Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies - Wang, Shudong; Chen, Wenan; Chen, Xiangning; Hu, Fengjiao; Archer, Kellie J.; Liu, Nianjun; Sun, Shumei; Gao, Guimin (Frontiers in Genetics, 2012)
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 a principal components analysis (PCA) correction method in meta-analysis. Results show that when the data consist of subpopulations, 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 generated much higher power in meta-analysis compared to the double GC correction method. We applied the double GC correction and PCA correction to meta-analysis of GWAS for two real data sets: the Atherosclerosis Risk in Communities (ARIC) and the Multi-Ethnic Study of Atherosclerosis (MESA). The results also suggest that PCA correction is more effective than the double GC correction in meta-analysis.