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Cited by 92School Choice and PerformanceIntergenerational and Educational Inequality StudiesEducation, Achievement, and Giftedness

Genetic associations with mathematics tracking and persistence in secondary school

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Harden, K. Paige, Domingue, Benjamin W., Belsky, Daniel W., Boardman, Jason D., Crosnoe, Robert, Malanchini, Margherita, Nivard, Michel, Tucker-Drob, Elliot M. +1 more·2020·npj Science of Learning
DOI 10.1038/s41539-020-0060-2PMC PMC7002519

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Abstract

Maximizing the flow of students through the science, technology, engineering, and math (STEM) pipeline is important to promoting human capital development and reducing economic inequality. A critical juncture in the STEM pipeline is the highly cumulative sequence of secondary school math courses. Students from disadvantaged schools are less likely to complete advanced math courses. Here, we conduct an analysis of how the math pipeline differs across schools using student polygenic scores , which are DNA-based indicators of propensity to succeed in education. We integrated genetic and official school transcript data from over 3000 European-ancestry students from U.S. high schools. We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years. Analyses using genetics as a molecular tracer revealed that the dynamics of the math pipeline differed by school advantage. Compared to disadvantaged schools, advantaged schools buffered students with low polygenic scores from dropping out of math. Across all schools, even students with exceptional polygenic scores (top 2%) were unlikely to take the most advanced math classes, suggesting substantial room for improvement in the development of potential STEM talent. These results link new molecular genetic discoveries to a common target of educational-policy reforms.

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  • The EA PGI predicts a range of cognitive phenotypes and adds non-trivial predictive power for 10 diseases even after controlling for disease-specific PGIs.

    “We first document that the EA PGI not only predicts a range of cognitive phenotypes as has been found in previous work2,4, but it also adds non-trivial predictive power for 10 diseases we examine, even after controlling for disease-specific PGIs.”

    From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [4]

    The Results sections on mathematics tracking and persistence report that the education polygenic score predicts advanced math placement and continued math coursetaking. These support prediction of specific educational outcomes, but not the broader claim about a range of cognitive phenotypes. The sentence presents the finding about 10 diseases as the citing authors’ own result, not as a finding attributed to this source (page numbers unavailable). (LLM, conf=0.82)

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Authors
Harden, K. P., Domingue, B. W., Belsky, D. W., Boardman, J. D., Crosnoe, R., Malanchini, M., Nivard, M., Tucker-Drob, E. M., & Harris, K. M.
Affiliations
  • The University of Texas at Austin (US)
  • Stanford University (US)
  • Columbia University (US)
  • University of Colorado Boulder (US)
  • Vrije Universiteit Amsterdam (NL)
  • University of North Carolina at Chapel Hill (US)
Corresponding author
Harden, K. Paige
ORCID
Venue
npj Science of Learning
Publisher
Springer Science and Business Media LLC
Published
2020-02-05
Volume
5
Issue
1
Pages
1-1
ISSN
2056-7936 (ISSN-L)
DOI
10.1038/s41539-020-0060-2
PMC
PMC7002519
OpenAlex
W3005380857
Cited by
92
References
11
Citations by year
2026: 2 · 2025: 14 · 2024: 20 · 2023: 10 · 2022: 19 · 2021: 16 · 2020: 8 · 2019: 3
FWCI
20.11
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Science of Learning (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • VU Research Portal (publishedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • John Templeton Foundation
  • Jacobs Foundation
  • Ford Foundation
Topics
  • Social Sciences › Education › School Choice and Performance
  • Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
Keywords
STEM pipeline, polygenic risk score, educational attainment, school disadvantage, socioeconomic status, high school mathematics, education policy

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