Leveraging functional annotations in genetic risk prediction for human complex diseases

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.
Author summary Genetic risk prediction plays a significant role in precision medicine. Accurate prediction models could have great impact on disease prevention and early treatment strategies. For example, mutations in BRCA1 and BRCA2 have been used to evaluate women’s breast cancer risk and as a guideline for early screening. However, genetic risk prediction models also present important challenges, including extreme high-dimensionality, limited access to and efficient computational methods for individual-level genotype data. To make use of rich GWAS summary statistics, we propose a novel method to address these challenges by integrating genomic functional annotations, which have been successfully applied in GWAS to generate biological insights. We demonstrate the improvement in accuracy in both extensive simulation studies and real data analysis of breast cancer, Crohn’s disease, celiac disease, rheumatoid arthritis and type-II diabetes.