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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 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=2.1×10-5 ) and within CPLX1 ( p=5.3×10-5 ). 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.

SUBMITTER: Posner DC

PROVIDER: S-EPMC7205561 | biostudies-literature |

REPOSITORIES: biostudies-literature

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