An Ant Colony Optimization and Tabu List Approach to the Detection of Gene-Gene Interactions in Genome-Wide Association Studies [Research Frontier]

Abstract

In this paper, a novel ant colony optimization and tabu list approach for the discovery of gene-gene interactions in genome-wide association study data is proposed. The method is tested on a number of diseases drawn from the large established database, the Wellcome Trust Case Control Consortium which contains hundreds of thousands of small DNA changes known as single nucleotide polymorphisms. To analyze full scale genome-wide association study data, the standard ant colony optimization algorithm has been adapted, with tournament path selection, a subset based approach, and tabu list included in the algorithm. These modifications, in addition to the use of a statistical test of significance of single nucleotide polymorphism interactions as a fitness function, greatly increase execution speeds and permit the discovery of combinations of single nucleotide polymorphisms that can discriminate cases and controls. The methodology is applied to several large-scale genome-wide association study disease datasets namely, inflammatory bowel disease, rheumatoid arthritis, type I diabetes and type II diabetes patients to discover putative gene-gene interactions in reasonable time on modest hardware.