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Within- and between-family genetic effects on educational achievement vary across countries and ages

Molecular Psychiatry (2025)Cite this article

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Abstract

Polygenic score (PGS) predictions of educational achievement are sizeable at the population level. Yet, population-level PGS predictions are environmentally confounded, due to gene-environment correlations, assortative mating, and population stratification. This confounding complicates the interpretation and application of PGS predictions of educational achievement. Here, we charted the variability of PGS predictions in N = 8115 dizygotic twins from UK, US, Swedish, and German samples aged 7 to 19 years. Population-level PGS predictions of educational achievement ranged from β = 0.16 to β = 0.37 across ages and countries. Discerning within- and between-family level estimates, we found that 10 to 65% of the population-level PGS predictions were due to environmental confounding, of which 29 to 100% were accounted for by family socioeconomic status. Variability in within-family and population-level PGS predictions was largely unsystematic across countries’ school systems (multi-tiered vs. comprehensive) and children’s ages. Therefore, interpretations regarding the sources of environmental confounding effects on educational achievement remain, at present, speculative.

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Fig. 1: Estimating population-level, within- and between-family genetic effects in family data.
Fig. 2: Overview of samples, analysis sample sizes, and assessment ages.
Fig. 3: Predictions of educational achievement from PGS and SES.
Fig. 4: PGS prediction of educational achievement at population level and within families.
Fig. 5: Within- and between family PGS predictions of educational achievement in the UK and Sweden with and without SES as covariate.

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Data availability

The datasets used for this study are not openly available due to privacy and ethical requirements, yet data access can be requested for research purposes from the respective data owners. TEDS data are available upon request (https://www.teds.ac.uk/researchers/teds-data-access-policy). Details on measurement and sample characteristics are available in the TEDS data dictionary (https://www.teds.ac.uk/datadictionary/home.htm). E-Risk data are available upon request (https://www.eriskstudy.com/data-access/). To access the MTFS data, external researchers can request to collaborate with MTFS researchers [69]. The Swedish sample comprises use individual-level register data provided by Statistics Sweden, combined with data from the STR, which is administered by the Steering Committee of the Swedish Twin Registry. To access the data, researchers they must obtain approval from the Swedish Ethical Review Authority and from the Steering Committee of the Swedish Twin Registry. STR data are available upon request (https://ki.se/en/research/swedish-twin-registry-for-researchers). In this study, TwinLife data release 7.1.0 was used (https://doi.org/10.4232/1.14186). Data are available upon request (https://search.gesis.org/research_data/ZA6701). Information on measurement and sample characteristics are available via the TwinLife data documentation website (https://www.twin-life.de/documentation/downloads).

Code availability

No custom code was used for the analyses in this study. Data analyses were conducted in R (version 4.4.0) [48] and RStudio (version 2024.04.1) [70]. The analysis code can be obtained here: https://osf.io/jzp7c/?view_only=10fd9c9885844b519339de63c5a485fe.

Notes

  1. Polygenic scores are also referred to as genome-wide score, polygenic index (PGI), or polygenic risk score/genetic risk score (in the context of medical risk).

References

  1. de Zeeuw EL, de Geus EJC, Boomsma DI. Meta-analysis of twin studies highlights the importance of genetic variation in primary school educational achievement. Trends Neurosci Educ. 2015;4:69–76.

    Article  Google Scholar 

  2. Eifler EF, Starr A, Riemann R The genetic and environmental effects on school grades in late childhood and adolescence. Meyre D, editor. PLOS ONE. 2019;14:e0225946.

  3. Rimfeld K, Malanchini M, Krapohl E, Hannigan LJ, Dale PS, Plomin R. The stability of educational achievement across school years is largely explained by genetic factors. Npj Sci Learn. 2018;3:16.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shakeshaft NG, Trzaskowski M, McMillan A, Rimfeld K, Krapohl E, Haworth CMA, et al. Strong genetic influence on a UK nationwide test of educational achievement at the end of compulsory education at age 16. Ansari D, editor. PLoS ONE. 2013;8:e80341.

  5. Allegrini AG, Selzam S, Rimfeld K, von Stumm S, Pingault JB, Plomin R. Genomic prediction of cognitive traits in childhood and adolescence. Mol Psychiatry. 2019;24:819–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50:1112–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Okbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat Genet. 2022;54:437–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wilding K, Wright M, von Stumm S. Using DNA to predict education: a meta-analytic review. Educ Psychol Rev. 2024;36:102.

    Article  Google Scholar 

  9. Selzam S, Ritchie SJ, Pingault JB, Reynolds CA, O’Reilly PF, Plomin R. Comparing within- and between-family polygenic score prediction. Am J Hum Genet. 2019;105:351–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Demange PA, Hottenga JJ, Abdellaoui A, Eilertsen EM, Malanchini M, Domingue BW, et al. Estimating effects of parents’ cognitive and non-cognitive skills on offspring education using polygenic scores. Nat Commun. 2022;13:4801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet. 2022;54:581–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Veller C, Coop GM. Interpreting population- and family-based genome-wide association studies in the presence of confounding. Moorjani P, editor. PLoS Biol 2024;22:e3002511.

  13. Malanchini M, Allegrini AG, Nivard MG, Biroli P, Rimfeld K, Cheesman R, et al. Genetic associations between non-cognitive skills and academic achievement over development. Nat Hum Behav [Internet]. 2024 [cited 2024 Aug 27]; Available from: https://www.nature.com/articles/s41562-024-01967-9.

  14. Bates TC, Maher BS, Medland SE, McAloney K, Wright MJ, Hansell NK, et al. The nature of nurture: Using a virtual-parent design to test parenting effects on children’s educational attainment in genotyped families. Twin Res Hum Genet. 2018;21:73–83.

    Article  PubMed  Google Scholar 

  15. Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI, Thorgeirsson TE, et al. The nature of nurture: Effects of parental genotypes. Science. 2018;359:424–8.

    Article  CAS  PubMed  Google Scholar 

  16. Wang B, Baldwin JR, Schoeler T, Cheesman R, Barkhuizen W, Dudbridge F, et al. Robust genetic nurture effects on education: a systematic review and meta-analysis based on 38,654 families across 8 cohorts. Am J Hum Genet. 2021;108:1780–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Plomin R, DeFries JC, Loehlin JC. Genotype-environment interaction and correlation in the analysis of human behavior. Psychol Bull. 1977;84:309–22.

    Article  CAS  PubMed  Google Scholar 

  18. Wertz J, Moffitt TE, Arseneault L, Barnes JC, Boivin M, Corcoran DL, et al. Genetic associations with parental investment from conception to wealth inheritance in six cohorts. Nat Hum Behav. 2023;7:1388–401.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Abdellaoui A, Hugh-Jones D, Yengo L, Kemper KE, Nivard MG, Veul L, et al. Genetic correlates of social stratification in Great Britain. Nat Hum Behav. 2019;3:1332–42.

    Article  PubMed  Google Scholar 

  20. Morris TT, Davies NM, Hemani G, Smith GD. Population phenomena inflate genetic associations of complex social traits. Sci Adv. 2020;6:eaay0328.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yengo L, Robinson MR, Keller MC, Kemper KE, Yang Y, Trzaskowski M, et al. Imprint of assortative mating on the human genome. Nat Hum Behav. 2018;2:948–54.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie GÅ, et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun. 2020;11:3519.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pingault J, Allegrini AG, Odigie T, Frach L, Baldwin JR, Rijsdijk F, et al. Research review: how to interpret associations between polygenic scores, environmental risks, and phenotypes. J Child Psychol Psychiatry. 2022;63:1125–39.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhou Q, Gidziela A, Allegrini AG, Cheesman R, Wertz J, Maxwell J, et al. Gene-environment correlation: the role of family environment in academic development. Mol Psychiatry [Internet]. 2024 [cited 2024 Oct 24]; Available from: https://www.nature.com/articles/s41380-024-02716-0.

  25. Kendler KS, Baker JH. Genetic influences on measures of the environment: a systematic review. Psychol Med. 2007;37:615.

    Article  PubMed  Google Scholar 

  26. van Hootegem A, Rogne AF, Lyngstad TH. Heritability of class and status: implications for sociological theory and research. Res Soc Stratif Mobil. 2024;92:100940.

    Google Scholar 

  27. von Stumm S, Smith-Woolley E, Ayorech Z, McMillan A, Rimfeld K, Dale PS, et al. Predicting educational achievement from genomic measures and socioeconomic status. Dev Sci. 2020;23:e12925.

    Article  Google Scholar 

  28. Haworth CMA, Wright MJ, Luciano M, Martin NG, De Geus EJC, Van Beijsterveldt CEM, et al. The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry. 2010;15:1112–20.

    Article  CAS  PubMed  Google Scholar 

  29. Ruks M, Starr A, Dierker P. Investigating the heritability of school grades from age 10 to 16 using biometric growth curve models [Internet]. PsyArXiv; 2023 [cited 2023 Oct 18]. Available from: https://osf.io/vecy9.

  30. Tucker-Drob EM, Briley DA, Harden KP. Genetic and environmental influences on cognition across development and context. Curr Dir Psychol Sci. 2013;22:349–55.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wößmann L. The importance of school systems: evidence from international differences in student achievement. J Econ Perspect. 2016;30:3–32.

    Article  Google Scholar 

  32. Borghans BL, Diris R, Smits W, De Vries J. Should we sort it out later? The effect of tracking age on long-run outcomes. Econ Educ Rev. 2020;75:101973.

    Article  Google Scholar 

  33. Burger K. Intergenerational transmission of education in Europe: Do more comprehensive education systems reduce social gradients in student achievement? Res Soc Stratif Mobil. 2016;44:54–67.

    Google Scholar 

  34. Engzell P, Raabe IJ. Within-school achievement sorting in comprehensive and tracked systems. Sociol Educ. 2023;96:324–43.

    Article  Google Scholar 

  35. Freeman RB, Viarengo M. School and family effects on educational outcomes across countries. Econ Policy. 2014;29:395–446.

    Article  Google Scholar 

  36. Terrin É, Triventi M. The effect of school tracking on student achievement and inequality: A meta-analysis. Rev Educ Res. 2023;93:236–74.

    Article  Google Scholar 

  37. van de Werfhorst HG. Early tracking and social inequality in educational attainment: Educational reforms in 21 European countries. Am J Educ. 2019;126:65–99.

    Article  Google Scholar 

  38. Dumont H, Klinge D, Maaz K. The many (subtle) ways parents game the system: mixed-method evidence on the transition into secondary-school tracks in Germany. Sociol Educ. 2019;92:199–228.

    Article  Google Scholar 

  39. Stull JC. Family socioeconomic status, parent expectations, and a child’s achievement. Res Educ. 2013;90:53–67.

    Google Scholar 

  40. Esping-Andersen G. Welfare regimes and social stratification. J Eur Soc Policy. 2015;25:124–34.

    Article  Google Scholar 

  41. Young AI. genome-wide association studies have problems due to confounding: are family-based designs the answer? PLoS Biol. 2024;22:e3002568.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Young AI, Benonisdottir S, Przeworski M, Kong A. Deconstructing the sources of genotype-phenotype associations in humans. Science. 2019;365:1396–400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Cumming G, Finch S. inference by eye: confidence intervals and how to read pictures of data. Am Psychol. 2005;60:170–80.

    Article  PubMed  Google Scholar 

  44. Gelman A, Stern H. The difference between “significant” and “not significant” is not itself statistically significant. Am Stat. 2006;60:328–31.

    Article  Google Scholar 

  45. Paternoster R, Brame R, Mazerolle P, Piquero A. Using the correct statistical test for the equality of regression coefficients. Criminology. 1998;36:859–66.

    Article  Google Scholar 

  46. Fisher RA. On the ’probable error’ of a coefficient of correlation deduced from a small sample. Metron. 1921;1:1–32.

    Google Scholar 

  47. Eid M, Gollwitzer M, Schmitt M. Statistik und Forschungsmethoden. 2nd ed. Weinheim: Beltz; 2011. 1024 p.

  48. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. R Foundation for Statistical Computing; 2024. Available from: https://www.R-project.org/.

  49. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw [Internet]. 2015 [cited 2023 Oct 23];67. Available from: http://www.jstatsoft.org/v67/i01/.

  50. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:702–9.

    Article  CAS  PubMed  Google Scholar 

  51. Silventoinen K, Jelenkovic A, Sund R, Latvala A, Honda C, Inui F, et al. Genetic and environmental variation in educational attainment: an individual-based analysis of 28 twin cohorts. Sci Rep. 2020;10:12681.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Esping-Andersen G. The three political economies of the welfare state. Int J Sociol. 1990;20:92–123.

    Article  Google Scholar 

  53. Osberg L. Long run trends in income inequality in the United States, UK, Sweden, Germany and Canada: A birth cohort view. East Econ J. 2003;29:121–41.

    Google Scholar 

  54. Gregg P, Jonsson JO, Macmillan L, Mood C. The role of education for intergenerational income mobility: A comparison of the United States, Great Britain, and Sweden. Soc Forces. 2017;96:121–52.

    Article  Google Scholar 

  55. Heisig JP, Elbers B, Solga H. Cross-national differences in social background effects on educational attainment and achievement: absolute vs. relative inequalities and the role of education systems. Comp J Comp Int Educ. 2020;50:165–84.

    Article  Google Scholar 

  56. Johnson W, Deary IJ, Silventoinen K, Tynelius P, Rasmussen F. Family background buys an education in Minnesota but not in Sweden. Psychol Sci. 2010;21:1266–73.

    Article  PubMed  Google Scholar 

  57. Ross NA, Dorling D, Dunn JR, Henriksson G, Glover J, Lynch J, et al. Metropolitan income inequality and working-age mortality: a cross-sectional analysis using comparable data from five countries. J Urban Health Bull N Y Acad Med. 2005;82:101–10.

    Google Scholar 

  58. Ehrenberg RG, Brewer DJ, Gamoran A, Willms JD. Class size and student achievement. Psychol Sci Public Interest. 2001;2:1–30.

    Article  CAS  PubMed  Google Scholar 

  59. López-Martín E, Gutiérrez-de-Rozas B, González-Benito AM, Expósito-Casas E. Why do teachers matter? A meta-analytic review of how teacher characteristics and competencies affect students’ academic achievement. Int J Educ Res. 2023;120:102199.

    Article  Google Scholar 

  60. Buckingham J, Wheldall K, Beaman-Wheldall R. Why poor children are more likely to become poor readers: The school years. Aust J Educ. 2013 Nov;57:190–213.

    Article  Google Scholar 

  61. Tan CY, Lyu M, Peng B. Academic benefits from parental involvement are stratified by parental socioeconomic status: A meta-analysis. Parenting. 2020;20:241–87.

    Article  Google Scholar 

  62. Coldron J, Cripps C, Shipton L. Why are english secondary schools socially segregated? J Educ Policy. 2010;25:19–35.

    Article  Google Scholar 

  63. Cullinane C, Hillary J, Andrade J, McNamara S. SELECTIVE COMPREHENSIVES 2017 Admissions to high-attaining non-selective schools for disadvantaged pupils [Internet]. The Sutton Trust; 2017. Available from: https://www.suttontrust.com/wp-content/uploads/2019/12/Selective-Comprehensives-2017.pdf.

  64. Hamnett C, Ramsden M, Butler T. Social background, ethnicity, school composition and educational attainment in East London. Urban Stud. 2007;44:1255–80.

    Article  Google Scholar 

  65. Moerenhout K, van den Berg D, de Wit M, Peyrot W, Abdellaoui A. Migration, assortative mating, and educational attainment. In London, UK; 2024.

  66. Hällsten M, Kolk M. The shadow of peasant past: Seven generations of inequality persistence in Northern Sweden. Am J Sociol. 2023;128:1716–60.

    Article  Google Scholar 

  67. Nivard MG, Belsky DW, Harden KP, Baier T, Andreassen OA, Ystrøm E, et al. More than nature and nurture, indirect genetic effects on children’s academic achievement are consequences of dynastic social processes. Nat Hum Behav [Internet]. 2024 Jan 15 [cited 2024 Apr 18]; Available from: https://www.nature.com/articles/s41562-023-01796-2.

  68. Singh C. A comparative analysis of attrition in household panel studies. (Research Papers on Comparative Analysis of Longitudinal Data; No. 10) [Internet]. CEPS/INSTEAD; 1995. Available from: https://liser.elsevierpure.com/files/11791910/Working%20Paper%20n%C2%B010.

  69. Iacono WG, McGue M. Minnesota twin family study. Twin Res. 2002;5:482–7.

    Article  PubMed  Google Scholar 

  70. Posit team. RStudio: Integrated Development Environment for R. [Internet]. Boston, MA: Posit software, PBC; 2024. Available from: http://www.posit.co/.

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Acknowledgements

We thank the participating families and many contributors to the Twins Early Development Study (TEDS), the Minnesota Twin Family Study, the Swedish Twin Registry (STR), and TwinLife. We are grateful to the Environmental Risk (E-Risk) Longitudinal Twin Study mothers and fathers, the twins, and the twins’ teachers for their participation. Our thanks to Professors Terrie Moffitt and Avshalom Caspi, the founders of the E-Risk Study, and to the E-Risk team for their dedication, hard work and insights. The views expressed are those of the authors and not necessarily those of the ESRC or King’s College London.

Funding

TEDS is supported by the UK Medical Research Council (MR/V012878/1 and previously MR/M021475/1), with additional support from the US National Institutes of Health (AG046938). The Environmental Risk (E-Risk) Study is funded by grants from the UK Medical Research Council (MRC) [G1002190; MR/X010791/1]. Additional support for E-Risk was provided by the US National Institute of Child Health and Human Development [HD077482] and the Jacobs Foundation. The Minnesota Twin Family Study was supported in part through grants from the National Institutes of Health R01 DA 042755, R01HG011035, R01 DA 054087, R01 DA 044283. The Swedish Twin Registry (STR) is managed by the Karolinska Institute and receives additional funding through the Swedish Research Council (2017-00641). Additional funding for STR comes from the Ragnar Söderberg Foundation (E9/11), and the Swedish Research Council (421-2013-1061). TwinLife is funded by grants from the German Research Foundation (DFG; project number: 220286500 and 428902522). SvS held fellowships from the Jacobs Foundation (2022-2027) and the Paris Institute of Advanced Study (2023-2024) during the writing of this manuscript. HLF was supported by the UK ESRC Centre for Society and Mental Health at King’s College London [ES/S012567/1]. The position of CP is funded by the DFG (funding number 428902552). OP, RA, and SO were supported by the Swedish Research Council (2019-00244; 2023-01343).

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Authors and Affiliations

  1. Department of Education, University of York, York, UK

    Alexandra Starr & Sophie von Stumm

  2. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Alexandra Starr

  3. Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Alexandra Starr

  4. Amsterdam Public Health Research Institute, Amsterdam, The Netherlands

    Alexandra Starr

  5. Department of Sociology, Bielefeld University, Bielefeld, Germany

    Mirko Ruks

  6. Department of Psychology, University of Bielefeld, Bielefeld, Germany

    Mirko Ruks & Christian Kandler

  7. Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA

    Alexandros Giannelis, Emily Willoughby, Matt McGue & Scott Vrieze

  8. Department of Government, Uppsala University, Uppsala, Sweden

    Oskar Pettersson, Rafael Ahlskog & Sven Oskarsson

  9. Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany

    Charlotte K. L. Pahnke, Andreas J. Forstner & Markus M. Nöthen

  10. Centre for Human Genetics, University of Marburg, Marburg, Germany

    Carlo Maj

  11. Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany

    Carlo Maj

  12. Department of Psychology, Saarland University, Saarbrücken, Germany

    Anastasia Andreas & Frank M. Spinath

  13. Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Louise Arseneault & Helen L. Fisher

  14. ESRC Centre for Society and Mental Health, King’s College London, London, UK

    Helen L. Fisher

  15. Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany

    Andreas J. Forstner

  16. Department of Psychology, University of Bremen, Bremen, Germany

    Christian Kandler

  17. Department of Psychology, University of Edinburgh, Edinburgh, UK

    Jasmin Wertz

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  1. Alexandra Starr
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Contributions

Conceptualization: AS and SvS, Methodology: AS and MR, Formal analysis: AS, MR, AG, OP, and CKLP, Resources: RA, LA, HLF, AJF, CK, MM, MMN, SO, FMS, SV, JW, and SvS, Data curation: AS, MR, AG, EW, OP, CKLP, CM, and AA, Writing – Original draft: AS and SvS, Writing – Review & Editing: All authors, Visualization: AS, Supervision: RA, LA, HLF, AJF, CK, MM, MMN, SO, FMS, SV, JW, and SvS, Funding acquisition: RA, LA, HLF, AJF, CK, MM, MMN, SO, FMS, SV, and SvS, All authors approved the final version of the manuscript.

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Correspondence to Alexandra Starr.

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Starr, A., Ruks, M., Giannelis, A. et al. Within- and between-family genetic effects on educational achievement vary across countries and ages. Mol Psychiatry (2025). https://doi.org/10.1038/s41380-025-03342-0

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