Genomic SEM analyses that were used to produce the summary statistics for construction of polygenic scores for out-of-sample prediction omit the PGHC MDD 2018 GWAS and SCZ 2018 GWAS and replace them with the PGC MDD 201358 and PGC SCZ 201459 GWAS to prevent overlap between discovery and target samples. This resulted in a Genomic SEM-based multivariate GWAS using 930,581 SNPs. Analyses used to construct a phenotypic p-factor for polygenic prediction in the UKB dataset were restricted to data on up to N=332,050 European participants. The Genomic SEM of the p-factor employed case-control GWAS statistics to construct summary statistics for a general factor of liability for clinically-severe levels of psychopathology as the discovery phenotype. For out-of-sample prediction, we selected a set of psychiatric symptoms (rather than diagnoses) to construct liability for general and domain-specific factors of psychiatric symptomology across the subclinical-to-clinical ranges as the target phenotypes. From the UKB dataset, we chose symptoms falling within the following domains: psychosis, mania, depression, post-traumatic stress, and anxiety. We fit a confirmatory factor model (diagram shown in Supplementary Fig. 29) to the phenotypic
