Powerful SNP Set Analysis for Case-Control GenomeWide Association Studies

Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects.

As an alternative analytical strategy, we propose grouping SNPs together into SNP sets based on proximity to genomic features such as genes or haplotype blocks, and then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel machine based test which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility as well as the ability to naturally adjust for covariate effects are important features of our test that make it appealing compared to individual SNP tests and existing multi-marker tests.