Genetic ancestry in lung-function predictions

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

194 Scopus citations


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 (FEV1) 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 FEV1) 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.)

Original languageEnglish (US)
Pages (from-to)321-330
Number of pages10
JournalNew England Journal of Medicine
Issue number4
StatePublished - Jul 22 2010

ASJC Scopus subject areas

  • Medicine(all)


Dive into the research topics of 'Genetic ancestry in lung-function predictions'. Together they form a unique fingerprint.

Cite this