A paper by Pickrell [9] combines GWAS data with functional data to identify enriched and depleted functional categories, and leverages the resulting model to increase GWAS power. The method, called fgwas, is effective at increasing association mapping power and identifies many interesting enrichments in the published paper. In our simulations we saw good null calibration, but low power to detect enrichment. Of the four simulations with true enrichment, fgwas performed best for when identifying enrichment of the smaller category (fetal brain) in the more polygenic trait (pcausal = 0.05); however, stratified LD score regression had higher power than fgwas in all four situations. Fgwas could have an advantage for annotations smaller than the ones tested in this manuscript, but we do not explore that issue here.
