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 among ancestral North Asians or among 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 among 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).

 

References

 

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. https://doi.org/10.1093/molbev/mss207

 

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.

https://doi.org/10.1007/s00414-009-0348-5

 

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. https://doi.org/10.1038/s41559-019-0871-9

 

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. https://doi.org/10.1534/g3.113.007484

 

Frost, P. (2014). The puzzle of European hair, eye, and skin color. Advances in Anthropology 4(2): 78-88. http://www.scirp.org/journal/PaperInformation.aspx?PaperID=46104

 

Frost, P. (2020). White Skin Privilege: Modern Myth, Forgotten Past. Evolutionary Studies in Imaginative Culture 4(2): 63-82. https://doi.org/10.26613/esic/4.2.190

https://www.jstor.org/stable/10.26613/esic.4.2.190

 

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

https://doi.org/10.1126/science.316.5823.364a

 

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. https://doi.org/10.1242/bio.056523

 

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. https://doi.org/10.1038/nature13673

 

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. https://doi.org/10.1038/nature12960

 

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.

 

 

References

 

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.

https://www.gwern.net/docs/genetics/selection/2021-bird.pdf

 

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

https://evoandproud.blogspot.com/2019/09/differences-in-genetic-architecture-of.html

 

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.

https://academicworks.medicine.hofstra.edu/cgi/viewcontent.cgi?article=5038&context=articles  

 

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. https://doi.org/10.3390/psych1010005   

 

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.

https://www.researchgate.net/profile/M-Ramsay/publication/13190390_High_Polymorphism_at_the_Human_Melanocortin_1_Receptor_Locus/links/596b13eeaca2728ca6821b9e/High-Polymorphism-at-the-Human-Melanocortin-1-Receptor-Locus.pdf

 

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

Tuesday, March 30, 2021

Recent cognitive evolution in West Africa

 


If we look at alleles associated with higher educational attainment, we find more of them among the Yoruba of Nigeria than among the Mende of Sierra Leone. The reason may be differences in social evolution over the past 1,000 years, particularly in trade, urban settlement, State formation, and other forms of social complexity. 

Ife king's head (14th or early 15th century) (Wikicommons - Vassil)

 

 

How can we measure the genetic component of cognitive ability? We have long used IQ tests to get a rough idea, but they are not an ideal yardstick. Twin studies have shown that genetic factors explain about two thirds of the variance in IQ results, perhaps even less for comparisons between people of different cultural backgrounds.

 

In recent years we've found a new yardstick: the polygenic score. It's a more direct genetic measurement, being a summation of alleles that have been linked to higher educational attainment. As a method for estimating the mean cognitive ability of a population, it seems to be as good as IQ tests. Piffer (2019) found a 90% correlation between the two methods. In his latest study, he has again found the same correlation (Piffer 2021, see Figure 8).

 

Interestingly, that study shows differences in mean cognitive ability within West Africa: the Mende of Sierra Leone score much lower than the Yoruba of Nigeria. In fact, the Yoruba have almost the same polygenic score as do African Americans, even though the latter have about 20% European admixture. Unfortunately, we have no data on the Igbo of Nigeria, who are known to be high achievers at school and in other areas of life (Frost 2015).

 

These differences within West Africa support the argument that mean cognitive ability has continued to increase in some human populations, even in relatively recent times. With respect to the Yoruba, their cognitive ability may have increased in tandem with their advances in trade, urban settlement, and State formation from the tenth century onward (Akintoye 2014; McIntosh and McIntosh 1988). Meanwhile, the Mende remained at a lower level of social complexity.

 

There is one problem with using polygenic scores for West Africans, or for any non-European population. To identify alleles associated with higher educational attainment, researchers have used genomes of European origin. There is evidence, however, that the architecture of cognitive ability may differ in different human populations. The same alleles might not explain high cognitive ability in West Africans and Europeans. Indeed, Lasker et al. (2019) found a lower correlation between polygenic scores and cognitive ability in African Americans than in European Americans.

 

References

 

Akintoye, S.A. (2014). A History of the Yoruba People. Dakar: Amalion.

 

Frost, P. (2015). The Jews of West Africa. The Unz Review, July 4

https://www.unz.com/pfrost/the-jews-of-west-africa/

 

Lasker, J., B.J. Pesta, J.G.R. Fuerst, and E.O.W. Kirkegaard. (2019). Global ancestry and cognitive ability. Psych 1(1)

https://www.mdpi.com/2624-8611/1/1/34  

 

McIntosh, S.K., and McIntosh, R.J. (1988). From stone to metal: New perspectives on the later prehistory of West Africa. Journal of World Prehistory 2: 89-133. https://doi.org/10.1007/BF00975123  

 

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. https://doi.org/10.3390/psych1010005  

 

Piffer, D. (2021). Divergent selection on height and cognitive ability: evidence from Fst and polygenic scores. OpenPsych

https://openpsych.net/files/submissions/14_Divergent_selection_on_height_and_cognitive_ability_evidence_from_Fst_and_13c3ICJ.pdf  

Monday, March 22, 2021

The big bird that takes away water

 


The constellation of the Southern Cross has inspired similar myths among indigenous peoples as far apart as Australia and South America. Why?

Southern Cross (Wikipedia – Yulanlu97). 

 

 

Did people cross the Pacific in pre-Columbian times? This question has aroused renewed interest with the discovery of sweet potato remains at Polynesian sites dated to A.D. 1000. There also seem to be loan-words of Polynesian origin in some Amerindian languages (Jones et al. 2011). Finally, we have strong evidence that Polynesians introduced chickens to the west coast of South America in prehistoric times, probably A.D. 1300-1420 (Fitzpatrick and Callaghan 2009).

 

A new piece of evidence is the similarity between a myth told by Aboriginal Australians, particularly those of southeast Australia, and a myth told by indigenous peoples in Argentina and central Brazil. In both cases, one finds the same two elements:

 

- A large flightless bird that can cause the land to dry up.

- The constellation of the Southern Cross and two adjacent regions of the sky: the Southern Pointers (Alpha and Beta Centauri) and the Coalsack Nebula

 

These similarities are mentioned by Gullberg et al. (2020) in a cross-cultural study of beliefs about the 'Dark Constellations':

 

Wiradjuri, Kamilaroi, Euahlayi, and others (Australia)

 

The Emu in the Sky is perhaps the best-known Aboriginal dark constellation (Figure 2). It is the silhouette of an emu traced out by the dark nebulae within the plane of the Milky Way and is featured in the traditions of Aboriginal people across Australia. The Coalsack Nebula, near the Southern Cross, forms the head, and the body extends along the dust lanes through Centaurus in the Milky Way, to the body as outlined by the galactic bulge in Scorpius and Sagittarius (Gullberg et al. 2020, p. 392)

 

When the celestial emu swings to where it is low on the horizon in October and November, the galactic bulge is now seen as the backside of an emu sitting in a waterhole, displacing the water and causing the land to dry up as the hot summer months approach. (Gullberg et al. 2020, p. 393)

 

Moqoit (Argentina)

 

Due to this crucial role of the Milky Way and the fact that it is a huge area of diffuse brightness interrupted by dark spots, it is not surprising that the Moqoit pay attention to dark patterns on it. The most important of all of them is the Mañic, the master of the South American rheas, a large flightless bird similar to an emu or ostrich shown in (Figure 5)

 

[…] We know many Moqoit stories mention that in the time of the origins, the master of Mañic used to shelter in a number of burrows, under the roots of an ombú (a very big tree, seen as the world tree—the Milky Way), and eat humans. Lapilalaxachi, a powerful human ancestor of the Moqoit people identified with the Pleiades, decided to face the Mañic. He chased the Mañic throughout the world and the cornered Mañic climbed up the ombú trunk to the sky.

 

Today, the shadow-soul (la 'al) of the Mañic can be seen in the Milky Way's dark clouds, with its head in what we know as the Coalsack (around -59° 50' galactic longitude). Alpha and Beta Centauri are the dogs of the man chasing the Mañic and bite at its neck (López and Giménez-Benítez, 2008). The Mañic's head is the Coalsack.

(Gullberg et al. 2020, p. 396)

 

Tupi (central Brazil)

 

In a similar view, the Tupi people of central Brazil also perceive a rhea in the sky, making essentially the same shape as the Aboriginal emu. The rhea and the emu are both large, flightless birds with a similar appearance and breeding cycle. Just as in Moqoit traditions, the head of the rhea is the Coalsack, and the body is traced out by dust lanes in Centaurus and Scorpius. The Tupi associate the rhea with the end of the world. The stars of Crux are holding the head of this animal. If it escapes, it will drink all the water of the world (Alencar, 2011)

(Gullberg et al. 2020, p. 397)

 

 

Why is this myth found only in Australia and South America? Why is it absent in-between? Actually, a version does exist on the Polynesian island of Tonga, except that the large flightless bird is a giant duck and it simply keeps people from getting access to water:

 

Tongans (Polynesia)

 

Polynesians of the Pacific recognise dark spaces in the Milky Way, focusing on the Coalsack Nebula and relating it to fish or fishing. Polynesian traditions of Tonga describe it as Humu (a giant triggerfish). In their traditions (Gifford, 1924), a Tongan chief named Ma'afu took a lizard wife and had twin sons, which they wanted gone as the chief's subjects were afraid of the pair. Ma'afu sneakily instructed the brothers to collect water from a waterhole containing a giant duck that would kill and consume anyone who came too close. The boys were attacked by the duck but grabbed it by the neck and killed it. When the boys returned unharmed, the father instructed them to obtain water from a more distant waterhole, inhabited by Humu, a triggerfish (these are large aggressive animals with powerful teeth designed for crushing shellfish). The boys killed the triggerfish and in anger at this, the father blurted out his secret to have the boys killed. The boys walked away and ascended to the stars, each carrying one of the two animals they killed. The twins became the Magellanic Clouds, the duck became the Southern Cross (with the duck's bill as γ Crucis), and Humu became the Coalsack Nebula

(Gullberg et al. 2020, p. 398)

 

 

My thoughts

 

This myth seems to have begun in one of three areas (Australia, Polynesia, South America) and then spread to the other two. If it began among Aboriginal Australians, the myth could be very old, going back perhaps 65,000 years. If it began among the Amerindian peoples of South America, it may go back 10,000 years. Finally, if it began among the Polynesians, the time depth would be no more than 3,500 years. The first and last scenarios seem most likely, given that oceanic travel was much easier from Polynesia to South America than the reverse (Fitzpatrick and Callaghan 2009).

 

Nonetheless, all of the scenarios run into a big problem: the myth is known to South American groups on the east side of the continent but not to those on the west side (which would be more consistent with trans-Pacific contact). I can think of only one other scenario. Given that the Americas were once inhabited by a population related to Aboriginal Australians and similar groups in Southeast Asia (Frost 2015), the myth may have originated in Asia more than 65,000 years ago and then spread in two directions: to Australia via Southeast Asia and to the Americas via the Bering Strait. But could a myth survive intact for that long?

 

Two other things leave me wondering. Why would the sky around the Southern Cross be seen as a large flightless bird? Gullberg et al. (2020) provide several pictures of that part of the sky and trace the outline of a bird on them. To my eyes, one could just as easily trace the outline of many other animals.

 

Finally, is this evidence, à la Von Däniken, of extraterrestrial contact? Keep in mind that the region of the Southern Cross includes the closest star system to ours. And if that system does have intelligent life, should we be reaching out to them and inviting them over? The last time around they didn’t leave a good impression.

 

Enough! I shouldn’t let myself get carried away. Extraordinary claims require extraordinary evidence, and a single myth hardly qualifies as extraordinary.

 

 

References

 

Fitzpatrick, S.M. and R. Callaghan. (2009). Examining dispersal mechanisms for the translocation of chicken (Gallus gallus) from Polynesia to South America. Journal of Archaeological Science 36(2): 214-223.

https://doi.org/10.1016/j.jas.2008.09.002

 

Frost, P. (2015). Guess who first came to America? Evo and Proud. August 1

http://evoandproud.blogspot.com/2015/08/guess-who-first-came-to-america.html

 

Gullberg, S.R., D.W. Hamacher, A. Martin-Lopez, J. Mejuto, A.M. Munro, and W. Orchiston. (2020). A Cultural Comparison of the 'Dark Constellations' in the Milky Way. Journal of Astronomical History and Heritage 23(2): 390-404.

http://www.narit.or.th/files/JAHH/2020JAHHvol23/2020JAHH...23..390G.pdf

 

Jones, T.L., A.A. Storey, E.A. Matisoo-Smith, and J.M. Ramirez-Altamira (eds). (2011). Polynesians in America: Pre-Columbian Contacts with the New World. Rowman Altamira.

Monday, March 15, 2021

Nigerians, Scrabble, and the GCSE

 


Exam hall at Hull Collegiate School (Wikicommons – Robin S. Taylor). The GCSE exam is a poor measure of raw cognitive ability. If some students get tutored and others do not, there will be more environmental variance in IQ, and the exam results will say less about the genetic potential for cognitive ability.

 

 

Chanda Chisala has written more about cognitive ability in sub-Saharan Africa. His argument is straightforward:

 

[…] if it is true that on average black Africans in Africa score extremely low on scholastic/intelligence tests because they grow up with much less educational and other modern cultural resources (as Flynn would agree), then they should perform "extremely well" (by comparison) in those "g-loaded" cognitive contests that do not require too much of such quality cultural exposure (as Jensen would agree). (Chisala 2021)

 

Chanda argues that raw cognitive ability is better measured in Africa by a Scrabble championship than by an IQ test, since most Africans lack "access to well-trained teachers, big libraries, computers or even TVs" (Chisala 2021). Africans are good at Scrabble:

 

Nigeria happens to be the world's top performing nation in English Scrabble, while francophone African countries are also the most dominant in French Scrabble, despite the fact that the top players in Western countries are super-high-IQ nerds with visibly exceptional mathematical talents (Chisala 2021)

 

Correlation isn't causation. Is a high IQ needed to do well at Scrabble? Not according to this study:

 

Forty tournament-rated SCRABBLE players (20 elite, 20 average) and 40 unrated novice players completed a battery of domain-representative laboratory tasks and standardized verbal ability tests. The analyses revealed that elite- and average-level rated players only significantly differed from each other on tasks representative of SCRABBLE performance. Furthermore, domain-relevant practice mediated the effects of SCRABBLE tournament ratings on representative task performance, suggesting that SCRABBLE players can acquire some of the knowledge necessary for success at the highest levels of competition by engaging in activities deliberately designed to maximize adaptation to SCRABBLE-specific task constraints. (Tuffiash, Roring, and Ericsson 2007)

 

Success at Scrabble seems to be due largely to practice and is thus a poor measure of raw cognitive ability.

 

A curious detail: Nigeria's top performers come overwhelmingly from one part of the country: the Niger Delta, which is home to the Igbo and related tribes. Since the peoples of the Niger Delta used to dominate trade between the coast and the interior, and since trade selects for cognitive ability, mean IQ should be higher in those populations that have long practiced it, like the Igbo (Frost 2015).

 

Young Nigerians in the UK - Academic achievement on the GCSE

 

Although many African immigrants do poorly in British schools, some actually do well. A study of six secondary schools in inner London found that results on the General Certificate of Secondary Education (GCSE) were higher for African students who spoke Igbo, Yoruba, Luganda, and Ga than for White British students who spoke only English (Demie 2013, p. 9). Chanda sees the GCSE as a proxy for IQ and argues that IQ differences between African immigrants and White British must be highly malleable:

 

Africans speaking Luganda and Krio did better than the Chinese students in 2011. The igbo were even more impressive given their much bigger numbers (and their consistently high performance over the years, gaining a 100 percent pass rate in 2009!). The superior Igbo achievement on GCSEs is not new and has been noted in studies that came before the recent media discovery of African performance. A 2007 report on "case study" model schools in Lambeth also included a rare disclosure of specified Igbo performance (recorded as Ibo in the table below) and it confirms that Igbos have been performing exceptionally well for a long time (5 + A*-C GCSEs); in fact, it is difficult to find a time when they ever performed below British whites. (Chanda 2015)

 

Igbo students stood out as high achievers on the GCSE, as did Yoruba students to a lesser extent. In both groups, however, the mean results were highly variable from one year to the next:

 

2009: Igbo - 100%, Yoruba - 39%

2010: Igbo - 80%, Yoruba - 68%

2011: Igbo - 76%, Yoruba - 75% 

(Demie 2013, p. 9)

 

Chanda attributes this variability to statistical noise caused by small sample size. If so, there should be an inverse correlation between sample size and variability. GCSE scores should be more variable for smaller groups than for larger ones. Yet the reverse seems to be true for the years 2009 to 2011:

 

Yoruba: 90 students / gain of 36 percentage points

Somali: 53 students / gain of 13 percentage points

Twi-Fante: 37 students / loss of 3 percentage points

Igbo: 16 students / loss of 24 percentage points

Krio: 12 students / gain of 4 percentage points

Tigrinya: 12 students / loss of 8 percentage points

Lingala: 12 students / loss of 5 percentage points

Ga: 8 students / gain of 9 percentage points

Swahili: 8 students / gain of 10 percentage points 

(Demie 2013, pp. 7, 9)

 

The two largest gains were made by the two largest groups: the Yoruba and the Somali. If the differences between 2009 and 2011 are statistical noise, why are the largest ones associated with the largest groups? Shouldn't we see the reverse? Shouldn't the smallest groups show the most variability?

 

Something seems to be causing those impressive GCSE gains. Since the students are not the same from one year to the next, and since the gains differ considerably from one ethnic community to another, the "something" must be the community itself. Over time, the Yoruba community became better at assisting its students, and this kind of assistance was available only in larger communities like the Yoruba.

 

The most obvious forms of assistance are tutoring and coaching. Such assistance is mentioned by parents in interviews for the above study:

 

Parent A: Father of daughter in Year 9. Generally supportive of the school which was not his first choice but is supplementing his daughter's education with a home tutor. He also calls on his extended family, his oldest son who is a graduate is also expected to help. (Demie 2013, p. 14)

 

Although tutoring and coaching are perfectly legitimate, they invalidate the GCSE as a means to measure IQ, particularly its genetic component. If some students get tutored and others do not, there will be more environmental variance in IQ, and the exam results will say less about the genetic potential for cognitive ability. Therefore, GCSE results tell us what we already know: if you get tutored and coached before an exam, you'll do better.

 

Are tutoring and coaching the only forms of community assistance? There is another one: impersonation. In other words, the parents hire a smart student from their community to take the exam in their child's place. This strategy is feasible only if the community has enough individuals who are (1) intelligent and (2) similar in age and appearance to the student in question. Such individuals are lacking in a small community, as are the middlemen who can refer an anxious parent to a suitable source of assistance.

 

How common is this strategy? Adebayo (2013) studied cheating behavior among Nigerian university students and British university students. He found that impersonation services were used or provided by 20% of the former and 1% of the latter. In general, cheating took non-collaborative forms among British students and collaborative forms among Nigerian students:

 

These include behaviours like writing somebody's coursework, colluding with others to communicate answers to one another, over marking one another's course work etc. This is quite different from plagiarism and non-collaborative cheating characteristic of the British sample reported by Newstead et al (1996). Reasons for these differences may be attributable to differences in population, differences in cultural ethnic, differences in emphasis placed on examination as part of educational assessment (Adebayo 2013, p. 146)

 

Adebayo (2013, p. 148) found high rates of collaborative cheating among Nigerian students:

 

Permitting own coursework to be copied - 72.6%

Copying another student's coursework with consent - 47.3%

Collaborative generous marking of coursework - 64.6%

Submitting joint work as an individual's - 49.3%

Doing another student's coursework for them - 77.3%

Collusion with another student to communicate answers - 83%

 

We live in a world that has low-trust and high-trust societies. In a high-trust society, like the UK, cheating is considered shameful and disreputable, regardless of whom you cheat. In a low-trust society, like Nigeria, cheating is wrong only when you do it to friends and relatives.

 

What happens when individuals from a low-trust society migrate to a high-trust one? If they come in sufficient numbers, their opportunities for collaborative cheating are greatly increased. Imagine you're supervising an exam in an English school, and you suspect an African student is filling in for another. He shows you his school card and another piece of ID. Both are correct. So what do you do now? Do you really want to make a fuss and risk being accused of racial profiling? No you don't.

 

Future research

 

The GCSE study by Demie (2013) leaves much to be desired. It does not provides the number of students who had to retake that exam (which must be a large number); nor does it provide a breakdown of the number of students taking it per year.

 

In any case, the GCSE is a poor substitute for an IQ test. Even if we exclude cheating, the results are distorted by legitimate activities like tutoring and coaching. The latter are more available to some students than to others. Consequently, GCSE results tell us nothing about differences in raw cognitive ability, either between individuals or between communities.

 

Chanda promises to write an article that will rule out cheating as an explanation for Nigerian success on the GCSE. Again, the issue isn't just cheating. It's any assistance that goes to some students and not to others. If you want to measure raw cognitive ability, you need a level playing field. In particular, you need a test that does not offer high achievers the lure of personal gain, which may push test-takers to do well by hook or by crook. In the UK, an African with good GCSE results has access to a wide range of good-paying jobs, in large part because of "diversity quotas" of one sort or another.

 

This motive comes out in interviews with the parents of African students:

 

● 'Without an education you cannot earn a decent salary, without qualifications you cannot get a good job. The best thing is to push your children as hard as you can.'

● 'Being a Black woman if you don't have education in this country, what job will you have to do, clean people's toilets?'  (Demie 2013, p. 13)

 

This subject should definitely be a research priority. We need IQ data on Nigerians, and not inadequate substitutes like GCSE scores. We also need data on alleles associated with educational attainment (i.e., polygenic scores). Furthermore, we need data on each of Nigeria's ethnic groups, particularly the Igbo. It's hard to fake intelligence in the real world, and the Igbo have a long history of doing better at business and other endeavors. Unfortunately, intelligent people are also better at cheating, so there is some confounding between real intelligence and the fake kind.

 

References

 

Adebayo, S.O. (2011). Common Cheating Behaviour among Nigerian University Students: A Case Study of University of Ado-Ekiti, Nigeria. World Journal of Education 1(1): 144-149.

https://files.eric.ed.gov/fulltext/EJ1159043.pdf

 

Chisala, C. (2015). The IQ Gap Is No Longer a Black and White Issue. The Unz Review, June 25

https://www.unz.com/article/the-iq-gap-is-no-longer-a-black-and-white-issue/

 

Chisala, C. (2020). Nigerians, Jews and Scrabble: An Update on the IQ Debate. The Unz Review, February 27

https://www.unz.com/article/nigerians-jews-and-scrabble-an-update-on-the-iq-debate/#comment-4520966

 

Demie, F. (2013). Raising Achievement of Black African Pupils. Good Practice in Schools. London: Lambeth Research and Statistics Unit, Lambeth Council.

https://www.lambeth.gov.uk/rsu/sites/www.lambeth.gov.uk.rsu/files/Raising_the_Achievement_of_Black_African_Pupils-Good_Practice_in_Schools_2013.pdf

 

Frost, P. (2015). The Jews of West Africa. The Unz Review, July 4

https://www.unz.com/pfrost/the-jews-of-west-africa/

 

Tuffiash, M., R.W. Roring, and K.A. Ericsson. (2007). Expert performance in SCRABBLE: Implications for the study of the structure and acquisition of complex skills. Journal of Experimental Psychology: Applied, 13(3), 124-134. https://doi.org/10.1037/1076-898X.13.3.124