Summary: 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.

Availability and implementation: The algorithm of EW_dmGWAS is implemented in the R package dmGWAS_3.0 and is available at http://bioinfo.mc.vanderbilt.edu/dmGWAS.

Contact: zhongming.zhao@vanderbilt.edu or peilin.jia@vanderbilt.edu

Supplementary information: Supplementary materials are available at Bioinformatics online.