Editor’s Note: Nicholas Wade’s A Troublesome Inheritance: Genes, Race, and Human History attempts to report on the concept of race from a modern genetic and evolutionary perspective. In a book on a volatile topic for a general audience such as this, it is important to evaluate whether it accurately reports on the full range of scientific opinion. TVOL is therefore pleased to provide this authoritative review by Dr. Joseph L. Graves Jr , an evolutionary geneticist who has written widely on the biological concept of race in humans in addition to his research on life history theory. Dr. Graves’ books include The Emperor’s New Clothes: Biological Theories of Race at the Millennium and The Race Myth: Why We Pretend Race Exists in America. An interview with Dr. Graves was featured in a 2012 TVOL article titled “Evolution and Black History Month”.According to Nicholas Wade, “Humans cluster into five continental groups or races, and within each race there are further subclusters. So the number of human races depends on the number of clusters one wishes to recognize.”Nicholas Wade is a freelance science writer. He has done stints as a deputy editor of Nature and has reported for Science, as well as for the New York Times. He has a Bachelor’s Degree in Natural Sciences from King’s College at Cambridge. Wade is now at the center of a firestorm resulting from Penguin Press’ 2014 publication of his latest work, A Troublesome Inheritance: Genes, Race, and Human History. In this book, he asserts that biological or geographic races exist within the human species. He also claims that these races result from the fact that since our species left Africa, the populations of each continent have evolved largely independently of one another as each adapted to its own regional environment (p. 2). He also claims that the existence of biological races within our species is a widely understood fact amongst evolutionary biologists and that only “fear” of reprisal and “political correctness” prevents us from publicizing this fact. Indeed, he even claims that “researchers at present routinely ignore the biology of race, or tiptoe around the subject, lest they be accused of racism by their academic rivals and see their careers destroyed (p. 7). Furthermore, he asserts that the reality of biological races existing within our species is important for understanding the development of human cultures and societies. In this review, I shall demonstrate how each of Wade’s central claims is problematic.Wade is not a professional population or human geneticist. In his book, he reports (and often misrepresents) the views of some geneticists – particularly those who are convinced that biological or geographic races can be legitimately defined in the human species. His literature review concerning the population genetics theory behind these analyses and their interpretation is not comprehensive. He tends to rely on the views of those who believe that biological races are real and consequential in modern humans and to ignore and minimize those of us who do not hold that position.Much of his assertion that biological races exist within humans is contingent upon both his uncritical acceptance and misrepresentation of the significance of STRUCTURE type analyses of human genetic diversity. STRUCTURE is an algorithm designed to infer population structure (cluster individuals into ancestry groups) within a species (Pritchard, Stephens, and Donnelly 2000.) STRUCTURE produces for individuals an estimate of the probability that a randomly chosen genetic marker (e.g. single tandem repeats, STR, or single nucleotide polymorphisms, SNP) from that individual originated from one of a set of ancestral groups. The number of ancestral groups, K, is chosen to produce a best estimate of these probabilities, which are averaged over all genetic markers to assign a membership coefficient, namely a fraction of each individual’s ancestry to one of the ancestral groups (Feldman 2010.) The ancestral groups are not specified in advance, and the population membership of individuals is removed prior to analysis. Rosenberg et al. (2005) showed that the results of STRUCTURE style analyses are dependent upon whether allele frequencies are correlated or uncorrelated across populations, the number of loci used, the number of clusters specified, and the sample size. At very small numbers of loci and individuals examined, the results can be strongly influenced by random factors; thus, we have more confidence in the results of larger studies (more loci and more individuals.) For example, in their simulation with 993 loci and 1,048 individuals, the correlated model returned cluster coefficients of 0.51, 0.76, 0.84, 0.86, and 0.86 for K = 2, 3, 4, 5, and 6 respectively, and the uncorrelated model returned cluster coefficients of 0.49, 0.75, 0.80, 0.63, and 0.64 for K = 2, 3, 4, 5, and 6 respectively. Thus, the correlated model states that ancestry five clusters are just as valid as ancestry six clusters, and the uncorrelated model suggests that four clusters are better than five or six.Wade suggests that such results are evidence of the existence of biological races within the human species. He starts by assuming/claiming that the definition of biological races is determined by “how the genomes of individuals throughout the world cluster together in terms of their genetic similarity” (p. 96). This of course is a new definition – either coined by Wade himself or by some of those he interviewed. The historical definitions of biological race, used in evolutionary biology, are far more nuanced (e.g. Templeton 2002). Of course Wade cites little of this discussion in this book; instead, he resorts to using the Straw Man Fallacy in dismissing arguments against the existence of biological races in modern humans (particularly citing Jared Diamond, Richard Lewontin, and Ashley Montagu, pp. 117 – 122).As opposed to Wade’s definition, evolutionary biologists have equated biological races with subspecies. Subspecies result from adaptation to local conditions and represent some amount of genetic change within species that occurs as new species are formed. The amount of genetic difference between populations that should be considered sufficient to name biological races has never been precise (Futuyma 1986). For this reason, evolutionary biologists have tended to look for some means to quantify just how much genetic difference between subpopulations within a species should be considered legitimate to delineate biological races. In this regard, two terms have been used in the non-human literature. Races have been described as geographically circumscribed, genetically differentiated populations and as “distinct evolutionary lineages” within a species (Shaffer and McKnight 1996; Templeton 2002).One method used to quantify whether human populations can be thought of under the first definition is the use of Wright’s population subdivision statistic. Wade (p. 20) specifically discusses this where he quotes Henry Harpending and Alan Rodgers, who are supposedly speaking for Sewall Wright concerning the significance of his FST statistic. FST is the population subdivision statistic and can be calculated as:

FST = (HT – HS)/HT

Where FST is the average for multiple loci, HT is the average of the expected heterozygosity in the total population over loci, and HS is the average expected heterozygosity over subpopulations. Actually Wright never gave an explicit value for which FST would be considered great enough to indicate the existence of geographical/biological races. The Sewall Wright quote that Wade refers to via Harpending and Rodgers is: “We will take F = 0.25 as an arbitrary value above which there is very great differentiation, the range of 0.15 to 0.25 as indicating moderately great differentiation. Differentiation is by no means negligible if F is as small as 0.05 or even less as bought out in the preceding chapter” (Wright 1978, p. 85).In the non-human literature, the value of FST that has been used to describe subspecies or biological races is FST > 0.250, Wright’s value for very great differentiation (Smith et al. 1997; Templeton 2002.) Subsequent studies of multiple loci, including whole genome analyses, have generally shown human FST values at much less than Wright’s critical value. These values range from as low as 0.095 to as high as 0.130 (Barbujani and Colonna 2010) . They also show that variation within regions is smaller than variation between regions (Barbujani and Colonna 2010). These data indicate that while there is genetic structure in the human species, there are no natural divisions in our species equivalent to biological races or corresponding to our socially defined notions of race. Neither is quantification of the amount of genetic difference alone indicative of populations acting in ways associated with the evolutionary meaning of race. For example, if assortative mating is strong enough, populations with very little genetic difference can act as biological races, even within the same geographical region. This has appeared to happen in some insects: With the introduction of corn in Europe, corn borers have adapted over the last 500 years to form populations that feed on different plants and predominantly mate with members of their same ecological race. FST values within races were 0.004 and between them was 0.132. The latter values are less than Wright's threshold. In this case, population subdivision is less because of less genetic polymorphism in insects, and the mating preferences result from strong selection on a few genes of major multiple effects related to host plant preference (Malausa et al 2005). Thus one could argue that a wide range of genetic differentiation is consistent with identifying biological races, depending upon the action of the specific loci involved, in the context of a given species biology. However, clearly human mating behavior is not determined by a few loci and therefore, such a low value of FST would not be consistent with biological races in species such as ours.Theoretical studies have further examined the limitations of Wright’s FST as a measure of population divergence. One study showed the statistic is biased toward smaller values due to a failure of certain core assumptions utilized in its formulation, such as its assumption that the effective population sizes of all the subpopulations analyzed are equivalent and that subpopulations are evolving independently of each other (significant gene flow between regions violates this assumption). Relaxing those assumptions allows FST values to become larger. Yet, this doesn’t alter the fact that all human populations derive from a common ancestral group and have great genetic diversity with a complex pattern of variation and no major discontinuities (Long and Kittles (2003.)It is also instructive to compare human substructure to that of other closely related species. Other large bodied mammals show much higher population subdivision: white tailed deer (0.600), Grant’s gazelle (0.650) and North American gray wolves (0.750; Templeton 2002). Our closest relatives, chimpanzees and gorillas, have more subdivision between their populations (Kaessman, Wiebe, and Paabo 1999). It would be more legitimate to identify geographically based races in these species. Human activity has resulted in large differences in allele frequencies between chimp and gorilla subpopulations. We have reduced their population sizes and fractionated their habitats. Conversely, anatomically modern humans have always maintained relatively large amounts of gene flow and are contiguous in habitat. For this reason, Lawson-Handley et al. (2007) argued that isolation-by-distance models explain 75% of the variance in genetic distance between human populations. Utilizing pairwise FST calculated for populations from the HGDP-CEPH panel, their study concluded that human genetic variation is mainly clinal (77% of variance explained by geographic distance). Their data also suggest that there is no unambiguous way to decide where along the clines we should decide the existence of biological races. Finally, the sampling schemes used in studies of human genetic variation limit their interpretation. To accurately represent the genetic diversity of the world’s people would require a systematic collection along geographic distance between world regions. In addition, within each region, suitable numbers of individuals would have to be examined, particularly to discover genetic variants that are present in low frequency. Wade’s chapter five “The Genetics of Race” lacks all of the sophistication that I have described above.Can STRUCTURE Identify Human Biological Races?Nicholas Wade argues in A Troublesome Inheritance (pp. 96 –100) that STRUCTURE provides proof positive of the existence of biological races in the human species. To support this, he cites Neil Risch who also claimed that the Rosenberg et al. (2005) analysis is best understood as meaning that there are five continental races matching the classical definitions of races: African, Caucasian (European and Middle Eastern), Asian, Pacific Islander (for example Australian, New Guinean, and Melanesian) and Native American1. Marcus Feldman, one of the authors of that paper, did not use the term biological race when referring to this analysis. Feldman suggested that a better name for these clusters was “ancestry groups” (Feldman 2010, p. 157). However even if we were to accept STRUCTURE analysis uncritically, we still would not come to the conclusion that identifying five continental groups (or races) is the only statistically valid interpretation of the data. For example, as I mentioned above, the results of STRUCTURE analyses strongly depends upon the sampling scheme used to generate the individuals who are entered into the analysis. The CEPH-HGDP panel used in Rosenberg et al. (2005) contained a very limited sampling of individuals from around the world (individuals from about 57 ethnic populations around the world). In another use of STRUCTURE, Tishkoff et al. (2009) utilizing 1,327 polymorphic markers and 2,432 African individuals from 113 geographically separated populations (98 African Americans, 21 Yemenites, 952 individuals worldwide from the CEPH-HGDP diversity panel, 432 individuals of East Indian descent, and 10 native Australians) identified 14 clusters (ancestry groups) worldwide. This analysis separated Saharan Africa, Central Africa, Eastern Africa, Southern Africa, African Americans, Europeans, Middle Easterners, Central Asians, East Indians, Eastern Asia, Oceania, and the Americas. Thus even by Wade’s own admission, if we were to take STRUCTURE type analyzes of human genetic variation at face value, we could recognize a great number of “so-called” human biological races. These biological races do not match socially-defined notions of race. However, there is no reason to assume that STRUCTURE type analyses should be accepted uncritically.Weiss and Long (2009) examined the assumptions behind the STRUCTURE algorithm. Its default assumptions include the idea that all genetic markers are unlinked, at linkage equilibrium with one another within populations, and at Hardy-Weinberg equilibrium within populations. It also assumes that the genetic information of individuals is derived from a single population (cluster). The model can handle admixture, but that must be specified in the run. STRUCTURE also treats individuals as if they are members of discrete populations or are admixed descendants of such populations. This of course means that you think ancestral populations were discrete. Thus, individuals are ascribed ancestry as if panmictic (non-internally structured) parental populations actually existed in history. Probably the worse feature of STRUCTURE type analysis is that the character of the ancestral populations is assumed by a discrete modern sample (such as the CEPH for Europeans). Weiss and Long (2009) considered this thinking Platonic. These populations are abstractions that never existed, yet they are used to infer reality as if they did exist. Generally, there is no explicit accounting of the fact that the “parental” populations must share common ancestry with each other (thus different parental populations ultimately share varying degrees of ancestry). Yet there is no reason to believe that there were ever isolated, homogeneous parental populations at any time in our human past.In addition to utilizing STRUCTURE analysis to claim the existence of five biological races in the human species, Wade attempts to claim the scientific high ground against those who analyze human genetic variation differently. This is accomplished via the use of Straw Man arguments, particularly singling out Jared Diamond, Richard Lewontin, and Ashley Montagu. The attack on Lewontin’s reasoning concerning the criteria for biological races is most significant. He does so by contrasting Lewontin’s judgment of the significance of within population versus between population genetic variation to Wade’s own characterization of Sewall Wright’s view. Of course, I have already demonstrated that Wade’s characterization of Wright’s FST is not correct. He also goes on to use the argument contained within “Lewontin’s Fallacy.” This argument relies on a 2003 paper published in Bioessays by Cambridge statistician A.W.F. Edwards. Edwards shows that a single genetic locus is insufficient to classify the ancestry of individuals. If one looks at many loci, it is possible to unambiguously identify an individual’s geographic ancestry. Of course, the problem here is that inferring the ancestry of an individual is not the same as claiming that biological races exist within a species. Wade again goes back to cluster analysis thinking that the existence of genetic structure within a population is sufficient to claim the existence of biological races.The Tyranny of Political Correctness and Race ResearchProbably the most galling claim of Wade’s book is that biological researchers are being prevented from discussing the significance of biological races in modern humans due to the fear of having their “careers” destroyed by politically correct anti-racist vigilantes lurking in their universities. This claim only betrays how little he knows about the academic environment. It is absolutely ludicrous to claim that human genetics researchers are afraid of having their careers ruined due to such fear. Indeed, the current predominant belief amongst human geneticists and biomedical researchers is that the socially-defined races of U.S. society are biological races, and that genetic differences between these groups have important biological and medical consequences (Graves 2011). For example, the terms “Negroid,” “Caucasoid,” and “Mongoloid” race are still used to organize research results on Entrez Pubmed. A recent search of that database returned: 66, 398; 50,893; and 42,026 research publications respectively. I have argued elsewhere that the absence of evolutionary training within the curriculum experienced by human geneticists and biomedical researchers is at fault for the persistence of their typological thinking and confusion of socially-defined, biological categories of race (Graves 2011).Even more insidious is the idea that scientists who reject the notion that the human species does not contain biological races are motivated by “political” agendas, and those who assert that races exist are apolitical and are simply dispassionately interpreting the data (p. 120). For example, it is correct that Richard Lewontin is a Marxist (see Levins and Lewontin 1985). It is not a correct assertion that all Marxists were/are anti-racists. Nor is it correct to assume that Lewontin’s population genetics research was dominated by his political views, particularly that of his early career. At the same time that Lewontin characterized the amount of genetic variation within and between purported human biological races, Matatoshi Nei and Arun Roychoudhury came to the same conclusions (Nei and Roychoudhury 1972; 1974). Neither of these men were accused of being Marxists, nor were their results assailed for being consistent with Marxist ideology. Certainly, Lewontin was not the only prominent population genetics or evolutionary biology pioneer who had Marxist views; others included J.B.S. Haldane and John Maynard Smith. It is also not a correct assertion that all non-Marxists are racists or vice versa. We know nothing about Sewall Wright’s racial ideology by his science. Having read Wright’s treatment of human races in Variability within and among Natural Populations (1978), it is clear that he suffered from most of the same racialist misconceptions of Americans of his time period (1889 –1988.) Nor should it be claimed that someone not being involved in political activism is a statement of them being “apolitical.” Sewall Wright lived in an America that was violently stratified by socially-defined race for most of his life. Yet, we have no record of him taking an active role in addressing racism. For example, evolutionary biologist Theodosius Dobzhansky and geneticists Leslie Dunn and T.H. Huxley were signatories of the 1950 and 1951 UNESCO statements on race (Wright was not). Physicist Albert Einstein wrote to the National Urban League and to President Harry Truman concerning the violation of the Negro’s human rights by the United States government (Jerome and Taylor 2006). The inactivity of American scientists during Jim Crow and the Civil Rights struggle can just as well be interpreted as their support for the status-quo of racial injustice in the United States rather than them being apolitical. Thus their acceptance of racial injustice could have influenced their interpretation of data concerning human genetic variation, just as Lewontin’s activism against racial injustice might have influenced his work in this area. What is clear today is that no one is being prevented in the American academy from doing research on genetic variation associated with conceptions of race. The abundance of papers published by human geneticists and biomedical researchers suggesting genetically causal differences in health disparity is evidence against the “anti-racist” conspiracy fantasy (Graves 2011).Racialist Just-So-Stories“Such achievement requires an explanation, and the best and the simplest is that Jews have adapted genetically to a way of life that requires higher than usual cognitive capacity.” This line is written as an explanation for the disproportionate achievement of Jews in receiving Nobel Prizes and in music and the arts. Wade argues that this achievement cannot be cultural, since others would easily be able to copy the behaviors resulting in such great success. He bases his arguments on one study (Cochran, Hardy, and Harpending 2006). Their claim is that high Ashkenazim Jewish intelligence led to their differential reproductive success compared to other European populations. They claim that the Ashkenazim focused on a few professions (money lending and tax farming, etc.) in the period between 900 and 1700 C.E. These professions, they argued, required higher intelligence and calculating the heritability of intelligence at 0.80, they claim that only 20 generations (500 years) was required for the Ashkenazim to increase their mean IQ by 16 points compared to the rest of the European population. They also suggested that the Ashkenazim carry a set of mutations that impact the storage of sphingolipids. These mutations are associated with Tay-Sachs, Gaucher’s Disease, Niemann-Pick, and mucolipidosis type IV. Thus, the high frequency of these mutations in the Ashkenazim is due to heterozygote superiority. Individuals who are homozygous for these mutations develop the diseases mentioned above with the resulting negative fitness consequences. Conversely, they suggested that heterozygous individuals would not suffer from a lipid storage disease and would develop greater than average intelligence. Greater than average intelligence, they argued, meant greater than average reproductive success.Taken at face value, this seems like a well-constructed analysis of Ashkenazim intelligence and lipid storage disease. However, this argument is an example of a just-so-story of adaptation. If any one of the claims of this argument is invalid, then the entire analysis collapses like a house of cards. To evaluate any adaptive hypothesis, we must determine the following:

1. Is there individual variation (or geographic) variation for the trait?2. Is this variation heritable?3. Does the trait influence reproductive success?4. Can a mechanism be established between the trait and reproductive success?5. Can the mechanism and the link with reproductive success be established by experimental means?

Clearly there is individual variation in different aspects of intelligence. Intelligence however is a complex phenotype whose definition is far from precise or agreed upon. For this reason, the heritability (h2) of intelligence is hotly debated. Not since the fraudulent studies of Sir Cyril Burt has anyone thought that the h2 of intelligence was as high as 0.80. Plomin, considered the foremost behavioral researcher studying this question, has claimed the h2 for this trait around 0.51. There are a number of reasons to question even an h2 estimate this high, but even if the h2 for intelligence was like that of other complex traits in humans at around 0.30, there still would be ample time for natural selection to increase Ashkenazim intelligence if it were indeed contributing to differential reproductive success. The problem of course is that we have no way of determining that greater intelligence was accounting for differential reproductive success amongst Europeans between the period of 1500 and 1939 CE. While we can easily suggest plausible mechanisms by which intelligence might have increased the reproductive success of individuals in this time period, we can just as easily suggest ways in which it could have worked against their reproductive success. This sort of study also suffers from the impossibility of developing an experimental means of testing the relationship between intelligence and reproductive success, let alone specific mutations, intelligence, and reproductive success. Similar just-so-stories appear throughout the text including his discussion of the MAO-A gene and aggression.ConclusionNicholas Wade’s A Troublesome Inheritance: Genes, Race, and Human History is organized around three claims:1. Geographic/biological races exist within the human species.2. The existence of biological races within our species is a widely understood fact amongst evolutionary biologists, and that only “fear” of reprisal and “political correctness” prevents us from publicizing this fact.3. Understanding the reality of biological race provides us with important insights concerning the nature of human society and culture.Wade is simply wrong on the first point. Geographically based genetic variation within our species is real. It is best described as isolation by distance, meaning that individuals with ancestry in particular geographic regions are more likely to share genes than those from disparate regions. The overall amount of measured human genetic variation, however, is very small. Utilizing enough genetic markers it is possible to describe populations that share more recent ancestry with each other compared to other groups. This does not mean, however, that such ancestry groups comprise biological races.Evolutionary and population genetics has revised its thinking about how such variation should be described in the 20th century. Despite the atrocities committed in the name of eugenics and racism, the revision of population genetics thinking was not primarily driven by any attempt to placate a liberal/equalitarian agenda as Wade claims. The difficulties of his sources (Harpending and company) in publishing some of their work does not result from any bias against their politics as opposed to the weaknesses in some of their claims.Given that his first claim does not follow, neither can the third. Human populations do show significant adaptations to their environments. Some of these might have had some influence on the way societies and cultures developed. Yet Wade attempts to take the correspondence between human genetic adaptations and their impact on our society and cultures too far. For this reason, this work certainly should be read – not as a definitive treatise on the meaning and significance of human genetic variation – but rather as a cautionary tale: a tale about what happens when someone has a little bit of knowledge about a complex subject, seems to have an axe to grind, and is unwilling to provide the reader with a fair and balanced presentation of the field s/he is reporting on.Notes1. Of course, Risch is incorrectly stating the classical definition of the continental races. For example African would have been “Negroid,” and Asian would have been “Mongoloid.”ReferencesBarbujani. G. & Colonna, V. (2010). Human genome diversity: Frequently asked questions. Trends in Genetics, 26: 285—295.Cochran, G., Hardy, J., & Harpending, H. (2006). Natural history of Ashkenazi intelligence. J.Biosocial Science, 38(5): 659—693.Edwards, A.W.F. (2003). Human genetic diversity: Lewontin’s Fallacy. Bioessays,25: 798-801.Feldman, M.W. (2010). The biology of ancestry: DNA, genomic variation, and race. In H. Markus, H. & P.M.L. Moya, (Eds.), Doing Race: 21 essays for the 21st century. New York: W.M. Norton & Co.Futuyma, D. (1986). Evolutionary biology. Sunderland, MA: Sinauer & Associates.Graves, J.L. (2001). The emperor’s new clothes: Biological theories of race at the millennium, (pp. 149 – 151). New Brunswick, NJ: Rutgers University Press.Graves, J.L. (2011). Evolutionary versus racial medicine: Why it matters? In S. Krimsky & K. Sloan (Eds.), Race and the genetic revolution: Science, myth and culture. New York: Columbia University Press.Kaessman, H., Wiebe, V., & Paabo, S. (1999, November 5). Extensive nuclear DNA sequence diversity amongst chimpanzees. Science, 286: 1159-62.Jerome, F. & Taylor, R. (2006). Einstein on race and racism. New Brunswick, NJ: Rutgers University Press.Lawson-Handley, L.J., Manica, A., Goudet, J., & Balloux, F. (2007). Going the distance: Humanpopulation genetics in a clinal world. Trends in Genetics, 23(9): 432—439.Levins, R. & Lewontin, R. (1985). The dialectical biologist. Cambridge, MA: Harvard University Press.Long, J.C. & Kittles, R. (2003). Human genetic diversity and the non-existence of biological races. Human Biology, 74(4): 449 – 471.Malausa, T., Bethonod, M., Bontemps, A., Bourguet, D. Cornuet, J., & Ponsard, S. (2005, April 8). Assortative mating in sympatric host races of the European corn borer, Science, 308, 258-260.Nei, M. & Roychoudhury, A.K. (1972). Gene differences between Caucasian, Negro, and Japanese populations, Science, 177: 434-435.Nei, M. & Roychoudhury, A.K. (1974). Genetic variation within and between three major races of man, Caucasoids, Negroids, and Mongoloids, Am. J. Human Genetics, 26:421-443.Plomin, R. & Spinath, F.M. (2004) Intelligence: Genetics, genes, and genomics, J. Personality and Social Psychology, 86(1): 112—129.Rosenberg, N.A., Mahajan, S., Ramachandran, S., Zhao, C., Pritchard, J.K., & Feldman, M.W. (2005). Clines, clusters, and the effect of study design on the inference of human population structure, PloS Genetics, 1(6) e70: 660—671.Schaffer, H.B. & McKnight, M.L. (1996). The polytypic species revisited: Genetic differentiation and molecular phylogenetics of the tiger salamander Ambystoma tigrinum (Amphibia: Caudata) complex, Evolution, 50: 417—433.Smith, H.M., Chiszar, D., & Montanucci, R.R. (1997). Subspecies and classification, Herpetological Review, 28: 13—16.Templeton, A.R., (2002). The genetic and evolutionary significance of human races. In Fish, J. (Ed.), Race and intelligence: Separating science from myth. Mahwah, NJ: Lawrence Erlbaum Associates.Tishkoff, S., Reed, F.A., Friedlaender, F.R., et al., The genetic structure and history of Africansand African Americans, Science, 324: 1035—1044, 2009.Weiss, K.M. & Long, J.C. (2009). Non-Darwinian estimation: My ancestors, my genes’ ancestors. Genome Research, 19: 703—710.Wright, S. (1978). Genetic variability in natural populations: Methods (Vol 4) Variability within populations and among natural populations, evolution and the genetics of populations (2nd ed.) Chicago: University of Chicago PressFor another review of this book, see Echoes of the Past: Hereditarianism and A Troublesome Inheritance by Marcus Feldman.