EpIntMC: Detecting Epistatic Interactions Using Multiple Clusterings

Detecting epistatic interaction between multiple single nucleotide polymorphisms (SNPs) is crucial to identify susceptibility genes associated with complex human diseases. Stepwise search approaches have been extensively studied to greatly reduce the search space for follow-up SNP interactions detection. However, most of these stepwise methods are prone to filter out significant polymorphism combinations and thus have a low detection power. In this paper, we propose a two-stage approach called EpIntMC, which uses multiple clusterings to significantly shrink the search space and reduce the risk of filtering out significant combinations for the follow-up detection. EpIntMC firstly introduces a matrix factorization based approach to generate multiple diverse clusterings to group SNPs into different clusters from different aspects, which helps to more comprehensively explore the genotype data and reduce the chance of filtering out potential candidates overlooked by a single clustering. In the search stage, EpIntMC applies Entropy score to screen SNPs in each cluster, and uses Jaccard similarity to merge the most similar clusters into candidate sets. After that, EpIntMC uses exhaustive search on these candidate sets to precisely detect epsitatic interactions. Extensive simulation experiments show that EpIntMC has a higher (comparable) power than related competitive solutions, and results on Wellcome Trust Case Control Consortium (WTCCC) dataset also expresses its effectiveness.