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Multivariable G-E interplay in the prediction of educational achievement
Andrea G Allegrini1, Ville Karhunen2, Jonathan R I Coleman1,3
1Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
Plos Genetics
|November 17, 2020
Summary
This study shows that combining genetic information (polygenic scores) and environmental factors significantly improves prediction of educational achievement. Genetic factors influence environments, and environments are partly heritable, highlighting gene-environment correlation.
Area of Science:
- Behavioral Genetics
- Psychology
- Genomics
Background:
- Polygenic scores (GPS) predict educational achievement (EA), but their joint performance with environmental measures is unclear.
- Environmental measures are heritable, and GPS can capture environmental influences, necessitating joint analysis.
- Gene-environment correlation (rGE) and interaction (GxE) are key factors in understanding the interplay between genes and environment in EA.
Purpose of the Study:
- To systematically investigate rGE and GxE in the joint prediction of EA using multiple GPS and environmental measures.
- To determine how multiple GPS and environmental factors, analyzed together, predict EA.
- To assess the predictive accuracy of a combined model of GPS and environmental factors for EA.
Main Methods:
- Utilized penalized regression models to jointly analyze 20 GPS and 13 environmental measures for predicting EA in 7,026 adolescents.
- Employed out-of-sample comparisons to evaluate prediction accuracy.
- Investigated rGE and GxE effects within the prediction models.
Main Results:
- Joint modeling of multiple GPS and environmental factors significantly improved EA prediction.
- Cognitive-related GPS provided unique predictive information beyond socioeconomic status (SES), home environment, and life events.
- Evidence for significant rGE (rGE = .38) was found, with 40% of GPS effects on EA mediated by environment and 18% of environmental effects accounted for by the genetic model (genetic confounding).
- No significant GxE effects were found to contribute to multivariable prediction.
Conclusions:
- Widespread rGE and unsystematic GxE contribute to adolescent educational achievement.
- Joint analysis of genetic and environmental factors offers a more comprehensive understanding of EA prediction.
- Environmental factors mediate genetic influences on EA, and genetic factors confound environmental influences on EA.
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