Literature DB >> 30702313

Using DNA to predict educational trajectories in early adulthood.

Ziada Ayorech1, Robert Plomin1, Sophie von Stumm2.   

Abstract

At the end of compulsory schooling, young adults decide on educational and occupational trajectories that impact their subsequent employability, health and even life expectancy. To understand the antecedents to these decisions, we follow a new approach that considers genetic contributions, which have largely been ignored before. Using genomewide polygenic scores (EA3) from the most recent genomewide association study of years of education in 1.1 million individuals, we tested for genetic influence on early adult decisions in a United Kingdom-representative sample of 5,839 at 18 years of age. EA3 significantly predicted educational trajectories in early adulthood (Nagelkerke R2 = 10%), χ2(4) = 571.77, p < .001, indicating that young adults partly adapt their aspirations to their genetic propensities-a concept known as gene-environment correlation. Compared to attending university, a 1 standard deviation increase in EA3 was associated on average with a 51% reduction in the odds of pursuing full-time employment (OR = .47; 95% CI [.43, .51]); an apprenticeship (OR = .49; 95% CI [.45, .54]); or not going in education, employment, or training (OR = .50; 95% CI [.41, .60]). EA3 associations were attenuated when controlling for previous academic achievement and family socioeconomic status. Overall this research illustrates how DNA-based predictions offer novel opportunities for studying the sociodevelopmental structures of life outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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Year:  2019        PMID: 30702313      PMCID: PMC6522355          DOI: 10.1037/dev0000682

Source DB:  PubMed          Journal:  Dev Psychol        ISSN: 0012-1649


  37 in total

1.  Emerging adulthood. A theory of development from the late teens through the twenties.

Authors:  J J Arnett
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3.  Principal components analysis corrects for stratification in genome-wide association studies.

Authors:  Alkes L Price; Nick J Patterson; Robert M Plenge; Michael E Weinblatt; Nancy A Shadick; David Reich
Journal:  Nat Genet       Date:  2006-07-23       Impact factor: 38.330

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7.  Educational attainment, deprivation-affluence and self reported health in Britain: a cross sectional study.

Authors:  I R White; D Blane; J N Morris; P Mourouga
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Review 8.  Common disorders are quantitative traits.

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  4 in total

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