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Multivariable G-E interplay in the prediction of educational achievement

View ORCID ProfileA.G. Allegrini, V. Karhunen, View ORCID ProfileJ. R. I. Coleman, S. Selzam, K. Rimfeld, S. von Stumm, J.-B. Pingault, R. Plomin
doi: https://doi.org/10.1101/865360
A.G. Allegrini
1Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
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  • For correspondence: andrea.allegrini@kcl.ac.uk
V. Karhunen
2Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
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J. R. I. Coleman
1Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
3NIHR Maudsley Biomedical Research Centre, King’s College London, UK
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S. Selzam
1Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
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K. Rimfeld
1Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
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S. von Stumm
4Department of Education, University of York, UK
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J.-B. Pingault
1Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
5Division of Psychology and Language Sciences, University College London, UK
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R. Plomin
1Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
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Abstract

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 = .36; 95% CIs = .29, .43). We estimated that 38% (95% CIs = 29%, 49%) of the GPS 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 GPS model. Lastly, we did not find evidence that GxE effects collectively contributed to multivariable prediction.

Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 06, 2019.
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Multivariable G-E interplay in the prediction of educational achievement
A.G. Allegrini, V. Karhunen, J. R. I. Coleman, S. Selzam, K. Rimfeld, S. von Stumm, J.-B. Pingault, R. Plomin
bioRxiv 865360; doi: https://doi.org/10.1101/865360
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Multivariable G-E interplay in the prediction of educational achievement
A.G. Allegrini, V. Karhunen, J. R. I. Coleman, S. Selzam, K. Rimfeld, S. von Stumm, J.-B. Pingault, R. Plomin
bioRxiv 865360; doi: https://doi.org/10.1101/865360

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