Epistasis Detection using Heterogeneous Bio-molecular Network

Detecting epistasis between single nucleotide polymorphisms (SNPs) is crucial to explain the missing heritability of complex diseases in genome-wide association studies (GWAS). Many methods have been proposed for detecting SNP interactions, most of them only focus on reducing search space but ignore the relations of SNP with other bio-molecules. In this paper, we proposed a heterogeneous molecular network based method called EpiNet to detect high-order SNP interactions. EpiNet firstly uses samples (case/control) data to construct an SNP statistical network to capture the SNP distribution information in samples. In addition, EpiNet applies meta-path based similarity search in a heterogeneous molecular network composed with SNPs, genes, lncRNAs, miRNAs and diseases to construct an SNP relational network, which mines diverse associations between molecules and diseases to supplement the SNP statistical network and search the most associated SNPs. Next, EpiNet combines the two networks to get a composite SNP network, utilizes modularity based clustering to group all SNPs into different clusters and finally detects SNP interactions within each cluster. Simulation experiments on two-locus and three-locus disease models show that EpiNet has a better performance than competitive methods, even without the heterogeneous network. It also shows expressive power to identify high-order SNP interactions from real WTCCC breast cancer data.