Multivariable G-E interplay in the prediction of educational achievement.
Multivariable G-E interplay in the prediction of educational achievement.
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DOI：
10.1371/journal.pgen.1009153
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发表时间：
2020-11
期刊：
影响因子：
4.5
通讯作者：
Plomin R
中科院分区：
文献类型：
--
作者：
Allegrini AG;Karhunen V;Coleman JRI;Selzam S;Rimfeld K;von Stumm S;Pingault JB;Plomin R
多基因分数越来越成为教育成就的有力预测指标。然而，目前还不清楚，在教育成就的预测模型中，部分反映环境影响的多基因评分组如何与本身具有遗传性的环境测量组一起发挥作用。在这里，我们首次系统地研究了基因-环境相关性（rGE）和相互作用（GxE），对多个全基因组多基因评分（GPS）和多种环境测量进行联合分析，以预测测试的教育成就（EA）。我们在 7,026 名 16 岁青少年的代表性样本中预测 EA，并使用 20 个用于精神、认知和人体测量特征的 GPS，以及在生命早期测量的 13 个环境（包括生活事件、家庭环境和 SES）。考虑到环境和 GPS 预测变量之间的相互作用对模型性能的影响，在惩罚回归模型中对环境和 GPS 预测变量进行单独和联合建模，并对预测精度进行样本外比较。联合建模多个 GPS 和环境因素显着改善了 EA 的预测，与认知相关的 GPS 添加了 SES、家庭环境和生活事件之外的独特独立信息。我们发现了 EA 中 rGE 潜在变化的证据（rGE = .38；95% CI = .30、.45）。我们估计 40%（95% CI = 31%、50%）的 EA 多基因评分效应是由环境影响介导的，而 18%（95% CI = 12%、25%）的环境影响是由多基因模型解释的，表明存在遗传混杂。最后，我们没有发现证据表明 GxE 效应对多变量预测有显着贡献。我们的多变量多基因和环境预测模型表明广泛的 rGE 和非系统的 GxE 对青春期 EA 的贡献。我们的研究调查了教育成就（EA）背后的遗传和环境贡献之间复杂的相互作用。多基因评分正在成为 EA 越来越强大的预测因子。虽然新出现的证据表明多基因评分并不是遗传倾向的纯粹衡量标准，但之前的定量遗传学研究结果表明，环境衡量标准本身是可遗传的。在这方面，尚不清楚这些个体倾向的测量如何联合起来预测 EA。我们在英国 7,026 名 16 岁青少年的代表性样本中调查了这个问题，我们提供了关于 EA 变异背后的基因-环境相关性和相互作用的实质性结果。我们表明 EA 的多基因评分和环境预测模型有很大重叠。多基因评分对 EA 的影响部分是由于其与环境影响的相关性所致；同样，环境对 EA 的影响与多基因评分效应相关。尽管如此，联合考虑多基因分数和测量环境显着改善了 EA 的预测。我们还发现，虽然多基因分数和测量环境之间的相关性很大，但它们之间的相互作用在 EA 的预测中并没有发挥重要作用。我们的研究结果与基因组和环境预测模型都具有相关性，因为它们显示了个体的遗传倾向和环境影响相互交织的方式。这表明在 EA 等复杂行为特征的预测模型中必须考虑遗传和环境影响。
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. Our study investigates the complex interplay between genetic and environmental contributions underlying educational achievement (EA). Polygenic scores are becoming increasingly powerful predictors of EA. While emerging evidence indicates that polygenic scores are not pure measures of genetic predisposition, previous quantitative genetics findings indicate that measures of the environment are themselves heritable. In this regard it is unclear how such measures of individual predisposition jointly combine to predict EA. We investigate this question in a representative UK sample of 7,026 16-year-olds where we provide substantive results on gene-environment correlation and interaction underlying variation in EA. We show that polygenic score and environmental prediction models of EA overlap substantially. Polygenic scores effects on EA are partly accounted for by their correlation with environmental effects; similarly, environmental effects on EA are linked to polygenic scores effects. Nonetheless, jointly considering polygenic scores and measured environments significantly improves prediction of EA. We also find that, although correlation between polygenic scores and measured environments is substantial, interactions between them do not play a significant role in the prediction of EA. Our findings have relevance for genomic and environmental prediction models alike, as they show the way in which individuals’ genetic predispositions and environmental effects are intertwined. This suggests that both genetic and environmental effects must be taken into account in prediction models of complex behavioral traits such as EA.
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影响因子：
2.1
作者：
Dudbridge F;Pashayan N;Yang J
通讯作者：
Yang J
影响因子：
11
作者：
International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS)
通讯作者：
International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS)
影响因子：
5.6
作者：
Abdelhakim, Ahmed A.;Tenreiro Machado, Jose A.
通讯作者：
Tenreiro Machado, Jose A.
影响因子：
11
作者：
Krapohl E;Patel H;Newhouse S;Curtis CJ;von Stumm S;Dale PS;Zabaneh D;Breen G;O'Reilly PF;Plomin R
通讯作者：
Plomin R
影响因子：
11
作者：
Allegrini, A. G.;Selzam, S.;Plomin, R.
通讯作者：
Plomin, R.