 0028-0836
VL - 447
SP - 661
EP - 678
JO - Nature
JF - Nature
IS - 7145
ER -
SDG 3 Good Health and Well-being