An Ant Colony Optimization and Tabu List Approach to the Detection of Gene-Gene Interactions in Genome-Wide Association Studies [Research Frontier]
IEEE Computational Intelligence Magazine
Institute of Electrical and Electronics Engineers (IEEE)
This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.
© 2015 IEEE. 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.
The work contained in this paper was supported by an EPSRC First Grant (EP/J007439/1). This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113.
Vol. 10, Iss. 4, pp. 54 - 65