We estimated hg2 tagged by PIP>0.95 SNPs and by lead GWAS SNPs via a multivariate linear regression. We regressed all the covariates used in BOLT-LMM out of the phenotypes, performed multivariate linear regression on the residuals (using all PIP>0.95 SNPs as explanatory variables) and reported the adjusted R2 as the hg2 tagged by these SNPs. We verified that the results remained nearly identical regardless of whether we excluded related individuals (Supplementary Table 14). We estimated MAF>0.001 SNP-heritability for trait selection and for Figure 2b by running S-LDSC with all the baseline-LF annotations. We overrode the automatic removal of very large effect SNPs employed by S-LDSC for hair color, because this removal led to hg2 estimates that were smaller than the linear regression-based estimates, due to the large proportion of SNP-heritability originating from very large-effect SNPs.
