Multivariable G-E interplay in the prediction of educational achievement

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 = .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.

Predicting alcohol use from genome-wide polygenic scores, environmental factors, and their interactions in young adulthood

“…Our findings contribute to a growing body of literature that struggles to identify GxE effects (27-30), because of three key challenges. First, studies that examine only one or two environmental moderators are likely to identify false positives, because of residual confounding that is unaccounted for (cf.…”

“…We partly overcome this challenge in the current research by comprehensively assessing the environment and testing 30 environmental moderators simultaneously. Second, testing GxE effects requires extremely large samples that afford sufficient statistical power to detect associations of very small effect size, which are likely to be true for GxE terms (25, 27). In the current study, we analyzed data from more than 3,000 individuals, which compares well with other GxE studies on alcohol use but is small relative to the populations that are typically assessed in GWA studies (31).…”

From Genome-Wide to Environment-Wide: Capturing the Environome

“…Although an extensive, conclusive body of empirical evidence has shown that correlations between genetic factors and environments are commonplace, G×Es remain to be reliably demonstrated (Duncan & Keller, 2011;Manuck & McCaffery, 2014;Tucker-Drob & Bates, 2016). For example, a recent large-scale study that tested GrEs and G×Es across multiple genotypes, phenotypes, and environments in the prediction of educational achievement observed systematic GrEs but concluded that the contributions of G×Es were random, weak, and negligible (Allegrini et al, 2020). Compared with detecting direct effects of genotypes on phenotypes (i.e., GrE), identifying G×Es is statistically more demanding because to be adequately powered, interaction models require much larger sample sizes than tests of direct effects (Duncan & Keller, 2011;Moffitt et al, 2006).…”

Using DNA to predict behaviour problems from preschool to adulthood

“…Our methods for obtaining DNA, genotyping, quality control and constructing polygenic scores have been described previously (Rimfeld et al, 2019; Selzam et al, 2018). In the present analyses, we included 15 genome-wide polygenic scores (GPS) of behaviour problems and psychopathology, derived from the most powerful genome-wide association (GWA) studies, which were used in our previous research (Allegrini et al, 2019; Allegrini et al, 2020a). For the list of polygenic scores, please refer to Supporting information 3 .…”

“…We estimated the joint prediction of the 15 GPS (multi-GPS heritability) in a penalized regression elastic net model with out-of-sample evaluation of prediction accuracy. For details on the elastic net regularization analytic procedure, please refer to Supporting information 9 and Allegrini et al (2020a).…”