Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

ABSTRACT: Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as

SUBMITTER: Hu Y 

PROVIDER: S-EPMC5482506 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature