An Improved Ant Colony Optimization Algorithm for the Detection of SNP-SNP Interactions

An increasing number of studies have found that one of the most important factors for emergence and development of complex diseases is the interactions between SNPs, that is to say, epistasis or epistatic interactions. Though many efforts have been made for the detection of SNP-SNP interactions, the algorithm of such studies is still ongoing due to the computational and statistical complexities. In this work, we proposed an algorithm IACO based on ant colony optimization and a novel introduced fitness function Svalue, which combined both Bayesian networks and mutual information, for detecting SNP-SNP interactions. Furthermore, a memory based strategy is also employed to improve the performance of IACO, which effectively avoids ignoring the optimal solutions that have already been identified. Experiments of IACO are performed on both simulation data sets and a real data set of age-related macular degeneration (AMD). Results show that IACO is promising in detecting SNP-SNP interactions, and might be an alternative to existing methods for inferring epistatic interactions.