We ran fastPAINTOR 3.1 in MCMC mode. We specified a per-locus causal effect size variance (specified via the -variance argument) using our modified HESS approach (as in PolyFun + SuSiE). We avoided truncating the LD matrix (using prop_ld_eigenvalues=1.0) because we used in-sample summary LD information. As fastPAINTOR is generally not designed to work with >10 annotations18,19 (and was too slow in our simulations to estimate the significance of each annotation and include only conditionally significant annotations as done in ref. 18), we selected a subset of 10 highly informative annotations by (1) scoring each annotation based on its average contribution to effect variance |aicτc| across all SNPs, using the true τc of the generative model; (2) iteratively selecting top-ranked annotations such that no annotation has correlation >0.3 (in absolute value) with a previously selected annotation, until selecting 10 annotations. We determined that 10 annotations yielded approximately optimal power while maintaining correct calibration (Supplementary Table 4).
