Our results should be interpreted in light of several limitations. First, while our dataset is the largest genome-wide cross-disorder analysis to date, data available for individual disorders varied substantially—from a minimum of 9,725 cases and controls for OCD to 461,134 cases and controls for MD. This imbalance of sample size may have limited our power to detect pleiotropic effects on underrepresented disorders. The future availability of larger samples will improve power for detection of cross-disorder effects. Second, it is possible that comorbidity among disorders contributed to apparent pleiotropy; we found, however, that fewer than 2% of cases overlapped between disorder datasets (excluding 23andMe data) and we adjusted for sample overlap in meta-analysis. Third, the method we applied to detect cross-phenotype association, which combines an all-subsets fixed-effects GWAS meta-analysis with a Bayesian method for evaluating the best-fit configuration of genotype-phenotype associations, is one of several approaches (Solovieff et al., 2013). However, we have previously shown that this method outperforms a range of alternatives for detecting pleiotropy under various settings (Zhu et al., 2018). Fourth, our designation of loci as pleiotropic vs.
