Genetic Endowments, Educational Outcomes and the Mediating Influence of School Investments

Arold, Benjamin W.; Hufe, Paul; Stoeckli, Marc

doi:10.2139/ssrn.4167444

Cited by 9 publications

(12 citation statements)

References 120 publications

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“…Years of education has been constructed by converting education levels into years of education following previous studies (Arold, Hufe, and Stoeckli 2022;Domingue et al 2015). It consists of the following categories: 8 years (some high school), 10 years (some high school), 12 years (high school graduate, General Educational Development (GED)), 13 years (some vocational/technical training), 14 years (some community college, some college, completed vocational/technical training, associate or junior college degree), 16 years (completed college), 17 years (some graduate school), 18 years (completed a master's degree, some postbaccalaureate professional education), 19 years (some graduate training beyond a master's degree, completed post-baccalaureate professional education), and 20 years (completed a doctoral degree) of education.…”

Section: National Longitudinal Study Of Adolescent To Adult Healthmentioning

confidence: 99%

“…Thirdly, over the last decades, research has moved away from the debate over whether observed social inequalities are predominantly genetically or structurally determined ii . Instead, one of the most active areas in the field has focused on gene-environment interactions in jointly shaping various outcomes, including educational attainment (e.g., Arold, Hufe, and Stoeckli 2022;Conley et al 2015;. The key question in this respect is whether the PGI for education has a greater impact on the final educational attainment of high-or low-SES students.…”

Section: Introductionmentioning

confidence: 99%

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Compensating or Boosting Genetic Propensities? Gene-Family Socioeconomic Status Interactions by Educational Outcome Selectivity

Ghirardi¹,

Bernardi²

2023

Preprint

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This study investigates the extent to which the genetic propensity for education matters for the final educational attainment of high- or low-SES students. Previous research addressing this question has been guided by the Scarr-Rowe hypothesis which predicts that genetic influences are maximized in high-SES families. However, the empirical evidence is mixed. We propose a model that integrates compensatory and boosting advantage models from social stratification research in sociogenomics highlighting the role of educational outcome selectivity. Our model predicts that in the case of educational outcomes characterized by a low level of selectivity, such as high-school completion, genes matter more for low-SES individuals, while for outcomes characterized by a high level of selectivity, such as graduate school completion, genes matter more for high-SES individuals. We test our model, replicating the same analyses on three different datasets with genotype information, the National Longitudinal Study of Adolescent to Adult Health, the Health and Retirement Study, and the Wisconsin Longitudinal Study. The results are in line with our predictions. Our theoretical model can explain previous heterogeneous findings in the literature and can be generalized to develop testable hypotheses for other cohorts in the United States and other countries.

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Section: National Longitudinal Study Of Adolescent To Adult Healthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compensating or Boosting Genetic Propensities? Gene-Family Socioeconomic Status Interactions by Educational Outcome Selectivity

Ghirardi¹,

Bernardi²

2023

Preprint

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“…DNA-based predictions of educational success can help improve teaching and learning in three ways. First, they offer the possibility of personalizing education to better meet students' different learning needs, for example by recommending students with lower polygenic scores for additional classes or tutoring or by allocating higher quality teachers and more teaching staff to them (Arold et al, 2022). Second, polygenic scores can serve as an early 'warning system' that signals potential problems before they have manifested as maladaptive behaviors, such as learned helplessness which occurs when students withdraw from educational opportunities after experiencing an academic setback (e.g., Ghasemi & Karimi, 2021).…”

Section: Safeguarding Polygenic Score Predictions In Educationmentioning

confidence: 99%

“…A less oxymoronic way for achieving fairness in education is prioritizing equity in educational outcomes (Knigge et al, 2022;Sokolowski & Ansari, 2018), for example allocating higher quality teachers or more teaching staff to children who are at risk of academic failure, while better performing children are looked after by fewer teachers (Arold et al, 2022). Prioritizing equity of outcomes rather than equality of opportunity will reduce the extent to which children are held personally responsible for their academic achievements and failures, an attribution that prevails in meritocratic education systems.…”

Section: Introductionmentioning

confidence: 99%

Using DNA to predict Education: A Meta-Analytic Review

Wilding¹,

Wright²,

Stumm³

2023

Preprint

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Recent advances in genomics have made it possible to predict people’s differences in educational success from person-specific aggregates of DNA, so-called genome-wide polygenic scores. Here, we review the validity and strength of these DNA-based predictions for educational success across studies. We identified 18 publications that reported associations between polygenic scores, based on the summary statistics of Lee et al.’s (2018) Genome-Wide Association Study for years spent in education, and phenotypic measures of educational success, including academic tests scores, school grades, highest educational qualifications, and years spent in education. A multilevel meta-analysis with k=39 estimates from 16 independent samples (Ntotal = 398,545) yielded an estimate of ρ=.27 (95% CI from .23 to .31) between polygenic scores and educational success. A meta-regression model controlling for sample characteristics, methodological factors, and phenotype measure differences yielded an estimate of ρ=.40 (95% CI from .25 to .54), indicative of a large effect size. This association varied as a function of ancestry, with the prediction being almost thrice as strong for European ancestry than African American ancestry samples. Our findings confirm that DNA-based predictions of educational success are substantial, with a Standard Deviation (SD) increase in polygenic score being associated with a .32 score increase in high school GPA or spending 1.6 more years in full-time education. We discuss how understanding DNA-based predictions may help address students’ heterogeneity in learning abilities and improving teaching and education provisions.

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“…Consequently, contemporary GWAS analyses have shifted focus away from individual SNPs, embracing methodologies that account for the polygenic nature of these traits ( Bulik-Sullivan et al, 2015 ; Finucane et al, 2015 ; Miao et al, 2023 ). Similarly, G × E studies are undergoing a similar transition, placing greater emphasis on understanding how the polygenic basis of a trait varies across different environments ( Arold et al, 2022 ; Barth et al, 2020 ; Bernabeu et al, 2021 ; Dahl et al, 2020 ; Martin et al, 2021 ; Robinson et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Statistical methods for gene–environment interaction analysis

Miao,

Wu,

2023

WIREs Computational Stats

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Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene–environment interactions (G × E). In this review, we present state‐of‐the‐art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single‐variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.This article is categorized under: Applications of Computational Statistics > Genomics/Proteomics/Genetics Data: Types and Structure > Massive Data Statistical Models > Linear Models

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Genetic Endowments, Educational Outcomes and the Mediating Influence of School Investments

Cited by 9 publications

References 120 publications

Compensating or Boosting Genetic Propensities? Gene-Family Socioeconomic Status Interactions by Educational Outcome Selectivity

Compensating or Boosting Genetic Propensities? Gene-Family Socioeconomic Status Interactions by Educational Outcome Selectivity

Using DNA to predict Education: A Meta-Analytic Review

Statistical methods for gene–environment interaction analysis

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