Given a ranking of SNPs by posterior per-SNP heritability (i.e., the posterior mean of their squared effect size; see Methods), we define M50% as the size of the smallest set of top-ranked common SNPs causally explaining 50% of common SNP heritability (resp. Mp for proportion p of common SNP heritability). We estimate M50% (resp. Mp) by (1) partitioning SNPs into 50 ranked bins of similar posterior per-SNP heritability estimates from PolyFun + SuSiE and stratifying the lowest-heritability bin into 10 equally-sized MAF bins, yielding 59 bins; (2) running S-LDSC using a different set of samples to re-estimate the average per-SNP heritability in each bin; and (3) computing the number of top-ranked common SNPs (with respect to the original ranking) whose estimated per-SNP heritabilities (from step 2) sum up to 50% (resp. the proportion p) of the total estimated SNP-heritability. We refer to this method as PolyLoc. The analysis of new samples in step 2 of PolyLoc prevents winner’s curse; although PolyFun + SuSiE is robust to winner’s curse, PolyLoc would be susceptible to winner’s curse if it reused the data
