GWAS meta-analysis has multiple benefits over standard replication, just as standard replication has multiple benefits over a single study. Instead of replicating only a handful of significant variants from the original study, meta-analysis allows one to increase sample size for all variants. This increases the odds that novel variants not significant in the original study will be discovered and, once the summary statistics for all variants are released (e.g., to dbGaP, or hosted on a website), it becomes a valuable (quasi-) public resource for other investigators interested in the same trait/disorder. Meta-analysis is an elegant solution when multiple replication datasets exist. Instead of vote-counting or other crude methods (e.g., 1 replicated, 1 did not), meta-analysis provides an estimate of the overall effect size and p-value. Meta-analysis allows for estimating many secondary statistics of interest, such as effect size heterogeneity and effect size moderators. Finally, GWAS meta-analysis in genetic association studies has led to a sea change in how genetic association studies are done. Instead of lone investigators guarding their own valuable data, scientists have begun sharing their data to accumulate
