Using DNA to Predict Education: a Meta-analytic Review

Abstract

Recent advances in genomics make it possible to predict individual differences in education from polygenic scores that are person-specific aggregates of inherited DNA differences. Here, we systematically reviewed and meta-analyzed the strength of these DNA-based predictions for educational attainment (e.g., years spent in full-time education) and educational achievement (e.g., school grades). For educational attainment (k = 20, n = 16, Ntotal = 314,757), a multilevel meta-analysis showed an association with polygenic scores of ρ = .27 (95% CI from .22 to .32). For educational achievement (k = 19, n = 10, Ntotal = 83,788), the association was ρ = .24 (95% CI from .18 to .30). Eurocentric biases were evident with only 15% of estimates being reported in samples of non-European ancestry. After accounting for sample ancestry, age at assessment, and education measure, the meta-analytic estimates increased to ρ = .29 (95% CI from .24 to .33) for educational attainment and ρ = .50 (95% CI from .39 to .61) for educational achievement, indicative of large effect sizes. All meta-analytic estimates were associated with significant heterogeneity. Our findings suggest that DNA-based predictions of education are sizeable but vary across samples and studies. We outline three steps to safeguard potential applications of polygenic score predictions in education to maximize their benefits for personalizing learning, while minimizing the bioethical risks of perpetuating social, cultural, and economic inequalities.