Regardless of the reason, if genetic heterogeneity between samples is indeed at play, this study’s findings have implications both for the field’s understanding of AUDs and for the design of research protocols. The results suggest, first, that sample characteristics (including, but also beyond the typical demographic contenders like sex and age) may play a critical role in defining distinctive genetic and/or phenotypic etiologies. The poor polygenic prediction across sample types in this study, and even now in some consortia, is evidence of a massive degree of heterogeneity across populations that must be better confronted. Second, these findings suggest caution in research methods that combine participants across different populations, e.g. in large-scale meta-analyses and replications. Combining across genetically heterogeneous populations can undermine a study’s power to detect or replicate true effects. Although a brute force approach with large sample sizes can certainly be effective (e.g. Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), it may be less tractable for disorders like AUDs that have stronger environmental and gene-environment interaction effects. Smaller studies can take complementary approaches to increase power by
