Jun 10, 2017

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

PLoS Genetics
Yiming HuHongyu Zhao

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 its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk ...Continue Reading

Mentioned in this Paper

Genome-Wide Association Study
EXOSC3 gene
Diabetes Mellitus, Non-Insulin-Dependent
Chromosomes, Human, Pair 1
Treatment Protocols
Meta-Analysis (Publications)
Genome
Coronary Artery Disease
Stratification
GZMK gene

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