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PLoS Comput Biol. 2017 Jun 8;13(6):e1005589. doi: 10.1371/journal.pcbi.1005589. eCollection 2017 Jun.

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

Hu Y1, Lu Q1, Powles R2, Yao X3, Yang C4, Fang F1, Xu X1, Zhao H1,2,5,6.

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Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America.
Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America.
Yale College, New Haven, CT, United States of America.
Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong.
Department of Genetics, Yale University School of Medicine, New Haven, CT, United States of America.
Clinical Epidemiology Research Center (CERC), Veterans Affairs (VA) Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, CT, United States of America.


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 using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.

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