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. 2011 Jan 1;27(1):95-102.
doi: 10.1093/bioinformatics/btq615. Epub 2010 Nov 2.

dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks

Peilin Jia  1 , Siyuan Zheng, Jirong Long, Wei Zheng, Zhongming Zhao
Affiliations

dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks

Peilin Jia et al. Bioinformatics. .

Abstract

Motivation: An important question that has emerged from the recent success of genome-wide association studies (GWAS) is how to detect genetic signals beyond single markers/genes in order to explore their combined effects on mediating complex diseases and traits. Integrative testing of GWAS association data with that from prior-knowledge databases and proteome studies has recently gained attention. These methodologies may hold promise for comprehensively examining the interactions between genes underlying the pathogenesis of complex diseases.

Methods: Here, we present a dense module searching (DMS) method to identify candidate subnetworks or genes for complex diseases by integrating the association signal from GWAS datasets into the human protein-protein interaction (PPI) network. The DMS method extensively searches for subnetworks enriched with low P-value genes in GWAS datasets. Compared with pathway-based approaches, this method introduces flexibility in defining a gene set and can effectively utilize local PPI information.

Results: We implemented the DMS method in an R package, which can also evaluate and graphically represent the results. We demonstrated DMS in two GWAS datasets for complex diseases, i.e. breast cancer and pancreatic cancer. For each disease, the DMS method successfully identified a set of significant modules and candidate genes, including some well-studied genes not detected in the single-marker analysis of GWA studies. Functional enrichment analysis and comparison with previously published methods showed that the genes we identified by DMS have higher association signal.

Availability: dmGWAS package and documents are available at http://bioinfo.mc.vanderbilt.edu/dmGWAS.html.

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Figures

Fig. 1.
Fig. 1.
dmGWAS workflow.
Fig. 2.
Fig. 2.
PPI subnetwork for breast cancer using the CGEMS GWAS dataset. (A) The module having the best score. (B) PPI subnetwork constructed using the top 10 modules. (C) The subnetwork containing 166 candidate genes from the top 93 modules (top 1% of all modules generated). The darkness of a node is proportional to its P-value.
Fig. 3.
Fig. 3.
Module size using different values of r. (A) Impact of r on breast cancer GWAS dataset. (B) Impact of r on pancreatic cancer GWAS dataset.
See this image and copyright information in PMC

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