the likelihood of model using annotations with model using and not using annotations jointly) results in non-significant p-values for most tests (S11 Table), which further demonstrates that PRS incorporating functional annotations mostly encompasses the information of PRS without annotations. To test different methods’ ability to stratify individuals with high risk, we compared the proportion of cases among testing samples with high PRS. AnnoPred outperformed all other methods in CD, CEL, RA, and T2D (S1 Fig). Next, we tested AnnoPred’s performance using only the 53 baseline annotations and observed a substantial drop in prediction accuracy for all diseases (S3 Table). AnnoPred with GenoCanyon and GenoSkyline annotations only (nine annotation tracks in total) yields better performance than the 53 baseline annotations (S10 Table). For CD and T2D, by using these 9 categories AnnoPred even achieved higher accuracy than the model with all 61 annotation tracks added. These results highlight the importance of annotation quality in genetic risk prediction, and also demonstrate GenoCanyon and GenoSkyline’s ability to accurately identify functionality in the human genome. Since different diseases have various enrichment patterns, we also run AnnoPred with significantly enriched annotations (enrichment test p value less than 0.05) for each disease (S10 Table). In general,
