Joint Modeling of Genetically Correlated Diseases and Functional Annotations Increases Accuracy of Polygenic Risk Prediction

• Activity Number: 667 - Statistical Genetics
• Type: Contributed
• Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Yiming Hu* and Qiongshi Lu and Wei Liu and Yuhua Zhang and Mo Li and Hongyu Zhao
• Companies: Yale University and Yale University and Peking University and Shanghai Jiao Tong University and Yale University and Yale University
• Keywords: genetic risk prediction ; GWAS ; functional annotations ; pleiotropy

Abstract

Genetic risk prediction is an important goal in human genetics research and advanced models will lead to more effective disease prevention and early 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, which uses GWAS summary statistics as input, and jointly models genetically correlated diseases and functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses, we demonstrate our approach can substantially increase the accuracy of risk prediction and population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages. Furthermore, we showcase that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.