Meta-analysis of genome-wide association summary statistics, in contrast to direct analysis of pooled individual-level data, alleviates common concerns with privacy of study participants and avoids cumbersome integration of genotype and phenotypic data from different studies. Meta-analysis allows for custom analyses of individual studies to conveniently account for population substructure, the presence of related individuals, study-specific covariates and many other ascertainment-related issues. It has been shown that meta-analysis of summary statistics is as efficient (in terms of statistical power) as pooling individual-level data across studies, but much less cumbersome (Lin and Zeng, 2009). Since GWAS routinely examine evidence for association at millions of directly genotyped and imputed SNPs across dozens or even hundreds of individual studies, it is important to use a fast and flexible tool to perform meta-analysis.
