Hostname: page-component-77f85d65b8-2tv5m Total loading time: 0 Render date: 2026-04-17T18:39:26.186Z Has data issue: false hasContentIssue false
  • English
  • Français

Social scientists would do well to steer clear of polygenic scores

Published online by Cambridge University Press:  11 September 2023

David Curtis Open the ORCID record for David Curtis [Opens in a new window]
David Curtis*
Affiliation:
UCL Genetics Institute, University College London, London, UK d.curtis@ucl.ac.uk; http://www.davecurtis.net/dcurtis.html Centre for Psychiatry, Queen Mary University of London, London, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

The problems with polygenic scores (PGSs) have been understated. The fact that they are ancestry-specific means that biases related to sociodemographic factors would be impossible to avoid. Additionally, the requirement to obtain DNA would have profound impacts on study design and required resources, as well as likely introducing recruitment bias. PGSs are unhelpful for social science research.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e212
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Alsaffar, M. M., Hasan, M., McStay, G. P., & Sedky, M. (2022). Digital DNA lifecycle security and privacy: An overview. Briefings in Bioinformatics, 23(2), 1–16. https://doi.org/10.1093/BIB/BBAB607CrossRefGoogle ScholarPubMed
Curtis, D. (2018). Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatric Genetics, 28(5), 85–89. https://doi.org/10.1097/YPG.0000000000000206CrossRefGoogle ScholarPubMed
Ho, W. K., Tai, M. C., Dennis, J., Shu, X., Li, J., Ho, P. J., … Teo, S. H. (2022). Polygenic risk scores for prediction of breast cancer risk in Asian populations. Genetics in Medicine, 24(3), 586–600. https://doi.org/10.1016/J.GIM.2021.11.008CrossRefGoogle ScholarPubMed
Ilyas, M., Mir, A., Efthymiou, S., & Houlden, H. (2020). The genetics of intellectual disability: Advancing technology and gene editing. F1000Research, 9. https://doi.org/10.12688/F1000RESEARCH.16315.1CrossRefGoogle ScholarPubMed
Marnetto, D., Pärna, K., Läll, K., Molinaro, L., Montinaro, F., Haller, T., … Pagani, L. (2020). Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals. Nature Communications, 11(1), 1–9. https://doi.org/10.1038/s41467-020-15464-wCrossRefGoogle ScholarPubMed
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591. https://doi.org/10.1038/s41588-019-0379-xCrossRefGoogle ScholarPubMed