title: 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.
authors: Department of Biomedical Informatics.
authors: Quan
authors: Q
authors: Wang
authors: Department of Biomedical Informatics.
authors: Hui
authors: H
authors: Yu
authors: Department of Biomedical Informatics, Center for Quantitative Sciences, Department of Psychiatry and Department of Cancer Biology, Vanderbilt University, Nashville, TN 37232, USA.
authors: Zhongming
authors: Z
authors: Zhao
authors: Department of Biomedical Informatics, Center for Quantitative Sciences.
authors: Peilin
authors: P
authors: Jia
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D005260:Female; D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D060066:Protein Interaction Maps / Q000235:genetics*; D012559:Schizophrenia / Q000235:genetics* / Q000378:metabolism; D059467:Transcriptome