Typically, GWAS summary statistics for quantitative phenotypes are not reported in terms of covariances, but are reported as ordinary least squared (OLS) unstandardized regression coefficients, with the phenotypes standardized prior to analyses (i.e., the coefficients are standardized with respect to the outcome, but not the predictor). In order to transform these partially standardized regression coefficient (bSNP,P) of a SNP effect on phenotype P to a covariance, we multiply by the variance of scores on the SNP. The variance (σSNP2) of scores (0, 1, 2) of a biallelic autosomal SNP is estimated as 2pq, assuming Hardy-Weinberg-Equilibrium, where p = the minor allele frequency (MAF) and q = 1-MAF, with the MAF typically obtained from a reference sample. As the latent genetic factors estimated in LDSC are scaled relative to unit-variance scaled phenotypes (by virtue of the SNP heritability estimates being placed on the diagonal of S), no further scaling is needed to transform this SNP-phenotype covariance into a SNP-genotype covariance.
