Detection of complex interactions of multi-locus SNPS

Detection of interacting SNPs predictive of complex disease will help identify individuals at high risk, make personalized treatment possible, and provide novel insights into the pathophysiology of the conditions in question. Although the interaction effect of multi-locus SNPs is widely expected, the existing strategies have limited power in detecting SNPs with interaction effects. This paper presents a new method (SCA-HCIG) to detect complex interaction effects. The method is tested on a realistic simulated data with 17 embedded ground-truth SNPs under 5 interaction models. Compared to six existing methods, SCA-HCIG achieves the best result in terms of both high sensitivity and specificity.