Meta-analysis is a useful tool to increase the statistical power to detect gene-disease associations by combining results from the original and subsequent replication studies. Recently, consortium-based meta-analyses of several genome-wide association (GWA) data sets have discovered new susceptibility genes of common diseases. We reviewed the process and the methods of meta-analysis of genetic association studies. To conduct and report a transparent meta-analysis, the search strategy, the inclusion or exclusion criteria of studies and the statistical procedures should be fully described. Assessing consistency or heterogeneity of the associations across studies is an important aim of meta-analysis. Random effects model (REM) meta-analysis can incorporate between-study heterogeneity. We illustrated properties of test for and measures of between-study heterogeneity and the effect of between-study heterogeneity on conclusions of meta-analyses through simulations. Our simulation shows that the power of REM meta-analysis of GWA data sets (total case-control sample size: 5000-20,000) to detect a small genetic effect (odds ratio (OR)=1.4 under dominant model) decreases as between-study heterogeneity increases and then the mean of OR of the simulated meta-analyses passing the genome-wide significance threshold would be upwardly biased (winner's curse phenomenon). Addressing observed between-study heterogeneity may be challenging but give a new insight into the gene-disease association.