Correlations between the calculated PRS and disease status (COR) for different approaches are summarized in Table 2. AnnoPred showed consistently improved prediction accuracy compared with all other methods across five diseases. Notably, PRSsig and PRSall showed suboptimal performance in these datasets, reaffirming the importance of modeling LD and other external information. A likelihood ratio test was used to test for the difference in the prediction accuracy between models comparing the likelihood of a logistic regression fitting PRS of one method to that of a logistic regression fitting PRS of two methods jointly (S11 Table). From the test, AnnoPred with 61 annotations performed significantly better than LDpred (p = 1.2E-22 for CD, p = 0.045 for BC, p = 4.2E-7 for RA, p = 3.3E-4 for T2D and p = 1.3E-3 for CEL). Reversing the order of test (that is, comparing 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
