Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease.

ABSTRACT: Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of fun

SUBMITTER: Kim SS 

PROVIDER: S-EPMC7721881 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature