Convex combination sequence kernel association test for rare‐variant studies. Issue 4 (26th February 2020) - Posner, Daniel C.; Lin, Honghuang; Meigs, James B.; Kolaczyk, Eric D.; Dupuis, Josée
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 × 1 0 − 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 to detect any genome‐wide significant associations between fasting glucose and 4‐kb windows of rare variants ( p < 1 0 − 7 ) in 6, 419 FHS participants, our method identifies suggestive associations between fasting glucose and rare variants near ROCK2 ( p = 2.1 × 1 0 − 5 ) and within CPLX1 ( p = 5.3 × 1 0 − 5 ). These two genes were previously reported to be involved in obesity‐mediated insulin resistance and glucose‐induced insulin secretion by pancreatic beta‐cells, respectively. These findings will need to be replicated in other cohorts and validated by functional genomic studies.