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Tractable limitations of current polygenic scores do not excuse genetically confounded social science

Published online by Cambridge University Press:  11 September 2023

Damien Morris Open the ORCID record for Damien Morris [Opens in a new window] ,
Stuart J. Ritchie  and
Alexander I. Young
Damien Morris
Affiliation:
Social, Genetic, and Developmental Psychiatry Centre, King's College London, London, UK damien.morris@kcl.ac.uk https://www.kcl.ac.uk/people/damien-morris stuart.j.ritchie@kcl.ac.uk https://www.kcl.ac.uk/people/stuart-ritchie
Stuart J. Ritchie
Affiliation:
Social, Genetic, and Developmental Psychiatry Centre, King's College London, London, UK damien.morris@kcl.ac.uk https://www.kcl.ac.uk/people/damien-morris stuart.j.ritchie@kcl.ac.uk https://www.kcl.ac.uk/people/stuart-ritchie
Alexander I. Young
Affiliation:
UCLA Anderson School of Management, Los Angeles, CA, USA Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA AlexTISyoung@gmail.com https://geneticvariance.wordpress.com/
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Abstract

Burt's critique of using polygenic scores in social science conflates the “scientific costs” of sociogenomics with “sociopolitical and ethical” concerns. Furthermore, she paradoxically enlists recent advances in controlling for environmental confounding to argue such confounding is scientifically “intractable.” Disinterested social scientists should support ongoing efforts to improve this technology rather than obstructing progress and excusing genetically confounded research.

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Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e222
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Baier, T., Eilertsen, E. M., Ystrom, E., Zambrana, I. M., & Lyngstad, T. H. (2022). An anatomy of the intergenerational correlation of educational attainment – Learning from the educational attainments of Norwegian twins and their children. Research in Social Stratification and Mobility, 79, 100691. https://doi.org/10.1016/j.rssm.2022.100691.CrossRefGoogle Scholar
Björklund, A., & Salvanes, K. G. (2011). Chapter 3 – Education and family background: Mechanisms and policies. In Hanushek, E. A., Machin, S. & Woessmann, L. (Eds.), Handbook of the economics of education (Vol. 3, pp. 201–247). Elsevier. https://doi.org/10.1016/B978-0-444-53429-3.00003-XGoogle Scholar
Chen, C.-Y., Tian, R., Ge, T., Lam, M., Sanchez-Andrade, G., Singh, T., … Runz, H. (2022). The impact of rare protein coding genetic variation on adult cognitive function. medRxiv. (p. 2022.06.24.22276728). https://doi.org/10.1101/2022.06.24.22276728CrossRefGoogle Scholar
Frisell, T., Pawitan, Y., Långström, N., & Lichtenstein, P. (2012). Heritability, assortative mating and gender differences in violent crime: Results from a total population sample using twin, adoption, and sibling models. Behavior Genetics, 42(1), 3–18. https://doi.org/10.1007/s10519-011-9483-0CrossRefGoogle ScholarPubMed
Holmlund, H., Lindahl, M., & Plug, E. (2011). The causal effect of parents’ schooling on children's schooling: A comparison of estimation methods. Journal of Economic Literature, 49(3), 615–651. https://doi.org/10.1257/jel.49.3.615CrossRefGoogle Scholar
Hyytinen, A., Ilmakunnas, P., Johansson, E., & Toivanen, O. (2019). Heritability of lifetime earnings. The Journal of Economic Inequality, 17(3), 319–335. https://doi.org/10.1007/s10888-019-09413-xCrossRefGoogle Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112. https://doi.org/10.1038/s41588-018-0147-3CrossRefGoogle ScholarPubMed
Rowe, D. C. (1994). The limits of family influence: Genes, experience, and behavior. Guilford PressGoogle Scholar
Sariaslan, A., Mikkonen, J., Aaltonen, M., Hiilamo, H., Martikainen, P., & Fazel, S. (2021). No causal associations between childhood family income and subsequent psychiatric disorders, substance misuse and violent crime arrests: A nationwide Finnish study of >650 000 individuals and their siblings. International Journal of Epidemiology, 50(5), 1628–1638. https://doi.org/10.1093/ije/dyab099CrossRefGoogle ScholarPubMed
Silventoinen, K., Jelenkovic, A., Sund, R., Latvala, A., Honda, C., Inui, F., … Kaprio, J. (2020). Genetic and environmental variation in educational attainment: An individual-based analysis of 28 twin cohorts. Scientific Reports, 10(1), 12681. https://doi.org/10.1038/s41598-020-69526-6CrossRefGoogle ScholarPubMed
Wolfram, T., & Morris, D. (2022). Conventional twin studies overestimate the environmental differences between families relevant to educational attainment. PsyArXiv. https://doi.org/10.31234/osf.io/m4eqvCrossRefGoogle Scholar