Our work demonstrates that functional annotations can effectively improve performance of genetic risk prediction. AnnoPred jointly analyzes diverse types of annotation data and GWAS summary statistics to upweight SNPs with a higher likelihood of functionality, which lead to consistently better prediction accuracy for multiple complex diseases. Our method is not without limitation. First, despite the consistent improvement compared with existing PRS-based methods, accuracies for most diseases remain moderate. In order to effectively stratify risk groups for clinical usage, our model remains to be further calibrated using large cohorts with measured environmental and clinical risk factors [1]. Second, accurate estimation of GWAS signal enrichment and SNP effect sizes requires a large sample size for the training dataset. This could potentially be improved by new estimators for annotation-stratified heritability [19]. A few Bayesian models combining GWAS summary statistics with functional annotations have been proposed for the purpose of fine-mapping functional variants [16, 20, 21]. Whether these models could be adapted to benefit risk prediction accuracy remains to be investigated in the future. Importantly, the rich collection of publicly available integrative annotation data,
