Mixed Race Studies

An Unexpectedly Complex Architecture for Skin Pigmentation in Africans

Cell
Volume 171, Issue 6, 2017-11-30
pages 1340–1353.e14
DOI: 10.1016/j.cell.2017.11.015

Alicia R. Martin, Meng Lin, Julie M. Granka, Justin W. Myrick, Xiaomin Liu, Alexandra Sockell, Elizabeth G. Atkinson, Cedric J. Werely, Marlo Möller, Manjinder S. Sandhu, David M. Kingsley, Eileen G. Hoal, Xiao Liu, Mark J. Daly, Marcus W. Feldman, Christopher R. Gignoux, Carlos D. Bustamante, Brenna M. Henn

Highlights

• Skin pigmentation in Africans is far more polygenic than light skin in Eurasians
• Southern African KhoeSan populations have lighter skin compared to equatorial Africans
• Highly heritable KhoeSan skin color variation is poorly explained by known genes
• The study of African skin color identifies novel and canonical pigmentation genes

Approximately 15 genes have been directly associated with skin pigmentation variation in humans, leading to its characterization as a relatively simple trait. However, by assembling a global survey of quantitative skin pigmentation phenotypes, we demonstrate that pigmentation is more complex than previously assumed, with genetic architecture varying by latitude. We investigate polygenicity in the KhoeSan populations indigenous to southern Africa who have considerably lighter skin than equatorial Africans. We demonstrate that skin pigmentation is highly heritable, but known pigmentation loci explain only a small fraction of the variance. Rather, baseline skin pigmentation is a complex, polygenic trait in the KhoeSan. Despite this, we identify canonical and non-canonical skin pigmentation loci, including near SLC24A5, TYRP1, SMARCA2/VLDLR, and SNX13, using a genome-wide association approach complemented by targeted resequencing. By considering diverse, under-studied African populations, we show how the architecture of skin pigmentation can vary across humans subject to different local evolutionary pressures.

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The Great Migration and African-American Genomic Diversity

PLOS Genetics
2016-05-27
27 pages
DOI: 10.1371/journal.pgen.1006059

Soheil Baharian
Department of Human Genetics
McGill University, Montreal, Quebec, Canada
Genome Quebec Innovation Centre, Montreal, Quebec, Canada

Maxime Barakatt
McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada

Christopher R. Gignoux
Department of Genetics
Stanford University School of Medicine, Stanford, California

Suyash Shringarpure
Department of Genetics
Stanford University School of Medicine, Stanford, California

Jacob Errington
Department of Human Genetics
McGill University, Montreal, Quebec, Canada
Genome Quebec Innovation Centre, Montreal, Quebec, Canada

William J. Blot
Division of Epidemiology
Vanderbilt University School of Medicine, Nashville, Tennessee
International Epidemiology Institute, Rockville, Maryland

Carlos D. Bustamante
Department of Genetics
Stanford University School of Medicine, Stanford, California

Eimear E. Kenny
Department of Genetics and Genomic Sciences
The Icahn School of Medicine at Mount Sinai, New York, New York

Scott M. Williams
Department of Genetics
Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire

Melinda C. Aldrich
Division of Epidemiology, Department of Thoracic Surgery
Vanderbilt University School of Medicine, Nashville, Tennessee

Simon Gravel
Department of Human Genetics
McGill University, Montreal, Quebec, Canada
Genome Quebec Innovation Centre, Montreal, Quebec, Canada

Fig 3. Pairwise genetic relatedness across US census regions among (A) African-Americans, (B) European-Americans, and (C) African-Americans and European-Americans. (D) Census-based prediction for African-Americans (see Materials and Methods). On each map, the line connecting two regions shows the average relatedness between individuals in those regions, and the thickness and opacity of the lines are on a linear scale between the minimum and maximum values shown above the map. Relatedness between regions with fewer than 10,000 possible pairs of individuals is not shown (see Materials and Methods for details). All numbers are in units of cM. (E) Decay of average IBD (shown in logarithmic scale) as a function of distance using IBD segments of length 18cM or longer from HRS (dots), compared to the analytical model (lines).

Abstract

We present a comprehensive assessment of genomic diversity in the African-American population by studying three genotyped cohorts comprising 3,726 African-Americans from across the United States that provide a representative description of the population across all US states and socioeconomic status. An estimated 82.1% of ancestors to African-Americans lived in Africa prior to the advent of transatlantic travel, 16.7% in Europe, and 1.2% in the Americas, with increased African ancestry in the southern United States compared to the North and West. Combining demographic models of ancestry and those of relatedness suggests that admixture occurred predominantly in the South prior to the Civil War and that ancestry-biased migration is responsible for regional differences in ancestry. We find that recent migrations also caused a strong increase in genetic relatedness among geographically distant African-Americans. Long-range relatedness among African-Americans and between African-Americans and European-Americans thus track north- and west-bound migration routes followed during the Great Migration of the twentieth century. By contrast, short-range relatedness patterns suggest comparable mobility of ∼15–16km per generation for African-Americans and European-Americans, as estimated using a novel analytical model of isolation-by-distance.

Author Summary

Genetic studies of African-Americans identify functional variants, elucidate historical and genealogical mysteries, and reveal basic biology. However, African-Americans have been under-represented in genetic studies, and relatively little is known about nation-wide patterns of genomic diversity in the population. Here, we study African-American genomic diversity using genotype data from nationally and regionally representative cohorts. Access to these unique cohorts allows us to clarify the role of population structure, admixture, and recent massive migrations in shaping African-American genomic diversity and sheds new light on the genetic history of this population.

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Genetic Ancestry in Lung-Function Predictions

New England Journal of Medicine
2010-07-07
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.

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 (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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