Our work has several limitations. First, our PIP>0.95 FDR estimates for PolyFun and for other methods are conservative, demonstrating the challenges of exact calibration in fine-mapping. Second, subtle population stratification may lead to spurious fine-mapping results57. However, our fine-mapped SNPs are concentrated in associated loci with larger estimated effects, which are relatively less likely to be spurious. Third, we restricted fine-mapping to N=337K unrelated British-ancestry individuals, consistent with previous studies12. Hence, our published summary LD information files do not support fine-mapping of UK Biobank data that includes non-British individuals. Fourth, PolyLoc requires analyzing samples distinct from the samples analyzed by PolyFun to avoid winner’s curse. Researchers with access to individual-level genetic data can partition the samples as we have done (we recommend using approximately 75% of the data for fine-mapping and 25% for polygenic localization). Fifth, PolyFun does not support X-chromosome analysis. Sixth, PolyLoc only provides an upper bound on the proportion of SNPs causally explaining a given proportion of SNP-heritability. Finally, multi-ethnic fine-mapping58 and incorporation of tissue-specific functional annotations9,13,15,17 may further increase fine-mapping power. Incorporating these into our fine-mapping framework is an avenue for future work.
