A block-based evolutionary optimization strategy to investigate gene-gene interactions in genetic association studies

Multi-locus interactions in genetic association studies are believed to influence the heritability of a number of common diseases. In this study, we propose a discrete optimization strategy to improve the power and computational efficiency of multi-locus association analysis. We implemented an adaptive evolutionary algorithm in combination with a linkage disequilibrium-based discretization approach to reduce the need for exhaustive search for combinations by taking advantage of inherent genomic structure. The method was applied to several simulated disease models as well as to a real genome-wide association study. The results indicate that our method performs as well as or better than the most powerful competing methods for detecting true interactions, and it achieves this performance with improved computational efficiency.