Following Finucane et al.32, the multivariate S-LDSC model is estimated by regressing the product of z statistics against the annotation-specific LD scores using a weighted regression model (see online supplement of Finucane et al.32 for a description of how weights are calculated). Standard errors and dependencies among estimation errors (i.e., sampling covariances) are estimated using a multivariate block jackknife. As sample overlap creates a dependency between z statistics for the two traits, thus increasing their products, the S-LDSC intercept (ρNs/√(N1N2) + a) is affected, but the regression slope is unaffected, and the estimates of partitioned genetic covariance and their standard errors are not biased.
