Confident identification of subgroups from SNP testing in RCTs with binary outcomes

Abstract

In modern drug development, genotype information becomes more frequently collected in randomized controlled trials (RCTs) for individualized risk prediction and personalized medicine development. Finding single nucleotide polymorphisms (SNPs) that are predictive of differential treatment efficacy, measured by a clinical outcome, is fundamentally different and more challenging than the traditional association test for a quantitative trait. With the objective to confidently identify and infer genetic subgroups with enhanced treatment efficacy from a large RCT for an eye disease, age-related macular degeneration (AMD), where the clinical endpoint is binary (progressed or not), we propose a novel SNP-testing procedure for binary clinical outcomes. Specifically, we formulate four contrasts to simultaneously assess all possible genetic effects on a logic-respecting efficacy measure, the relative risk (between treatment and control). Our method controls both within- and across-SNP multiplicity rigorously. We then use real genotype data to perform chromosome-wide simulations to evaluate our method performance and to provide practical recommendations. Finally, we apply the proposed method to perform a genome-wide SNP testing for the AMD trial and successfully identify multiple gene regions with genetic subgroups exhibiting enhanced efficacy in terms of decreasing the AMD progressionrate.