Washington University School of Medicine

Division of Biostatistics
Seminar Series Spring 2008

Population Substructure and Control Selection in Genome-wide Association Studies

Kai Yu
Biometry and Mathematical Statistics Branch
National Institute of Health

Friday, April 18, 2008, 12:30–1:30 pm

GEMS classroom, 3rd Floor in Shriner's Building
Coffee, tea, and cookies will be provided


Abstract

The importance of meeting the classic but burdensome epidemiologic criteria for control selection and of aggressive handling of population stratification (PS) are two intertwined questions in 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 their corresponding prospective cohorts, a minor confounding effect due to PS (inflation factor 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 (inflation factor 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 r2 < 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 inflation factor 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 GWAS in European Americans, PS does not appear to be a major problem in well-designed studies. Especially with effective correction of PS, use of suboptimal controls seems to have acceptable type I error performance.