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PLoS Genet. 2017 Jun 9;13(6):e1006836. doi: 10.1371/journal.pgen.1006836. eCollection 2017 Jun.

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

Hu Y1, Lu Q1, Liu W2, Zhang Y3, Li M1, Zhao H1,4,5,6.

Author information

1
Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
2
Peking University, Beijing, China.
3
Shanghai Jiao Tong University, Shanghai, China.
4
Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
5
Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America.
6
Clinical Epidemiology Research Center (CERC), Veterans Affairs (VA) Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, Connecticut, United States of America.

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 population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

PMID:
28598966
PMCID:
PMC5482506
DOI:
10.1371/journal.pgen.1006836
[Indexed for MEDLINE]
Free PMC Article

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