As we have robust genome-wide polygenic scores and identified genes from large-scale gene identification consortia, developmental scientists can play an active role in mapping the behavioral phenotypes that represent earlier manifestations of genetic predispositions and how these outcomes are moderated by the environment (Dick, 2017). Characterizing these pathways will inform our understanding of how genetic risk unfolds across time, and the nature of malleability of associated outcomes as a function of intervention. The intermediary behavioral phenotypes mapped to genetic risk may very well be more useful in making decisions about which children are likely to be responsive to which interventions than the genetic risk scores themselves. Identifying child characteristics that modify intervention effectiveness is not a new area for prevention scientists (Bates, Pettit, Dodge, & Ridge, 1998), but genetic information may help inform our understanding of the complex web of etiological pathways, which can be used to inform prevention science. It is possible that a combination of behavioral and genetic information may be most effective in providing information to make decisions about health interventions. Machine learning methods hold promise in identifying combinations of factors that yield the best prediction (Walsh, Ribeiro, & Franklin, 2017).
