Convex combination sequence kernel association test for rare-variant studies

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 $\alpha =2.5\times 10^{-6}$ and has greater power than SKAT(-O) when SNV weights are not misspecified and sample sizes are large ( $N\ge 5,000$ ). 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 ( $p<10^{-7}$ ) in 6,419 FHS participants, our method identifies suggestive associations between fasting glucose and rare variants near ROCK2 ( $p=2.1\times 10^{-5}$ ) and within CPLX1 ( $p=5.3\times 10^{-5}$ ). 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.