from the four modes with and (results for other scenarios are shown in Figures S10-S20 in Text S1). Because all the simulation parameters were the same for the two diseases, only the results for the first disease are shown. We can see that incorporating either annotation information or pleiotropy between the two diseases improved the prioritization performance. In particular, as the proportion of shared risk SNPs increased, the prioritization performance also improved. Given the local FDR obtained using GPA, we controlled the global FDR at 0.2 and calculated the average power to identify the true risk SNPs. GPA performance measured by partial AUC and power for and are shown in the middle and right panels of Figure 1, respectively (results for other scenarios are shown in Figures S10–S20 in Text S1). We also evaluated the actual FDR and found that the FDR was indeed controlled at 0.2 with occasional slight conservativeness (Figure 2 and Figures S21–S31 in Text S1). In addition, we evaluated the actual FDR in the presence of linkage disequilibrium (LD) among SNPs. The details of the simulations and results are given in Figure S1 in Text S1. These results suggest that GPA can improve the power of
