A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics.

ABSTRACT: While genome-wide association studies (GWASs) have been widely used to uncover associations between diseases and genetic variants, standard SNP-level GWASs often lack the power to identify SNPs that individually have a moderate effect size but jointly contribute to the disease. To overcome this problem, pathway-based GWASs methods have been developed as an alternative strategy that complements SNP-level approaches. We propose a Bayesian method that uses the generalized fused hierarchical structured variable selection prior to identify pathways associated with the disease using SNP-level summary statistics. Our prior has the flexibility to take in pathway structural information so that it can model the gene-level correlation based on prior biological knowledge, an important feature that mak

SUBMITTER: Yang Y 

PROVIDER: S-EPMC7690328 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature