How many 'ethnics' here vs WEIRDs?

[Jason]

I'll just post the commentary of some PhD guys on some more recent studies (performed by PhD guys) speak for themselves (I, sadly, do not have a PhD so I bow before their cumulative doctorates).


Steve Hsu (some PhD guy writes this stuff after 9 years of scientific progress since the Templeton study)
http://infoproc.blogspot.ca/2007/01/met ... s-and.html

Thursday, January 04, 2007
Metric on the space of genomes and the scientific basis for race
[Note: the science has advanced considerably since I wrote this post. See here for a nice picture of clustering by ethnicity.]

Suppose that the human genome has 30,000 distinct genes, which we will label as i = 1,2, ... N, where N = 30k. Next, suppose that there are n_i variants or alleles (mutations) of the i-th gene. Then, each human's genetic information can be described as a point on a lattice of size n_1 x n_2 x n_3 ... n_N, or equivalently an N-tuple of integers, each of whose values range from 1 to n_i. For the simplified case where there are exactly 10 variants of each gene, the number of points in this N dimensional space is 10^N or 10^{30k}, one for each distinct 30k digit number. It's a space of very high dimension, but this doesn't stop us from defining a metric, or measure of distance between any two points in the space. (For simplicity we ignore restrictions on this space which might result from incompatibility of certain combinations, etc.)

Note that the genomes of all of the humans who have ever lived occupy only a small subset of this space -- most possible variations have never been realized. For this reason, the surprise expressed by biologists that humans have so few genes (not many more than a worm, and far less than the 100k of earlier estimates) is no cause for concern -- the number of possible organisms that might result from 30k genes is enormous -- far more than the number of molecules in the visible universe.

To define a metric, we need a notion of how far apart two different alleles are. We can do this by counting base pair differences -- most mutations only alter a few base pairs in the genetic code. We can define the distance between two alleles in terms of the number of base pair changes between them (this is always a positive number). Then, we can define the distance between two genomes as the sum of each of the i=1,2,..,N individual gene distances. It is natural, although perhaps not always possible, to choose the n_i labeling of alleles to reflect relative distances, so variants n_1 and n_2 are close together, and both very far from n_10.

The exact definition of the metric and the allele labeling are somewhat arbitrary, but you can see it is easy to define a meaningful measure of how far apart any two individuals are in genome space.

Now plot the genome of each human as a point on our lattice. Not surprisingly, there are readily identifiable clusters of points, corresponding to traditional continental ethnic groups: Europeans, Africans, Asians, Native Americans, etc. (See, for example, Risch et al., Am. J. Hum. Genet. 76:268–275, 2005.) Of course, we can get into endless arguments about how we define European or Asian, and of course there is substructure within the clusters, but it is rather obvious that there are identifiable groupings, and as the Risch study shows, they correspond very well to self-identified notions of race.


From the conclusions of the Risch paper (Am. J. Hum. Genet. 76:268–275, 2005):

Attention has recently focused on genetic structure in the human population. Some have argued that the amount of genetic variation within populations dwarfs the variation between populations, suggesting that discrete genetic categories are not useful (Lewontin 1972; Cooper et al. 2003; Haga and Venter 2003). On the other hand, several studies have shown that individuals tend to cluster genetically with others of the same ancestral geographic origins (Mountain and Cavalli-Sforza 1997; Stephens et al. 2001; Bamshad et al. 2003). Prior studies have generally been performed on a relatively small number of individuals and/or markers. A recent study (Rosenberg et al. 2002) examined 377 autosomal micro-satellite markers in 1,056 individuals from a global sample of 52 populations and found significant evidence of genetic clustering, largely along geographic (continental) lines. Consistent with prior studies, the major genetic clusters consisted of Europeans/West Asians (whites), sub-Saharan Africans, East Asians, Pacific Islanders, and Native Americans. ... We have shown a nearly perfect correspondence between genetic cluster and SIRE [self-reported ethnicity] for major ethnic groups living in the United States, with a discrepancy rate of only 0.14%.


This clustering is a natural consequence of geographical isolation, inheritance and natural selection operating over the last 50k years since humans left Africa.

Every allele probably occurs in each ethnic group, but with varying frequency. Suppose that for a particular gene there are 3 common variants (v1, v2, v3) all the rest being very rare. Then, for example, one might find that in ethnic group A the distribution is v1 75%, v2 15%, v3 10%, while for ethnic group B the distribution is v1 2% v2 6% v3 92%. Suppose this pattern is repeated for several genes, with the common variants in population A being rare in population B, and vice versa. Then, one might find a very dramatic difference in expressed phenotype between the two populations. For example, if skin color is determined by (say) 10 genes, and those genes have the distribution pattern given above, nearly all of population A might be fair skinned while all of population B is dark, even though there is complete overlap in the set of common alleles. Perhaps having the third type of variant v3 in 7 out of 10 pigmentation genes makes you dark. This is highly likely for an individual in population B with the given probabilities, but highly unlikely in population A.

We see that there can be dramatic group differences in phenotypes even if there is complete allele overlap between two groups - as long as the frequency or probability distributions are distinct. But it is these distributions that are measured by the metric we defined earlier. Two groups that form distinct clusters are likely to exhibit different frequency distributions over various genes, leading to group differences.

This leads us to two very distinct possibilities in human genetic variation:

Hypothesis 1: (the PC mantra) The only group differences that exist between the clusters (races) are innocuous and superficial, for example related to skin color, hair color, body type, etc.

Hypothesis 2: (the dangerous one) Group differences exist which might affect important (let us say, deep rather than superficial) and measurable characteristics, such as cognitive abilities, personality, athletic prowess, etc.

Note H1 is under constant revision, as new genetically driven group differences (e.g., particularly in disease resistance) are being discovered. According to the mantra of H1 these must all (by definition) be superficial differences.

A standard argument against H2 is that the 50k years during which groups have been separated is not long enough for differential natural selection to cause any group differences in deep characteristics. I find this argument quite naive, given what we know about animal breeding and how evolution has affected the (ever expanding list of) "superficial" characteristics. Many genes are now suspected of having been subject to strong selection over timescales of order 5k years or less. For further discussion of H2 by Steve Pinker, see here.

The predominant view among social scientists is that H1 is obviously correct and H2 obviously false. However, this is mainly wishful thinking. Official statements by the American Sociological Association and the American Anthropological Association even endorse the view that race is not a valid biological concept, which is clearly incorrect.

As scientists, we don't know whether H1 or H2 is correct, but given the revolution in biotechnology, we will eventually. Let me reiterate, before someone labels me a racist: we don't know with high confidence whether H1 or H2 is correct.

Finally, it is important to note that group differences are statistical in nature and do not imply anything definitive about a particular individual. Rather than rely on the scientifically unsupported claim that we are all equal, it would be better to emphasize that we all have inalienable human rights regardless of our abilities or genetic makeup.

Commentary on the above commentary with links to more studies.
http://www.gnxp.com/blog/2007/01/race-c ... sensus.php

There has been surprisingly little outrage in the internets over Steve Hsu's argument that the concept of "race" has a biological basis. But still, it might be worth going over in a bit more detail the evidence supporting him, so that's what this post will aim to do; it will hopefully be worthwhile to have this all compiled in a single spot.

First, an important preliminary-- there are millions of places in the human genome where any two given people could possible differ, either by a single base change, the addition of an entire chunk of DNA, the inversion of a chunk of DNA, or whatever. Keep that in mind: millions and millions of places (for a database of many of the single base changes, see the HapMap). Now, the intuitive argument: after humans arose in Africa, they dispered themselves throughout the world. By both chance and in response to selection due to their new environments, populations in different parts of the world ended up with different frequencies of those millions of DNA variants. Simple enough. Now, below the fold, I will present the evidence that 1. the patterns of genetic variation form clusters on a world-wide scale, 2. genetic clusters coincide with what is commonly called "race", and 3. genetic variation between clusters is relevant phenotypically.

I. Genetic variation in humans forms clusters that correspond to geography

The fact that one can cluster humans together by geography based solely on their genetic information was most convincingly demonstrated in two papers (the second one is open access) by a group out of Stanford. These studies looked at several hundred variable places in the genome in 52 populations scattered across the globe. The hypothesis was as follows-- on applying a clustering algorithm to these data, individuals from similar geographic regions would end up together. I've put a representation on the right, where colors represent poplations-- on top is a pattern of variation that would lead to no clustering (the colors all blend one into the next) while on the bottom is a pattern of variation that would lead to clustering (there are subtle but noticable jumps from yellow to green, for example, though there is much variation within each color). Note that the lack of clustering would not mean that all populations are genetically the same (in the top figure, yellow and orange are not "the same" even though you couldn't find a fixed boundry between them). But indeed, the researchers found the situation corresponding to the bottom figure-- the individuals formed five clusters which represented, in the authors' words, "Africa, Eurasia (Europe, Middle East, and Central/South Asia), East Asia, Oceania, and the Americas". Some populations were exceptions, of course (there are always exceptions in biology)-- they seemed to be a mix between two clusters, or could even form their own cluster in certain models.

But in general, the second model in the figure is a good fit for human variation based on the spots in the genome used by these researchers-- continents correspond to clusters, and geographic barriers like the Himalayas or an ocean correspond to those areas where a "jump" from one cluster to the next occurrs.

II. Clusters and race

The fact that humans cluster together based on genetic information could, in theory, be entirely orthoganal to the concept of race. However, at least in the United State (where this has been explicitly tested), this is not the case. The most important reason for this, in my mind, is that the ancestors of European-Americans and African-Americans were not randomly sampled from the globe (there's a bias towards points on the globe that are quite distant), and this non-random sampling accentuates the genetic differences between the two groups. But in any case, the reasons for this are irrelevant to the argument; let's look at the data.

The basis for this assertion comes from a paper (open access) by a different set of researchers at Stanford, who assembled a group of Americans who identified themselves as either African-American, white, East Asian, or Hispanic. They followed a similar protocal as the studies in the first section-- they took DNA from all individuals, looked a hundreds of different DNA variants, and applied a clustering algorithm. They then looked to see if their clusters corresponded to self-reported group. And indeed, in 3631 out of 3636 cases (99.85%), the individuals were clustered by the algorithm into the "correct" racial group.

This result is obviously only valid in America, but presumably it could be repeated in other parts of the world (though there is some evidence that skin color and genetic ancestry are becoming independent in Brazil). But it is certainly the case that knowing someone's race will give you some probabilistic insight into their genetics[1].

III. Genotype and Phenotype

Once one accepts that genetic information clusters people together according to geography and that these clusters sometimes correspond to race, the next question is, do these genetic differences add up to phenotypic differences? The answer to this question is slowly emerging, and in the shadows I see the outline of a "YES".

All of the studies I will cite are based on the HapMap, a resource with genetic data as well as cell lines for individuals from four populations-- one of Western European ancestry, an Nigerian population, a Chinese population, and a Japanese population. Does the Nigerian population represent all populations in the African cluster, or the European population represent all the populations in the Eurasian cluster? Of course not, but analyzing them certainly gives an insight as to what makes one population different from any other.

First, the genetic data from the different populations can be analyzed to search for areas of the genome that have been under recent selection-- i.e. that have recently become beneficial for Nigerians, or Chinese, or whichever group. That analysis was done by two groups (both papers are open access), though I will discuss the second one. What they found was that each of the populations (they group the Chinese and Japanese together into a single population) has been under, and probably continues to be under, natural selection. It would be theoretically possible (if remarkable) to find that all humans are undergoing the same selective pressures and responding identically to them, but that is not the case. I've posted on the right a Venn diagram from the paper showing that most of the loci identified as under selection are detected in only one of the three groups, indicating that selection is causing people in different parts of the globe to become more distinct. The precise effects of the genetic variation between populations is unclear, but (as it's under selection) it's certainly phenotypically relevant. And lest you think the genes under selection are related only to "boring" physiological traits, note that one of the papers found that a number of genes involved in "neuronal function" have been under selection.

Even more recently, another group analyzed gene expression in both the Asian HapMap samples and the European HapMap samples and found that around 25% of the genes in the two were differentially expressed, and that this differential expression is due to genetic differences in many cases. The road from genotype to phenotype goes through gene expression, so this is a major step in connecting genetic variation to phenotypic variation.

So it's clear that populations differ genetically and that these differences are relevant phenotypically and informative about race. So, do genetic differences explain racial differences in any given phenotype? I hope that for phenotypes like eye color and skin color people accept the answer as obviously yes; these sorts of things have been convincingly demonstrated. For other phenotypes like IQ or personality, if you're inclined to react negatively, I say wait a few years before you get too confident; the study of human genetic variation is in its infancy, and once it hits adolescence it's going to start becoming a real pain in the ass.

[1]A note on race being a societal construct. To some extent, of course it is--some people that would be called "black" in the US might not be called "black" in France, for example (and not because of the language difference, for all you smartasses. The word "black" in French specifically refers to racial classification). I have enough faith in human intelligence to think that the first person who called race a societal construct did not mean that it had no biological component as well--note that the Wikipedia entry on adolesence refers to it as a "cultural and social phenomenon" but also "the transitional stage of human development in which a juvenile matures into an adult". People seem to somehow be able to keep the cultural and biological aspects of adolescence in their heads at the same time, as I imagine the first sociologists to study race were able to do (I may, of course, be wrong), yet somehow the fact that biological differences are interpreted through a cultural lens has somehow morphed into the idea that the biological differences don't exist to begin with (see, e.g. the ASA statement on race). Weird.

[Jason]

Perhaps later I'll post some Biomedical science shizz from more guys with PhDs.

[Jason]

Little blurb from Steven Pinker (some other guy with a PhD) that was printed in Edge magazine in 2006, but is no longer available:

Groups of people may differ genetically in their average talents and temperaments

The year 2005 saw several public appearances of what will I predict will become the dangerous idea of the next decade: that groups of people may differ genetically in their average talents and temperaments.

In January, Harvard president Larry Summers caused a firestorm when he cited research showing that women and men have non-identical statistical distributions of cognitive abilities and life priorities.

In March, developmental biologist Armand Leroi published an op-ed in the New York Times rebutting the conventional wisdom that race does not exist. (The conventional wisdom is coming to be known as Lewontin's Fallacy: that because most genes may be found in all human groups, the groups don't differ at all. But patterns of correlation among genes do differ between groups, and different clusters of correlated genes correspond well to the major races labeled by common sense. )

In June, the Times reported a forthcoming study by physicist Greg Cochran, anthropologist Jason Hardy, and population geneticist Henry Harpending proposing that Ashkenazi Jews have been biologically selected for high intelligence, and that their well-documented genetic diseases are a by-product of this evolutionary history.

In September, political scientist Charles Murray published an article in Commentary reiterating his argument from The Bell Curve that average racial differences in intelligence are intractable and partly genetic.

Whether or not these hypotheses hold up (the evidence for gender differences is reasonably good, for ethnic and racial differences much less so), they are widely perceived to be dangerous. Summers was subjected to months of vilification, and proponents of ethnic and racial differences in the past have been targets of censorship, violence, and comparisons to Nazis. Large swaths of the intellectual landscape have been reengineered to try to rule these hypotheses out a priori (race does not exist, intelligence does not exist, the mind is a blank slate inscribed by parents). The underlying fear, that reports of group differences will fuel bigotry, is not, of course, groundless.

The intellectual tools to defuse the danger are available. "Is" does not imply "ought. " Group differences, when they exist, pertain to the average or variance of a statistical distribution, rather than to individual men and women. Political equality is a commitment to universal human rights, and to policies that treat people as individuals rather than representatives of groups; it is not an empirical claim that all groups are indistinguishable. Yet many commentators seem unwilling to grasp these points, to say nothing of the wider world community.

Advances in genetics and genomics will soon provide the ability to test hypotheses about group differences rigorously. Perhaps geneticists will forbear performing these tests, but one shouldn't count on it. The tests could very well emerge as by-products of research in biomedicine, genealogy, and deep history which no one wants to stop.

The human genomic revolution has spawned an enormous amount of commentary about the possible perils of cloning and human genetic enhancement. I suspect that these are red herrings. When people realize that cloning is just forgoing a genetically mixed child for a twin of one parent, and is not the resurrection of the soul or a source of replacement organs, no one will want to do it. Likewise, when they realize that most genes have costs as well as benefits (they may raise a child's IQ but also predispose him to genetic disease), "designer babies" will lose whatever appeal they have. But the prospect of genetic tests of group differences in psychological traits is both more likely and more incendiary, and is one that the current intellectual community is ill-equipped to deal with.

[Jason]

A little Biomed shiz.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC139378/

Categorization of humans in biomedical research: genes, race and disease
Neil Risch,corresponding author1,2 Esteban Burchard,3 Elad Ziv,3 and Hua Tang4
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This article has been cited by other articles in PMC.
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Abstract

A debate has arisen regarding the validity of racial/ethnic categories for biomedical and genetic research. Some claim 'no biological basis for race' while others advocate a 'race-neutral' approach, using genetic clustering rather than self-identified ethnicity for human genetic categorization. We provide an epidemiologic perspective on the issue of human categorization in biomedical and genetic research that strongly supports the continued use of self-identified race and ethnicity.

A major discussion has arisen recently regarding optimal strategies for categorizing humans, especially in the United States, for the purpose of biomedical research, both etiologic and pharmaceutical. Clearly it is important to know whether particular individuals within the population are more susceptible to particular diseases or most likely to benefit from certain therapeutic interventions. The focus of the dialogue has been the relative merit of the concept of 'race' or 'ethnicity', especially from the genetic perspective. For example, a recent editorial in the New England Journal of Medicine [1] claimed that "race is biologically meaningless" and warned that "instruction in medical genetics should emphasize the fallacy of race as a scientific concept and the dangers inherent in practicing race-based medicine." In support of this perspective, a recent article in Nature Genetics [2] purported to find that "commonly used ethnic labels are both insufficient and inaccurate representations of inferred genetic clusters." Furthermore, a supporting editorial in the same issue [3] concluded that "population clusters identified by genotype analysis seem to be more informative than those identified by skin color or self-declaration of 'race'." These conclusions seem consistent with the claim that "there is no biological basis for 'race'" [3] and that "the myth of major genetic differences across 'races' is nonetheless worth dismissing with genetic evidence" [4]. Of course, the use of the term "major" leaves the door open for possible differences but a priori limits any potential significance of such differences.

In our view, much of this discussion does not derive from an objective scientific perspective. This is understandable, given both historic and current inequities based on perceived racial or ethnic identities, both in the US and around the world, and the resulting sensitivities in such debates. Nonetheless, we demonstrate here that from both an objective and scientific (genetic and epidemiologic) perspective there is great validity in racial/ethnic self-categorizations, both from the research and public policy points of view.
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Definition of risk factors: human categorization

The human population is not homogeneous in terms of risk of disease. Indeed, it is probably the case that every human being has a uniquely defined risk, based on his/her inherited (genetic) constitution plus non-genetic or environmental characteristics acquired during life. It is the goal of etiological epidemiological research to characterize such risks, both on an individual as well as population level, for the effective planning of prevention and/or treatment strategies. This public health perspective applies not only to disease, but to variation in normal traits (for example, for quantitative variables that are risk factors for disease such as blood pressure) as well as treatment response and adverse effects of pharmacologic agents.

The term 'risk factor' is widely used in epidemiology to define a characteristic associated either directly or indirectly with risk of disease. Some risk factors are fixed at birth (for example, sex or ethnicity) while others are acquired during life (for example, exposure to tobacco smoke or other environmental toxins). It is often assumed that risk factors fixed at birth are non-modifiable while those acquired after birth are modifiable and thus amenable to intervention strategies. But a multifactorial model of risk requires the interplay of multiple inherited and non-inherited factors in producing a particular risk profile. Identification of inherited factors can both aid in the discovery of their environmental counterparts as well as provide a rational strategy for identifying, a priori, the most vulnerable members of our population, on whom prevention strategies can be focused. These concepts apply not only to disease prevention but to disease treatment as well, given that providing timely and efficacious treatment to individuals benefits both patients and health-care providers.

The ultimate goal of characterizing each individual's unique risk would require knowledge of every causal factor and the quantitative relationship of all possible combinations of such factors. In most cases, causal variables are not known, however, so epidemiologists resort to other means of categorizing people by the use of surrogate variables. Examples of such variables include gender, occupation, geographic location, socioeconomic status and dietary intake of a given food. It is understood that these variables do not themselves reflect a direct causal relationship with disease but rather are correlated with such a causal variable or variables.

Each of these classification systems masks within it inherent risk heterogeneity that could be further resolved if the specific agent(s) were identified. For example, geographic gradients in disease rates are well known, but it is not the geographic location, per se, that is causally related but rather some underlying correlated causal factor(s) such as temperature, humidity, rainfall, sunlight or presence of ground toxins. Even geographic categorizations, for example those based on latitude, mask heterogeneity within strata. Vancouver has a similar latitude to Winnipeg, but individuals born and raised in these two locations have very different climatologic experiences.

Although risk factor associations do not usually imply direct causal links, they do provide a starting point for further investigation. For example, there are sex differences in the rate of a variety of disorders. Sometimes these differences are related to endogenous (and hence non- or poorly-modifiable) differences between men and women (for example, the differential rates of breast cancer in men and women), while other examples are due to behavioral (presumably modifiable) differences (for example, differential lung cancer rates in men and women due to different smoking experiences). When direct causal factors are identified, risk estimates on both an individual and population basis can be made much more precise. Before such identification, however, the use of cruder surrogate factors can still provide valuable input for prevention and treatment decisions, even while acknowledging the latent heterogeneity within strata defined by such variables.
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Rationale for the genetic categorization of humans

The discussion above provides an objective perspective from which to examine the question of genetic categorization of humans for biomedical research purposes. As for non-genetic risk factors, the ultimate goal of genetic research is to identify those specific genes and gene variants that influence the risk of disease, a quantitative outcome of interest, or response to a particular drug. Once all such genes are identified, every individual's unique risk can be assessed according to some quantitative model (if the number of genetic factors involved is large, however, problems will arise from the large number of possible combinations). In this case, categorization can occur at the level of the individual, irrespective of his/her racial, ethnic or geographic origins, moving us "closer to the ultimate goal of individualized therapy" [3]. But unless financial considerations in genetic testing become moot, there may still be practical issues concerning which genes to test in which individuals, and whether all genes should be tested in everyone.

To date, few genes underlying susceptibility to common diseases or influencing drug response have been identified. A question then arises, as it does when considering non-genetic factors, as to whether humans can or should be categorized genetically according to a surrogate scheme in the absence of known, specific gene effects. Wilson et al. [2] argue forcefully that genetic structure exists in the human population, that it can easily be identified even with a relatively modest number of marker loci, and that such structure is highly predictive of drug response: "We conclude that it is not only feasible but a clinical priority to assess genetic structure as a routine part of drug evaluation." [2]. This conclusion was based on their identification of four genetic "clusters" within a diverse sample of humans, and significant differences in the frequencies across these clusters for functional allelic variants in drug metabolizing enzymes.
Human evolution

Probably the best way to examine the issue of genetic subgrouping is through the lens of human evolution. If the human population mated at random, there would be no issue of genetic subgrouping because the chance of any individual carrying a specific gene variant would be evenly distributed around the world. For a variety of reasons, however, including geography, sociology and culture, humans have not and do not currently mate randomly, either on a global level or within countries such as the US. A clearer picture of human evolution has emerged from numerous studies over the past decade using a variety of genetic markers and involving indigenous populations from around the world. In summary, populations outside Africa derive from one or more migration events out of Africa within the last 100,000 years [5,6,7,8,9,10,11]. The greatest genetic variation occurs within Africans, with variation outside Africa representing either a subset of African diversity or newly arisen variants. Genetic differentiation between individuals depends on the degree and duration of separation of their ancestors. Geographic isolation and in-breeding (endogamy) due to social and/or cultural forces over extended time periods create and enhance genetic differentiation, while migration and inter-mating reduce it.

With this as background, it is not surprising that numerous human population genetic studies have come to the identical conclusion - that genetic differentiation is greatest when defined on a continental basis. The results are the same irrespective of the type of genetic markers employed, be they classical systems [5], restriction fragment length polymorphisms (RFLPs) [6], microsatellites [7,8,9,10,11], or single nucleotide polymorphisms (SNPs) [12]. For example, studying 14 indigenous populations from 5 continents with 30 microsatellite loci, Bowcock et al. [7] observed that the 14 populations clustered into the five continental groups, as depicted in Figure ​Figure1.1. The African branch included three sub-Saharan populations, CAR pygmies, Zaire pygmies, and the Lisongo; the Caucasian branch included Northern Europeans and Northern Italians; the Pacific Islander branch included Melanesians, New Guineans and Australians; the East Asian branch included Chinese, Japanese and Cambodians; and the Native American branch included Mayans from Mexico and the Surui and Karitiana from the Amazon basin. The identical diagram has since been derived by others, using a similar or greater number of microsatellite markers and individuals [8,9]. More recently, a survey of 3,899 SNPs in 313 genes based on US populations (Caucasians, African-Americans, Asians and Hispanics) once again provided distinct and non-overlapping clustering of the Caucasian, African-American and Asian samples [12]: "The results confirmed the integrity of the self-described ancestry of these individuals". Hispanics, who represent a recently admixed group between Native American, Caucasian and African, did not form a distinct subgroup, but clustered variously with the other groups. A previous cluster analysis based on a much smaller number of SNPs led to a similar conclusion: "A tree relating 144 individuals from 12 human groups of Africa, Asia, Europe and Oceania, inferred from an average of 75 DNA polymorphisms/individual, is remarkable in that most individuals cluster with other members of their regional group" [13]. Effectively, these population genetic studies have recapitulated the classical definition of races based on continental ancestry - namely African, Caucasian (Europe and Middle East), Asian, Pacific Islander (for example, Australian, New Guinean and Melanesian), and Native American.
Figure 1
Figure 1
The evolutionary tree of human races. Population genetic studies of world populations support the categorization into five major groups, as shown. See text for further details.

The terms race, ethnicity and ancestry are often used interchangeably, but some have also drawn distinctions. For the purpose of this article, we define racial groups on the basis of the primary continent of origin, as discussed above (with some modifications described below). Ethnicity is a self-defined construct that may be based on geographic, social, cultural and religious grounds. It has potential meaning from the genetic perspective, provided it defines an endogamous group that can be differentiated from other such groups. Ancestry refers to the race/ethnicity of an individual's ancestors, whatever the individual's current affiliation. From the genetic perspective, the important concept is mating patterns, and the degree to which racially or ethnically defined groups remain endogamous.

The continental definitions of race and ancestry need some modification, because it is clear that migrations have blurred the strict continental boundaries. For example, individuals currently living in South Africa, although currently Africans, have very different ancestry, race and ethnicity depending on the ancestry of their forbears (for example from Europe or Asia) and the degree to which they have remained endogamous. For our purposes here, on the basis of numerous population genetic surveys, we categorize Africans as those with primary ancestry in sub-Saharan Africa; this group includes African Americans and Afro-Caribbeans. Caucasians include those with ancestry in Europe and West Asia, including the Indian subcontinent and Middle East; North Africans typically also are included in this group as their ancestry derives largely from the Middle East rather than sub-Saharan Africa. 'Asians' are those from eastern Asia including China, Indochina, Japan, the Philippines and Siberia. By contrast, Pacific Islanders are those with indigenous ancestry from Australia, Papua New Guinea, Melanesia and Micronesia, as well as other Pacific Island groups further east. Native Americans are those that have indigenous ancestry in North and South America. Populations that exist at the boundaries of these continental divisions are sometimes the most difficult to categorize simply. For example, east African groups, such as Ethiopians and Somalis, have great genetic resemblance to Caucasians and are clearly intermediate between sub-Saharan Africans and Caucasians [5]. The existence of such intermediate groups should not, however, overshadow the fact that the greatest genetic structure that exists in the human population occurs at the racial level.

Most recently, Wilson et al. [2] studied 354 individuals from 8 populations deriving from Africa (Bantus, Afro-Caribbeans and Ethiopians), Europe/Mideast (Norwegians, Ashkenazi Jews and Armenians), Asia (Chinese) and Pacific Islands (Papua New Guineans). Their study was based on cluster analysis using 39 microsatellite loci. Consistent with previous studies, they obtained evidence of four clusters representing the major continental (racial) divisions described above as African, Caucasian, Asian, and Pacific Islander. The one population in their analysis that was seemingly not clearly classified on continental grounds was the Ethiopians, who clustered more into the Caucasian group. But it is known that African populations with close contact with Middle East populations, including Ethiopians and North Africans, have had significant admixture from Middle Eastern (Caucasian) groups, and are thus more closely related to Caucasians [14]. Furthermore, the analysis by Wilson et al. [2] did not detect subgroups within the four major racial clusters (for example, it did not separate the Norwegians, Ashkenazi Jews and Armenians among the Caucasian cluster), despite known genetic differences among them. The reason is clearly that these differences are not as great as those between races and are insufficient, with the amount of data provided, to distinguish these subgroups.
Are racial differences merely cosmetic?

Two arguments against racial categorization as defined above are firstly that race has no biological basis [1,3], and secondly that there are racial differences but they are merely cosmetic, reflecting superficial characteristics such as skin color and facial features that involve a very small number of genetic loci that were selected historically; these superficial differences do not reflect any additional genetic distinctiveness [2]. A response to the first of these points depends on the definition of 'biological'. If biological is defined as genetic then, as detailed above, a decade or more of population genetics research has documented genetic, and therefore biological, differentiation among the races. This conclusion was most recently reinforced by the analysis of Wilson et al. [2]. If biological is defined by susceptibility to, and natural history of, a chronic disease, then again numerous studies over past decades have documented biological differences among the races. In this context, it is difficult to imagine that such differences are not meaningful. Indeed, it is difficult to conceive of a definition of 'biological' that does not lead to racial differentiation, except perhaps one as extreme as speciation.

A forceful presentation of the second point - that racial differences are merely cosmetic - was given recently in an editorial in the New England Journal of Medicine [1]: "Such research mistakenly assumes an inherent biological difference between black-skinned and white-skinned people. It falls into error by attributing a complex physiological or clinical phenomenon to arbitrary aspects of external appearance. It is implausible that the few genes that account for such outward characteristics could be meaningfully linked to multigenic diseases such as diabetes mellitus or to the intricacies of the therapeutic effect of a drug." The logical flaw in this argument is the assumption that the blacks and whites in the referenced study differ only in skin pigment. Racial categorizations have never been based on skin pigment, but on indigenous continent of origin. For example, none of the population genetic studies cited above, including the study of Wilson et al. [2], used skin pigment of the study subjects, or genetic loci related to skin pigment, as predictive variables. Yet the various racial groups were easily distinguishable on the basis of even a modest number of random genetic markers; furthermore, categorization is extremely resistant to variation according to the type of markers used (for example, RFLPs, microsatellites or SNPs).

Genetic differentiation among the races has also led to some variation in pigmentation across races, but considerable variation within races remains, and there is substantial overlap for this feature. For example, it would be difficult to distinguish most Caucasians and Asians on the basis of skin pigment alone, yet they are easily distinguished by genetic markers. The author of the above statement [1] is in error to assume that the only genetic differences between races, which may differ on average in pigmentation, are for the genes that determine pigmentation.
Common versus rare alleles

Despite the evidence for genetic differentiation among the five major races, as defined above, numerous studies have shown that local populations retain a great deal of genetic variation. Analysis of variance has led to estimates of 10% for the proportion of variance due to average differences between races, and 75% of the variance due to genetic variation within populations. Comparable estimates have been obtained for classical blood markers [15,16], microsatellites [17], and SNPs [12]. Unfortunately, these analysis of variance estimates have also led to misunderstandings or misinterpretations. Because of the large amount of variation observed within races versus between races, some commentators have denied genetic differentiation between the races; for example, "Genetic data ... show that any two individuals within a particular population are as different genetically as any two people selected from any two populations in the world." [18]. This assertion is both counter-intuitive and factually incorrect [12,13]. If it were true, it would be impossible to create discrete clusters of humans (that end up corresponding to the major races), for example as was done by Wilson et al. [2], with even as few as 20 randomly chosen genetic markers. Two Caucasians are more similar to each other genetically than a Caucasian and an Asian.

In these variance assessments, it is also important to consider the frequency of the allelic variants examined. These studies are based primarily on common alleles, and may not reflect the degree of differentiation between races for rare alleles. This is an important concern because alleles underlying disease susceptibility, especially deleterious diseases, may be less frequent than randomly selected alleles. Similarly, it has also been shown that among different classes of SNPs, those that lead to non-conservative amino-acid substitutions (which most frequently are associated with clinical outcomes) occur least often, and when they do occur they tend to have lower allele frequencies than non-coding or synonymous coding changes [12,19,20].

It is likely that genetic differentiation among races is enhanced for disease-predisposing alleles because such alleles tend to be in the lower frequency range. It is well known that rarer alleles are subject to greater fluctuation in frequency due to genetic drift than common alleles. Indeed, for most Mendelian diseases, even higher frequency alleles are found only in specific races (for example, cystic fibrosis and hemochromatosis in Caucasians). Furthermore, recent SNP surveys of the different races have shown that lower frequency variants are much more likely to be specific to a single race or shared by only two races than are common variants [12,19,20]. In one study, only 21% of 3,899 SNPs were found to be pan-ethnic, and some race-specific SNPs were found to have a frequency greater than 25% [12].
Admixture and genetic categorization in the United States

Most population genetic studies that focus on human evolution and the relatedness of people, including the ones cited above, utilize indigenous groups from the various continents. These groups would not necessarily adequately depict the US population, for example, where admixture between races has occurred over many centuries. Nonetheless, during the same period of time, as well as currently, mating patterns are far from random. The tendency toward endogamy is reflected within the 2000 US Census [21], which allowed individuals to report themselves to be of a single race or of mixed race. Six racial categories were provided (White; Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and other Pacific Islander; Some other race). In response to this question, 97.6% of subjects reported themselves to be of one race, while 2.4% reported themselves to be of more than one race; 75% reported themselves as White, 12.3% as Black or African American, 3.6% as Asian, 1% as American Indian or Alaska Native, 0.1% as Hawaiian or Pacific Islander; and 5.5% of other race. Of the 5.5% who reported themselves as 'other race', most (97%) also reported themselves to be Hispanic. According to these numbers, if mating were at random with respect to these racial categories, 42% of individuals would result from 'mixed' matings and hence derive from more than one race, as opposed to the 2.4% reported. These figures highlight the strong deviation from random mating in the US.

What are the implications of these census results and the admixture that has occurred in the US population for genetic categorization in biomedical research studies in the US? Gene flow from non-Caucasians into the US Caucasian population has been modest. On the other hand, gene flow from Caucasians into African Americans has been greater; several studies have estimated the proportion of Caucasian admixture in African Americans to be approximately 17%, ranging regionally from about 12% to 23% [22]. Thus, despite the admixture, African Americans remain a largely African group, reflecting primarily their African origins from a genetic perspective. Asians and Pacific Islanders have been less influenced by admixture and again closely represent their indigenous origins. The same is true for Native Americans, although some degree of Caucasian admixture has occurred in this group as well [23].

The most complex group is made up of those who self-identify as Hispanic/Latino. The US Census did not consider this group as a separate race, although 42% of respondents who considered themselves Hispanic checked the category 'other race' for the racial question, while 48% checked 'White'. Hispanics are typically a mix of Native American, Caucasian and African/African American, with the relative proportions varying regionally. Southwest Hispanics, who are primarily Mexican-American, appear to be largely Caucasian and Native American; recent admixture estimates are 39% Native American, 58% Caucasian and 3% African [24]. By contrast, East Coast Hispanics are largely Caribbean in origin, and have a greater proportion African admixture [25]. Thus, depending on geography, self-identified Hispanics could aggregate genetically with Caucasians, Native Americans, African Americans or form their own cluster.

The persistence of genetic differentiation among these US racial groups (as defined by the US Census) has also been verified recently in a study of nearly 4,000 SNPs in 313 genes [12]. These authors found distinct clusters for Caucasian Americans, African Americans and Asian Americans; the Hispanic Americans did not form a separate cluster but were either grouped with Caucasians or not easily classified. Although the US Census results suggest the large majority of individuals can be categorized into a single ancestral group, there remain individuals of mixed ancestry who will not be easily categorized by any simple system of finite, discrete categories. On the other hand, such individuals can be particularly informative in epidemiologic studies focused on differentiating genetic versus environmental sources for racial/ethnic difference, as we describe further below.
Genetic clustering versus self-reported ancestry

A major conclusion from the study of Wilson et al. [2], reiterated in accompanying editorials, is that "Clusters identified by genotyping... are far more robust than those identified using geographic and ethnic labels" [26]. But closer examination of the study and other data actually leads to the opposite conclusion: namely, that self-defined race, ethnicity or ancestry are actually more genetically informative than clusters based on analysis of random genetic markers.

In their analysis, Wilson et al. [2] found greater variation in allele frequencies for drug metabolizing enzymes based on four "genetically" defined clusters than in three "ethnically" defined clusters. The ethnic clusters included Caucasians (Norwegians, Ashkenazi Jews and Armenians), Africans (Bantus, Afro-Caribbeans and Ethiopians), and Asians (Chinese and Papua New Guineans). The inclusion of Papua New Guineans as Asians would be considered highly controversial by most population geneticists, as all prior studies of this group show them to cluster with Pacific Islanders [7,8], and as we discussed above, population genetic studies have shown Pacific Islanders to be distinct from Asians [6,7,8,9]. Futhermore, the racial categories of the US census would also not merge Chinese with New Guineans. Nonetheless, examination of the variance in allele frequencies for the six drug-metabolizing enzymes across the four "genetically defined" clusters versus the three "racial" groups does not reveal greater differentiation of the former (Table ​(Table1).1). In fact, for 5 of the 6 loci, the variance is greater among the three "racial" groups, although for most loci the variance is very similar for the two categorization schemes. This is not surprising, as the racial categories aligned nearly perfectly with genotype clusters. The only exception was for the Ethiopians, who (as we discussed above) are known to genetically resemble Caucasians, probably as a result of considerable Caucasian admixture [14]. On the other hand, neither of these ethnic categorizations (of New Guineans and Ethiopians) would have much impact on studies in the US, as these groups represent only a tiny fraction of the US population, even among Pacific Islanders and African Americans, respectively.
Table 1
Table 1
Allele frequency differentiation of drug metabolizing enzymes on the basis of "genetic clusters" versus "racial groups," from the data of Wilson et al. [2]

Indeed, in another respect, the results of Wilson et al. [2] demonstrate the superiority of ethnic labels over genetic clustering. Consider the group they labeled Caucasian, consisting of Norwegians, Ashkenazi Jews and Armenians. Their genetic cluster analysis lumped these three populations together into a single (Caucasian) cluster. Yet numerous genetic studies of these groups have shown them to differ in allele frequencies for a variety of loci. For example, the hemochromatosis gene mutation C282Y has a frequency of less than 1% in Armenians and Ashkenazi Jews but of 8% in Norwegians [27]. Thus, in this case, self-defined ethnicity provides greater discriminatory power than the single genotype cluster obtained by Wilson et al. [2].

This conclusion obtains not just for the ethnically homogeneous but for admixed subjects as well. A study by Williams et al. [28] considered the degree of Caucasian admixture in a Pima Indian population. These authors compared self-report (the number of Caucasian and Pima grandparents) with a genetic estimate of admixture based on 18 conventional blood markers. Using type 2 diabetes mellitus (high frequency in Pimas, low frequency in Caucasians) as the outcome, disease frequency correlated more strongly with self-reported admixture than with genetically estimated admixture.

As seen in the study of Wilson et al. [2], genetic cluster analysis is only powerful in separating out individuals whose ancestors diverged many millennia ago, leading to substantial genetic differences. It is much less capable of differentiating more recently separated groups, whose genetic differentiation is smaller. This conclusion may also be a result of the small number of genetic markers employed, however, and finer resolution might be possible with a much larger number of loci.
How many loci are needed for clustering?

A natural question arises as to the number of loci required to categorize individuals into ancestrally defined clusters. The answer depends on the degree of genetic differentiation of the populations in question. Two groups with ancient separation and no migration will require far fewer markers than groups that have separated more recently or have been influenced by recent migrations and/or admixture.

A simple quantification of this question is possible, as described in Box 1. The number of biallelic loci necessary for given misclassification rates is given in Table ​Table2;2; when δav is high, 20 or fewer loci are adequate for accurate classification, but even for low δav values 200 markers are more than adequate. How do these numbers relate to the ability to differentiate genetically various racial/ethnic groups? Recent large-scale surveys of 257 SNPs [29] and 744 short tandem repeat polymorphisms (STRPs, or microsatellites) [30] provide an answer to this question, based on the distribution of δ values observed in these surveys for various population comparisons. Both studies included Caucasian Americans (CA), Asian Americans (AS) and African Americans (AA); Dean et al. [29] also included Native Americans (NA), while Smith et al. [30] included Hispanic Americans (HA). Table ​Table33 provides the median δ values for all markers, for the top 50th percentile of δ values, and for the top 20th percentile of δ values.
Box 1
Box 1
Table 2
Table 2
The number of markers required for clustering as a function of the misclassification rate (calculated as shown in Box 1)
Table 3
Table 3
Median δ values for different racial/ethnic group comparisons, from data of Dean et al. [29] and Smith et al. [30]

In conjunction with Table ​Table2,2, we can estimate that about 120 unselected SNPs or 20 highly selected SNPs can distinguish group CA from NA, AA from AS and AA from NA. A few hundred random SNPs are required to separate CA from AA, CA from AS and AS from NA, or about 40 highly selected loci. STRP loci are more powerful and have higher effective δ values because they have multiple alleles. Table ​Table33 reveals that fewer than 100 random STRPs, or about 30 highly selected loci, can distinguish the major racial groups. As expected, differentiating Caucasians and Hispanic Americans, who are admixed but mostly of Caucasian ancestry, is more difficult and requires a few hundred random STRPs or about 50 highly selected loci. These results also indicate that many hundreds of markers or more would be required to accurately differentiate more closely related groups, for example populations within the same racial category.
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Gene-environment correlation and confounding - the real problem

From an epidemiologic perspective, the use of 'genetic' clusters, as suggested by Wilson et al. [2], instead of self-reported ethnicity will not alleviate but rather will actually create and/or exacerbate problems associated with genetic inferences based on racial differences. The true complication is due to the fact that racial and ethnic groups differ from each other on a variety of social, cultural, behavioral and environmental variables as well as gene frequencies, leading to confounding between genetic and environmental risk factors in an ethnically heterogeneous study. For example, with respect to treatment response, "An individual's response to a drug depends on a host of factors, including overall health, lifestyle, support system, education and socioeconomic status - all of which are difficult to control for and likely to be affected, at least in the United States, by a person's 'race'" [3].

Specifically, let us consider the practical implications of the "race-neutral approach" [3] advocated by Wilson et al. [2]. As an example, we revisit a recent study of the efficacy of inhibitors of angiotensin-converting enzyme (ACE) in 1,200 white versus 800 black patients with congestive heart failure [31] that generated a great deal of controversy [1,32]. In that study, the authors showed that black patients on the ACE inhibitor Enalapril showed no reduction in hospitalization compared with those on placebo, whereas white patients showed a strong, statistically significant difference between treatment versus placebo arms. Let us suppose that instead of using racial labels, the authors had performed genotype cluster analysis on their combined sample. They would have obtained two clusters - cluster A containing approximately 1,200 subjects, and cluster B, containing approximately 800 subjects. They would then demonstrate that cluster A treated subjects show a dramatic response to Enalapril compared to placebo subjects, while cluster B subjects show no such response. The direct inference from this analysis would be that the difference in responsiveness between individuals in cluster A and cluster B is genetic - that is, due to a frequency difference in one or more alleles between the two groups. But the problem should be obvious: cluster A is composed of the Caucasian subjects and cluster B the African Americans. Although a genetic difference in treatment responsiveness between these two groups is inferred, the conclusion is completely confounded with the myriad other ways these two groups might differ from each other; hence the culprit may not be genetic at all.

A racial difference in the frequency of some phenotype of interest (disease, or drug response) or quantitative trait is but a first clue in the search for etiologic causal factors. As we have illustrated, without such racial/ethnic labels, these underlying factors cannot be adequately investigated. Although some investigators might quickly jump to a genetic explanation for an ethnic difference, this is rarely the case with epidemiologists, who have a broad view of the complex nature of most human traits [33]. Indeed, epidemiologists employ several different approaches to disentangling genetic from environmental causes of ethnic differences, including migrant studies and stratified analyses.

The rationale underlying migrant studies is to compare the frequency of a trait (such as disease) between members of the same ethnic group (who are assumed to be genetically homogeneous) who are residing in different environments. For example, breast cancer rates in Asian (Chinese and Japanese) women are vastly lower than the rates among US Caucasians. However, the breast cancer rates of Chinese and Japanese women living in Hawaii and the San Francisco Bay Area are comparable to those of US Caucasians [34]. These results suggest an environmental source of the racial difference. Asians are also known to have much lower rates of multiple sclerosis than European Caucasians [35]. But within a single country, namely Canada, this racial difference persists [36], increasing support for (but not proving) a genetic explanation.

The best approach to resolve confounding is through matched, adjusted or stratified analyses, but this depends on having the confounding variables (or their surrogates). Such analyses can be performed in a racially heterogeneous sample, but it is potentially more powerful when performed within a single group. The reason is that the correlation between confounding variables (such as genes and environment) may be stronger in a heterogeneous study population than in a more homogeneous one. The ability to disentangle the effects of confounding variables is greater when their sample correlation is low.

A simple example is provided in Figure ​Figure2.2. Here we assume two populations (for example, races), groups A and B. As shown in the figure, where both environmental and genetic factors differ between the populations, it is impossible to determine which is the functional cause of the racial difference if the genetic and environmental effects are completely correlated in frequency within the two groups. More importantly, if the relative frequency in the two groups of the environmental factor was not measured, analysis stratified on the genetic differences yields the correct interpretation that the genetic difference does not contribute to the racial difference if the genetic and environmental factors are not correlated. But if they are fully correlated, analysis stratified on the genetic factor alone would lead to the incorrect conclusion that it is the cause of the racial difference.
Figure 2
Figure 2
An example of confounding and a stratified analysis of environmental and genetic factors. Here we assume two populations (for example, races), groups A and B. G1 and G2 represent dichotomous genotype classes at a candidate gene locus (here one of the ...

Epidemiologists often perform analyses of racial differences stratified on numerous environmental variables, such as socio-economic status, access to health care, education, and so on. The persistence of racial differences after accounting for these covariates raises the index of suspicion that genetic differences may be involved. For example, Karter et al. [37] recently demonstrated persistence of racial differences in diabetes complications in a health maintenance organization after controlling for numerous potential confounders including measures of socioeconomic status, education, and health-care access and utilization. Such evidence is indirect, however, as other unmeasured factors may still be responsible [38]. Ultimate proof depends on identifying a specific gene effect within each population, with an allele frequency difference between populations. One such example involves the lower risk of type 1 diabetes in US Hispanic versus Caucasian children. The HLA allele DR3 is predisposing to type 1 diabetes in both populations but has a lower frequency in Hispanics than Caucasians [39].

Another approach often taken by genetic epidemiologists is to consider the prevalence of disease or drug response (D) in individuals who are admixed between groups A and B - for example, in individuals who are 100%A, 75%A-25%B, 50%A-50%B, 25%A-75%B and 100%B (corresponding to 4, 3, 2, 1 and 0 grandparents that are group A, respectively). A continuous cline in the frequency of D with genome proportion that is group A is taken as suggestive evidence of genetic factors explaining the prevalence difference between groups A and B. An example of this type of analysis is the decreasing trend in type 2 diabetes in Pima Indians with degree of Caucasian admixture [23]. Analyses stratified on environmental factors can again strengthen the argument. But the same caveat applies here as described above. If an unmeasured environmental variable (such as socio-economic status) covaries in the same fashion as the proportion group A, the racial difference could be due to this unmeasured variable. At best, one could argue that the racial difference is not explained by any of the measured covariates.
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Identical treatment is not equal treatment

Both for genetic and non-genetic reasons, we believe that racial and ethnic groups should not be assumed to be equivalent, either in terms of disease risk or drug response. A 'race-neutral' or 'color-blind' approach to biomedical research is neither equitable nor advantageous, and would not lead to a reduction of disparities in disease risk or treatment efficacy between groups. Whether African Americans, Hispanics, Native Americans, Pacific Islanders or Asians respond equally to a particular drug is an empirical question that can only be addressed by studying these groups individually. Differences in treatment response or disease prevalence between racial/ethnic groups need to be studied carefully; naive inferences about genetic causation without evidence should be avoided. At the same time, gratuitous dismissal of a genetic interpretation without evidence for doing so is also unjustified.

We strongly support the search for candidate genes that contribute both to disease susceptibility and treatment response, both within and across racial/ethnic groups. Identification of such genes can help provide more precise individualized risk estimates. Environmental variables that influence risk and interact with genetic variables also require identification. Only if consideration of all these variables leaves no residual difference in risk between racial/ethnic groups is it justified to ignore race and ethnicity.

Is there any advantage to using random genetic markers and genetically defined clusters over self-reported ancestry in attempting to assess risk? In the case where a sample was collected without ancestry information, or for individuals on whom ancestral background is missing - for example, adoptees - such data could substitute for self-identified ancestry. On the other hand, we see considerable disadvantage in avoiding self-reported ancestry in favor of a 'color-blind' approach of genetically defined clusters. Any study in the US that randomly samples subjects without regard to ancestry will obtain, on average, 75% Caucasians, 12% African Americans, 4% Asians, 12% Hispanics (broadly defined) and few Pacific Islanders, although these frequencies would vary regionally. Thus, results from such studies would be largely derived from the Caucasian majority, with obtained parameter estimates that might not apply to the groups with minority representation. It might be possible in such studies to subsequently identify racial/ethnic differences, either based on self-reported ancestry of the study subjects or by genetic cluster analysis. However, the low frequency of the non-Caucasian groups in the total sample would lead to reduced power to detect and investigate any racial/ethnic differences that might be present. The best way to avoid this power issue is to specifically over-sample the lower frequency racial/ethnic groups to obtain larger sample sizes. Obviously, the only way to effectively and economically do so is based on self-identified ancestry, as genotype information from the population at large is not available and not likely to become so. Fortunately, the National Institutes of Health have instituted policies to encourage the inclusion of US ethnic minorities, and we strongly support continuation and expansion of this policy.

Finally, we believe that identifying genetic differences between races and ethnic groups, be they for random genetic markers, genes that lead to disease susceptibility or variation in drug response, is scientifically appropriate. What is not scientific is a value system attached to any such findings. Great abuse has occurred in the past with notions of 'genetic superiority' of one particular group over another. The notion of superiority is not scientific, only political, and can only be used for political purposes.

As we enter this new millennium with an advancing arsenal of molecular genetic tools and strategies, the view of genes as immutable is too simplistic. Every race and even ethnic group within the races has its own collection of clinical priorities based on differing prevalence of diseases. It is a reflection of the diversity of our species - genetic, cultural and sociological. Taking advantage of this diversity in the scientific study of disease to gain understanding helps all of those afflicted. We need to value our diversity rather than fear it. Ignoring our differences, even if with the best of intentions, will ultimately lead to the disservice of those who are in the minority.
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Acknowledgements

We are grateful to Andrew Karter and Catherine Schaefer for many helpful comments and discussion on an earlier version of this manuscript. N.R. was supported by NIH grant GM057672, E.B. and E.Z. by the Sandler Family Supporting Foundation, and H.T. by a Howard Hughes Medical Institute fellowship.
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Esteban Burchard was on NPR Science Friday in 2010 for the show "Is There A Biological Basis For Race?"
http://www.npr.org/templates/story/stor ... =122620064

It's about

[Cormac]

What is this "Social Science" that is mentioned?

[FBM]

Bum custard.

[Jason]

Moar from Steve Hsu (PhD guy) links at link - pictures too.
http://infoproc.blogspot.ca/2007/06/its ... sters.html

It's all in the clusters
Nicholas Wade in the Times on genetic clustering, differential selection and race. See here for previous discussion of a metric on the space of genomes and the scientific meaning of race.

I hear the voice of physicist turned evolutionary biologist Greg Cochran in Wade's writing :-)


Wade: Historians [and social scientists!] often assume that they need pay no attention to human evolution because the process ground to a halt in the distant past. That assumption is looking less and less secure in light of new findings based on decoding human DNA.

People have continued to evolve since leaving the ancestral homeland in northeastern Africa some 50,000 years ago, both through the random process known as genetic drift and through natural selection. The genome bears many fingerprints in places where natural selection has recently remolded the human clay, researchers have found, as people in the various continents adapted to new diseases, climates, diets and, perhaps, behavioral demands. ...


Cochran: There is something new under the sun — us.

Thucydides said that human nature was unchanging and thus predictable — but he was probably wrong. If you consider natural selection operating in fast-changing human environments, such stasis is most unlikely. We know of a number of cases in which there has been rapid adaptive change in humans; for example, most of the malaria-defense mutations such as sickle cell are recent, just a few thousand years old. The lactase mutation that lets most adult Europeans digest ice cream is not much older.

There is no magic principle that restricts human evolutionary change to disease defenses and dietary adaptations: everything is up for grabs. Genes affecting personality, reproductive strategies, cognition, are all able to change significantly over few-millennia time scales if the environment favors such change — and this includes the new environments we have made for ourselves, things like new ways of making a living and new social structures. I would be astonished if the mix of personality types favored among hunter-gatherers is "exactly" the same as that favored among peasant farmers ruled by a Pharaoh. In fact they might be fairly different.



(Larger version here.)

From Dienekes:


A new article in BMC Genomics discusses the issue of predicting continental origin using randomly selected markers. The pdf is freely available.

One of the arguments of those who deny the existence of biological races is that their reality is subjective. Some extremists have argued that race is totally socially constructed; this is, however, disproven by the fact that socially constructed race is correlated with physical characteristics. Thus, rather than being separated from biology, the social phenomenon of race is rooted in biology.

A different argument holds that race is correlated with biology, but the differences are "skin-deep", i.e., involve only superficial, visible, (and by some strange logic unimportant) characteristics. According to the proponents of this view, the idea of biological race places an undue emphasis on a set of traits: it is a result of the subjective choice of a set of traits as race-defining. Thus, the commonly recognized races of traditional physical anthropology are discounted as subjective organizations of the biological data: we could just as simply speak of a "lactose-intolerant race" according to this view.

In forensic science and admixture analysis scientists often discover and use polymorphisms which exhibit large inter-population differences. Decoding DNA isn't free, thus, it makes sense to use the most informative, most "biased" markers when one is trying to discover the origin of a biological sample. For example, if Africans have 55% of gene version A and 45% of gene version B, and Europeans have 53% of A and 47% of B, it makes little sense to type this particular gene, since it cannot really tell us whether a sample is European or African. A gene where Africans have 90% of A while Europeans have 5%of A would be much more useful. Race skeptics claim, as with the physical anthropological data, that to privilege such carefully chosen genes is to stress the differences between groups; the implication is that in randomly chosen genes these differences are minor.

The new paper is one of many (you can click on the Clusters label to find more) recent papers that have discovered that no matter what genetic markers you choose: SNPs, STRs, no matter how you choose them: randomly or based on their "informativeness", it is relatively easy to classify DNA into the correct continental origin. Depending on the marker types (e.g., indel vs. microsatellite), and their informativeness (roughly the distribution differences between populations), one may require more or less markers to achieve a high degree of accuracy. But, the conclusion is the same: after a certain number of markers, you always succeed in classifying individuals according to continental origin.

Thus, the emergent pattern of variation is not at all subjectively constructed: it does not deal specifically with visible traits (randomly chosen markers could influence any trait, or none at all), nor does it privilege markers exhibiting large population differences. The structuring of humanity into more or less disjoint groups is not a subjective choice: it emerges naturally from the genomic composition of humans, irrespective of how you study this composition. Rather than proving that race is skin-deep, non-existent, or unimportant, modern genetic science is both proving that it is in fact existent, but also sets the foundation for the study of its true importance, which is probably somewhere in between the indifference of the sociologists and the hyperbole of the racists.

[Jason]

What is this "Social Science" that is mentioned?

They're mostly the twats that claim there's no biological basis for race.

[FBM]

What is this "Social Science" that is mentioned?

They're mostly the twats that claim there's no biological basis for race.

Yeah, like the hippie flakes running the Human Genome Project.

[Jason]

Yep. It's all bum custard.

[FBM]

Yeah, pretty much. I'm happy with saying that the concept of race exists and leaving the details up to the pros.

[Cormac]

What is this "Social Science" that is mentioned?

They're mostly the twats that claim there's no biological basis for race.

Ach. From a practical, everyday point of view, people I meet will be generally much the same as me except for externalities. Whether or not there are biological differences is not all that relevant to human interaction - although, clearly, they can be medically very important.

Regarding intelligence and mental aptitudes, you would have to wonder how much this is influenced by culture, economic development, attitude, and access to education.

But the so-called "social sciences" are not scientific, really.

[pErvinalia]

Bum custard.

I love ratz!

[FBM]

[Gawdzilla Sama]