Explicit modeling and incorporation of external information, e.g. pleiotropy [7, 8] and LD [10], has been shown to effectively improve risk prediction accuracy. Recent advancements in integrative genomic functional annotation, coupled with the rich collection of summary statistics from GWAS, have enabled increase of statistical power in several different settings [14–16]. To our knowledge, the impact of functional annotations on performance of genetic risk prediction has not been systematically studied. Here, we introduce AnnoPred (available at https://github.com/yiminghu/AnnoPred), a principled framework that integrates GWAS summary statistics with various types of annotation data to improve risk prediction accuracy. We compare AnnoPred with state-of-the-art PRS-based approaches and demonstrate its consistent improvement in risk prediction performance using both simulations and real data of multiple complex diseases.
