Polygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement. Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance. Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE = .38; 95% CIs = .30, .45). We estimated that 40% (95% CIs = 31%, 50%) of the polygenic scores effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs = 12%, 25%) of environmental effects were accounted for by the polygenic model, indicating genetic confounding. Lastly, we did not find evidence that GxE effects significantly contributed to multivariable prediction. Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence.

多基因分数越来越成为教育成就的有力预测指标。但是，目前尚不清楚在教育成就的预测模型中，部分捕获环境影响的多基因分数集与本身可遗传的环境度量集如何共同发挥作用。在这里，我们首次系统地研究基因-环境相关性（rGE）和相互作用（GxE），以联合分析全基因组范围的多基因评分（GPS）和多种环境措施，因为它们可以预测经过测试的教育成绩（EA）。我们预测有代表性的7,026名16岁儿童的EA，其中20种用于精神，认知和人体测量学特征的GPS，以及13种在生命早期测量的环境（包括生活事件，家庭环境和SES）。对环境和GPS预测器进行建模，在考虑预测误差相互影响对模型性能的影响的情况下，在具有预测精度的样本外比较的惩罚性回归模型中分别进行评估。对多个GPS和环境因素进行联合建模可显着改善EA的预测，而与认知相关的GPS可以添加SES，家庭环境和生活事件以外的独特独立信息。我们发现了EA中rGE潜在变异的证据（rGE = 0.38； 95％CIs = 0.30，0.45）。我们估计，对EA的多基因得分影响中有40％（95％CIs = 31％，50％）是由环境影响介导的，反过来，有18％（95％CIs = 12％，25％）是环境影响。由多基因模型解释，表明遗传混杂。最后，我们没有发现证据表明GxE效应显着促进了多变量预测。