We have introduced PolyFun, a framework that improves fine-mapping by prioritizing variants that are a-priori more likely to be causal based on their functional annotations. Across 49 UK Biobank traits, PolyFun + SuSiE confidently fine-mapped 3,025 SNP-trait pairs (PIP >0.95), a 32% increase over non-functionally informed SuSiE. 223 of the fine-mapped SNPs were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We further leveraged the results of PolyFun to perform polygenic localization by constructing minimal SNP sets causally explaining a given proportion of common SNP heritability, demonstrating that 50% of common SNP heritability can be explained by sets ranging in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). We note that these set sizes impose a (possibly loose) upper bound on the size of the smallest sets causally explaining 50% of common SNP heritability. We have publicly released the PIPs and the prior and posterior means and variances of effect sizes for all SNPs and traits analyzed.
