Mixed Race Studies

The Seminole Indians of Florida: Morphology and Serology

American Journal of Physical Anthropology
Volume 32, Number 1 (January, 1970)
pages 65-81

William S. Pollitzer
University of North Carolina, Chapel Hill

Donald L. Rucknagel
University of Michigan

Richard E. Tashian
University of Michigan

Donald C. Shreffler
University of Michigan

Webster C. Leyshon
National Institute of Dental Research, NIH

Kadambari Namboodiri
Carolina Population Center
University of North Carolina

Robert C. Elston
University of North Carolina

The Seminole Indians of Florida were studied on their three reservations for blood types, red cell enzymes, serum proteins, physical measurements, and relationships. Both serologic and morphologic factors suggest their close similarity to other Indians and small amount of admixture. The Florida Seminoles are similar to Cherokee “full-bloods” in their absence of Rho and their incidence of O and M. In the presence of Di^a they are similar to other Indians, especially those of South America. While the presence of G-6-P-D A and the frequency of Hgb. S are indicative of Negro ancestry, the absence of Rho suggests that the Negro contribution must have been small. Physical traits give parallel results. Both serology and morphology further show that the Seminoles of the Dania and Big Cypress reservations are more similar to each other than to those of the Brighton reservation, in keeping with their history.

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Self-Reported Race and Genetic Admixture

The New England Journal of Medicine
Number 354, Number 4 (2006-01-26)
pages 431-422
DOI: 10.1056/NEJMc052515

Moumita Sinha, M.Stat.
Case Western Reserve University, Cleveland, Ohio

Emma K. Larkin, M.H.S.
Case Western Reserve University, Cleveland, Ohio

Robert C. Elston, Ph.D.
Case Western Reserve University, Cleveland, Ohio

Susan Redline, M.D., M.P.H.
Case Western Reserve University, Cleveland, Ohio

To the Editor:

The use of data on self-reported race in health research has been highly debated. For example, Burchard et al. recently argued that important information on disease susceptibility may be derived from the use of data on self-reported race, whereas Cooper et al. cited Wilson et al., who argued that ethnic labels “are inaccurate representations of the inferred genetic clusters.” Cooper et al., however, ignored later work that identified limitations in the analyses of Wilson et al. — specifically, inappropriate classification of groups, the use of a suboptimal model for cluster identification, and reliance on only 39 microsatellite markers for cluster analyses. With larger numbers of markers, it was shown that genetically distinct groups can be almost completely inferred from self-reported race…

…With support from a U.S. Public Health Service grant, we applied an admixture analysis to a sample population in Cleveland. Participants were clearly separated into unique groups with the use of this genetic approach. Whereas 93 percent of self-reported whites were classified as having predominantly European ancestry, less than 2 percent of blacks were so classified. Only 4 percent who reported their race as black had predominantly African ancestry; yet, the admixture proportions of this group made it possible to separate the population into two groups, in which 94 percent of self-reported blacks and 7 percent of self-reported whites were classified as being of mixed race (Figure 1: Frequency Histogram Showing the Percentage of African Ancestry in a Population Living in Cleveland). The sharp peak at the left in Figure 1 indicates that there are many persons who have no African ancestry (i.e., the values correspond to those of self-reported whites), and the broad peak at the right indicates that most blacks are of mixed race and do not originate from any single population. Thus, self-reported race and genetic ethnic ancestry appear to be highly correlated as a dichotomy, with those who self-report as being black comprising, as expected from historical and cultural practices in the United States, a broad range of African ancestry…

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The Estimation of Admixture in Racial Hybrids

Annals of Human Genetics
Volume 35, Issue 1 (July 1971)
pages 9–17
DOI: 10.1111/j.1469-1809.1956.tb01373.x

Robert C. Elston, Professor & Chair, Distinguished University Professor
Case Western Reserve University

When a racial hybrid population has arisen from the intermarriage of two or more parental populations, a problem of interest is to determine what the relative contributions are from each parental population to the hybrid. Various distance measures have been proposed whereby, on the basis of several traits, the distance between the hybrid and each of the parental populations can be estimated: these distances are then sometimes interpreted, as a first approximation, as being inversely proportional to the parental contributions (Pollitzer, 1964). In the particular case that all the traits considered are discrete in nature and each is determined by alleles at a single locus (or system of tightly linked loci), it is possible to estimate the parental contributions more directly. It in the purpose of this paper to reconsider two main methods of doing this when the traits involved are determined by a random set of independently assorting loci.

Robers &. Hiorns (1962, 1965) proposed a least-squares solution to the problem, and Krieger et al. (1965) gave a maximum-likelihood solution. Both methods, as given by these authors, can be improved. We shall here restate both methods, using a common notation, and point out the improvements possible; furthermore, some resumes of using these method will also be presented, so that the methods may be compared empirically.

Least-Squares Method

Suppose ther are p (> 1) parental populations and for each we have gene frequency estimates of the same k genes.  Let X = (x_ij) be a k x p matrix, x_ij being the estimate of the ith gene frequency in the jth parental population. Let the k x 1 vector y have as its elements the corresponding gene frequency estimates in the hybrid population; and let the proportion of the hybrid population’s genes that come from the jth parental populatio be µ_ij the jth element of th p x 1 vector µ.  Then if the estimates are all exactly equal to the gene frequencies; and if the k chosen genes represent perfectly all the genes for which there has been no selection or drift, y-Xµ = 0, where 0 is the null vector. The least squares estimate of µ is that value of µ, m say, which minimizes the sum of squares of the diserepancies given by y-Xµ, i.e. which minimizes (y — Xµ)′(y — Xµ), where the prime denotes transposition. The least squares estimate is accordingly

m = (X′X)^-1 X′y provided X′X is non-singular.

Now it should be noted that the k genes fall into allelic systems, the sum of the gene frequencies for each syatem being unity in each population. Thus, for example, the gene frequency for M and N add to unity, and it is impossible to estimate a gene  frequency for M without at the same time implicitly estimating a gene frequency for N. When Roberts & Hiorns (1963) use (1) to obtain least squares estimates they eliminate one allele from each system, so that tho rows of X and y can no longer be grouped by system with the column totals for each group adding…

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