Convex combination sequence kernel association test for rare-variant studies.

ABSTRACT: We propose a novel variant set test for rare-variant association studies, which leverages multiple single-nucleotide variant (SNV) annotations. Our approach optimizes a convex combination of different sequence kernel association test (SKAT) statistics, where each statistic is constructed from a different annotation and combination weights are optimized through a multiple kernel learning algorithm. The combination test statistic is evaluated empirically through data splitting. In simulations, we find our method preserves type I error at α=2.5×10-6 and has greater power than SKAT(-O) when SNV weights are not misspecified and sample sizes are large ( N≥5,000 ). We utilize our method in the Framingham Heart Study (FHS) to identify SNV sets associated with fasting glucose. While we are unable t

SUBMITTER: Posner DC 

PROVIDER: S-EPMC7205561 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature