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Motivation: Genome-wide association studies (GWAS) have successfullyidentified thousands of genetic risk loci for complex traits and diseases. Mostof these GWAS loci lie in regulatory regions of the genome and the gene throughwhich each GWAS risk locus exerts its effects is not always clear. Manycomputational methods utilizing biological data sources have been proposed toidentify putative casual genes at GWAS loci; however, these methods can beimproved upon. Results: We present the Relations-Maximization Method, a densemodule searching method to identify putative causal genes at GWAS loci throughthe generation of candidate sub-networks derived by integrating associationsignals from GWAS data into the gene co-regulation network. We employ ourmethod in a chronic obstructive pulmonary disease GWAS. We perform anextensive, comparative study of Relations-Maximization Method's performanceagainst well-established baselines.

Network Based Approach to Gene Prioritization at Genome-Wide Association Study Loci