Identifying disease associated genes by network propagation

Abstract

Background: Genome-wide association studies have identified many individual genes associated with complex traits. However, pathway and network information have not been fully exploited in searches for genetic determinants, and including this information may increase our understanding of the underlying biology of common diseases.

Results: In this study, we propose a framework to address this problem in a principled way, with the underlying hypothesis that complex disease operates through multiple connected genes. Associations inferred from GWAS are translated into prior scores for vertices in a protein-protein interaction network, and these scores are propagated through the network. Permutation is used to select genes that are guilty-by-association and thus consistently obtain high scores after network propagation. We apply the approach to data of Crohn's disease and call candidate genes that have been reported by other independent GWAS, but not in the analysed data set. A prediction model based on these candidate genes show good predictive power as measured by Area Under the Receiver Operating Curve (AUC) in 10 fold cross-validations.

Conclusions: Our network propagation method applied to a genome-wide association study increases association findings over other approaches.

Figure 1

Number of reported genes. Overlap of validated genes among top 150 genes for each method, all of them map SNPs to genes within 10kb boundary. Validated genes denote genes that are reported as candidate in later GWAS studies. Method GWAS means ranking genes by minimum p value.