Race and genetics

The relationship between race and genetics is relevant to the controversy concerning race. Genetic analysis enables us to determine the geographic ancestry of a person. Such analyses may pinpoint the migrational history of a person's ancestors with a high degree of accuracy.

Due to endogamy, allele frequencies cluster around kin groups and lineages or national, cultural or linguistic boundaries. This correlates genetic clusters and population groups when a number of alleles are evaluated.

The biological variation of a human genetic trait is clinal, with a gradual change in trait frequency between population clusters. Clines do not align around the same centers, resulting in more complex variations than those caused by large continental groups.

Research in genetics offers a means to classify humans which is more precise than broad phenotypes (such as skin color), and does not correlate with geographic ancestry. Some anthropologists, particularly forensic anthropologists, consider race a useful biological category. It is possible to determine race from physical remains; what is identified is the geographic phenotype.

Evolution

Map of early human migrations
1. Homo sapiens
2. Neanderthals
3. Early Hominids

Main articles: Human evolution and Early human migrations

Humans and chimpanzees diverged from a common ancestor about 5–7 million years ago. The genus Homo evolved by about 2.3 to 2.4 million years ago from Australopithecines. Several species and subspecies of Homo evolved, which are now extinct; these included Homo erectus (who inhabited Asia) and Homo sapiens neanderthalensis (who inhabited western Eurasia). Archaic Homo sapiens evolved from 400,000 to 250,000 years ago.

The dominant view among scientists of the origin of anatomically modern humans is the "out of Africa" hypothesis; this believes that Homo sapiens arose in Africa and migrated from the continent 50,000 to 100,000 years ago, replacing populations of Homo erectus in Asia and Homo neanderthalensis in Europe. This theory has replaced the multiregional hypothesis, which argues that Homo sapiens evolved as geographically separate (but interbreeding) populations stemming from a worldwide migration of Homo erectus out of Africa about 2.5 million years ago. Although scientists have generally replaced Homo erectus with Homo sapiens as the common ancestor of modern humans, DNA evidence demonstrates that Neanderthal genes may have contributed about four percent of non-African heredity and the recently discovered Denisova hominin may have contributed six percent of the Melanesian genome.

Genetic variation

Main article: Human genetic variation

Genetic variation arises from mutations in genetic material, migration between populations (gene flow) and the reshuffling of genes through sexual reproduction. Evolution is caused by natural selection and genetic drift. Genetic drift may be hampered by the founder effect. Epigenetic inheritance are heritable changes in phenotype (appearance) or gene expression caused by mechanisms other than changes in the DNA sequence.

Human phenotypes are polygenic (dependent on interaction by many genes) and may be influenced by environment as well as genetics. The measure of genetic role in phenotypes is heritability.

Nucleotide diversity is based on single mutations, single nucleotide polymorphisms (SNPs). The nucleotide diversity between humans is about 0.1 percent (one difference per one thousand nucleotides between two humans chosen at random). This amounts to approximately three million SNPs (since the human genome has about three billion nucleotides). There are an estimated ten million SNPs in the human population.

Research has shown that non-SNP (structural) variation accounts for more human genetic variation than single nucleotide diversity. Structural variation includes copy-number variation and results from deletions, inversions, insertions and duplications. It is estimated that approximately 0.4 percent of the genomes of unrelated people differ, apart from copy number. When copy-number variation is included, human-to-human genetic variation is estimated to be at least 0.5 percent.

Research methods

Trait, protein and gene studies

See also: Race (classification of human beings)

Early classification attempts measured surface traits.

Geographic distribution of blood group A

Geographic distribution of blood group B

Before the discovery of DNA, scientists used blood proteins (the human blood group systems) to study human genetic variation. Research by Ludwik and Hanka Herschfeld during World War I found that the incidence of blood groups A and B differed by region; for example, among Europeans 15 percent were group B and 40 percent group A. Eastern Europeans and Russians had a higher incidence of group B; people from India had the greatest incidence. The Herschfelds concluded that humans comprised two "biochemical races", originating separately. It was hypothesized that these two races later mixed, resulting in the patterns of groups A and B. This was one of the first theories of racial differences to include the idea that human variation did not correlate with genetic variation. It was expected that groups with similar proportions of blood groups would be more closely related, but instead it was often found that groups separated by great distances (such as those from Madagascar and Russia), had similar incidences. Researchers currently use genetic testing, which may involve hundreds (or thousands) of genetic markers or the entire genome.

Population genetics

Structure

Multi-Locus Allele Clusters In a haploid population, when a single locus is considered (blue), with two alleles, + and - we can see a differential geographical distribution between Population I (70% +) and Population II (30% +). When we want to assign an individual to one of these populations using this single locus we will assign any + to population I because the probability (p) of this allele belonging to Population I is p = 0.7, the probability (q) of incorrectly assigning this allele to Population I is q = 1 − p, or 0.3. This amounts to a Bernoulli trial because the answer to the question "is this the correct population?" is a simple yes or no. This makes the test binomially distributed but with a single trial. But when three loci per individual are taken into account, each with p = 0.7 for a + allele in Population I the average number of + alleles per individual becomes kp = 2.1 (number of trials (k = 3) × probability for each allele (p = 0.7)) and 0.9 (3 × 0.3) + alleles per individual in Population II. This is sometimes referred to as the population trait value. Because alleles are discrete entities we can only assign an individual to a population based on the number of whole + alleles it contains. Therefore we will assign any individual with three or two + alleles to Population I, and any individual with one or fewer + alleles to population II. The binomial distribution with three trials and a probability of 0.7 shows that the probability of an individual from this population having a single + allele is 0.189 and for zero + alleles it is 0.027, which gives a misclassification rate of 0.189 + 0.027 = 0.216, which is a smaller chance of misclassification than for a single allele. Misclassification becomes much smaller as we use more alleles. When more loci are taken into account, each new locus adds an extra independent test to the binomial distribution, decreasing the chance of misclassification. Using modern computer software and the abundance of genetic data now available, it is possible not only to distinguish such correlations for hundreds or even thousands of alleles, which form clusters, it is also possible to assign individuals to given populations with very little chance of error. It should be noted, however, that genes tend to vary clinally, and there are likely to be intermediate populations that reside in the geographical areas between our sample populations (Population III, for example, may lie equidistantly from Population I and Population II). In this case it may well be that Population III may display characteristics of both population I and Population II and have intermediate frequencies for many of the alleles used for classification, causing this population to be more prone to misclassification.

World map based on genetic principal component analysis of human populations from Luigi Luca Cavalli-Sforza's 1994 History and Geography of Human Genes.

Several methods to examine and quantify genetic subgroups exist, including cluster and principal components analysis. Genetic markers from individuals are examined to find a population's genetic structure. While subgroups overlap when examining variants of one marker only, when a number of markers are examined different subgroups have different average genetic structure. An individual may be described as belonging to several subgroups. These subgroups may be more or less distinct, depending on how much overlap there is with other subgroups. One such mathematical method is.. The population genetic structure found is often similar.

In cluster analysis, the number of clusters to search for K is determined in advance; how distinct the clusters are varies. The results obtained from cluster analyses depend on several factors:

• A large number genetic markers studied facilitates finding distinct clusters.
• Some genetic markers vary more than others, so fewer are required to find distinct clusters. Ancestry-informative markers exhibits substantially different frequencies between populations from different geographical regions. Using AIMs, scientists can determine a person's ancestral continent of origin based solely on their DNA. AIMs can also be used to determine someone's admixture proportions.
• The more individuals studied, the easier it becomes to detect distinct clusters (statistical noise is reduced).
• Low genetic variation makes it more difficult to find distinct clusters. Greater geographic distance generally increases genetic variation, making identifying clusters easier.
• A similar cluster structure is seen with different genetic markers when the number of genetic markers included is sufficiently large. The clustering structure obtained with different statistical techniques is similar. A similar cluster structure is found in the original sample with a subsample of the original sample.

Distance

Genetic distance is genetic divergence between species or populations of a species. It may compare the genetic similarity of related species, such as humans and chimpanzees. Within a species, genetic distance measures divergence between subgroups.

Genetic distance significantly correlates to geographic distance between populations, a phenomenon sometimes known as "isolation by distance". Genetic distance may be the result of physical boundaries restricting gene flow such as islands, deserts, mountains or forests.

Genetic distance is measured by the fixation index (F_ST). F_ST is the correlation of randomly chosen alleles in a subgroup to a larger population. It is often expressed as a proportion of genetic diversity. This comparison of genetic variability within (and between) populations is used in population genetics. The values range from 0 to 1; zero indicates the two populations are freely interbreeding, and one would indicate that two populations are separate.

History and geography

Cavalli-Sforza has described two methods of ancestry analysis. Current-population genetic structure does not imply that differing clusters or components indicate only one ancestral home per group; for example, a genetic cluster in the US comprises Hispanics with European, Native American and African ancestry.

Geographic analyses attempt to identify places of origin, their relative importance and possible causes of genetic variation in an area. The results can be presented as maps showing genetic variation. Cavalli-Sforza and colleagues argue that if genetic variations are investigated, they often correspond to population migrations due to new sources of food, improved transportation or shifts in political power. For example, in Europe the most significant direction of genetic variation corresponds to the spread of agriculture from the Middle East to Europe between 10,000 and 6,000 years ago. Such geographic analysis works best in the absence of recent large-scale, rapid migrations.

Historic analyses use differences in genetic variation (measured by genetic distance) as a molecular clock indicating the evolutionary relation of species or groups, and can be used to create evolutionary trees reconstructing population separations.

Validation

Results of genetic-ancestry research are supported if they agree with research results from other fields, such as linguistics or archeology. Cavalli-Sforza and colleagues have argued that there is a correspondence between language families found in linguistic research and the population tree they found in their 1994 study. There are generally shorter genetic distances between populations using languages from the same linguistic family. Exceptions to this rule are Sami, Tibetans and Ethiopians, who are genetically associated with populations speaking languages from other linguistic families. The Sami speak a Uralic language, but are genetically primarily European. This is argued to have resulted from migration (and interbreeding) with Europeans while retaining their original language. Agreement also exists between research dates in archeology and those calculated using genetic distance.

Ancestral populations

A 1994 study by Cavalli-Sforza and colleagues evaluated genetic distances among 42 native populations based on 120 blood polymorphisms. The populations were grouped into nine clusters: African (sub-Saharan), Caucasoid (European), Caucasoid (extra-European), northern Mongoloid (excluding Arctic populations), northeast Asian Arctic, southern Mongoloid (mainland and insular Southeast Asia), Pacific islander, New Guinean and Australian, and American (Amerindian). Although the clusters demonstrate varying degrees of homogeneity, the nine-cluster model represents a majority (80 out of 120) of single-trait trees and is useful in demonstrating the historic phylogenetic relationship among these populations.

The greatest genetic distance between two continents is between Africa and Oceania, at 0.2470. Based on physical appearance this is counterintuitive, since indigenous Australians and New Guineans resemble Africans (with dark skin and curly hair). This measure of genetic distance reflects the isolation of Australia and New Guinea since the end of the last glacial maximum, when the continent was isolated from mainland Asia due to rising sea levels. The next-largest genetic distance is between Africa and the Americas, at 0.2260. This is expected, since the longest geographic distance by land is between Africa and South America. The shortest genetic distance, 0.0155, is between European and extra-European Caucasoids. Africa is the most genetically divergent continent, with all other groups more related to each other than to sub-Saharan Africans. This is expected, according to the single-origin hypothesis. Europe has a general genetic variation about three times less than that of other continents; the genetic contribution of Asia and Africa to Europe is thought to be two-thirds and one-third, respectively.

Recent studies have been published using an increasing number of genetic markers. Research has also been done on smaller regions (for example, the genetic history of Europe, the genetic history of Italy and genetic studies on Jews).

Population structures

Definitions of "race" are rooted in taxonomic classifications first developed in 18th- and 19th-century Europe. "Race" has overlapped with a debate about species known as the species problem.

Since the 1960s scientists have understood race as a social construct imposed on phenotypes in culturally determined ways, rather than a biological concept. A 2000 study by Celera Genomics found that human DNA does not differ significantly across populations. Citizens of any village in the world, in Scotland or Tanzania, have 90 percent of the genetic variability humanity has to offer. Only .01 percent of genes account for a person's appearance. Biological adaptation plays a role in bodily features and skin type. According to Luigi Luca Cavalli-Sforza, "From a scientific point of view, the concept of race has failed to obtain any consensus; none is likely, given the gradual variation in existence. It may be objected that the racial stereotypes have a consistency that allows even the layman to classify individuals. However, the major stereotypes, all based on skin color, hair color and form, and facial traits, reflect superficial differences that are not confirmed by deeper analysis with more reliable genetic traits and whose origin dates from recent evolution mostly under the effect of climate and perhaps sexual selection".

Group size

Research techniques can be used to detect genetic population differences if enough genetic markers are used; the Japanese and Chinese East Asian populations have been identified. Sub-Saharan Africans have greater genetic diversity than other populations.

Lewontin's argument and criticism

In 1972, Richard Lewontin performed a F_ST statistical analysis using 17 markers (including blood-group proteins). He found that the majority of genetic differences between humans (85.4 percent) were found within a population, 8.3 percent were found between populations within a race and 6.3 percent were found to differentiate races (Caucasian, African, Mongoloid, South Asian Aborigines, Amerinds, Oceanians, and Australian Aborigines in his study). Since then, other analyses have found F_ST values of 6–10 percent between continental human groups, 5–15 percent between different populations on the same continent and 75–85 percent within populations.

While acknowledging Lewontin's observation that racial groups are genetically homogeneous, A. W. F. Edwards in his 2003 paper "Human Genetic Diversity: Lewontin's Fallacy" argued that his conclusion (that races cannot be genetically distinguished from each other) is incorrect. Edwards argued that when multiple alleles are taken into account, genetic differences cluster in geographic patterns (roughly corresponding to groups commonly defined as "races"). Most information distinguishing populations from each other is hidden in the correlation structure of allele frequency, making it possible to classify individuals using mathematical techniques. Edwards argued that even if the probability of misclassifying an individual based on a single genetic marker is as high as 30 percent (as Lewontin reported in 1972), the misclassification probability nears zero if enough genetic markers are studied simultaneously. Edwards saw Lewontin's argument as based on a political stance, denying biological differences to argue for social equality.

Neil Risch (2005), who was involved in research published in the article Genetic Structure, Self-Identified Race/Ethnicity, and Confounding in Case-Control Association Studies, noted: "In a recent study, when we looked at the correlation between genetic structure [based on microsatellite markers] versus self-description, we found 99.9 percent concordance between the two. We actually had a higher discordance rate between self-reported sex and markers on the X chromosome! So you could argue that sex is also a problematic category". Henry Harpending (2002) has argued that the magnitude of human F_ST values implies that "kinship between two individuals of the same human population is equivalent to kinship between grandparent and grandchild or between half siblings. The widespread assertion that this is small and insignificant should be reexamined". Sarich and Miele (2004) have argued that estimates of genetic differences between individuals in different populations fail to take into account human diploidity: "The point is that we are diploid organisms, getting one set of chromosomes from one parent and a second from the other. To the extent that your mother and father are not especially closely related, then, those two sets of chromosomes will come close to being a random sample of the chromosomes in your population. And the sets present in some randomly chosen member of yours will also be about as different from your two sets as they are from one another. So how much of the variability will be distributed where? First is the 15 percent that is interpopulational. The other 85 percent will then split half and half (42.5 percent) between the intra- and interindividual within-population comparisons. The increase in variability in between-population comparisons is thus 15 percent against the 42.5 percent that is between-individual within-population. Thus, 15/42.5 is 32.5 percent, a much more impressive and, more important, more legitimate value than 15 percent".

Anthropologists (such as C. Loring Brace) and Jonathan Kaplan and geneticist Joseph Graves have argued that while it is possible to find biological and genetic variation roughly corresponding to race, this is true for almost all geographically distinct populations: the cluster structure of genetic data is dependent on the initial hypotheses of the researcher and the populations sampled. When one samples continental groups, the clusters become continental; with other sampling patterns, the clusters would be different. Weiss and Fullerton note that if one sampled only Icelanders, Mayans and Maoris, three distinct clusters would form; all other populations would be composed of genetic admixtures of Maori, Icelandic and Mayan material. Kaplan argues that Lewontin and Edwards are correct, concluding that while racial groups are characterized by allele frequency racial classification is a taxonomy of the human species; other genetic patterns can be found in human populations which crosscut racial distinctions. In this view, races are social constructs with a biological reality (largely an artifact of the category's construction).

Self-identification

Jorde and Wooding (2004) wrote that clusters from genetic markers did not correspond to subjects' self-identified race or ethnic group. However, the studies cited were based on relatively few genetic markers and deemed insufficient. In contrast, studies based on a higher number of genetic markers have found more agreement.

A 2005 study by Tang and colleagues used 326 genetic markers to determine genetic clusters. The 3,636 subjects, from the United States and Taiwan, self-identified as belonging to white, African American, East Asian or Hispanic ethnic groups. The study found "nearly perfect correspondence between genetic cluster and SIRE for major ethnic groups living in the United States, with a discrepancy rate of only 0.14 percent".

Paschou et al. (2010) found "essentially perfect" agreement between 51 self-identified populations and the population's genetic structure, using 650,000 genetic markers. Selecting for informative genetic makers allowed a reduction to less than 650, while retaining near-total accuracy.

Correspondence between genetic clusters in a population (such as the current US population) and self-identified race or ethnic groups does not mean that such a cluster (or group) corresponds to only one ethnic group. African Americans have an estimated 10–20-percent European genetic admixture; Hispanics have European, Native American and African ancestry. In Brazil there has been extensive admixture between Europeans, Amerindians and Africans, resulting in no clear differences in skin color and relatively weak associations between self-reported race and African ancestry.

Genetic-distance increase

A change in a gene pool may be abrupt or clinal.

Genetic distances generally increase continually with geographic distance, which makes a dividing line arbitrary. Any two neighboring settlements will exhibit some genetic difference from each other, which could be defined as a race. Therefore, attempts to classify races impose an artificial discontinuity on a naturally occurring phenomenon. This explains why studies on population genetic structure yield varying results, depending on methodology.

Rosenberg and colleagues (2005) have argued, based on cluster analysis, that populations do not always vary continuously and a population's genetic structure is consistent if enough genetic markers (and subjects) are included. "Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions." They also wrote, regarding a model with five clusters corresponding to Africa, Eurasia (Europe, Middle East, and Central/South Asia), East Asia, Oceania, and the Americas: "For population pairs from the same cluster, as geographic distance increases, genetic distance increases in a linear manner, consistent with a clinal population structure. However, for pairs from different clusters, genetic distance is generally larger than that between intracluster pairs that have the same geographic distance. For example, genetic distances for population pairs with one population in Eurasia and the other in East Asia are greater than those for pairs at equivalent geographic distance within Eurasia or within East Asia. Loosely speaking, it is these small discontinuous jumps in genetic distance—across oceans, the Himalayas, and the Sahara—that provide the basis for the ability of STRUCTURE to identify clusters that correspond to geographic regions". This applies to populations in their ancestral homes when migrations and gene flow were slow; large, rapid migrations exhibit different characteristics. Tang and colleagues (2004) wrote, "we detected only modest genetic differentiation between different current geographic locales within each race/ethnicity group. Thus, ancient geographic ancestry, which is highly correlated with self-identified race/ethnicity—as opposed to current residence—is the major determinant of genetic structure in the U.S. population".

Number of clusters

Cluster analysis has been criticized because the number of clusters to search for is decided in advance, with different values possible (although with varying degrees of probability). Principal component analysis does not decide in advance how many components for which to search, and it has been used in an increasing number of studies.

Utility

It has been argued that knowledge of a person's race is limited in value, since people of the same race vary from one another. Witherspoon and colleagues (2007) have argued that when individuals are assigned to population groups, two randomly chosen individuals from different populations can resemble each other more than a randomly chosen member of their own group. They found that many thousands of genetic markers had to be used for the answer to "How often is a pair of individuals from one population genetically more dissimilar than two individuals chosen from two different populations?" to be "never". This assumed three population groups, separated by large geographic distances (European, African and East Asian). The global human population is more complex, and studying a large number of groups would require an increased number of markers for the same answer. They conclude that "caution should be used when using geographic or genetic ancestry to make inferences about individual phenotypes", and "The fact that, given enough genetic data, individuals can be correctly assigned to their populations of origin is compatible with the observation that most human genetic variation is found within populations, not between them. It is also compatible with our finding that, even when the most distinct populations are considered and hundreds of loci are used, individuals are frequently more similar to members of other populations than to members of their own population".

This is similar to the conclusion reached by anthropologist Norman Sauer in a 1992 article on the ability of forensic anthropologists to assign "race" to a skeleton, based on craniofacial features and limb morphology. Sauer said, "the successful assignment of race to a skeletal specimen is not a vindication of the race concept, but rather a prediction that an individual, while alive was assigned to a particular socially constructed 'racial' category. A specimen may display features that point to African ancestry. In this country that person is likely to have been labeled Black regardless of whether or not such a race actually exists in nature".

Race and medicine

Main article: Race and health

Neil Risch states that studies have documented biological differences among the races in susceptibility to chronic disease. Genes change in response to local diseases; for example, people who are Duffy-negative tend to have a higher resistance to malaria (most Africans are Duffy-negative, and most non-Africans are Duffy-positive). A number of genetic diseases prevalent in malaria-endemic areas may provide genetic resistance to malaria, including sickle cell disease, thalassaemias and glucose-6-phosphate dehydrogenase. Cystic fibrosis is the most common life-limiting autosomal recessive disease among people of European ancestry; a hypothesized heterozygote advantage, providing resistance to diseases earlier common in Europe, has been challenged.

Information about a person's population of origin may aid in diagnosis, and adverse drug responses may vary by group. Because of the correlation between self-identified race and genetic clusters, medical treatments influenced by genetics have varying rates of success between self-defined racial groups. For this reason, some physicians consider a patient's race in choosing the most effective treatment, and some drugs are marketed with race-specific instructions. Jorde and Wooding (2004) have argued that because of genetic variation within racial groups, when "it finally becomes feasible and available, individual genetic assessment of relevant genes will probably prove more useful than race in medical decision making". However, race continues to be a factor when examining groups (such as epidemiologic research).

See also

• Y-chromosome haplogroups by populations
• Human genetic clustering

Regional: Archaeogenetics

• Archaeogenetics of the Near East
• Genetics and archaeogenetics of South Asia
• Genetic history of Africa
• Genetic history of Europe
• Genetic history of Italy
• Genetic history of North Africa
• Genetic history of indigenous peoples of the Americas
• Genetic history of the Iberian Peninsula
• Genetic history of the British Isles

Further reading

• Helms JE, Jernigan M, Mascher J (2005). "The meaning of race in psychology and how to change it: a methodological perspective". The American Psychologist. 60 (1): 27–36. doi:10.1037/0003-066X.60.1.27. PMID 15641919. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
• Jackson FL (2004). "Human genetic variation and health: new assessment approaches based on ethnogenetic layering". British Medical Bulletin. 69: 215–35. doi:10.1093/bmb/ldh012. PMID 15226208.
• Keita, Shomarka O. Y. (1993). "The Subspecies Concept in Zoology and Anthropology: A Brief Historical Review and Test of a Classification Scheme". Journal of Black Studies. 23 (3): 416–45. doi:10.1177/002193479302300309. {{cite journal}}: Unknown parameter |month= ignored (help)
• Keita SO, Kittles RA, Royal CD; et al. (2004). "Conceptualizing human variation". Nature Genetics. 36 (11 Suppl): S17–20. doi:10.1038/ng1455. PMID 15507998. {{cite journal}}: Explicit use of et al. in: |author= (help); Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
• Leroi, Armand Marie (March 14, 2005). "A Family Tree in Every Gene". The New York Times. Retrieved February 2, 2010.
• Lieberman, Leonard; Kirk, Rodney C.; Corcoran, Michael (2003). "The Decline of Race in American Physical Anthropology" (PDF). Przegląd Antropologiczny – Anthropological Review. 66: 3–21. ISSN 0033-2003.
• Long JC, Kittles RA (2003). "Human genetic diversity and the nonexistence of biological races". Human Biology. 75 (4): 449–71. doi:10.1353/hub.2003.0058. PMID 14655871. {{cite journal}}: Unknown parameter |month= ignored (help)
• Miththapala, Sriyanie; Seidensticker, John; O'Brien, Stephen J. (1996). "Phylogeographic Subspecies Recognition in Leopards (Panthera pardus): Molecular Genetic Variation". Conservation Biology. 10 (4): 1115. doi:10.1046/j.1523-1739.1996.10041115.x.
• Ossorio P, Duster T (2005). "Race and genetics: controversies in biomedical, behavioral, and forensic sciences". The American Psychologist. 60 (1): 115–28. doi:10.1037/0003-066X.60.1.115. PMID 15641926. {{cite journal}}: Unknown parameter |month= ignored (help)
• Parra EJ, Kittles RA, Shriver MD (2004). "Implications of correlations between skin color and genetic ancestry for biomedical research". Nature Genetics. 36 (11 Suppl): S54–60. doi:10.1038/ng1440. PMID 15508005. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
• Sawyer SL, Mukherjee N, Pakstis AJ; et al. (2005). "Linkage disequilibrium patterns vary substantially among populations". European Journal of Human Genetics. 13 (5): 677–86. doi:10.1038/sj.ejhg.5201368. PMID 15657612. {{cite journal}}: Explicit use of et al. in: |author= (help); Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
• Rohde DL, Olson S, Chang JT (2004). "Modelling the recent common ancestry of all living humans". Nature. 431 (7008): 562–6. Bibcode:2004Natur.431..562R. doi:10.1038/nature02842. PMID 15457259. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
• Serre D, Pääbo S (2004). "Evidence for Gradients of Human Genetic Diversity Within and Among Continents". Genome Research. 14 (9): 1679–85. doi:10.1101/gr.2529604. PMC 515312. PMID 15342553. {{cite journal}}: Unknown parameter |month= ignored (help)
• Smedley A, Smedley BD (2005). "Race as biology is fiction, racism as a social problem is real: Anthropological and historical perspectives on the social construction of race". The American Psychologist. 60 (1): 16–26. doi:10.1037/0003-066X.60.1.16. PMID 15641918. {{cite journal}}: Unknown parameter |month= ignored (help)
• Barbara A. Koenig, Sandra Soo-Jin Lee, Sarah S. Richardson, ed. (2008). Revisiting race in a genomic age. Rutgers University Press. ISBN 978-0-8135-4324-6.{{cite book}}: CS1 maint: multiple names: editors list (link)

External links

• The Race FAQ by John Goodrum
• Minorities, Race, and Genomics via Human Genome Project information.
• All non-Africans have Neanderthal DNA
• Race and human evolution by Alan Templeton