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Meta-analysis of genome-wide association studies is increasingly popular andmany meta-analytic methods have been recently proposed. A majority ofmeta-analytic methods combine information from multiple studies by assumingthat studies are independent since individuals collected in one study areunlikely to be collected again by another study. However, it has becomeincreasingly common to utilize the same control individuals among multiplestudies to reduce genotyping or sequencing cost. This causes those studies thatshare the same individuals to be dependent, and spurious associations may ariseif overlapping subjects are not taken into account in a meta-analysis. In thispaper, we propose a general framework for meta-analyzing dependent studies withoverlapping subjects. Given dependent studies, our approach "decouples" thestudies into independent studies such that meta-analysis methods assumingindependent studies can be applied. This enables many meta-analysis methods,such as the random effects model, to account for overlapping subjects. Anotheradvantage is that one can continue to use preferred software in the analysispipeline which may not support overlapping subjects. Using simulations and theWellcome Trust Case Control Consortium data, we show that our decouplingapproach allows both the fixed and the random effects models to account foroverlapping subjects while retaining desirable false positive rate and power.

A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping