In order to examine the effect of sample overlap on estimates obtained from Genomic SEM, the GCTA package package3 was used to generate a 50% heritable, quantitative phenotype with 30,000 causal SNPs. The phenotype was paired with genetic data from 100,000 randomly selected, unrelated individuals of European descent from UKB data for 1,209,498 HapMap3 SNPs. Three sets of 60,000 participants each were created using this same phenotype, with 40,000 participants overlapping across all three identical phenotypes and 20,000 participants unique to each phenotype (i.e., 100,000 total participants). These three subsamples were individually analyzed in PLINK56 to produce univariate GWAS summary statistics. The multivariable LDSC function was then used to construct the genetic covariance and sampling covariance matrix using the three sets of summary statistics, and Genomic SEM was used to fit a one factor model with the SNP predicting the common factor. Two key results were verified at this stage. First, we confirmed that the standardized factor loadings on the common factor were 1 for the identical phenotypes. Second, we verified that the bivariate ld-score intercepts that are used to
