Our sample included participants of European and African ancestry. For the European ancestry sample, we used estimates from the above referenced discovery GWAS to derive the externalizing polygenic scores. For the African ancestry sample, we calculated externalizing polygenic scores using two methods, given prior evidence that PGS are most predictive when individuals in the discovery GWAS and the target sample are matched on ancestral background (Martin et al. 2017; Peterson et al. 2019). First, we created the PGS based on the weights from the discovery GWAS of European ancestry individuals (Karlsson Linnér et al. in press). Second, we used a multi-ethnic polygenic scoring approach (Márquez-Luna et al. 2017) to calculate PGS for the African ancestry sample. This multi-ethnic scoring approach combines GWAS results from the European ancestry discovery sample with training GWAS data from the target (i.e., African ancestry) population. Specifically, we combined GWAS results weights from Karlson Linnér et al. (in press) with results from a 10-fold GWAS method of a latent externalizing factor constructed by a phenotypic externalizing factor that corresponded with the seven indicator phenotypes in the
