Friday, July 9, 2021

Lewontin's legacy


Lewontin assumed that genetic diversity between populations is qualitatively similar to genetic diversity within populations. So comparing the two would be like comparing apples with apples. He was wrong. The second kind of diversity is less functionally significant.



The geneticist Richard Lewontin died last Sunday at the age of 92. He became prominent during the 1970s, particularly through his 1972 paper "The Apportionment of Human Diversity." Using data from blood groups, serum proteins, and red blood cell enzymes, he found far more genetic diversity within human populations than between them:


The results are quite remarkable. The mean proportion of the total species diversity that is contained within populations is 85.4%, with a maximum of 99.7% for the Xm gene, and a minimum of 63.6% for Duffy. Less than 15% of all human genetic diversity is accounted for by differences between human groups!


It is clear that our perception of relatively large differences between human races and subgroups, as compared to the variation within these groups, is indeed a biased perception and that, based on randomly chosen genetic differences, human races and populations are remarkably similar to each other, with the largest part by far of human variation being accounted for by the differences between individuals.


His reasoning seems sound. It ignores, however, two aspects of population genetics:


1. Genetic differences between populations are qualitatively different from genetic differences within populations. A population boundary is usually a boundary between different environments, either natural environments or cultural environments. It is thus a boundary between different pressures of natural selection and, hence differences in adaptation. An allele may work just fine on one side of the boundary, but not so well on the other side. Conversely, genetic diversity within a population is less meaningful because the end result tends to be the same. Everyone is adapting to the same environment. Genetic differences are less likely to produce real functional differences.


2. Genetic differences vary considerably in their functional significance, with the overwhelming majority having little or none. Many of these differences are found in junk DNA.


Lewontin discovered the obvious. Most genetic differences have little or no functional significance, and such differences account for most of the diversity within human populations. The more a genetic difference has real consequences, the less likely it will be found within a population because that is where the pressures of selection are uniform. It will more likely be found at a population boundary where the pressures of selection are different.


We see this, for example, in dog breeds. Although they differ considerably in anatomy and behavior, they are barely discernable in the genetic data. There is much more diversity within breeds than between them:


... genetic and biochemical methods ... have shown domestic dogs to be virtually identical in many respects to other members of the genus. ... Greater mtDNA differences appeared within the single breeds of Doberman pinscher or poodle than between dogs and wolves. ... there is less mtDNA difference between dogs, wolves, and coyotes than there is between the various ethnic groups of human beings, which are recognized as a single species. (Coppinger & Schneider 1995)


Well, dog breeds have been created through human-directed selection. What about subspecies that have arisen through natural selection? We see the same fuzziness, not only between subspecies but also between many sibling species that are anatomically distinct. In the deer family, genetic diversity is greater within some species than between some genera (Cronin 1991). Some masked shrew populations are genetically closer to prairie shrews than they are to other masked shrews (Stewart et al. 1993). Only a minority of mallards cluster together on an mtDNA tree, the rest being scattered among black ducks (Avise et al. 1990). All six species of Darwin's ground finches seem to form a genetically homogeneous genus while showing very little concordance between mtDNA, nuclear DNA, and morphology (Freeland & Boag 1999). In terms of genetic distance, redpoll finches from the same species are not significantly closer to each other than they are to sibling species (Seutin et al. 1995). Different species of haplochromine cichlids cannot be easily told apart by means of nuclear or mitochondrial genes, yet they are well differentiated morphologically and behaviorally (Klein et al., 1998). Neither mtDNA nor allozyme alleles can distinguish the various species of Lycaedis butterflies, despite clear differences in morphology (Nice & Shapiro 1999). An extreme example is a dog tumor that spreads through sexual contact: canine transmissible venereal sarcoma. It looks and acts like an infectious pathogen, yet its genes would show it to be a canid, and some beagles may be genetically more similar to it than they are to Great Danes (Yang 1996; see Frost 2011 for a full discussion).


When populations diverge under the impact of divergent pressures of natural selection, changes initially occur only within a fraction of the genome. Later, with the passage of time, the two populations will drift apart over the rest of the genome. But the human species is still young. The genetic split between Africans and non-Africans goes back only 60,000 years, and other splits are younger still.


This doesn't mean that genetic diversity between human populations is trivial. In fact, almost the opposite is true. It is the diversity within populations that is largely trivial.





Avise, J.C., C.D. Ankney, and W.S. Nelson. (1990). Mitochondrial gene trees and the evolutionary relationship of mallard and black ducks. Evolution 44: 1109-1119.


Coppinger, R. and R. Schneider (1995). Evolution of working dogs. In: J. Serpell (ed.) The Domestic Dog: Its Evolution, Behaviour and Interactions with People. Cambridge: Cambridge University Press, pp. 21-47.


Cronin, M. (1991). Mitochondrial-DNA phylogeny of deer (Cervidae). Journal of Mammalogy 72: 533-566.


Freeland, J.R. and P.T. Boag. (1999). The mitochondrial and nuclear genetic homogeneity of the phenotypically diverse Darwin's ground finches. Evolution 53: 1553-1563.


Frost, P. (2011). Human nature or human natures? Futures 43: 740-748.  


Klein, J., A. Sato, S. Nagl, and C. O'hUigin. (1998). Molecular trans-species polymorphism. Annual Review of Ecology and Systematics 29: 1-21.


Lewontin, R.C. (1972). The apportionment of human diversity. Evolutionary Biology 6: 381-398.


Nice, C.C. and A.M. Shapiro. (1999). Molecular and morphological divergence in the butterfly genus Lycaeides (Lepidoptera: Lycaenidae) in North America: evidence of recent speciation. Journal of Evolutionary Biology 12: 936-950.


Seutin, G., L.M. Ratcliffe, and P.T. Boag. (1995). Mitochondrial DNA homogeneity in the phenotypically diverse redpoll finch complex (Aves: Carduelinae: Carduelis flammea-hornemanni). Evolution 49: 962-973.


Stewart, D.T., A.J. Baker, and S.P. Hindocha. (1993). Genetic differentiation and population structure in Sorex Haydeni and S. Cinereus. Journal of Mammalogy 74: 21-32.


Yang, T.J. (1996). Parasitic protist of metazoan origin. Evolutionary Theory 11: 99-103.

Tuesday, June 29, 2021

American Indians in decline


After three decades of sharp decline, American Indians now have the lowest fertility rate of all ethnic groups in the U.S. The trend is real and is not due to sub-fertile Whites self-identifying as American Indians.



The pandemic has reduced the American birth rate. According to data from 2020 and early 2021, almost all ethnic groups have taken a hit, but the magnitude has been greater for some than for others.


Asian Americans took the biggest hit. At first thought, this makes sense. Asians, especially East Asians (who make up a majority of Asian Americans) tend to take infectious diseases more seriously. They are generally more willing to wear masks, practice social distancing, and wash their hands, and it seems logical that they would also be more willing to postpone childbearing.

But that's not the whole story. The pandemic has accelerated an ongoing fertility decline among East Asians at home and abroad. With the exception of North Korea, East Asia was already a zone of ultra-low fertility—about one child per woman. When the pandemic is over, I predict that East Asian fertility will not return to pre-pandemic levels. The decline will continue. The pandemic has merely acted as a social accelerant (Frost 2020).


This view is strengthened if we return to the above graph and look at the group that took the second-biggest hit: American Indians and Alaskan Natives. Their fertility rate has likewise been declining. It was still high in the 1980s, but sometime around 1990 it began to plummet, falling below the fertility rate of any other ethnic group in the U.S. by the early 2000s.

What's going on? Is the decline real? Or is it a statistical fluke? Perhaps sub-fertile Whites are self-identifying as American Indians in growing numbers. This hypothesis was tested by Cannon and Percheski (2017):


Concurrent with this decline in estimated TFRs, the self-identified AI/AN population enumerated in the decennial US Census increased in size, largely because of changes in the racial categories and in the wording of racial identity items on the census forms.


The increase in the census counts of the American Indian population means that there are several possible explanations for the decline in American Indian fertility rates published by Vital Statistics. First, the decline could be a mechanical artifact of differential changes in racial identification between the two data systems Vital Statistics used to calculate fertility rates. Second, the decline could be driven by compositional changes in who identifies as American Indian. Third, there may be real changes infertility behavior that are unrelated to changes in who identifies as American Indian.


To control for these differences in definition and self-identification, Cannon and Percheski (2017) used a single data system (the American Community Survey) for the period 1980 to 2010. They also examined the fertility decline on the basis of three definitions of American Indian/Alaskan native: 1) women who identify as AI/AN only, 2) any woman who identifies as AI/AN, whether identifying one or more races, and 3) women who list a specific tribe or American Indian for the ancestry question. The second definition seems to be the one most vulnerable to "ethnic reassignment."


Cannon and Percheski (2017) found that all three definitions showed a fertility decline, particularly the first one. The decline was steepest among younger women. However, there was no indication that lower fertility at younger ages was being offset by higher fertility at older ages. The authors concluded: "This finding of declining TFRs estimated within a single data system is evidence against the explanation that fertility declines are merely artifacts of data collection changes or incongruences."


So what is the explanation? The main cause seems to be the declining marriage rate: "fertility rates among married and unmarried women have remained fairly stable, while the share of women ever married has declined across birth cohorts. Thus declines in fertility rates seem to be linked with changes in marriage for this population."


In this respect, American Indians are more vulnerable than most other ethnic groups in the U.S. Their women seem to prefer having children when a man is in the home. As the authors note, "other population subgroups in the United States who have experienced substantial declines in marriage have not experienced such drastic declines in fertility levels" (Cannon and Percheski 2017, pp. 8-9). 


Anthropologists have long noted that the Indigenous peoples of the Americas still retain many "Arctic" adaptations in their anatomy. Could the same be true for their behavioral predispositions? Some 12,000 years ago, their ancestors lived in northeast Asia and Beringia. In that environment, women had almost no food autonomy and could not raise children on their own. Perhaps their female descendants are still making a half-conscious link between having a baby and having a male provider.



Cannon, S., and C. Percheski. (2017). Fertility change in the American Indian and Alaska Native population, 1980-2010. Demographic Research 37: 1-12.


Frost, P. (2020). An Accelerant of Social Change? The Spanish Flu of 1918-19. International Political Anthropology Journal 13(2): 123-133.


Hamilton, B.E., M.J.K. Osterman, and J.A. Martin. (2021). Declines in births by month: United States, 2020. NVSS Vital Statistics Rapid Release. Report no. 14, June

Thursday, June 17, 2021

Getting the message


Gaps between perceived and actual crime rates, by immigrant group in the Netherlands. Dutch people underestimate the crime rate of immigrants from sub-Saharan Africa and the Caribbean while overestimating the crime rate of lighter-skinned immigrants, including Roma, Turks, and Chinese. The latter don’t benefit from the messaging of modern culture.



The crime news is unfair to Negroes, on the one hand, in that it emphasizes individual cases instead of statistical proportions [...] and, on the other hand, in that all other aspects of Negro life are neglected in the white press which gives the unfavorable crime news an undue weight. Sometimes the white press "creates" a Negro crime wave where none actually exists. (Myrdal 1944, pp. 655-656)


Gunnar Myrdal wrote An American Dilemma during the early years of the civil rights movement. He won over many young educated people, particularly when he argued that prejudice was making Black American criminality seem worse than it really was:


The popular belief that all Negroes are inherently criminal operates to increase arrests, and the Negro's lack of political power prevents a white policeman from worrying about how many Negro arrests he makes. Some white criminals have made use of these prejudices to divert suspicion away from themselves onto Negroes: for example, there are many documented cases of white robbers blackening their faces when committing crimes. (Myrdal 1944, p. 968)


The theme of the "framed Black man" would be central to a work of fiction, To Kill a Mockingbird. Since its publication in 1960 it has never been out of print. In 2006, it was the book most often mentioned when British librarians were asked: "Which book should every adult read before they die?" (Pauli 2006). Thus, for at least six decades, there has been a social norm of downplaying Black crime.


This norm has spread not only within the United States but also to all countries where English is widely used, particularly among the university-educated. In fact, it has spread to countries that never had black slavery or Jim Crow, or even a substantial African minority until recent times.


Perceptions and realities of crime in the Netherlands


One such country is the Netherlands. In a recent survey, 615 Dutch adults were given the following instructions:


There are many different immigrant groups in the Netherlands. For each of the groups, adjust the slider to your estimation of the crime rate relative to Dutch natives. This means you should adjust the slider to two (2) if you think the crime rate of this group is twice that of natives. (Kirkegaard and Gerritsen 2021, p. 4)


The actual crime rate of each immigrant group is known from public data published by the government. It was thus possible to measure how much the survey respondents overestimated or underestimated the criminality of each immigrant group. The respondents were chosen by two polling firms. A little over two-thirds of them came from a firm that tended to select younger and more university-educated people.


The findings are shown in the above graph. On the y-axis, the crime rate is overestimated at values higher than zero and underestimated at values lower than zero. The x-axis shows the percentage of Muslims in the immigrants' home country.


Kirkegaard and Gerritsen (2021, pp. 12-17) argue that the results show a pro-Muslim bias: the respondents tended to underestimate the crime rate of Muslim immigrants. But the bias was not favorable toward all Muslims. In fact, the crime rate was overestimated for immigrants from Indonesia, Syria, Turkey, Pakistan, and Afghanistan and more or less correctly estimated for those from Egypt, Iran, and Iraq. In addition, the respondents showed much larger gaps between perception and reality when estimating the crime rates of different non-Muslim groups.


For source countries less than 25% Muslim, the crime rate was greatly underestimated (by a factor of 1 or more) for people from Congo, Angola, Cape Verde, the Netherlands Antilles, and the Dominican Republic. Conversely, it was greatly overestimated for people from Hungary, Poland, Romania, and Mexico.


Do you see a pattern? The respondents were underestimating the crime rate of immigrants from sub-Saharan Africa and the Caribbean. Their pro-Black bias was much stronger than their supposed pro-Muslim bias. In fact, it was so strong that it affected their perceptions of different Muslim groups. The respondents perceived North Africans and Somalis as being better than they really are, while perceiving Turks as being worse than they really are.


It looks like people in the Netherlands, and probably throughout the West, are being conditioned to view the Black African phenotype positively and the White European phenotype negatively. This bias caused the respondents to overestimate the arrest rate not only of European immigrants but also of any group that deviates too far from the Black African phenotype, including Chinese, Mexicans and, apparently, Roma.(1)


What about the respondents who voted for Nationalist parties? You know, the “far right.” Although Nationalist voters were more inclined to overestimate the crime rate of Muslim immigrants, they were just as inclined to underestimate the crime rate of sub-Saharan African and Caribbean immigrants. The pro-Black bias seems very pervasive.





1. Roma in Western Europe identify themselves to the authorities by their country of origin, not by their ethnicity. The Dutch respondents greatly overestimated the crime rate of immigrants from Romania, and the recent wave of Romanian migrants is widely perceived to be mostly Roma:


In these figures, the number relating to the Roma is indeterminate since the ethnicity of asylum seekers is not recorded. Nonetheless, the assumption is that the majority of these applications were made by Roma. Certainly, the press is of this view. Articles discussing Czech or Romanian asylum seekers refer frequently to the Roma. As a result, it is easy for the ordinary member of the public to assume that such groups of applicants are of Roma extraction (Stevens 2003, p. 440)





Kirkegaard, E.O.W., and A. Gerritsen. (2021). A study of stereotype accuracy in the Netherlands: immigrant crime, occupational sex distribution, and provincial income inequality. OpenPsych, June 14


Myrdal, G. (1944). An American Dilemma. The Negro Problem and Modern Democracy. New York: Harper and Row.


Pauli, M. (2006). Harper Lee tops librarians' must-read list. The Guardian, March 2  


Stevens, D.E. (2003). The Migration of the Romanian Roma to the UK: A Contextual Study. European Journal of Migration and Law 5(4): 439-461.  



Friday, May 14, 2021

Damunwha in South Korea


Graffiti in Ansan (Wikicommons – Piotrus)


I've published a paper on the Damunwha children of South Korea. In that country, foreign brides, mainly from Southeast Asia, produce almost 6% of births. These children do poorly at school, ostensibly because of discrimination and imperfect learning of spoken Korean from their mothers. Yet they actually do well in subjects that emphasize social interaction and spoken language. Their learning deficit is in subjects that require abstraction and memorization, such as mathematics.


This is the abstract:


In South Korea, over 10% of new couples involve a foreign bride. Most come from Southeast Asia (Vietnam, Philippines, Thailand, Cambodia, Indonesia), and others from East Asia (China, Japan). Such couples now produce almost 6% of births. Their children tend to do badly at school and many drop out, the commonly cited reason being the child's poor acquisition of language skills from a foreign mother. In reality, Damunwha ("multicultural children") have no trouble with spoken Korean. Their deficiency is in written Korean, particularly in literary and specialized vocabulary that is largely learned at school. They actually do well in subjects that emphasize the spoken language and social interaction, like music, painting, and physical education. They do badly only in those subjects that require abstraction and memorization, like mathematics and social studies. Damunwha children are also more prone to hyperactivity, impulsivity, and non-compliance with rules. These divergences in cognition and behavior seem confined to children of Southeast Asian mothers, since children of Chinese or Japanese mothers perform as well as those of unmixed Korean parentage. It looks as if the country's social norms, particularly those of Confucianism, favored the spread of certain cognitive and behavioral traits within the Korean population, and more broadly among East Asians. These traits include not only high cognitive ability but also a high capacity to obey rules, to defer gratification, and to control impulsive behavior.




Frost, P. (2021). Damunwha in South Korea: A case study of divergences in cognition and behavior. Advances in Anthropology 11(2): 153-162.




Tuesday, April 27, 2021

Polygenic scores and Black Americans


Sunday Morning in Virginia (1877), by Winslow Homer. 

The ability to acquire language may be the mental domain where people of sub-Saharan African descent have undergone the most cognitive evolution since their separation from other humans.




If we look at SNP alleles associated with educational attainment, we see differences between Europeans and sub-Saharan Africans. In a previous post I asked whether the cause was genetic drift or natural selection (Frost 2021).


That post brought a comment on Twitter:


Or maybe the fact that educational attainment is based on whiteness and familial wealth in the USA but not in Africa? And familial wealth tends to be concentrated in specific closed groupings of people who only breed with each other?


I don’t think so. First, the alleles were identified in subjects from the Netherlands Twin Registry, the Finnish Twin Cohort, the Swedish Twin Registry, the Avon Longitudinal Study of Parents and Children, the UK Biobank and 23andme. Of those sources, only 23andme had American subjects.


Second, let's suppose that those alleles are incidentally related to educational attainment. Maybe they are just something that wealthy Europeans share with each other through inbreeding, a bit like the Habsburg jaw. Those alleles should therefore be useless for predicting educational attainment in other populations. Are they?


Let me answer that question by discussing two recent studies:



The Guo et al. study


Guo et al. (2019) used the same alleles to predict success on a cognitive test (verbal ability) by 8,078 Americans of different ethnic backgrounds. Two polygenic scores were calculated: one based on alleles associated with educational attainment (education PGS) and the other based on alleles associated with IQ (IQ PGS).


The polygenic scores significantly correlated with test results for all major ethnic backgrounds, except one:


The education PGS was significantly predictive of verbal ability in all estimated models and its coefficients were similar in size except for the black sample in which the coefficient was much smaller. The IQ PGS significantly predicted verbal ability in all samples except the black sample. (Guo et al. 2019)


[...] The incremental R2 s or the R2 s of "pure" PGS effects were 1.8%, 0.1%, 1%, 1.8%, 1.7%, and 1% for whites, blacks, Asians, Hispanic whites, the combined sample and the overall sample, respectively.


The literature is showing a consistent trend: polygenic scores have much less power to predict cognitive ability in people of sub-Saharan African descent than in people of European or Asian descent. In this case, the polygenic scores were ten to eighteen times worse at predicting verbal ability in Black Americans than they were at predicting verbal ability in White, Asian, and Hispanic White Americans.


Why? The reason may be that Eurasians and sub-Saharan Africans have different gene pools. Some alleles for higher cognitive ability are available in one gene pool but not in the other. There is undoubtedly overlap between the two, but not total overlap. Intelligent Nigerians, for instance, may owe their intelligence to alleles that exist only in sub-Saharan Africa.


To return to the Twitter comment, it seems clear that polygenic scores are predicting something that correlates with cognitive ability, and that "something" is not an artefact of wealthy people being related to each other and sharing the same genes. It's already a stretch to believe that close family ties are shared by high achievers throughout the United Kingdom, the Netherlands, Sweden, and Finland. Does the same family clique also include high achievers of Asian American origin? 



The Rabinowitz et al. study


Rabinowitz et al. (2019) used an education PGS to predict cognitive ability in Black American participants, specifically three cohorts from first grade to young adulthood (at which point their DNA was collected and analyzed).


The results? The PGS significantly correlated with pursuit of postsecondary education. The correlation was weak or insignificant, however, for performance on school tests. The PGS did not predict performance on a standardized reading test for any of the three cohorts, and it predicted performance on a standardized math test for only one of them. In addition, the PGS negatively correlated with having a criminal record (but only in the male subjects).


A problem here may be the young age of the participants. Cognitive ability seems to become less malleable and more hardwired with age. We can help children do better on IQ tests, but the improvement tends to disappear by adulthood (Frost 2008). Consequently, academic success in childhood may be too clouded by environmental factors to show a significant correlation with genetic factors.


On the other hand, the PGS did predict some things better than others. It predicted general academic success (pursuit of postsecondary education) and compliance with rules (absence of a criminal record). For actual school tests, it had some power to predict success on the math test but none at all on the reading test. The ability to acquire language may be the mental domain where people of sub-Saharan African descent have undergone the most cognitive evolution since their separation from other humans. The PGS cannot predict superior reading ability among Black Americans because too many of the relevant alleles are exclusive to the sub-Saharan African gene pool and remain to be identified by scientific studies.


The take-home message? At present, we can create polygenic scores that provide a rough idea of cognitive ability in people of sub-Saharan African descent. To get more than a rough idea, we need to identify the relevant alleles specific to that population.





Frost, P. (2008). IQ: Interaction between race and age. The Unz Review, May 20


Frost, P. (2021). The mismeasure of genetic differentiation. Evo and Proud, April 13


Guo, G., Lin, M.J., and K.M. Harris. (2019). Socioeconomic and Genomic Roots of Verbal Ability. bioRxiv, 544411.


Rabinowitz, J.A., S.I.C. Kuo, W. Felder, R.J. Musci, A. Bettencourt, K. Benke, ... and A. Kouzis. (2019). Associations between an educational attainment polygenic score with educational attainment in an African American sample. Genes, Brain and Behavior, e12558.

Tuesday, April 20, 2021

Selection for fair skin in Europeans and North Asians


Selection for fair skin in different human populations (Huang et al. 2021)

Selection for fair skin was about four times stronger among ancestral Europeans than it was among ancestral North Asians or the earlier shared ancestors of both groups. So says a recent genome study.


Huang et al. (2021) examined genes that influence skin pigmentation to calculate the strength of selection for lighter skin among the ancestors of today’s Europeans and North Asians. They concluded that selection for lighter skin was strongest among the unique ancestors of present-day Europeans, with a selection pressure of 25.9. It was about four times weaker among the unique ancestors of North Asians (5.61) and the earlier shared ancestors of both groups (6.5). East Asians actually became darker after they split from North Asians, with a negative selection pressure of -5.53.


Our estimate shows that the modern European lineage had the largest selective pressure (s4=0.0259/generation) on light pigmentation than the other branches, suggesting that recent natural selection favoured light pigmentation in Europeans. Recent studies using ancient DNA could support our observation of recent directional selection in Europeans (Huang et al. 2021, p. 3)


This finding supports earlier findings. Modern humans remained dark-skinned in Europe long after they had spread north into northern latitudes some 45,000 years ago. It was not until 20,000 years ago that alleles for white skin made their appearance (Beleza et al. 2013; Canfield et al. 2014; Norton and Hammer 2007). As a Science correspondent concluded: "The implication is that our European ancestors were brown-skinned for tens of thousands of years" (Gibbons 2007).


Those ancestors were initially proto-Eurasians, and it was only later that they differentiated to become respectively Europeans and North Asians. Only then, and only in the European lineage, did skin color begin to lighten at a fast rate. This rapid evolution seems to have been confined to a relatively small area that stretched from the Baltic to central Siberia. Elsewhere, in western and southern Europe, people remained dark-skinned until almost the dawn of history, as shown by DNA dated to 11,000 years ago from England, 8,000 years ago from Luxembourg, and 7,000 years ago from Spain (Brace et al. 2019; Lazaridis et al. 2014; Olalde et al. 2014).


The fair skin phenotype, together with a variety of hair and eye colors, would later spread throughout all of Europe, while going extinct east of the Urals. In the latter region it would persist into historic times. At sites in south-central Siberia, dating from the third millennium BC to the fourth century AD, genetic analysis has shown that most of the buried individuals had blue or green eyes, light hair (blond, red, light brown), and light skin (Bouakaze et al. 2009). South Siberian peoples were, in fact, described as having "green eyes" and "red hair" in old Chinese records (Keane 1886, p. 703).


It seems that Europeans acquired their current appearance very fast, perhaps ten to twenty thousand years ago during the last ice age. Initially confined to northeastern Europe and parts of Siberia, the new phenotype would in time spread to the rest of the continent ... on the eve of recorded history. Only then did all Europeans come to look “European” (Frost 2014; Frost 2020).




Beleza, S., A.M. Santos, B. McEvoy, I. Alves, C. Martinho, E. Cameron, et al. (2013). The timing of pigmentation lightening in Europeans. Molecular Biology and Evolution 30(1): 24-35.


Bouakaze, C., C. Keyser, E. Crubézy, D. Montagnon, and B. Ludes. (2009). Pigment phenotype and biogeographical ancestry from ancient skeletal remains: inferences from multiplexed autosomal SNP analysis. International Journal of Legal Medicine 123(4): 315-325.


Brace, S., Y. Diekmann, T.J. Booth, Z. Faltyskova, N. Rohland, S. Mallick, et al. (2019). Ancient genomes indicate population replacement in Early Neolithic Britain. Nature Ecology & Evolution 3(5): 765-771.


Canfield, V.A., A. Berg, S. Peckins, S.M. Wentzel, K.C. Ang, S. Oppenheimer, and K.C. Cheng. (2014). Molecular phylogeography of a human autosomal skin color locus under natural selection. G3, 3(11): 2059-2067.


Frost, P. (2014). The puzzle of European hair, eye, and skin color. Advances in Anthropology 4(2): 78-88.


Frost, P. (2020). White Skin Privilege: Modern Myth, Forgotten Past. Evolutionary Studies in Imaginative Culture 4(2): 63-82.


Gibbons, A. (2007). American Association of Physical Anthropologists Meeting: European skin turned pale only recently, gene suggests. Science 20 April 2007, 316(5823): 364.


Huang, X., S. Wang, L. Jin, and Y. He. (2021). Dissecting dynamics and differences of selective pressures in the evolution of human pigmentation. Biology Open 15 February 2021; 10(2): bio056523.


Keane, A.H. (1886). Asia with Ethnological Appendix. London: Edward Stanford.


Lazaridis, I., N. Patterson, A. Mittnik, G. Renaud, S. Mallick, K. Kirsanow, et al. (2014). Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513(7518): 409-413.


Norton, H.L., and M.F. Hammer. (2007). Sequence variation in the pigmentation candidate gene SLC24A5 and evidence for independent evolution of light skin in European and East Asian populations. Program of the 77th Annual Meeting of the American Association of Physical Anthropologists, p. 179.


Olalde, I., M.E. Allentoft, F. Sanchez-Quinto, G. Santpere, C.W.K. Chiang, M. DeGiorgio, et al. (2014). Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European. Nature 507 (7491): 225-228.


Tuesday, April 13, 2021

The mismeasure of genetic differentiation


Red Tree, Piet Mondrian (1908-10)

If we look at SNP alleles associated with educational attainment, we see differences between Europeans and sub-Saharan Africans. Is genetic drift the cause? Or natural selection?



IQ has long been the yardstick of cognitive ability. As such, it describes phenotype, not genotype: it measures how your inborn potential has developed in your environment. Genotype is the inborn component of IQ. It can be inferred from twin studies, family studies, and adoption studies, but those approaches are indirect and far from perfect.


To measure genotype directly, we need to identify the alleles that affect the development of cognitive ability. We also need to measure the size of each allele’s effect. Recently, much progress has been made. By using genome-wide association studies (GWAS), researchers have identified many alleles that are associated with educational attainment (EA). EA is not quite the same as IQ—it also includes things like sitting still in class and brownnosing the teacher—but it's a good approximation.


In the most recent study of this sort, Lee et al. (2018) identified 1,271 single-nucleotide polymorphisms (SNPs) that are significantly associated with high EA in a sample of over one million people of European ancestry. Together, the SNPs can explain 11-13% of the variance in EA among individuals. This new yardstick is called the "polygenic score."


The polygenic score is more accurate for populations than for individuals. If we compare the mean polygenic score of a population and its mean IQ, the correlation is 90% (Piffer 2019). This high correlation is due to the logic of sampling: to estimate the mean cognitive ability of a population, we don't have to identify all of the relevant SNPs, just a large enough sample.


Like mean IQ, the mean polygenic score differs among human populations. It seems to have increased during the northward spread of modern humans out of Africa and into the temperate zone of Europe and Asia, with East Asians having the highest scores. This geographic pattern is in line with IQ data. The mean polygenic score is also very high among Ashkenazi Jews and Finns, again in line with IQ data (Piffer 2019).



Kevin Bird’s paper


The above findings have been disputed by the American researcher Kevin Bird in a recent paper. Although Europeans and sub-Saharan Africans have different alleles at genes associated with educational attainment, he argues that these differences correspond to small differences in cognitive ability. In fact, they are more consistent with genetic drift than with natural selection.


To prove his argument, he performed two analyses of the data: an Fst and a test for polygenic selection. In my opinion, both analyses have serious problems.


The Fst


This is the most common measure of genetic differentiation. If the Fst is low, differentiation is trivial and consistent with genetic drift. If it is high, differentiation is significant and consistent with natural selection.


For SNPs associated with EA, Kevin Bird reports an Fst of 0.111. Is that low or high? When Sewall Wright (1978, pp. 82-85) created this measure, he defined four categories of differentiation:


0 to 0.05 - little genetic differentiation

0.05 to 0.15 - moderate genetic differentiation

0.15 to 0.25 - great genetic differentiation

0.25 to 1 - very great genetic differentiation


Those categories are widely cited in the literature. A search in Google Scholar for "moderate genetic differentiation" and "0.05 - 0.15" shows over two hundred papers.


So does an Fst of 0.111 mean moderate genetic differentiation? Not according to Kevin Bird, who sees nothing at all below a benchmark of 0.118. That benchmark may be valid, but it cannot be easily verified and does not appear elsewhere in the literature. Nor does Kevin explain why it is better than the ones put forward by Sewall Wright. In fact, he makes no reference to them.


One may also question the Fst of 0.111. For the data source, the reader is referred to Lee et al. (2018), but that study was done only with European subjects. Moreover, Kevin Bird used 1,259 SNPs to calculate that Fst, even though he found only 685 SNPs that had data on both Africans and Europeans.


The Fst of 0.111 seems to be the diversification of those SNPs in Europeans. That value is what would be expected, but it says nothing about diversification between Europeans and sub-Saharan Africans.


The polygenic selection analysis


The other analysis is more on subject. Kevin Bird compared European data with African data as follows:


1. First, he looked through the 1000 Genomes Project for SNP data on Europeans and sub-Saharan Africans. He found data on five European-descended populations (Utah residents, Tuscans, Finns, British, Iberians) and five African populations (Yoruba, Luhya, Gambians, Mende, Esan). The two datasets had information on 685 of the 1,271 SNPs associated with educational attainment.


2. For each SNP, he noted the allele frequencies in Europeans and the allele frequencies in sub-Saharan Africans.


3. He calculated the differences in allele frequencies between the two groups. He then weighted the differences for the allele's effect size (its estimated positive or negative effect on educational attainment). For each allele, he used two different estimates of effect size: one from between-family data and the other from within-family data.


4. Alongside this list of weighted alleles, he created a second list to simulate genetic drift by randomly flipping the sign of effect size for 10,000 permutations.


5. When effect size was calculated from between-family data, the two lists clearly differed from each other. When it was calculated from within-family data, the overall difference was much smaller and easily explained by genetic drift.


Bird (2021) prefers the second dataset to the first, whereas Piffer (2019) prefers the first. Who is right? All things being equal, data should come from within families. There is less statistical noise because siblings have similar upbringings. With less noise, group differences can more easily be identified.


Yet, here, we have the opposite. We see a significant difference between Europeans and Africans in the between-family data, but not in the within-family data. Why? The reason is that the between-family data came from over a million subjects whereas the within-family data came from 20,000 sibling pairs. Being smaller, the second dataset had a lot more noise. Sure, there should have been less noise, all things being equal. But some things weren't.



Doing the comparison again but better


I suspect Kevin Bird still prefers within-family data. Fine. Let's repeat the comparison with a much larger sample of sibling pairs. There would then be less noise and probably a significant difference between African and European alleles in their effect on educational attainment. Kevin seems to anticipate this eventuality:


While the results presented here are more consistent with neutral evolution rather than divergent natural selection, it is not possible to rule out that data sets with more power could present different results. Additionally, although within-family effect sizes are recommended over between-family effect sizes, if the within-family effect sizes are re-estimated for SNPs ascertained by a between-family GWAS, there is still likely to be some level of confounding from population structure. (Bird 2021, p. 7)


He elaborates on the last point:


[...] the [polygenic] scores might be biased by a variety of factors, including the nonrandom ways that society is geographically structured [...]. For instance, Black people in the US, for reasons unrelated to genetics, live in areas with poorer air quality and more exposure to environmental toxins (Bird 2021, p. 8)


Yet, as he notes further on, these SNP alleles were identified only in European subjects, and their effects on educational attainment were estimated only from European data. So how could different alleles among Europeans be spuriously associated with differences in educational attainment among Europeans because of socioeconomic deprivation among Black Americans? Where and when do the latter come into this presumably spurious association?


Kevin Bird is right to point out that the allele effects were calculated from European data and may be less applicable to people of other origins. In fact, there is growing evidence that the genetic architecture of cognition is different in sub-Saharan Africans (Frost 2019). By ignoring that factor, however, we introduce even more noise into the data and muddle even more any differences that may exist between Africans and Europeans. The data may indeed be of low quality, but that shortcoming would, if anything, obscure group differences. Again, Kevin is making a coherent point within an incoherent argument.



Other ways?


There are other ways to distinguish between genetic drift and natural selection. One way is to measure the ratio of nonsynonymous alleles to synonymous alleles. If a trait has little functional value and is thus vulnerable to genetic drift, nonsynonymous alleles will tend to proliferate and become as numerous as synonymous alleles (Tomoko 1995). Of course, if nonsynonymous alleles greatly outnumber synonymous alleles, there may be natural selection for diversity (Rana et al. 1999).


An SNP, by its very nature, has alleles that differ from each other by only one base substitution, and this fact limits our ability to distinguish between genetic drift and natural selection. It would thus be interesting to identify genetic polymorphisms that are associated with educational attainment but have several nucleotides.


If such a polymorphism is undergoing genetic drift, the most frequent alleles will be the ancestral allele and those that differ from it by one base substitution. The less frequent ones will be those that differ by two or more base substitutions. In short, the frequency of an allele will be inversely related to the number of base substitutions that separate it from the ancestral allele.


The picture is different with natural selection. The most frequent alleles will not necessarily be the ones that differ the least from the ancestral allele. If allele frequency is graphed as a function of base substitutions, the result will not be a smoothly decreasing exponential curve. The most successful allele may differ from the ancestral one by several base substitutions.





Bird, K.A. (2021). No support for the hereditarian hypothesis of the Black-White achievement gap using polygenic scores and tests for divergent selection. American Journal of Physical Anthropology. Feb. 1-12, DOI: 10.1002/ajpa.24216.


Frost, P. (2019). Differences in the genetic architecture of cognition? Evo and Proud, September 25


Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics 50(8): 1112-1121.  


Tomoko, O. (1995). Synonymous and nonsynonymous substitutions in mammalian genes and the nearly neutral theory. Journal of Molecular Evolution 40 (1): 56-63


Piffer, D. (2019). Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from Gwas Hits: A Replication of Previous Findings Using Recent Data. Psych 1(1): 55-75.   


Rana, B.K., D. Hewett-Emmett, L. Jin, B.H.J. Chang, N. Sambuughin, M. Lin, et al. (1999). High polymorphism at the human melanocortin 1 receptor locus. Genetics 151(4): 1547-1557.


Wright S. (1978). Evolution and Genetics of Populations, Volume 4. University of Chicago, Chicago, IL.