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    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, 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.
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