A fast algorithm to optimize SNP prioritization for gene‐gene and gene‐environment interactions

Detection of gene-environment interactions using an exhaustive search necessarily raises the multiple hypothesis problem. While frequently used to control for experiment-wise type I error, Bonferroni correction is overly conservative and results in reduced statistical power. We have previously shown that prioritizing SNPs on the basis of heterogeneity in quantitative trait variance per genotype leads to increased power to detect genetic interactions. Our proposed method, variance prioritization (VP), selects SNPs having significant heterogeneity in variance per genotype using a pre-determined P-value threshold. We now suggest prioritizing SNPs individually such that the optimal heterogeneity of variance P-value is determined for each SNP. The large number of SNPs in genome-wide studies calls for a fast algorithm to output the optimal prioritization threshold for each SNP. In this report, we present such an algorithm, the Gene Environment Wide Interaction Search Threshold (GEWIST), and show that the use of GEWIST will increase power under a variety of interaction scenarios. Furthermore, by integrating over possible interaction effect sizes, we provide a framework to optimize prioritization in situations where interactions are a priori unknown.