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. 2014 Jul;197(3):1045-9.
doi: 10.1534/genetics.114.164285. Epub 2014 Apr 29.

Improving the power of GWAS and avoiding confounding from population stratification with PC-Select

George Tucker  1 , Alkes L Price  2 , Bonnie Berger  3
Affiliations

Improving the power of GWAS and avoiding confounding from population stratification with PC-Select

George Tucker et al. Genetics. 2014 Jul.

Abstract

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models.

Keywords: GWAS; mixed models; population stratification.

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Figures

Figure 1
Figure 1
(A and B) Comparison of power for linear regression, linear regression with PCs, standard LMM, FaST-LMM Select, and PC-Select on simulated genotypes and phenotypes (A) and real genotypes and simulated phenotypes (B) with and without population stratification as the fraction of casual SNPs (P = 0.05, 0.005) varies. To measure power, we plot the mean Wald statistic on test causal SNPs. In all cases, PC-Select has the highest power of the methods that do not inflate statistics.
See this image and copyright information in PMC

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