Multivariable G-E interplay in the prediction of educational achievement

Andrea G. Allegrini*, Ville Karhunen, Jonathan R.I. Coleman, Saskia Selzam, Kaili Rimfeld, Sophie von Stumm, Jean Baptiste Pingault, Robert Plomin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere1009153
Number of pages20
JournalPLoS Genetics
Issue number11
Publication statusPublished - 17 Nov 2020

Bibliographical note

Funding Information:
TEDS is supported by a programme grant to RP from the UK Medical Research Council (MR/ M021475/1 and previously G0901245), with additional support from the US National Institutes of Health (AG046938). The research leading to these results has also received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/ 2007- 2013)/grant agreement n 602768 and ERC grant agreement n 295366. RP is supported by a Medical Research Council Professorship award (G19/2). AGA and VK have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 721567. JRIC is supported in part by the UK National Institute for Health Research (NIHR) as part of the Maudsley Biomedical Research Centre (BRC). This study represents independent research partly funded by the NIHR BRC at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. K.R. is supported by a Sir Henry Wellcome Postdoctoral Fellowship. SvS is supported by a Jacobs Fellowship (2017-2019) and a Nuffield award (EDO/44110). High performance computing facilities were funded with capital equipment grants from the GSTT Charity (TR130505) and Maudsley Charity (980). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2020 Allegrini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Copyright 2020 Elsevier B.V., All rights reserved.

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