A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.

ABSTRACT: Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Amon

SUBMITTER: Lu Q 

PROVIDER: S-EPMC5812911 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature