Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis.

Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping the SNPs underlying complex diseases, there is still difficulty in detecting epistatic interactions due to the lack of heuristic information to expedite the search process. In this study, a method EACO is proposed to detect epistatic interactions based on the ant colony optimization (ACO) algorithm, the highlights of which are the introduced heuristic information, fitness function, and a candidate solutions filtration strategy. The heuristic information multi-SURF* is introduced into EACO for identifying epistasis, which is incorporated into ant-decision rules to guide the search with linear time. Two functionally complementary fitness functions, mutual information and the Gini index, are combined to effectively evaluate the associations between SNP combinations and the phenotype. Furthermore, a strategy for candidate solutions filtration is provided to adaptively retain all optimal solutions which yields a more accurate way for epistasis searching. Experiments of EACO, as well as three ACO based methods (AntEpiSeeker, MACOED, and epiACO) and four commonly used methods (BOOST, SNPRuler, TEAM, and epiMODE) are performed on both simulation data sets and a real data set of age-related macular degeneration. Results indicate that EACO is promising in identifying epistasis.