DESeeker: Detecting Epistatic Interactions Using a Two-Stage Differential Evolution Algorithm

Abstract

Epistatic interactions are generally defined as the interactions between different single-nucleotide polymorphisms (SNPs). Identifying epistatic interactions is important for determining the individual susceptibility of complex diseases. In large-scale association studies, finding epistatic interactions in the large volume of SNP data is a challenging issue. Since the current search approaches are confronted with the serious problem of computational burden, developing an efficient algorithm for dealing with the intensive computing problem would be significant. In this paper, a novel differential evolution-based algorithm DEseeker is proposed to detect epistatic interactions. DEseeker, combined with a local search and a self-adapting parameter tuning strategy, employs a two-stage design of DE to enhance its search capability. DEseeker is compared with the other recent algorithms on a set of simulated datasets and a real biological dataset. The experimental results on the simulated datasets show that the proposed algorithm is superior to the other compared algorithms in terms of detection power. The discovery of the real biological dataset demonstrates that the proposed algorithm is promising for practical disease prognosis.