Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies

Abstract

Meta-analysis of genome-wide association studies (GWAS) has become a useful tool to identify genetic variants that are associated with complex human diseases. To control spurious associations between genetic variants and disease that are caused by population stratification, double genomic control (GC) correction for population stratification in meta-analysis for GWAS has been implemented in the software METAL and GWAMA and is widely used by investigators. In this research, we conducted extensive simulation studies to evaluate the double GC correction method in meta-analysis and compared the performance of the double GC correction with that of a principal components analysis (PCA) correction method in meta-analysis. Results show that when the data consist of population stratification, using double GC correction method can have inflated type I error rates at a marker with significant allele frequency differentiation in the subpopulations (such as caused by recent strong selection). On the other hand, the PCA correction method can control type I error rates well and has much higher power in meta-analysis compared to the double GC correction method, even though in the situation that the casual marker does not have significant allele frequency difference between the subpopulations. We applied the double GC correction and PCA correction to meta-analysis of GWAS for two real datasets from the Atherosclerosis Risk in Communities (ARIC) project and the Multi-Ethnic Study of Atherosclerosis (MESA) project. The results also suggest that PCA correction is more effective than the double GC correction in meta-analysis.

Exploring efficient linear mixed models to detect quantitative trait locus-by-environment interactions

“…, ) to correct marker P- values differs according to allele frequency and correlation with other covariates, and therefore use of a uniform overall inflation factor ( i.e. , ) may results in a loss of power ( Price et al 2006 ; Wang et al 2012 ; Moore et al 2019 ). Notably, ≈ 1 is theoretically correct for this study because we simulated only three major QTLs ( Table 3 ) and, therefore, most P- values should follow the expected distribution.…”

Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions

“…However, the applicability of a given inflation factor (i.e., λ GC ) to correct marker p values differs according to allele frequency and correlation with other covariates, and therefore use of a uniform overall inflation factor (i.e., λ GC ) may results in a loss of power (Price et al . 2006; Wang et al . 2012; Moore et al .…”

Power considerations for λ inflation factor in meta-analyses of genome-wide association studies

“…Accumulating evidence from recently published studies suggests that GC may not be effective in controlling population stratification in association studies (Edwards & Gao, 2012;Wang et al, 2012). This problem may be aggravated under meta-analysis settings where a double GC correction method might lead to more prominent inflation of type I error rates at a marker with significant allele frequency differentiation in subpopulations generated by recent strong selection (Bouaziz et al, 2011;Edwards & Gao, 2012;Wang et al, 2012).…”

“…Accumulating evidence from recently published studies suggests that GC may not be effective in controlling population stratification in association studies (Edwards & Gao, 2012;Wang et al, 2012). This problem may be aggravated under meta-analysis settings where a double GC correction method might lead to more prominent inflation of type I error rates at a marker with significant allele frequency differentiation in subpopulations generated by recent strong selection (Bouaziz et al, 2011;Edwards & Gao, 2012;Wang et al, 2012). Conversely, alternative methods, including principal component analysis (PCA) correction and Bayesian semiparametric algorithm for inferring population structure, could control type I error rates and yield much higher power in meta-analyses compared to the double GC correction method (Bouaziz et al, 2011;Edwards & Gao, 2012;Majumdar et al, 2013).…”