Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

ABSTRACT: Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test const

SUBMITTER: Hao X 

PROVIDER: S-EPMC5805369 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature