RT Journal Article
SR Electronic
T1 Leveraging Functional Annotations in Genetic Risk Prediction for Human Complex Diseases
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 058768
DO 10.1101/058768
A1 Yiming Hu
A1 Qiongshi Lu
A1 Ryan Powles
A1 Xinwei Yao
A1 Fang Fang
A1 Xinran Xu
A1 Hongyu Zhao
YR 2016
UL http://biorxiv.org/content/early/2016/06/13/058768.abstract
AB Genome wide association studies have identified numerous regions in the genome associated with hundreds of human diseases. Building accurate genetic risk prediction models from these data will have great impacts on disease prevention and treatment strategies. However, prediction accuracy remains moderate for most diseases, which is largely due to the challenges in identifying all the disease-associated variants and accurately estimating their effect sizes. We introduce AnnoPred, a principled framework that incorporates diverse functional annotation data to improve risk prediction accuracy, and demonstrate its performance on multiple human complex diseases.