Provides code and example data for analyzing GWAS data with a network-based approach. Finds modules of genes that are enriched for GWAS signal and have high confidence biological coherence.
Analyzing GWAS data in the context of biological pathways helps us understand how genetic variation influences phenotype and increases power to find associations. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine GWAS results with genetic networks to infer the key phenotype-modulating subnetworks combat these issues, but have primarily been limited to network definitions with yes/no labels for gene-gene interactions. A recent method (EW_dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this paper, we combine an algorithm for weighted-edge module searching and a probabilistic gene interaction network (STRING) in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and highly probable biologic coherence. Our method builds on EW_dmGWAS but does not require a secondary expression dataset and performs better in three test cases.
Sep 30, 2016
Provides code and example data for finding dense modules of GWAS data within the STRING interaction network. This code is licensed under GPL 2.0 and may be modified or used by any other GPL 2.0 or 3.0 projects.See all Downloads