Chunk #57 — STAR* METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Examination of the Impact of Sample Size Imbalance on Genetic Correlations and Genomic SEM Results
Next, we investigated the impact of variable sample sizes on the Genomic SEM analysis results by re-running Genomic SEM analysis using a Maximum Likelihood (ML) estimator that does not take into account the differing precisions of the genetic covariance estimates (resulting from, for example, uneven sample sizes across traits) when optimizing parameters. As shown in Data S1.4, the results were consistent with those from the primary analysis reported in the main text that is based on a Weighted Least Squares (WLS) estimator, which does take into account the differing precisions of the genetic covariance estimates. Specifically, the nontrivial standardized factor loadings of MD on two of the three factors is evident in both the WLS and ML solutions and is therefore unlikely to be an artifact of its large N. Note that, in both the WLS and the ML solution, the standard errors are smaller for the loadings involving the better-powered GWAS phenotypes, as we would expect.
