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.
Highlights
Genome-wide association studies (GWAS) are an important approach for identifying genetic variants associated with complex human diseases
To control the spurious associations caused by population stratification in meta-analysis of GWAS, genomic control (GC) correction within each study has been used (Devlin and Roeder, 1999; Devlin et al, 2001; Reich and Goldstein, 2001; Devlin et al, 2004; Lindgren et al, 2009)
DATA SIMULATION To evaluate the performance of the single GC correction, double GC correction, and principal component analysis (PCA) correction in meta-analysis of data with population stratification, we simulated datasets on K case– control studies in a similar way to those described in Pritchard and Donnelly (2001) and Price et al (2006)
Summary
Genome-wide association studies (GWAS) are an important approach for identifying genetic variants associated with complex human diseases. To adjust for stratification in meta-analysis of GWAS, another popular approach is the principal component analysis (PCA) correction method that adjusts for stratification by top principal components (PCs) of genotype data within each study (Price et al, 2006; Wang et al, 2009; Qayyum et al, 2012). Double GC is not effective in meta-analysis suggest that when population stratification exists, using double GC correction can have inflated false positive error rates at markers with significant allele frequency differentiation in the subpopulations (such as caused by recent strong selection), and can have lower power than using the PCA method for stratification correction in meta-analysis
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