Population structure, differential bias and genomic control in a large-scale, case-control association study

• Published in: Nature genetics Vol. 37; no. 11; pp. 1243 - 1246
• Main Authors: Clayton, David G, Todd, John A, Walker, Neil M, Smyth, Deborah J, Pask, Rebecca, Cooper, Jason D, Maier, Lisa M, Smink, Luc J, Lam, Alex C, Ovington, Nigel R, Stevens, Helen E, Nutland, Sarah, Howson, Joanna M M, Faham, Malek, Moorhead, Martin, Jones, Hywel B, Falkowski, Matthew, Hardenbol, Paul, Willis, Thomas D
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 01-11-2005
• Subjects: Adolescent; Bias; Biological and medical sciences; Case studies; Case-Control Studies; Deoxyribonucleic acid; Diabetes Mellitus, Type 1 - epidemiology; Diabetes Mellitus, Type 1 - genetics; Diagnosis; DNA; DNA - blood; False Positive Reactions; Fundamental and applied biological sciences. Psychology; Genetic aspects; Genetic screening; Genetics; Genetics of eukaryotes. Biological and molecular evolution; Genetics, Population; Genomics; Genotype; Health aspects; Humans; Lymphocytes - metabolism; Models, Genetic; Polymorphism, Single Nucleotide - genetics; Population structure; Risk factors; Single nucleotide polymorphisms; Type 1 diabetes; United Kingdom - epidemiology; United Kingdom; Case study; Population structure; Regulation(control); Association; Genomics
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng1653

The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure. In genetics the first of these, in the form of population structure, has dominated recent debate. 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 method by applying a variable downweighting to each SNP.