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Bioinformatics. 2015 Aug 1;31(15):2591-4. doi: 10.1093/bioinformatics/btv150. Epub 2015 Mar 24.

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

Author information

1
Department of Biomedical Informatics.
2
Department of Biomedical Informatics, Center for Quantitative Sciences, Department of Psychiatry and Department of Cancer Biology, Vanderbilt University, Nashville, TN 37232, USA.
3
Department of Biomedical Informatics, Center for Quantitative Sciences.

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.

PMID:
25805723
PMCID:
PMC4514922
DOI:
10.1093/bioinformatics/btv150
[Indexed for MEDLINE]
Free PMC Article

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