Bioinformatics and Systems Medicine Laboratory

 About dmGWAS

Genome-wide association studies (GWAS) have greatly expanded our knowledge of common diseases by discovering many susceptibility common variants. So far, analysis of genetic signal has been typically based on the single marker/gene approach. Gene set based analysis can be an alternative and effective approach to detecting the combined effect of multiple variants within a gene set from GWAS dataset(s).

dmGWAS is designed to identify significant protein-protein interaction (PPI) modules and, from which, the candidate genes for complex diseases by an integrative analysis of GWAS dataset(s) and PPI network. It implements a dense module searching method previously developed for gene expression data analysis (Ideker et al., 2002). We adapted the method specifically for GWAS datasets, including data preparation, integration, searching, and validation in GWAS permutation data. Specifically, we proposed two strategies to select modules for single GWAS and multiple GWAS datasets. In the later case, additional GWAS dataset(s) can be used for evaluation/validation of the modules identified by the primary (discovery) GWAS dataset.

 Current Version

  • (*Recommed) Release: dmGWAS version 2.4, May 1, 2014 (compatible with igraph ≥ 0.6.x, R platform ≥ 3.0)
  • Linux binary: dmGWAS_2.4.tar.gz
  • Install dmGWAS_2.4 in R: install.packages("PATH_TO/dmGWAS_2.4.tar.gz",repos=NULL,type="source")
Release Note
Bugs fixed in Version 2.3 in the function: zn.permutation, Aug 12, 2011
Bugs fixed in Version 2.2 in the function: zn.permutation, March 29, 2011
Bugs fixed in Version 2.1 in two functions: SNP2Gene.match and PCombine

 Previous Version
 GWAS resources
  • Ideker T, et al. (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18 Suppl 1, S233-240.
  • Jia P, Zheng S, Long J, Zheng W, and Zhao Z (2011) dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics 27(1):95-102 PubMed
  • Peilin Jia:
  • Zhongming Zhao:
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