Nature Genetics 37, 1243 - 1246 (2005)
Published online: 9 October 2005; | doi:10.1038/ng1653
Population structure, differential bias and genomic control in a large-scale, case-control association studyDavid G Clayton1, Neil M Walker1, Deborah J Smyth1, Rebecca Pask1, Jason D Cooper1, Lisa M Maier1, Luc J Smink1, Alex C Lam1, Nigel R Ovington1, Helen E Stevens1, Sarah Nutland1, Joanna M M Howson1, Malek Faham2, Martin Moorhead2, Hywel B Jones2, Matthew Falkowski2, Paul Hardenbol2, Thomas D Willis2
& John A Todd11
Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, University of Cambridge, Cambridge Institute for Medical Research, Wellcome Trust/MRC Building, Cambridge, CB2 2XY, UK. 2
ParAllele BioScience, 7300 Shoreline Court, South San Francisco, California 94080, USA.
Correspondence should be addressed to David G Clayton david.clayton@cimr.cam.ac.uk or John A Todd john.todd@cimr.cam.ac.uk The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure1. In genetics the first of these, in the form of population structure, has dominated recent debate2,
3,
4. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method2,
3,
4,
5 by applying a variable downweighting to each SNP.
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