polygenic scores in follow-up studies with diverse samples. Rather than limiting our analyses to focus on participants of European ancestry, we used GWAS results from European ancestry discovery samples to create polygenic scores for the African ancestry sample in our study. Although this approach is more inclusive, we recognize that it is not ideal because the mismatch in ancestry between the discovery and target samples make the polygenic scores less accurate and less predictive, creating difficulties in interpreting null findings associated with polygenic scores. However, we believe it is a scientific and ethical imperative to include individuals of African ancestry in genetic research as the exclusion of historically underrepresented individuals from health research has the potential to further perpetuate health disparities (Davis, 2021). Predictive power of polygenic scores in non-European ancestry groups will improve as large-scale GWAS in diverse populations becomes available. Additionally, because we conducted analyses separately by ancestry group to accommodate the inclusion of polygenic scores, our study is not informative about how socioenvironmental factors may operate differently across the ancestry groups, and it does not provide information on any ethnic-racial differences in externalizing behaviors or parenting. Clearly, concerted efforts to have adequate representation of diversity in genetic
