Towards Identification of Human Disease Phenotype-Genotype Association via a Network-Module Based Method

Abstract

Inspired by recent discovery that human disease phenome shows a modular organization on the genetic landscape, we introduce a network-module based method towards phenotype-genotype association inference and disease gene identification. This approach integrates protein-protein interaction network, phenotype similarity network and known phenotype-genotype associations, and then decomposes the resulted assembled network into modules (or communities) wherein we identified and prioritized the disease genes from the candidates within the loci associated with the query disease using a linear regression model and concordance score. For the known phenotype-gene associations in the OMIM database, we used the leave-one-out validation to evaluate the feasibility of our method, and successfully ranked known disease genes at top 1 in 887 out of 1807 cases. Moreover, applying this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.