Leveraging functional annotations in genetic risk prediction for human complex diseases.

ABSTRACT: Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy 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 the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained usin

SUBMITTER: Hu Y 

PROVIDER: S-EPMC5481142 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature