Comparing Genetic Ancestry and Self-Described Race in African Americans Born in the United States and in Africa

Posted in Articles, Health/Medicine/Genetics, Media Archive, United States on 2011-12-09 02:58Z by Steven

Comparing Genetic Ancestry and Self-Described Race in African Americans Born in the United States and in Africa

Cancer Epidemiology, Biomarkers & Prevention
Volume 17, Issue 6 (June 2008)
pages 1329-1338
DOI: 10.1158/1055-9965.EPI-07-2505

Rona Yaeger
Herbert Irving Comprehensive Cancer Center

Alexa Avila-Bront
Department of Medicine
College of Physicians and Surgeons of Columbia University

Kazeem Abdul
Herbert Irving Comprehensive Cancer Center

Patricia C. Nolan
Department of Medicine
College of Physicians and Surgeons of Columbia University

Victor R. Grann
Department of Medicine
College of Physicians and Surgeons of Columbia University

Mark G. Birchette
Department of Biology
Long Island University, Brooklyn, New York

Shweta Choudhry
Department of Biopharmaceutical Sciences and Medicine
University of California-San Francisco, San Francisco, California

Esteban G. Burchard
Department of Biopharmaceutical Sciences and Medicine
University of California-San Francisco, San Francisco, California
Kenneth B. Beckman
Children’s Hospital Oakland Research Institute, Oakland, California

Prakash Gorroochurn
Department of Biostatistics
Columbia University Medical Center, New York, New York

Elad Ziv
Division of General Internal Medicine
University of California-San Francisco, San Francisco, California

Nathan S. Consedine
Department of Psychology
Long Island University, Brooklyn, New York

Andrew K. Joe
Herbert Irving Comprehensive Cancer Center

Genetic association studies can be used to identify factors that may contribute to disparities in disease evident across different racial and ethnic populations. However, such studies may not account for potential confounding if study populations are genetically heterogeneous. Racial and ethnic classifications have been used as proxies for genetic relatedness. We investigated genetic admixture and developed a questionnaire to explore variables used in constructing racial identity in two cohorts: 50 African Americans and 40 Nigerians. Genetic ancestry was determined by genotyping 107 ancestry informative markers. Ancestry estimates calculated with maximum likelihood estimation were compared with population stratification detected with principal components analysis. Ancestry was approximately 95% west African, 4% European, and 1% Native American in the Nigerian cohort and 83% west African, 15% European, and 2% Native American in the African American cohort. Therefore, self-identification as African American agreed well with inferred west African ancestry. However, the cohorts differed significantly in mean percentage west African and European ancestries (P < 0.0001) and in the variance for individual ancestry (P ≤ 0.01). Among African Americans, no set of questionnaire items effectively estimated degree of west African ancestry, and self-report of a high degree of African ancestry in a three-generation family tree did not accurately predict degree of African ancestry. Our findings suggest that self-reported race and ancestry can predict ancestral clusters but do not reveal the extent of admixture. Genetic classifications of ancestry may provide a more objective and accurate method of defining homogenous populations for the investigation of specific population-disease associations.


Genome-wide case-control association studies provide a powerful tool for investigating possible genetic factors that may contribute to the health disparities observed among different racial and ethnic populations. Populations with different ancestral backgrounds may carry different genetic variants, and these may contribute to the variations in disease incidence and outcomes seen in specific racial and ethnic groups (1). Association studies can most easily identify disease-associated alleles when study groups are genetically similar, sharing a similar ancestral background (2). However, individual ancestry is not an easily assayed, simple category; consequently, race continues to be used as a proxy for genetic relatedness in clinical and other biological studies (3-6). There is currently no consensus on how best to examine or characterize different racial or ethnic groups when designing and conducting such studies.

Two main approaches have been used to approximate individual ancestry in biological studies: (a) using self identified race and ethnicity, which may capture common environmental influences as well as ancestral background, and (b) genotyping a panel of markers that show large frequency differentials between major geographic ancestral groupings (7, 8). Both approaches have limitations. Self-identified racial categories may not always consistently predict ancestral population clusters, and evidence suggests that it may take large sample sizes and numerous markers to describe genetic clusters that correspond to self-identified race and ethnicity groupings (9-11). Racial categories are also imprecise and inconsistent, because they may potentially vary within the same individual over time (12, 13). Furthermore, their use risks reinforcing racial divisions in society. On the other hand, more objective analyses that genotype markers that are highly informative for ancestry may not be economically practical and are limited by the requirement of serum or fresh tissue for DNA extraction. Genetically determined ancestry may not capture unmeasured social factors that may affect differences in health outcomes. There are also unique ethical challenges when linking biological phenotypes with genetic markers for specific racial groups, and caution must always be used when attributing biological differences (e.g., disease risk and treatment response) to different populations.

Understanding the ancestral background of study subjects is most important in genetic studies of admixed populations, such as African Americans, who represent an admixture of Africans, Europeans, and Native Americans (14). Genetic studies have shown that African Americans form a diverse group with percent European admixture estimated to range between 7% and 23% (14-16). Genotyping of self-identified African Americans participating in the Cardiovascular Health Study revealed that among self-reported Africans there are differences in genetic ancestry that are correlated with some clinically important endpoints (15).


The African American cohort in our study had a mean of 15% European admixture, which is consistent with previous reports of a range of 7% to 23% European admixture among U.S. African Americans (14-16). Of note, the estimates of 4% European and 1% Native American ancestry in the Nigerian population is likely due to bias in MLE due to the limited number of markers. We found that among participants there was a significantly higher proportion of admixture and higher variability in admixture proportions in the U.S.-born African American cohort compared with a population that emigrated from Africa (that is, Nigerians; Table 3). The significant variation in individual ancestry estimates among the African American cohort suggests that this group, like the Cardiovascular Health Study African American cohort (15), represents a diverse population consisting of several subpopulations. For participation in the African American cohort, subjects identified both parents as African Americans who were born in the United States. Although data regarding grandparental race were not used to screen study participation, these data were collected through a three-generation family tree during administration of the questionnaire. In this study population, all African American subjects described that the race of at least three of their four grandparents was consistent with African ancestry. Individuals and society have historically classified children of mixed-race ancestry as African American, even when one parent is Caucasian, Asian, or Native American. For African Americans, this is a remnant of the ‘‘Jim Crow’’ laws and the ‘‘One Drop’’ rule or ‘‘Rule of Hypodescent.’’ Thus, identification as African American would still occur in cases where the parents and grandparents were of mixed-race ancestry. This could also contribute to the greater European admixture and greater admixture variability seen in the African American cohort…

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Genetic Ancestry in Lung-Function Predictions

Posted in Articles, Health/Medicine/Genetics, New Media, United States on 2010-07-10 01:51Z by Steven

Genetic Ancestry in Lung-Function Predictions

New England Journal of Medicine
DOI: 10.1056/NEJMoa0907897

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


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.)

…There are some important limitations of our study. First, our analysis does not address population groups other than self-identified African Americans, such as Latinos, who have more complex patterns of ancestral admixture. Second, the association between lung function and ancestry found in our study may be the result of factors other than genetic variation, such as premature birth, prenatal nutrition, socioeconomic status, and other environmental factors. Third, we did not study a replication population with the same age range as that of the CARDIA cohort. Thus, we may have overestimated the association between ancestry and lung function in the CARDIA participants, who were young adults. Finally, some researcher groups used different statistical approaches to estimate ancestry in their respective study populations. We have found previously, however, that different approaches (e.g., Markov models and maximum-likelihood estimation) produce highly correlated results from the same set of markers. The consistency of our findings across three cohorts, despite the different methods for estimating ancestry, underscores the robustness of the association with ancestry…

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