Bioinformatics and Systems Medicine Laboratory

 dmGWAS 3.0

On October 4, 2014, we released an upgraded version, dmGWAS 3.0, which implements a new algorithm EW_dmGWAS: Edge-weighted dense module search for genome-wide association studies and gene expression profiles. In dmGWAS 3.0, the algorithm EW_dmGWAS introduces the following features:

  1. The new algorithm, EW_dmGWAS, combines node weight, which are computed based on GWAS signals, and edge weight, which are computed based on gene expression data.
  2. Allow tissue-specific gene expression profile. For example, for breast cancer GWAS analysis, gene expression data from breast cancer patients can be used; for schizophrenia, gene expression data in brain tissues can be used.
  3. Edge weights can be computed based on differential gene co-expression (DGCE) patterns, i.e., by comparing tumor to normal samples.
  4. In dmGWAS 3.0, users can still apply the "old" models that use only node weight from GWAS signal (see the user guide below).
  5. dmGWAS 3.0 is compatible to igraph 0.6.x.

All old versions, i.e., <3.0, are available from here.

gene expression profile
 Current Version
 Documentation
 Citation
  • Wang Q, Yu H, Zhao Z, and Jia P (2015) EW_dmGWAS: Edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics online access
  • 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
 Contact
  • Peilin Jia: peilin.jia@vanderbilt.edu
  • Zhongming Zhao: zhongming.zhao@uth.tmc.edu
UTHealth

Copyright 2008-Present - The University of Texas Health Science Center at Houston (UTHealth)

Web File Viewing | How to Report, Fraud, Waste and Abuse | State of Texas | Statewide Search | Texas Homeland Security | Site Policies