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 singlevariant 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 information from potentially causal and noncausal rare variants to boost power. Simulation studies show that for both continuous and dichotomous phenotypes, HSVS-A is powerful when there are multiple causal rare variants, either in the same or opposite direction of effect, with the presence of a large number of noncausal variants.We also apply HSVS-A to the Wellcome Trust Case Control Consortium Crohn's disease data for testing the association of Crohn's disease with rare variants in pathways. HSVS-A identifies two pathways harboring novel protective rare variants for Crohn's disease.

A Bayesian hierarchically structured prior for gene‐based association testing with multiple traits in genome‐wide association studies

“…Compared to the conventional approach that tests single‐nucleotide polymorphisms (SNPs) individually, gene‐based tests are often more powerful due to the reduced burden of multiple testing and the information borrowed from SNPs in linkage disequilibrium (LD). However, current gene‐based tests are mostly limited to univariate analysis (Pan et al, 2014, 2015; Wu et al, 2011; Yang et al, 2018, 2020, 2021) that tests the association of a gene with only one trait at a time. Multiple correlated traits are often measured and studied together in the context of complex diseases.…”

“…In this study, we propose the multivariate hierarchically structured variable selection (HSVS‐M) model for testing the association of a gene with multiple traits. The HSVS framework was previously considered for univariate gene‐ and pathway‐based GWASs using summary statistics (Yang et al, 2018, 2020) and genotype data (Yang et al, 2021). We extend the HSVS framework in this study to multivariate gene‐based GWASs that require only summary statistics, which are typically meant for common (minor allele frequency [MAF] 5%) and low‐frequency (1% MAF 5%) variants.…”

“…However, current gene-based tests are mostly limited to univariate analysis (Pan et al, 2014(Pan et al, , 2015Wu et al, 2011;Yang et al, 2018Yang et al, , 2020Yang et al, , 2021 that tests the association of a gene with only one trait at a time. Multiple correlated traits are often measured and studied together in the context of complex diseases.…”