Our fine-mapping analysis differs from several previous fine-mapping studies in two aspects. First, we applied PolyFun genome-wide. However, we envision that the PolyFun software will primarily be used to fine-map genome-wide significant loci, which harbor most PIP>0.95 SNPs. We discuss possible reasons for identifying PIP>0.95 SNPs with P>5×10−8 in the Supplementary Note. Second, PolyFun fine-maps all signals in a locus jointly to maximize power5,28. Researchers wishing to use PolyFun for a partitioned analysis47 may still do so by first partitioning a locus into multiple signals using a separate tool (e.g. GCTA-COJO47) and then applying PolyFun to each signal separately, restricting PolyFun to assume a single causal SNP per signal.
