Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits.

ABSTRACT: Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use

SUBMITTER: Chun S 

PROVIDER: S-EPMC9829185 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature