Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA.

ABSTRACT: Summary:Integration of genetic studies for multiple phenotypes is a powerful approach to improving the identification of genetic variants associated with complex traits. Although it has been shown that leveraging shared genetic basis among phenotypes, namely pleiotropy, can increase statistical power to identify risk variants, it remains challenging to effectively integrate genome-wide association study (GWAS) datasets for a large number of phenotypes. We previously developed graph-GPA, a Bayesian hierarchical model that integrates multiple GWAS datasets to boost statistical power for the identification of risk variants and to estimate pleiotropic architecture within a unified framework. Here we propose a novel improvement of graph-GPA which incorporates external knowledge about phenotype-

SUBMITTER: Kim HJ 

PROVIDER: S-EPMC6248798 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature