TY - JOUR T1 - Leveraging Functional Annotations in Genetic Risk Prediction for Human Complex Diseases JF - bioRxiv DO - 10.1101/058768 SP - 058768 AU - Yiming Hu AU - Qiongshi Lu AU - Ryan Powles AU - Xinwei Yao AU - Fang Fang AU - Xinran Xu AU - Hongyu Zhao Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/06/13/058768.abstract N2 - 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. ER -