Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank

Cheesman, Rosa; Hunjan, Avina; Coleman, Jonathan R. I.; Ahmadzadeh, Yasmin; Plomin, Robert; McAdams, Tom A.; Eley, Thalia C.; Breen, Gerome

doi:10.1101/707695

Cited by 23 publications

(21 citation statements)

References 34 publications

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“…Simulations in an Icelandic dataset suggest that although GREML-KIN gives unbiased heritability estimates when family environmental influences are present, it over-estimates heritability if phenotypes are also substantially influenced by passive gene-environment correlation (Young et al 2018). The influence of passive gene-environment correlation for educational attainment in the UK Biobank is suggested by the lower SNP heritability for adoptees, whose rearing environments are less correlated with their genotypes (Cheesman et al, 2019). In the future, methods that can distinguish direct from indirect influences should be applied to neuroticism, education and other complex, socially-contingent traits (Eaves et al 2014;Visscher et al 2006;Young et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Familial Influences on Neuroticism and Education in the UK Biobank

et al. 2019

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Genome-wide studies often exclude family members, even though they are a valuable source of information. We identified parent-offspring pairs, siblings and couples in the UK Biobank and implemented a family-based DNA-derived heritability method to capture additional genetic effects and multiple sources of environmental influence on neuroticism and years of education. Compared to estimates from unrelated individuals, total heritability increased from 10 to 27% and from 17 to 56% for neuroticism and education respectively by including family-based genetic effects. We detected no family environmental influences on neuroticism. The couple similarity variance component explained 35% of the variation in years of education, probably reflecting assortative mating. Overall, our genetic and environmental estimates closely replicate previous findings from an independent sample. However, more research is required to dissect contributions to the additional heritability by rare and structural genetic effects, assortative mating, and residual environmental confounding. The latter is especially relevant for years of education, a highly socially contingent variable, for which our heritability estimate is at the upper end of twin estimates in the literature. Family-based genetic effects could be harnessed to improve polygenic prediction.

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Section: Discussionmentioning

confidence: 99%

Familial Influences on Neuroticism and Education in the UK Biobank

et al. 2019

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“…The findings are also consequential for genetic analyses more broadly. Some recent innovative analysis linking genetics to outcomes has taken seriously the confounding between children's genetics and family background when explaining children's later outcomes as adults, seeking to decompose PGS into direct (child) and indirect (parent) effects (26,31,32). This work has been motivated, in part, by previous research that used sibling comparisons in PGS to predict education.…”

Section: Discussionmentioning

confidence: 99%

The production of within-family inequality: Insights and implications of integrating genetic data

Zhao

et al. 2020

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The integration of genetic data within large-scale social and health surveys provides new opportunities to test long standing theories of parental investments in children and within-family inequality. Genetic predictors, called polygenic scores, allow novel assessments of young children's abilities that are uncontaminated by parental investments, and family-based samples allow indirect tests of whether children's abilities are reinforced or compensated. We use over 16,000 sibling pairs from the UK Biobank to test whether the relative ranking of siblings' polygenic scores for educational attainment is consequential for actual attainments. We find strong evidence of compensatory processes, on average, where the association between genotype and phenotype of educational attainment is reduced by over 20% for the higher-ranked sibling compared to the lower-ranked sibling. These effects are most pronounced in high socioeconomic status areas. We find no evidence that similar processes hold in the case of height or for relatives who are not full biological siblings (e.g. cousins). Our results provide a new use of polygenic scores to understand processes that generate within-family inequalities and also suggest important caveats to causal interpretations the effects of polygenic scores using siblingdifference designs.

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“…The latter demonstrated that parental genotypes are associated with the environment they provide for the child (28,29). In fact, a growing body of evidence is showing the importance of considering gene-environment correlation when assessing polygenic effects on trait variation (30,31), especially for educationally relevant traits. Paralleling quantitative genetics results, a key point is that environmental measures are themselves heritable and GPS effects can be mediated by the environment, while environmental effects can be accounted for by genetics (genetic confounding).…”

Section: Introductionmentioning

confidence: 99%

Multivariable G-E interplay in the prediction of educational achievement

Allegrini

Karhunen

Coleman

et al. 2019

Preprint

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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 7,026 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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Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank

Cited by 23 publications

References 34 publications

Familial Influences on Neuroticism and Education in the UK Biobank

Familial Influences on Neuroticism and Education in the UK Biobank

The production of within-family inequality: Insights and implications of integrating genetic data

Multivariable G-E interplay in the prediction of educational achievement

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