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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.

SUBMITTER: Wang Q

PROVIDER: S-EPMC4514922 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles.

Wang Quan Q Yu Hui H Zhao Zhongming Z Jia Peilin P

Bioinformatics (Oxford, England) 20150324 15

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 ...[more]

PMID: 25805723

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