Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom.
PLoS Genet. 2020 Nov 17;16(11):e1009153. doi: 10.1371/journal.pgen.1009153. eCollection 2020 Nov.
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.
多基因分数是教育成就的强有力预测指标。然而，在教育成就的预测模型中，部分捕获环境效应的多基因分数集合与自身具有遗传性的环境测量集合如何共同发挥作用尚不清楚。在这里，我们首次系统地研究了在多个全基因组多基因分数（GPS）和多个环境测量的联合分析中，基因-环境相关性（rGE）和相互作用（GxE），因为它们预测了经过测试的教育成就（EA）。我们使用 20 个用于精神、认知和人体测量特征的 GPS 和 13 个在生命早期测量的环境（包括生活事件、家庭环境和 SES），对 7026 名 16 岁青少年的代表性样本进行了 EA 的预测。我们在惩罚回归模型中单独和联合地对环境和 GPS 预测因素进行建模，同时考虑了它们相互作用对模型性能的影响，并对预测准确性进行了样本外比较。联合建模多个 GPS 和环境因素显著提高了 EA 的预测能力，认知相关的 GPS 除了 SES、家庭环境和生活事件之外，还提供了独特的独立信息。我们发现 EA 变异的 rGE 存在证据（rGE =.38；95%置信区间为.30，.45）。我们估计，40%（95%置信区间为 31%，50%）的 GPS 对 EA 的影响是由环境效应介导的，反过来，18%（95%置信区间为 12%，25%）的环境效应是由多基因模型解释的，表明存在遗传混杂。最后，我们没有发现 GxE 效应对多变量预测有显著贡献的证据。我们的多变量遗传和环境预测模型表明，在青春期，广泛存在 rGE 和非系统性 GxE 对 EA 的影响。
