A key distinction between PolyFun and previous functionally-informed fine-mapping methods10,18–20 is the use of the entire genome and a large number of functional annotations to estimate prior causal probabilities. We exploited the computational scalability of PolyFun (together with SuSiE21) to fine-map up to 2,763 overlapping 3Mb loci spanning the entire genome (Methods). We subsequently used our fine-mapping results to perform polygenic localization, identifying minimal sets of common SNPs causally explaining a given proportion of common SNP heritability. Details of the PolyFun method are provided in the Methods section; we have released open-source software implementing PolyFun in conjunction with SuSiE21 and FINEMAP22. In all main simulations and analyses of real traits, we applied PolyFun using summary LD information estimated directly from the target samples (both for running S-LDSC and for running SuSiE or FINEMAP), as previously recommended for fine-mapping methods12,28.
