# Population Substructure and Control Selection in Genome-Wide Association Studies

 Title: Population Substructure and Control Selection in Genome-Wide Association Studies Author: Yu, Kai; Wang, Zhaoming; Li, Qizhai; Wacholder, Sholom; Hoover, Robert N.; Chanock, Stephen; Thomas, Gilles; Hunter, David J. Note: Order does not necessarily reflect citation order of authors. Citation: Yu, Kai, Zhaoming Wang, Qizhai Li, Sholom Wacholder, David J. Hunter, Robert N. Hoover, Stephen Chanock, and Gilles Thomas. 2008. Population substructure and control selection in genome-wide association studies. PLoS ONE 3(7): e2551. Full Text & Related Files: 2432498.pdf (617.4Kb; PDF) Abstract: Determination of the relevance of both demanding classical epidemiologic criteria for control selection and robust handling of population stratification (PS) represents a major challenge in the design and analysis of genome-wide association studies (GWAS). Empirical data from two GWAS in European Americans of the Cancer Genetic Markers of Susceptibility (CGEMS) project were used to evaluate the impact of PS in studies with different control selection strategies. In each of the two original case-control studies nested in corresponding prospective cohorts, a minor confounding effect due to PS (inflation factor $$\lambda$$ of 1.025 and 1.005) was observed. In contrast, when the control groups were exchanged to mimic a cost-effective but theoretically less desirable control selection strategy, the confounding effects were larger ($$\lambda$$ of 1.090 and 1.062). A panel of 12,898 autosomal SNPs common to both the Illumina and Affymetrix commercial platforms and with low local background linkage disequilibrium (pair-wise $$r^2$$<0.004) was selected to infer population substructure with principal component analysis. A novel permutation procedure was developed for the correction of PS that identified a smaller set of principal components and achieved a better control of type I error (to $$\lambda$$ of 1.032 and 1.006, respectively) than currently used methods. The overlap between sets of SNPs in the bottom 5% of p-values based on the new test and the test without PS correction was about 80%, with the majority of discordant SNPs having both ranks close to the threshold. Thus, for the CGEMS GWAS of prostate and breast cancer conducted in European Americans, PS does not appear to be a major problem in well-designed studies. A study using suboptimal controls can have acceptable type I error when an effective strategy for the correction of PS is employed. Published Version: doi:10.1371/journal.pone.0002551 Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2432498/pdf/ Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10198687 Downloads of this work: