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.