A Bayesian hierarchically structured prior for rare-variant association testing.

ABSTRACT: Although genome-wide association studies have been widely used to identify associations between complex diseases and genetic variants, standard single-variant analyses often have limited power when applied to rare variants. To overcome this problem, set-based methods have been developed with the aim of boosting power by borrowing strength from multiple rare variants. We propose the adaptive hierarchically structured variable selection (HSVS-A) before test for association of rare variants in a set with continuous or dichotomous phenotypes and to estimate the effect of individual rare variants simultaneously. HSVS-A has the flexibility to integrate a pairwise weighting scheme, which adaptively induces desirable correlations among variants of similar significance such that we can borrow infor

SUBMITTER: Yang Y 

PROVIDER: S-EPMC8597248 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature