EpiHNet: Detecting epistasis by heterogeneous molecule network.

Abstract

Epistasis between single nucleotide polymorphisms (SNPs) plays an important role in elucidating the missing heritability of complex diseases. Diverse approaches have been invented for detecting SNP interactions, but they canonically neglect the important and useful connections between SNPs and other bio-molecules (i.e., miRNAs and lncRNAs). To comprehensively model these disease related molecules, a heterogeneous bio-molecular network based solution EpiHNet is introduced for high-order SNP interactions detection. EpiHNet firstly uses case/control data to construct an SNP statistical network, and meta-path based similarity on the heterogeneous network composed with SNPs, genes, lncRNAs, miRNAs and diseases to define another SNP relational network. The SNP relational network can explore and exploit different associations between molecules and diseases to complement the SNP statistical network and search the significantly associated SNPs. Next, EpiHNet integrates these two networks into a composite network, applies the modularity based clustering with fast search strategy to divide SNP nodes into different clusters. After that, it detects SNP interactions based on SNP combinations derived from each cluster. Synthetic experiments on diverse two-locus and three-locus disease models manifest that EpiHNet outperforms competitive baselines, even without the heterogeneous network. For real WTCCC breast cancer data, EpiHNet also demonstrates expressive results on detecting high-order SNP interactions.