EW-dmGWAS: Edge-weighted dense module search for genome-wide association studies and gene expression profiles

Abstract

We previously developed dmGWAS to search for dense modules in a human protein-protein interaction (PPI) network; it has since become a popular tool for network-assisted analysis of genome-wide association studies (GWAS). dmGWAS weights nodes by using GWAS signals. Here, we introduce an upgraded algorithm, EW-dmGWAS, to boost GWAS signals in a node- and edge-weighted PPI network. In EW-dmGWAS, we utilize condition-specific gene expression profiles for edge weights. Specifically, differential gene co-expression is used to infer the edge weights. We applied EW-dmGWAS to two diseases and compared it with other relevant methods. The results suggest that EW-dmGWAS is more powerful in detecting disease-associated signals.