Race and genetics

The relationship between race and genetics has relevance for the ongoing controversies regarding race. Examples include genetic research on geographic ancestry and population genetic structure, as well as on possible genetic causes of average group differences between races.

Human evolution

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

Main articles: Human evolution and Early human migrations

The human lineage diverged from the common ancestor with chimpanzees 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 and are now extinct. These include Homo erectus, which inhabited Asia, and Homo sapiens neanderthalensis, which inhabited Europe. Archaic Homo sapiens evolved between 400,000 and 250,000 years ago.

The dominant view among scientists concerning the origin of anatomically modern humans is the "Out of Africa" or recent African origin hypothesis, which argues that Homo sapiens arose in Africa and migrated out of the continent around 50,000 to 100,000 years ago, replacing populations of Homo erectus in Asia and Homo neanderthalensis in Europe. An alternative multiregional hypothesis argue that Homo sapiens evolved as geographically separate but interbreeding populations stemming from a worldwide migration of Homo erectus out of Africa nearly 2.5 million years ago. This theory has been contradicted by recent evidence, although it has been suggested that non Homo sapiens Neanderthal genomes may have contributed about 4% of non-African heredity, and the recently discovered Denisova hominin may have contributed 6% of the genome of Melanesians.

Genetic variation

Main article: Human genetic variation

Genetic variation comes from mutations in genetic material, migration between populations (gene flow), and the reshuffling of genes through sexual reproduction. The two main mechanisms that produce evolution are natural selection and genetic drift. A special case of genetic drift is the founder effect. Epigenetic inheritance are heritable changes in phenotype (appearance) or gene expression caused by mechanisms other than changes in the underlying DNA sequence.

Many human phenotypes are polygenic, meaning that they depend on the interaction among many genes. Polygeneity makes the study of individual phenotypic differences more difficult. Additionally, phenotypes may be influenced by environment as well as by genetics. The measure of the genetic role in phenotypes is heritability.

Nucleotide diversity is based on single mutations called single nucleotide polymorphisms (SNPs). The nucleotide diversity between humans is about 0.1%, which is 1 difference per 1,000 nucleotides between two humans chosen at random. This amounts to approximately 3 million SNPs since the human genome has about 3 billion nucleotides. It is estimated that a total of 10 million SNPs exist in the human population.

Recent analysis has shown that non-SNP variation accounts for much more human genetic variation than single nucleotide diversity. This non-SNP variation includes copy number variation and results from deletions, inversions, insertions and duplications. It is estimated that approximately 0.4% of the genomes of unrelated people typically differ with respect to copy number. When copy number variation is included, human to human genetic variation is estimated to be at least 0.5%.

Methods in human ancestry and population genetic structure research

Visible traits, proteins, and genes studied

See also: Race (classification of human beings)

The earliest classification attempts were done using surface traits such as done in anthropometry. This is argued to have caused problems for early anthropologists whose simplistic approach was inadequate for classifying race based on visible traits.

Geographic distribution of blood group A.

Geographic distribution of blood group B.

Prior to the discovery of DNA as the hereditary material, 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 frequencies of blood groups A and B differed greatly from region to region. For example, among Europeans, 15% were group B and 40% were group A. Eastern Europeans and Russians had higher frequencies of group B, with people from India having the highest proportion. The Herschfelds concluded that humans were made of two different "biochemical races," each with its own origin. It was hypothesized that these two pure races later became mixed, resulting in the complex pattern of groups A and B. This was one of the first theories of racial differences to include the idea that visible human variation did not necessarily correlate with invisible genetic variation. It was expected that groups that had similar proportions of the blood groups would be more closely related in racial terms, but instead it was often found that groups separated by large distances, such as those from Madagascar and Russia, had similar frequencies. This confounded scientists who were attempting to learn more about human evolutionary history.

Today researchers often use direct genetic testing. Unlike earlier research using one or a few traits or proteins, today this often involve the simultaneous study of hundreds or thousands of genetic markers or even the whole genome.

Population genetic structure and genetic distance

Population genetic 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.

There are several mathematical methods for examining if a population have more or less distinct genetic subgroups and to quantify this. Many genetic markers from many individuals are examined simultaneously in order to find the population genetic structure. The basic idea is that while such subgroups are not distinct and overlap if looking at the distribution of the variants of one marker only, when many markers are examined simultaneously, then the different subgroups have distinctly different average genetic structure. An individual need not have exactly this average genetic structure and may be described as belonging, to varying degrees, to several subgroups. Such subgroups may be more or less distinct depending on how close a subgroup distribution is to the average genetic structure of the subgroup and how much overlap there are with the distributions of different subgroups. One such mathematical method is cluster analysis. Another is principal components analysis. 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 these clusters are from one another vary. The results obtained by clustering analyses are dependent on several factors:

• More genetic markers studied at the same time makes it easier to find distinct clusters.
• Certain genetic markers vary more than others which means 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, as statistical noise is reduced.
• Low genetic variation makes it more difficult to find distinct clusters. Larger geographic distances generally increases genetic variation which makes identifying clusters easier.
• A similar cluster structure is seen even if using different genetic markers, when the number of genetic markers included is sufficiently high. The clustering structure obtained with different statistical techniques is quite similar. A similar cluster structure is found in the original sample and if using a subsample of the original sample.

Genetic distance

Genetic distance refers to the genetic divergence between species or between populations within a species. Smaller genetic distances indicate a close genetic relationship whereas large genetic distances indicate a more distant genetic relationship. Genetic distance can be used to compare the genetic similarity between different species, such as humans and chimpanzees. Within a species genetic distance can be used to measure the divergence between different subgroups.

In its simplest form, the genetic distance between two populations is the difference in frequencies of a trait. For example the frequency of Rh- individuals is 50.4% among Basques, 41.2% in France and 41.1% in England. Thus the genetic difference between the Basques and French is 9.2% and the genetic difference between the French and the English is 0.1% for the RH negative trait. The genetic distance of several individual traits can then be averaged to compute an overall genetic distance.

Genetic distance significantly correlates to geographic distance between populations, a phenomenon referred to as "isolation by distance". Genetic distance can also be the result of physical boundaries which naturally restrict gene flow, such as islands, deserts, mountain ranges or dense forests.

Genetic distance is often measured by Fixation index (F_ST). F_ST is simply the correlation of randomly chosen alleles within the same sub-population relative to that found in the entire population. It is often expressed as the proportion of genetic diversity due to allele frequency differences among populations. This comparison of genetic variability within and between populations is frequently used in the field of population genetics. The values range from 0 to 1. A zero value implies complete panmixis, that the two populations are interbreeding freely. A value of one would imply the two populations are completely separate.

Historic and geographic analysis of ancestry

Cavalli-Sforza has described two major methods of ancestry analysis. Note that current population genetic structure does not necessarily imply that the different current clusters/components found correspond to only one ancestral home per group. One example being a genetic cluster in the US corresponding to Hispanics who have European, Native American, and African ancestries.

Geographic analyses attemp to identify the places of origin, relative importance, and the possible causes involved in the spread of genetic variation over an area. The results can be presented as maps showing how genes vary between populations. Cavalli-Sforza and colleagues have argued that if variations in many genes between populations are investigated simultaneously, they often correspond to population migrations due to, for example, new sources of food, improved transportation, or shifts in political power. For example, in Europe the single most significant direction of genetic variation corresponds to the spread of farming from the Middle East to Europe between 10,000 and 6,000 years ago. Such geographic analysis works best when describing the situation before recent large scale and fast migrations with intermixing of many populations far from their ancestral homes.

Historic analyses use differences in genetic variation, genetic distance being one way to measure this, as a molecular clock indicating the evolutionary relatedness of various species or groups. This method can be used to create evolutionary trees which attempt to reconstruct population separations over time,

Validating the genetic ancestry research

The results from the genetic ancestry research are argued to be supported if they agree with the results from other research such as from linguistics or archeology.

Cavalli-Sforza and colleagues have argued that there is a strong correspondence between the language families found in linguistic research and and the populations and the tree they found in their 1994 study. As a general rule, there is shorter genetic distances between populations using languages from the same language family. The notable exceptions to this rule are Sami, Tibetans, and Ethiopians, who are genetically associated with populations which speak languages belonging to different language families. For example, the Sami speak an Uralic language yet are according to the genetic analysis mainly Europeans. This is argued to have resulted from migration and interbreeding with Europeans while retaining the original language. There are similar explanations for the other exceptions. There is also a high agreement between dates from research done in archeology and as calculated using genetic distance.

Ancestral populations

Linkage tree and genetic distance matrix for the 9 main population clusters in the 1994 study by Cavalli-Sforza et al.

A widely cited 1994 study by Cavalli-Sforza et al. evaluated the genetic distances between 42 native populations from around the world based on 120 blood polymorphisms. These 42 populations can be grouped into 9 main clusters, which Cavalli-Sforza termed 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). Though the clusters evidence varying degrees of homogeneity, the 9-cluster model represents a majority (80 out of 120) of single-trait trees and is useful in demonstrating the historic phylogenetic relationship between these populations.

The largest genetic distance between any two continents is between Africa and Oceania at 0.2470. Based on physical appearance this may be counterintuitive, since Indigenous Australians and New Guineans resemble Africans with dark skin and sometimes frizzy hair. This large figure for genetic distance reflects the relatively long isolation of Australia and New Guinea since the end of the last glacial maximum when the continent was further 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 at 0.0155 is between European Caucasoids and Non-European Caucasoids. Africa is the most genetically divergent continent, with all other groups being more related to each other than to Sub-Saharan Africans. This is expected in accordance with the recent single-origin hypothesis. Europe has a genetic variation in general about three times less than that of other continents, and the genetic contribution of Asia and Africa to Europe is thought to be 2/3 and 1/3 respectively.

Many more recent worldwide studies have also been published. Often they use an increasing number of genetic markers. Many studies have also been done on more limited regions, (one example being studies on the genetic history of Europe), or on individual nations (one example being studies on the genetic history of Italy), or on specific groups (one example being genetic studies on Jews).

Race and population genetic structure

As described in the article on race (classification of humans), several different definitons of race have been proposed. Even for species there is controversy and many proposed definitions which is sometimes referred to as the "species problem".

Size of group

The research techniques can be used to detect subtle genetic population differences if enough genetic markers are used. One example being that the East Asian populations Japanese and Chinese can be identified if enough markers are used. Sub-Saharan Africans have higher genetic diversity than other populations which may be a problem to seeing them as a single race. At which point a group becomes to small or to large to be a race is thus not clear.

Lewontin's argument and criticism

If human height or body weight are measured alone (at the horizontal or vertical axis), the red and blue populations here would overlap strongly. If both traits are measured at the same time, however, natural clusters with little overlap emerge (in the middle between the axis). Thus, if only one trait (or genetic marker) is measured it will be difficult to decide which population a person belongs to. If two traits (or genetic markers) are measured at the same time much less so.

In 1972 Richard Lewontin performed a F_ST statistical analysis using 17 markers including blood group proteins. His results were that the majority of genetic differences between humans, 85.4%, were found within a population, 8.3% of genetic differences were found between populations within a race, and only 6.3% was found to differentiate races which in the study were Caucasian, African, Mongoloid, South Asian Aborigines, Amerinds, Oceanians, and Australian Aborigines. Since then, other analyses have found F_ST values of 6%-10% between continental human groups, 5-15% between different populations occupying the same continent, and 75-85% within populations. Lewontin's argument led a number of authors publishing in the 1990s and 2000s to follow Lewontin's verdict that race is biologically a meaningless concept.

However, this view was criticised by geneticist A. W. F. Edwards in the paper "Human Genetic Diversity: Lewontin's Fallacy" (2003). According to Edwards, claims to the effect that "race is biologically meaningless" are politically motivated, and that it is possible to construct a meaningful notion of race based on genetic differences. Edwards's argument is that it is fallacious to claim that racial classification is impossible because any particular allele may exist in most populations. Most of the information that distinguishes populations from each other is hidden in the correlation structure of allele frequencies, making it possible to highly reliably classify individuals using the mathematical techniques described above. Edwards argued that, even if the probability of misclassifying an individual based on a single genetic marker is as high as 30% (as Lewontin reported in 1972), the misclassification probability becomes close to zero if enough genetic markers are studied simultaneously.

Alan Templeton (2003) claimed that in the nonhuman literature an F_ST of at least 25%-30% is a standard criterion for the identification of a subspecies. Furthermore it has been observed that autosomal F_ST values derived for humans are typically equal to and in some instances greater than those derived for other species acknowledged to be polytypic with respect to subspecies.

Henry Harpending (2002) has argued that the magnitude of human F_ST values imply 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 difference between individuals of 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."

Self-identified race/ethnic group

Jorde and Wooding (2004) wrote that some studies have argued that clusters from genetic markers did not correspond well to the subjects' self-identified race/ethnic group. These studies, however, were based on only several dozen or fewer genetic markers, and such a number, unless carefully selected, are argued to not be sufficient. In contrast, studies based on more genetic markers have found high agreements.

A study by Tang el al. in 2005 used 326 genetic markers in order to determine genetic clusters. The 3,636 subjects involved in the study, from the United States and Taiwan, self-identified as belonging to white, African American, East Asian, or Hispanic (=self-identified race/ethnic group (SIRE)). 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%."

Paschou et al. (2010) found "essentially perfect" agreement between 51 self-reported populations of origin and the population genetic structure found using 650,000 genetic markers. Selecting for especially informative genetic makers allowed a reduction to less than 650 while still retaining close to 100% accuracy.

That there is correspondence between genetic clusters in a current population, such as the current US population, and self-identified race/ethnic groups does not necessarily mean that such a cluster/group corresponds to only one ancestral origin/population. African Americans have an estimated 10%–20% European admixture. The Hispanic group have European, Native American, and African ancestries. In Brazil, there has been extensive admixture between Europeans, Amerindians, and Africans resulting in no clear discontinuities in skin color in the population and relatively weak associations between between self-reported race (called Color in Brazil probably because it captures the continuous aspects) and African ancestry as well as between objectively measured skin color and African ancestry.

Continuous or discontinuous increase in genetic distance

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

One argument is that genetic distance on average increase in a continuous manner with geographic distance, which causes any threshold or dividing line to be arbitrary. Any two neighboring villages or towns will show some genetic differentiation from each other and thus could be defined as a race. Thus any attempt to classify races would be imposing an artificial discontinuity on what is otherwise a naturally occurring continuous phenomenon. This has been argued to explain why some studies on population genetic structure have yielded varying results depending on the methodology used.

Ring species show that also a continuous change in genetic variation can produce very large differences between different populations in a species.

Rosenberg et al. (2005) have argued, based on cluster analysis, that populations do not always vary continuously and that the population 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, that "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."

The above discussion applies to populations in their ancestral homes when migrations and gene flow were slow. Recent large and fast migrations due to changed technology have changed this. Thus, regarding the situation today in the United States, Tang et al. (2004) write that "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 for that number of clusters to search for are decided in advance with many different values possible although with varying probability. Principal components analysis does not decide the numbers of components to search for in advance. An increasing number of studies have used it in recent years.

Utility

While knowing a persons race can be helpful in some situations in medicine, it has been argued that this is of limited value since also persons from the same race vary from one another.

Witherspoon et al. (2007) have argued that even when individuals can be reliably assigned to specific population groups, it may still be possible for two randomly chosen individuals from different populations/clusters to be more similar to each other than to a randomly chosen member of their own cluster. They found that many thousands of genetic markers had to be used in order for the answer to the question "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 ranges (European, African and East Asian). The entire world population is much more complex and studying an increasing number of groups would require an increasing number of markers for the same answer. Witherspoon et al. conclude that "caution should be used when using geographic or genetic ancestry to make inferences about individual phenotypes."

Rushton and Jensen (2005 and 2010), who also see the research on population genetic structure as validating race, also state that the implications of race for a person on a single trait (such as IQ) may be limited (many blacks have higher IQ than than the average white IQ despite the black average IQ being lower). On the other hand, due to the effects of statistical regularity, when instead looking at groups, and especially the average effect of many group differences, then the effects for society become profound. Rushton in his book Race, Evolution, and Behavior thus argues that races due to genetic factors differ, on average, on many variables, and that the total effect of these group differences on society are very significant.

Race and physical characteristics

Human skin color vary for different populations. The leading explanation is that skin colour adapts to sunlight intensities which produce vitamin D deficiency or ultraviolet light damage to folic acid. Other hypotheses include protection from ambient temperature, infections, skin cancer or frostbite, an alteration in food, and sexual selection. The gene that causes light skin color in Europeans is different from the gene that causes light skin in East Asians. Europeans have a different version of the SLC24A5 than East Asians possibly indicating that they evolved light skin independently.

The most widely used human racial categories are based on various combinations of visible traits such as skin color, eye shape and hair texture. However, some argue that many of these traits are non-concordant in that they are not necessarily expressed together. For example, skin color and hair texture vary independently. Some examples of non-concordance include:

• Skin color varies all over the world in different populations.
• Epicanthal fold are typically associated with East Asian populations but are found in populations all over the world, including many Native Americans, the Khoisan, the Sami, and even amongst some isolated groups such as the Andamanese.
• Lighter hair colors are typically associated with Europeans, especially Northern Europeans, but blond hair is found amongst a limited, small number of the dark skinned populations of the south pacific, particularly the Solomon Islands and Vanuatu.

Others argue that this is just an example of Lewontin's Fallacy. On the contrary, if several traits are looked at the same time, then today forensic anthropoligists can classify a person's race with an accuracy close to 100% based on only skeletal remains.

A 2010 examination of 18 widely used English anatomy textbooks found that every one relied on the race concept. The study gives examples of how the textbooks claim that anatomical features vary between races.

Race and medicine

Main article: Race and health

Neil Risch states that numerous studies over past decades have documented biological differences among the races with regard to susceptibility and natural history of chronic diseases. Genes may be under strong selection in response to local diseases. For example, people who are duffy negative tend to have higher resistance to malaria. Most Africans are duffy negative and most non-Africans are duffy positive. A number of genetic diseases more prevalent in malaria-afflicted areas may provide some genetic resistance to malaria including sickle cell disease, thalassaemias, glucose-6-phosphate dehydrogenase, and possibly others. Cystic fibrosis is the most common life-limiting autosomal recessive disease among people of European heritage. Numerous hypotheses have suggested that it provides a heterozygote advantage by giving resistance to diseases earlier common in Europe.

Information about a person's population of origin may in some situations help making a diagnosis and adverse drug responses may vary between such groups. Because of the correlation between self-identified race and genetic clusters, medical treatments whose results are influenced by genetics often have varying rates of success between self-defined racial groups. For this reason, some doctors consider a patient’s race while attempting to identify the most effective possible treatment, and some drugs are marketed with race-specific instructions. Jorde and Wooding (2004) have argued that, because of the 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." Even so, race will continue to be important when looking at groups instead of individuals such as in epidemiologic research.

Race and food tolerance

Lactose tolerance and alcohol tolerance differ with geographic ancestry in part due to genetic factors. Lactose tolerance appears to be an evolutionarily recent adaptation to dairy consumption, and has occurred independently in both northern Europe and east Africa in populations with a historically pastoral lifestyle.

Race and sports

Main article: Race and sports

The overrepresentation of certain ethnicities with respect to certain sports has led some to question whether there is a genetic component giving certain races a competitive advantage. Others point out that such overrepresentations are not necessarily due to genetic causes. Such views differ between nations. Among Chinese, the proposition that there are genetic differences affecting sports performance is a widely accepted. A 1994 examination of 32 English sport/exercise science textbooks found that 7 (21.9%) claimed that there are biophysical differences due to race that might explain differences in sports performance, 24 (75%) did not mention nor refute the concept, and 1 (3.12%) expressed caution with the idea.

Race and intelligence

Main article: Race and intelligence

There is an ongoing scientific controversy regarding the role of genetics in explaining racial differences in IQ and other measures of intelligence. Some are agnostic about the causes, while others argue that environmental factors explain all of the differences, or that both genetics and environmental factors are important.

See also

• Y-DNA haplogroups by ethnic groups

Regional: Archaeogenetics

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

References

• 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 Maried (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: 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. 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)

External links

• The Race FAQ by John Goodrum
• Minorities, Race, and Genomics via Human Genome Project information.
• Race and human evolution by Alan Templeton