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 Li, Tamara B Harris, Deborah A Nickerson, Myriam Fornage, Paul Enright, Elad Ziv, Lewis J Smith, Kiang Liu, Esteban González Burchard, 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 Li, Tamara B Harris, Deborah A Nickerson, Myriam Fornage, Paul Enright, Elad Ziv, Lewis J Smith, Kiang Liu, Esteban González Burchard

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.)

2010 Massachusetts Medical Society

Figures

Figure 1. Ancestry, Forced Expiratory Volume in…
Figure 1. Ancestry, Forced Expiratory Volume in 1 Second (FEV1), and Differences in Predicted FEV1 among CARDIA Study Participants
The percentage of African ancestry, as estimated by means of genetic markers, in men and women who identified themselves as African American is shown in Panels A and B, respectively. The relation between African ancestry and FEV1 (indicated as a solid curve, with 95% confidence intervals as dashed curves) is also shown for men and women, in Panels C and D, respectively. The extent and distribution of the differences in predicted FEV1 between the ancestry-based models and the standard race-based models (not including ancestry) in men and women are shown in Panels E and F, respectively.

Source: PubMed

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