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Downward causation and vertical pleiotropy

Published online by Cambridge University Press:  11 September 2023

Evan Charney Open the ORCID record for Evan Charney [Opens in a new window]
Evan Charney*
Affiliation:
Samuel DuBois Cook Center on Social Equity, Duke University, Durham, NC, USA echar1@me.com
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Abstract

In discussing the relationship between genetically influenced differences and educational attainment (EA), Burt employs the concept of downward causation. I note the similarities between Burt's concept of downward causation and the sociogenomics concept of vertical pleiotropy and argue that her discussion of downward causation introduces an unnecessary normative component. The core problem concerns not the appropriateness of phenotypes that influence EA but mistaken assumptions about which phenotypes are being predicted.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e211
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Abdellaoui, A., Dolan, C. V., Verweiji, K. J. H., & Nivard, M. G. (2022). Gene–environment correlations across geographic regions affect genome-wide association studies. Nature Genetics, 54, 1345–1354. https://doi.org/10.1038/s41588-022-01158-0CrossRefGoogle ScholarPubMed
Hegelund, E. R., Grønkjær, M., Osler, M., Dammeyer, J., Flensborg-Madsen, T., & Mortensen, E. L. (2020). The influence of educational attainment on intelligence. Intelligence, 78, 101419. https://doi.org/10.1016/j.intell.2019.101419CrossRefGoogle Scholar
Hill, W. D., Neil, M. D., Ritchie, S. J., Skene, N. G., Bryois, J., Bell, S., … Deary, I. J. (2019). Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income. Nature Communications, 10(1), 5741. https://doi.org/10.1038/s41467-019-13585-5CrossRefGoogle ScholarPubMed
Rietveld, C. A., Esko, T., Davies, G., Pers, T. H., Turley, P., & Benyamin, B. (2014). Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences of the United States of America, 111(38), 13790–13794. https://doi.org/10.1073/pnas.1404623111CrossRefGoogle ScholarPubMed
van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H., & Wray, N. R. (2019). Genetic correlations of polygenic disease traits: From theory to practice. Nature Reviews Genetics, 20(10), 567–581. https://doi.org/10.1038/s41576-019-0137-zCrossRefGoogle ScholarPubMed