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Why genes don't count (for racial differences in health)

Open access
·2000·American Journal of Public Health

Abstract

There is a paradoxical relationship between "race" and genetics. Whereas genetic data were first used to prove the validity of race, since the early 1970s they have been used to illustrate the invalidity of biological races. Indeed, race does not account for human genetic variation, which is continuous, complexly structured, constantly changing, and predominantly within "races." Despite the disproof of race-as-biology, genetic variation continues to be used to explain racial differences. Such explanations require the acceptance of 2 disproved assumptions: that genetic variation explains variation in disease and that genetic variation explains racial variation in disease. While the former is a form of geneticization, the notion that genes are the primary determinants of biology and behavior, the latter represents a form of racialization, an exaggeration of the salience of race. Using race as a proxy for genetic differences limits understandings of the complex interactions among political-economic processes, lived experiences, and human biologies. By moving beyond studies of racialized genetics, we can clarify the processes by which varied and interwoven forms of racialization and racism affect individuals "under the skin."

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Metadata

Venue
American Journal of Public Health
Published
2000-11-01
Volume
90
Issue
11
Pages
1699-1702
ISSN
  • 0090-0036 (ISSN-L)
  • 1541-0048
OpenAlex
W1562813268
Cited by
325
References
0
FWCI
6.12
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Why genes don't count (for racial differences in health). https://app.cochat.ai/papers/10.2105/ajph.90.11.1699
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Why genes don't count (for racial differences in health). (2000). American Journal of Public Health. https://doi.org/10.2105/ajph.90.11.1699

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.