Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 256

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 access
Kumar, Rajesh, Seibold, Max A., Aldrich, Melinda C., Williams, L. Keoki, Reiner, Alex P., Colangelo, Laura, Galanter, Joshua, Gignoux, Christopher, Hu, Donglei, Sen, Saunak, Choudhry, Shweta, Peterson, Edward L., Rodriguez-Santana, Jose, Rodriguez-Cintron, William, Nalls, Michael A., Leak, Tennille S., O'Meara, Ellen, Meibohm, Bernd, Kritchevsky, Stephen B., Li, Rongling, Harris, Tamara B., Nickerson, Deborah A., Fornage, Myriam, Enright, Paul, Ziv, Elad, Smith, Lewis J., Liu, Kiang, Burchard, Esteban González·2010·New England Journal of Medicine

Abstract

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.)

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1056/nejmoa0907897

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 8 references

Cited by

See all 256

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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Genetic Ancestry in Lung-Function Predictions. https://app.cochat.ai/papers/10.1056/nejmoa0907897
The paper
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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.