The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Genetic Ancestry in Lung-Function Predictions
Open accessAbstract
BACKGROUND: Self-identified race or ethnic group is used to determine normal reference standards in the prediction of pulmonary function. We conducted a study to determine whether the genetically determined percentage of African ancestry is associated with lung function and whether its use could improve predictions of lung function among persons who identified themselves as African American. METHODS: We assessed the ancestry of 777 participants self-identified as African American in the Coronary Artery Risk Development in Young Adults (CARDIA) study and evaluated the relation between pulmonary function and ancestry by means of linear regression. We performed similar analyses of data for two independent cohorts of subjects identifying themselves as African American: 813 participants in the Health, Aging, and Body Composition (HABC) study and 579 participants in the Cardiovascular Health Study (CHS). We compared the fit of two types of models to lung-function measurements: models based on the covariates used in standard prediction equations and models incorporating ancestry. We also evaluated the effect of the ancestry-based models on the classification of disease severity in two asthma-study populations. RESULTS: African ancestry was inversely related to forced expiratory volume in 1 second (FEV(1)) and forced vital capacity in the CARDIA cohort. These relations were also seen in the HABC and CHS cohorts. In predicting lung function, the ancestry-based model fit the data better than standard models. Ancestry-based models resulted in the reclassification of asthma severity (based on the percentage of the predicted FEV(1)) in 4 to 5% of participants. CONCLUSIONS: Current predictive equations, which rely on self-identified race alone, may misestimate lung function among subjects who identify themselves as African American. Incorporating ancestry into normative equations may improve lung-function estimates and more accurately categorize disease severity. (Funded by the National Institutes of Health and others.)
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Fast model-based estimation of ancestry in unrelated individuals
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The Cardiovascular Health Study: design and rationale.
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Control of confounding of genetic associations in stratified populations
Hoggart, C. J., Parra, E. J., Shriver, M. D., Bonilla, C., Kittles, R. A., Clayton, D. G., & McKeigue, P. M. (2003) · The American Journal of Human Genetics · doi:10.1086/375613 · Cited by 490
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Reexamining the Sarcopenia Hypothesis: Muscle Mass versus Muscle Strength
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Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmms2004740 · Cited by 1,731
Lamprecht, B., McBurnie, M. A., Vollmer, W. M., Gudmundsson, G., Welte, T., Nizankowska-Mogilnicka, E., Studnicka, M., Bateman, E., Anto, J. M., Burney, P., et al. (2011) · Chest · doi:10.1378/chest.10-1253 · Cited by 526
The genetics of Mexico recapitulates Native American substructure and affects biomedical traits
Moreno-Estrada, A., Gignoux, C. R., Fernández-López, J. C., Zakharia, F., Sikora, M., Contreras, A. V., Acuña-Alonzo, V., Sandoval, K., Eng, C., Romero-Hidalgo, S., et al. (2014) · Science · doi:10.1126/science.1251688 · Cited by 478
Self-citationMersha, T. B., & Abebe, T. (2015) · Human Genomics · doi:10.1186/s40246-014-0023-x · Cited by 450
Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
Riley, R. D., Snell, K. I. E., Ensor, J., Burke, D. L., Harrell, F. E., Moons, K. G. M., & Collins, G. S. (2019) · Statistics in Medicine · doi:10.1002/sim.7993 · Cited by 351
Metadata
- Authors
- Kumar, R., Seibold, M. A., Aldrich, M. C., Williams, L. K., Reiner, A. P., Colangelo, L., Galanter, J., Gignoux, C., Hu, D., Sen, S., Choudhry, S., Peterson, E. L., Rodriguez-Santana, J., Rodriguez-Cintron, W., Nalls, M. A., Leak, T. S., O'Meara, E., Meibohm, B., Kritchevsky, S. B., Li, R., Harris, T. B., Nickerson, D. A., Fornage, M., Enright, P., Ziv, E., Smith, L. J., Liu, K., & Burchard, E. G.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2010-07-22
- Volume
- 363
- Issue
- 4
- Pages
- 321-330
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2032761460
- Cited by
- 256
- References
- 8
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Kumar, R., Seibold, M. A., Aldrich, M. C., Williams, L. K., Reiner, A. P., Colangelo, L., Galanter, J., Gignoux, C., Hu, D., Sen, S., Choudhry, S., Peterson, E. L., Rodriguez-Santana, J., Rodriguez-Cintron, W., Nalls, M. A., Leak, T. S., O'Meara, E., Meibohm, B., Kritchevsky, S. B., . . . Burchard, E. G. (2010). Genetic Ancestry in Lung-Function Predictions. New England Journal of Medicine. https://doi.org/10.1056/nejmoa0907897
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.