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Educational Tracking and the Polygenic Prediction of Education

By on March 18, 2024 in Articles

Hannu Lahtinen, Pekka Martikainen, Kaarina Korhonen, Tim Morris, Mikko Myrskylä

Sociological Science March 18, 2024
10.15195/v11.a8

Abstract

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Educational systems that separate students into curriculum tracks later may place less emphasis on socioeconomic family background and allow individuals’ personal skills and interests more time to manifest. We tested whether postponing tracking from age 11 to 16 results in stronger genetic prediction of education across a population, exploiting the natural experiment of the Finnish comprehensive school reform between 1972 and 1977. The association between polygenic score of education and achieved education strengthened after the reform by one-third among men and those from low-educated families. We observed no evidence for reform effect among women or those from high-educated families. The first cohort experiencing the new system had the strongest increases. From the perspective of genetic prediction, the school reform promoted equality of opportunity and optimal allocation of human capital. The results also suggest that turbulent circumstances, including puberty or ongoing restructuring of institutional practices, may strengthen genetic associations in education.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Hannu Lahtinen: Population Research Unit, University of Helsinki; Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland
E-mail: hannu.lahtinen@helsinki.fi

Pekka Martikainen: Population Research Unit, University of Helsinki; Max Planck Institute for Demographic Research, Rostock, Germany; Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland
E-mail: pekka.martikainen@helsinki.fi

Kaarina Korhonen: Population Research Unit, University of Helsinki; Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland
E-mail: kaarina.korhonen@helsinki.fi

Tim Morris: Centre for Longitudinal Studies, Social Research Institute, University College London
E-mail: t.t.morris@ucl.ac.uk

Mikko Myrskylä: Max Planck Institute for Demographic Research, Rostock, Germany; University of Helsinki, Helsinki, Finland; Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Rostock, Germany and Helsinki, Finland
E-mail: myrskyla@demogr.mpg.de

Acknowledgements: Special thanks for Aysu Okbay for providing education GWAS summary results excluding overlapping samples. We also thank the Finnish National Agency for Education for providing municipal-specific school-reform implementation years. The genetic samples used for the research were obtained from the THL Biobank (study number: THLBB2020_8), and we thank all study participants for their generous participation in the THL Biobank.

Supplemental Material

Replication Package: Instructions for data access and code to reproduce the analysis can be found at https://github.com/halahti/SocSci23

  • Citation: Lahtinen, Hannu, Pekka Martikainen, Kaarina Korhonen, Tim Morris, and Mikko Myrskylä. 2024. “Educational tracking and the polygenic prediction of education.” Sociological Science 11: 186-213.
  • Received: September 19, 2023
  • Accepted: October 31, 2023
  • Editors: Arnout van de Rijt, Nan Dirk de Graaf
  • DOI: 10.15195/v11.a8


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Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges

By on September 21, 2020 in Articles

Benjamin W. Domingue, Sam Trejo, Emma Armstrong-Carter, Elliot M. Tucker-Drob

Sociological Science September 21, 2020
10.15195/v7.a19

Abstract
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Interest in the study of gene–environment interaction has recently grown due to the sudden availability of molecular genetic data—in particular, polygenic scores—in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene–environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene–environment interaction studies.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Benjamin W. Domingue: Graduate School of Education, Stanford University
E-mail: bdomingu@stanford.edu

Sam Trejo: La Follette School of Public Affairs & Department of Sociology, University of Wisconsin–Madison
E-mail: sam.trejo@wisc.edu

Emma Armstrong-Carter: Graduate School of Education, Stanford University
E-mail: emmaac@stanford.edu

Elliot M. Tucker-Drob: Department of Psychology and Population Research Center, University of Texas at Austin
E-mail: tuckerdrob@utexas.edu

Acknowledgments: This work has been supported by the Russell Sage Foundation and the Ford Foundation (grant 96-17-04). S.T. was supported by the National Science Foundation (grant DGE-1656518) and the Institute of Education Sciences (grant R305B140009). E.M.T.-D. was supported by the National Institutes of Health (grants R01AG054628, R01MH120219, and R01HD083613) and by the Jacobs Foundation. Any opinions expressed are those of the authors alone and should not be construed as representing the opinions of any foundation. The authors would like to thank Jason Boardman and Jason Fletcher for comments on an early draft of this article.

  • Citation: Domingue, Benjamin W., Sam Trejo, Emma Armstrong-Carter, and Elliot M. Tucker-Drob. 2020. “Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges.” Sociological Science 7: 465-486.
  • Received: June 5, 2020
  • Accepted: August 24, 2020
  • Editors: Olav Sorenson
  • DOI: 10.15195/v7.a19


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