TY - JOUR
T1 - Leveraging functional annotations in genetic risk prediction for human complex diseases
AU - Hu, Yiming
AU - Lu, Qiongshi
AU - Powles, Ryan
AU - Yao, Xinwei
AU - YANG, Can
AU - Fang, Fang
AU - Xu, Xinran
AU - Zhao, Hongyu
N1 - Funding Information:
This study was supported in part by the National Institutes of Health (https://www.nih.gov/) grants R01 GM59507, the VA Cooperative Studies Program of the Department of Veterans Affairs, Office of Research and Development (http://www.research.va.gov/programs/csp/), and the Yale World Scholars Program (http://bbs.yale.edu/training/initiatives/csc.aspx) sponsored by the China Scholarship Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We sincerely thank DIAGRAM, GAME-ON, IIBDGC, and ImmunoBase for making their GWAS summary data publicly accessible. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113, 085475 and 090355. We also thank Dr. Bjarni J. Vilhj?lmsson for sharing his codes. And finally we thank Jina Li for her insightful suggestions and support.
PY - 2017/6
Y1 - 2017/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85021735285&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1005589
DO - 10.1371/journal.pcbi.1005589
M3 - Journal article
C2 - 28594818
AN - SCOPUS:85021735285
SN - 1553-734X
VL - 13
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 6
M1 - e1005589
ER -
