A Novel Two-Stage Multi-objective Ant Colony Optimization Approach for Epistasis Learning

Abstract

Recently, genome-wide association study (GWAS) which aims to discover genetic effects in phenotypic traits is a hot issue in genetic epidemiology. Epistasis known as genetic interaction is an important challenge in GWAS since it explains most individual susceptibility to complex diseases and it is difficult to detect due to its non-linearity. Here we present a novel two-stage method based on multi-objective ant colony optimization for epistasis learning. We conduct a lot of experiments on a wide range of simulated datasets and compare the outcome of our method with some other recent epistasis learning methods like AntEpiSeeker, Bayesian epistasis association mapping (BEAM) and BOolean Operation-based Screening and Testing (BOOST) method, finding that our method has a high power and is time efficient to learn epistatic interactions. We also do experiments in the real Late-onset Alzheimer’s disease (LOAD) dataset and the results substantiate that our method has a potential in searching the suspicious epistasis in large scale real GWAS datasets.