In genetic association studies, meta-analyses are widely used to increase the statistical power by aggregating information from multiple studies. In meta-analyses, participating studies often share the same individuals due to the shared use of publicly available control data or accidental recruiting of the same subjects. As such overlapping can inflate false positive rate, overlapping subjects are traditionally split in the studies prior to meta-analysis, which requires access to genotype data and is not always possible. Fortunately, recently developed meta-analysis methods can systematically account for overlapping subjects at the summary statistics level.