These results should be interpreted in the context of three primary considerations. First, because genetic correlations estimated from GWAS summary statistics utilize only common variants, genetic correlations will only be equivalent across genomic and family-based estimation methods as long as the genetic correlation is constant across rare and common variants (Van Rheenen et al., 2019). This largely remains an open question as sequencing efforts that capture rare variants are only just beginning to produce results in psychiatric genomics. Second, it is of note that the absolute level of genetic correlations can be upwardly biased when misclassification is present (Wray, Lee, & Kendler, 2012). However, simulations indicate that extremely high rates would be needed to explain the pattern of sizeable genetic correlations across psychiatric traits (Anttila et al., 2018). Third, simulation results indicate that ascertainment of controls in GWAS can upwardly bias estimates of genetic correlation when studies utilize super-normal controls that are screened both for the disorder of interest and related disorders (Kendler, Chatzinakos, & Bacanu, 2020). It is worth noting that the upward bias was found to be most
