English

Abstract

In recent years there has been much focus on the use of single nucleotide polymorphism (SNP) fine genome mapping to identify causative mutations for traits of interest; however, many studies focus only on the marginal effects of markers, ignoring potential gene interactions. Simulation studies have shown that this approach may not be powerful enough to detect important loci when gene interactions are present. Although several attempts have been made to study potential gene interaction, the number of SNP markers considered in these studies is often limited. Given the prohibitive computation cost of modeling interactions in studies involving a large number SNP, there is a need for methods that can account for potential gene interactions in a computationally efficient manner to be developed. In this study, the ant colony optimization algorithm (ACA) and logistic regression on large number of SNP genotypes were used. Our procedure was compared to sliding window (SW/H), and single locus genotype association (RG) methods used in haplotype analyses. A binary trait simulated using an epistatic model and HapMap ENCODE SNP genotypes was used to evaluate each algorithm. Results show that the ACA outperformed SW/H and RG under several simulation scenarios, yielding substantial increases in power to detect genomic regions associated with the simulated trait.