Population differences in intellectual capacity: a new polygenic analysis

 

PISA test documents at a German school (source: Theo Müller). PISA and IQ tests are informing us about differences in intellectual capacity by country. Meanwhile, genetic studies are informing us about genomic differences by country. Davide Piffer has been tapping into these two pools of data to explore the links between genes and intellectual capacity.

 

Between individuals and populations, intellectual capacity seems to differ through small differences at many genes. This is hardly surprising. Intelligence is a complex trait that involves many different genes interacting with each other and with the environment. If one gene changes, the immediate effect may be beneficial, but there will be side effects at other genes, and most of those side effects will likely be harmful. The bigger the effect at any one gene, the greater the likelihood of negative side effects elsewhere.

So evolution has proceeded through tinkering. A small effect here, a small effect there, but nothing that will rock the boat.

We must therefore pool data from many genes to understand the evolution of complex traits like intelligence. This is what Davide Piffer (2013) has done in a recent study. He began with seven genes (SNPs) whose different alleles are associated with differences in intellectual capacity, as measured by PISA or IQ tests. Then, for fifty human populations, he looked up the prevalences of the alleles that seem to increase intellectual capacity. Finally, for each population, he calculated their average prevalence at all seven genes.

The average prevalence was 39% among East Asians, 36% among Europeans, 32% among Amerindians, 24% among Melanesians and Papuan-New Guineans, and 16% among sub-Saharan Africans. The lowest scores were among San Bushmen (6%) and Mbuti Pygmies (5%). A related finding is that all but one of the alleles seem to be derived. In other words, they are specific to humans and not shared with ancestral primates.

Since these alleles have only small effects on intellectual capacity, there might be other causes for the above geographic pattern. For instance, as modern humans spread out of Africa, older alleles would have gradually given way to newer ones simply through founder effects and other random events. On the other hand, these derived alleles do not reach their highest prevalence in populations that are farthest removed from Africa, like the native inhabitants of the Americas and Oceania. The highest prevalences are actually reached less far away, in Europe and East Asia. Furthermore, the African/non-African difference is much greater for these alleles than for derived alleles in general. Derived alleles typically have a prevalence of 42% among sub-Saharan Africans and 56-57% among East Asians and Europeans (Watkins et al., 2001). This difference is tiny in comparison to the one for alleles that seem to increase intellectual capacity.

 

Principal component analysis

In this study and in a subsequent one (Piffer, 2014), principal component analysis has shown that a single factor explains much of the variability in the data (45%). Moreover, this one factor correlates highly with average IQ scores (r=0.9) and PISA scores (r=0.8) for each population. A common neural property thus seems to be the target of the various derived alleles. Could it be the elusive g factor?

The existence of such a large factor is further proof that we are dealing with some kind of selection pressure, and not random genetic changes like founder effects. It doesn’t follow, however, that the “unexplained variability” is without significance. Selection for intellectual capacity, like selection for any complex trait, may follow different paths in different cultural contexts. Moreover, there may be tradeoffs between different kinds of mental ability, and these tradeoffs may likewise vary according to the cultural context.

 

A final caveat

These seven genes are a small subset of the many genes that affect intellectual capacity. They thus provide only a rough picture of how this trait varies within the human species. Nonetheless, this picture is probably not far from reality. 

 

References

 

Piffer, D. (2013). Factor analysis of population allele frequencies as a simple, novel method of detecting signals of recent polygenic selection: The example of educational attainment and IQ, Interdisciplinary Bio Central, provisional manuscript

http://www.ibc7.org/article/journal_v.php?sid=312 

Piffer, D. (2014). Simple statistical tools to detect signals of recent polygenic selection, Interdisciplinary Bio Central, 6, article 1

http://www.ibc7.org/article/journal_v.php?sid=317

Watkins, W.S., C.E. Ricker, M.J. Bamshad, M.L. Carroll, S.V. Nguyen, M. A. Batzer, H.C. Harpending, A.R. Rogers, and L.B. Jorde. (2001). Patterns of ancestral human diversity: An analysis of Alu-insertion and restriction-site polymorphisms, American Journal of Human Genetics, 68, 738-752.

The paradox of the Visual Word Form Area

 

Luke the Evangelist (source: British Library). In the past, only a minority could read long texts of cursive writing. But many more could read short texts of block writing.

 

The Visual Word Form Area (VWFA) is a specialized part of the brain that helps us recognize written words and letters. If it is subjected to a surgical lesion, the patient will suffer a clear impairment to reading ability but not to recognition of objects, names, or faces or to general language abilities. There will be some improvement over the next six months, but reading will still take twice as long as it had before surgery (Gaillard et al, 2006).

Most of the initial skepticism over the existence of the VWFA has disappeared. There does seem to be, however, much variability in its size. An area that may fall within this mental organ in one person may fall outside it in someone else (Glezer and Riesenhuber, 2013).

In addition to word recognition, the VWFA may participate in higher-level processing of word meaning:

[It seems that] the VWFA would not only be recruited at an early stage for allowing low-level (script processing) word processing as has been previously instantiated (Pammer et al., 2004; Dehaene and Cohen, 2011), but also at a later stage for gating high-level (lexico-semantic) processing. Such late semantic gateway would not be selective to the VWFA but rather emerge in the posterior LOT and extend anteriorly to the VWFA. (Levy et al., 2013)

The VWFA is described in the above study as a “bottleneck to consciousness.” It helps us not only to recognize words on a page but also to understand what the words mean. To me, this makes sense. I’m better at thinking through an idea and its implications if I can write it down and then read it. There thus seems to be a single mental pathway that does double duty: processing character strings (words) and processing higher-level concepts.

 

Population differences 

The VWFA functions differently in different human populations. The difference is striking between people who use alphabetical script, where each symbol represents a sound, and those who use logographic script, where each symbol represents an idea. Chinese subjects process their idea-based symbols with assistance from other brain regions, whereas Westerners process their sound-based symbols only in the VWFA (Liu et al., 2008). Similarly, dyslexics activate this brain region in ways that differ by linguistic background, apparently because of differences in spelling and writing (Paulesu et al., 2001).

Hardwired or softwired?

For Dehaene and Cohen (2011), the VWFA is not a hardwired mental organ. They argue that it occupies the same area of the brain because that is where we can most easily recruit neurons when learning to recognize words. But why, then, does this recruitment happen so fast in young children? When kindergarten children were asked to play a grapheme/phoneme correspondence game, their VWFAs preferentially responded to pictures of letter strings after a total of 3.6 hours over an 8-week period. It is worth noting that only a few of these children could actually read, and even then only at a rudimentary level (Brem et al., 2010; Dehaene et al., 2010).

But the alternative view, hardwiring, is also hard to accept. Reading began not in the Paleolithic but in historic times, less than 6,000 years ago. Widespread literacy is even more recent, and there are still many societies where most people cannot read or write. How could an entirely new mental organ have evolved over so short a time?

Yet this alternative view may not be so farfetched. Let’s examine the two main objections.

Was there not enough time for natural selection to work?

The VWFA did not evolve out of nothing. It seems to be a population of neurons that originally served to recognize faces (Dehaene and Cohen, 2011). This sort of recycling is a common pathway for natural selection and explains much of the apparent rapidity of evolution. A complex mental adaptation may take a long time to evolve, but much less time is needed to develop an exaggerated version of it or to alter when and how it becomes activated (Harpending and Cochran, 2002).

Indeed, parallel to the way alphabetical reading ability has spread historically and geographically, there is a similar spread of the latest variant of ASPM, a gene implicated in the regulation of brain growth. In humans, a new variant arose about 6,000 years ago in the Middle East. It eventually became more prevalent in the Middle East (37-52% incidence) and Europe (38-50%) than in East Asia (0-25%) (Frost, 2011; Mekel-Bobrov et al., 2005). 

Would it have benefited too few people to have been favored by natural selection?

There is some debate over the relative recentness of literacy. It is true that before the modern era only a small minority could read long texts of cursive writing. But the ability to read short texts of block writing was much more widespread, as evidenced by the prevalence of graffiti and storefront signs. We should also keep in mind that the literate few contributed disproportionately to the gene pool of subsequent generations. Clark (2007) has shown that the English lower class is largely descended from people who were middle or upper class a few centuries ago. In the ancient world, there was a perception that scribes enjoyed reproductive success. The Book of Sirach [39: 11] states: “If [a scribe] lives long, he will leave a name greater than a thousand.” 

Gene-culture co-evolution?

There may have been positive feedback between reading ability and the cultural opportunities it created. One example is the scientific revolution in Western Europe (15th - 18th centuries), which took off once a critical mass of scholars could read each other’s papers. In short, reading and writing are advantageous to the extent that other people can read and write. While this kind of feedback loop is self-evident, its biological implications may be less so. The same feedback loop would have steadily ratcheted up selection for the VWFA and, subsequently, for higher-level faculties. This might explain why the VWFA evolved beyond word recognition per se and towards lexico-semantic tasks.

Future research

One priority would be to study the VWFA in populations that have become literate only in recent times. What form, if any, does it take in such people? A study in New York elementary schools found that VWFA activation varied with socioeconomic status. In students from high SES families, activation seemed to be more hardwired and less dependent on familiarity with the way sounds are visually represented. Unfortunately, there was no attempt to break the data down by ethnic background (Noble et al., 2006).

At present, high VWFA activation is attributed to an environment where reading material is accessible and parents very supportive, this being in turn attributed to high SES. Yet reading material is ubiquitous nowadays. And how crucial is parental support? As a child, I read almost always on my own with little encouragement at home or school. My teachers were in fact annoyed by my habit of sneaking into the small storage room where old textbooks and encyclopedias were kept (we had no library). “If you’ve finished your assignment, stay at your desk. Is that clear?!”

Nonetheless, I read voraciously, even when I couldn’t understand half of what I read. Strange new words were a source of pleasure, and I would often read and reread the same texts simply because I liked the flow of the words and the images they conjured up.

 

References 

Brem, S., S. Bach, K. Kucian, T.K. Guttorm, E. Martin, H. Lyytinen, D. Brandeis, and U. Richardson. (2010). Brain sensitivity to print emerges when children learn letter-speech sound correspondences, Proceedings of the National Academy of Sciences U.S.A., 107, 7939–7944.

http://psyserv06.psy.sbg.ac.at:5916/fetch/PDF/20395549.pdf

Clark, G. (2007). A Farewell to Alms. A Brief Economic History of the World, Princeton University Press, Princeton and Oxford.

Dehaene, S. and L. Cohen. (2011). The unique role of the visual word form area in reading, Trends in Cognitive Sciences, 15, 254-262.

http://www.cnbc.pitt.edu/~plaut/VisCog/papers/DehaeneCohen11TICS.VWFA.pdf  

Dehaene, S. et al. (2010). How learning to read changes the cortical networks for vision and language, Science, 330, 1359–1364.

http://gondabrain.ls.biu.ac.il/Neuroling/courses/877/Dehaene_Science2010.pdf

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

http://dx.doi.org/10.1016/j.futures.2011.05.017  

Gaillard, R., Naccache, L., P. Pinel, S. Clémenceau, E. Volle, D. Hasboun, S. Dupont, M. Baulac, S. Dehaene, C. Adam, and L. Cohen. (2006). Direct intracranial, fMRI, and lesion evidence for the causal role of left inferotemporal cortex in reading, Neuron, 50, 191-204.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.76.7620&rep=rep1&type=pdf

 

Glezer, L.S. and M. Riesenhuber. (2013). Individual variability in location impacts orthographic selectivity in the “Visual Word Form Area”, The Journal of Neuroscience, 33(27), 11221–11226.

http://www.jneurosci.org/content/33/27/11221.full  

Harpending, H., and G. Cochran. (2002). In our genes, Proceedings of the National Academy of Sciences USA, 99(1), 10-12.

http://www.wcas.northwestern.edu/nescan/2008-2009%20papers/harpending%20-%20in%20our%20genes.pdf  

Levy, J., J.R Vidal, R. Oostenveld, I. FitzPatrick, J-F. Démonet, and P. Fries. (2013). Alpha-band suppression in the Visual Word Form Area as a functional bottleneck to consciousness, NeuroImage,78C, 33-45.

http://hal.inria.fr/docs/00/81/96/67/PDF/Levy_et_al.pdf  

Liu, C., W-T. Zhang, Y-Y Tang, X-Q. Mai, H-C. Chen, T. Tardif, and Y-J. Luo. (2008). The visual word form area: evidence from an fMRI study of implicit processing of Chinese characters, NeuroImage, 40, 1350-1361.

http://psychbrain.bnu.edu.cn/teachcms/res_base/teachcms/upload/channel/file/2010_4/11_25/6hlcggx7rk3z.pdf  
Mekel-Bobrov, N., S.L. Gilbert, P.D. Evans, E.J. Vallender, J.R. Anderson, R.R. Hudson, S.A. Tishkoff, and B.T. Lahn. (2005). Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens, Science, 309, 1720-1722.
http://ftp.eebweb.arizona.edu/faculty/nachman/Archived%20Research%20Papers/mekel_bobrov_et_al_2005.pdf  

Noble, K.G., M.E. Wolmetz, L.G. Ochs, M.J. Farah, and B.D. McCandliss. (2006). Brain–behavior relationships in reading acquisition are modulated by socioeconomic factors, Developmental Science, 9, 642–654.

http://www.cumc.columbia.edu/dept/sergievsky/fs/publications/Noble-et-al-2006-2.pdf  

Paulesu E., J.F. Démonet, F. Fazio, E. McCrory, V. Chanoine, N. Brunswick et al (2001). Dyslexia: cultural diversity and biological unity, Science, 291, 2165–2167.

http://www.drru-research.org/data/resources/42/Paulesu-et-al-2001.pdf

Replacement or continuity?

 

Inuit meat cache, Kazan River (source: Library and Archives Canada / PA-101294). Because of their high meat diet, hunters produce more body heat than farmers do. Natural selection has thus favored certain mtDNA sequences over others in humans with this profile of heat production. A change in selection pressure may therefore explain, at least in part, the genetic divide between late hunter-gatherers and early farmers in Europe.

 

Who were the ancestors of present-day Europeans? The hunter-gatherers of the Paleolithic and the Mesolithic? Or the Neolithic farmers who began to spread out of the Middle East some 10,000 years ago?

This debate has teetered back and forth for the past thirty years. On the basis of various genetic polymorphisms, L.L. Cavalli-Sforza and his students argued that Europeans are largely descended from Middle Eastern farmers (Ammerman and Cavalli-Sforza, 1984; Cavalli-Sforza et al., 1994). On the basis of mtDNA and Y chromosomal data, two other research teams, one led by Martin Richards and the other by Ornella Semino, maintained that the European gene pool is over 75% of native hunter-gatherer origin (Richards et al., 2000; Semino et al., 2000). If we look only at the present-day gene pool, Europeans seem far too differentiated to be the descendants of Neolithic farmers from the Middle East.

Over the last few years, new evidence has swung the debate back to the model of population replacement. By retrieving DNA from ancient skeletal remains, we can now compare the latest hunter-gatherers with the earliest farmers, and what we see is a sharp genetic divide between the two (Bramanti et al., 2009). The farmers seem to have been immigrants who replaced the hunter-gatherers. This is direct evidence, so what more is there to say? Facts are facts.

Yet there is always more to say. Facts may be illusory or, if real, wrongly interpreted. For one thing, wherever we have a fairly continuous time series of ancient DNA, the genetic divide no longer appears between the latest hunter-gatherers and the earliest farmers. It appears between the earliest farmers and somewhat later farmers. This is particularly so when we examine haplogroup U lineages, whose disappearance is widely seen as evidence for population replacement. According to a study of 92 Danish remains, these lineages remained common after the Neolithic and reached their current low prevalence only during the Early Iron Age (Melchior et al., 2010).

If this genetic divide is not solely due to population replacement, what else might be responsible? Mishmar et al. (2003) were the first to suggest natural selection:

Thus, extensive global population studies have shown that there are striking differences in the nature of the mtDNAs found in different geographic regions. Previously, these marked differences in mtDNA haplogroup distribution were attributed to founder effects, specifically the colonizing of new geographic regions by only a few immigrants that contributed a limited number of mtDNAs. However, this model is difficult to reconcile with the fact that northeastern Africa harbors all of the African-specific mtDNA lineages as well as the progenitors of the Eurasia radiation, yet only two mtDNA lineages (macrohaplogroups M and N) left northeastern Africa to colonize all of Eurasia (1, 2) and also that there is a striking discontinuity in the frequency of haplogroups A, C, D, and G between central Asia and Siberia, regions that are contiguous over thousands of kilometers. Rather than Eurasia and Siberia being colonized by a limited number of founders, it seems more likely that environmental factors enriched for certain mtDNA lineages as humans moved to the more northern latitudes.

[...] We now hypothesize that natural selection may have influenced the regional differences between mtDNA lineages. This hypothesis is supported by our demonstration of striking differences in the ratio of nonsynonymous (nsyn)/synonymous (syn) nucleotide changes in mtDNA genes between geographic regions in different latitudes. We speculate that these differences may reflect the ancient adaptation of our ancestors to increasingly colder climates as Homo sapiens migrated out of Africa and into Europe and northeastern Asia.

This hypothesis has since received support from Balloux et al. (2009):

We show that populations living in colder environments have lower mitochondrial diversity and that the genetic differentiation between pairs of populations correlates with difference in temperature. These associations were unique to mtDNA; we could not find a similar pattern in any other genetic marker. We were able to identify two correlated non-synonymous point mutations in the ND₃ and ATP6 genes characterized by a clear association with temperature, which appear to be plausible targets of natural selection producing the association with climate. The same mutations have been previously shown to be associated with variation in mitochondrial pH and calcium dynamics. Our results indicate that natural selection mediated by climate has contributed to shape the current distribution of mtDNA sequences in humans.

Humans have to adapt to two sources of warmth: climate and internal body heat, which in turn varies with lifestyle and diet. Diet in particular results in different patterns of body heat production between hunter-gatherers and farmers, as explained by Speth (1983):

One aspect of protein metabolism relevant to this issue concerns the high "specific dynamic action" (SDA) of protein ingestion. The SDA of food refers to the rise in metabolism or heat production (diet-induced thermogenesis) resulting from the ingestion of food [...] The SDA of a diet consisting largely of fat is about 6- 14%, while that of a diet high in carbohydrates is about 6%. In striking contrast, the SDA of a diet consisting almost entirely of protein may be as high as 30%; or, in other words, for every 100 calories of protein ingested, up to 30 calories are needed to compensate for the increase in metabolism. Thus, persons whose diets are high in protein experience higher metabolic rates than those whose diets are composed largely of carbohydrate. For example, members of Eskimo populations, at least 90% of whose caloric needs were traditionally met by meat and fat (cf. Draper 1980:263; Hoygaard 1941), had basal metabolic rates 13 to 33% above the DuBois standard, which is based on the metabolic rates of populations consuming western diets (Itoh 1980:285).

Conclusion

Before ancient DNA became available, the prehistory of populations had to be inferred. The age of a genetic lineage was inferred from the degree of differentiation divided by the mutation rate. Since both variables could be known only approximately, the time depths of Europe's genetic lineages were likewise known only approximately.

Ancient DNA seems to promise a clearer picture because the only source of uncertainty is the age of the skeletal material. Unfortunately, this new method is more sensitive to uncertainty from another source: natural selection. Late hunter-gatherers and early farmers had to adapt to different environments. There certainly was a genetic divide between the two, but did it result from differences in origin or from differences in natural selection?

Natural selection distorts the picture if either method is used, since both assume that mtDNA is selectively neutral. The distortion is more serious, however, with the new method, which assumes selective neutrality across the genetic divide between late hunter-gatherers and early farmers—the very moment in prehistory when this assumption is most likely to fail. The old method assumes selective neutrality throughout the entire time depth of Europe’s genetic lineages—an assumption that may indeed be true over most of that time.

Even if the lineage has no selective value in and of itself, natural selection can still distort the picture. This is especially so for mtDNA:

Selection can change allele frequency even at a locus not responsible for fitness differences. Because there is little or no recombination in mitochondrial DNA, selection at one nucleotide affects the frequencies of all other variable nucleotides for the whole molecule. Selection on the nuclear genome, particularly nuclear-encoded proteins that are imported into the mitochondrion and X-linked markers that can have a high effective linkage to mtDNA, can also cause changes in the frequencies of mtDNA haplotypes. Equally importantly, selection on any other cytoplasmically inherited traits will directly affect the frequencies of mtDNA. (Ballard and Whitlock, 2004)

This is less of a problem with nuclear DNA because of recombination, but the problem remains if the presumably neutral gene is close to another gene of high selective value.

In raising these points, I am not trying to argue that Middle Eastern farmers made no contribution to the European gene pool. There is good archaeological evidence of these farmers pushing up the Danube and into central Europe. Elsewhere, however, the evidence for population replacement becomes weaker and the evidence for continuity correspondingly stronger. This is the conclusion that Zvelebil and Dolukhanov (1991) make with respect to northern and eastern Europe:

The transition to farming occurred very slowly and took a long time to complete, the whole process lasting 1500-4000 years. In the far north and northeast of Europe, the process was never completed. [...] Local hunter-gatherer societies played a significant role in the transition. There is strong evidence for continuity in material culture in most regions throughout the transition. Although this neither proves nor disproves the case for population movement associated with the transition (small groups of people could have migrated, leaving little or no trace in the archaeological record), such evidence does not support the colonization model for the transition to farming and it does indicate that local hunter-gatherer traditions were passed on from generation to generation during the long period of the adoption of farming.

And yet the advent of farming brought massive genetic change to northern and eastern Europe, including widespread decline of haplogroup U—the sort of change that is supposed to mean massive population replacement. Since farming began to spread to this region only 6,000 years ago, even later among the Finnish and Baltic peoples, there is only a very narrow time frame in which northern and eastern Europeans could have evolved their characteristic physical appearance, assuming of course that population replacement had actually happened.

Even in central Europe, where population replacement is well documented, we are still unsure whether it was permanent or temporary. Indeed, we see evidence of the replacers being later replaced, perhaps by natives who had never disappeared from the vicinity of the farming settlements (Haak et al., 2005; Rowley-Conwy, 2011).

 

References

Ammerman, A.J. and L.L. Cavalli-Sforza. (1984). The Neolithic Transition and the Genetics of Populations in Europe, New Jersey: Princeton University Press.

Ballard, J.W.O. and M.C. Whitlock. (2004). The incomplete natural history of mitochondria, Molecular Ecology, 13, 729-744.

http://dna.ac/filogeografia/PDFs/Ballard%26Whitlock_04_MTrev.pdf

Balloux F., L.J. Handley, T. Jombart, H. Liu, and A. Manica (2009). Climate shaped the worldwide distribution of human mitochondrial DNA sequence variation. Proceedings. Biological Sciences, 276 (1672), 3447-55.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2817182/?tool=pmcentrez

Bramanti, B., M. G. Thomas, W. Haak, M. Unterlaender, P. Jores, K. Tambets, I. Antanaitis-Jacobs, M.N. Haidle, R. Jankauskas, C.-J. Kind, F. Lueth, T. Terberger, J. Hiller, S. Matsumura, P. Forster, and J. Burger. (2009). Genetic discontinuity between local hunter-gatherers and Central Europe's first farmers, Science, 326 (5949), 137-140.

http://jsarf.free.fr/palanthsci/Europe's%20First%20Farmers%20Were%20Immigrants.pdf

Cavalli-Sforza, L.L., P. Menozzi, and A. Piazza. (1994). The History and Geography of Human Genes, New Jersey: Princeton University Press.

Haak, W., P. Forster, B. Bramanti, S. Matsumura, G. Brandt, M. Tänzer, R. Villems, C. Renfrew, D. Gronenborn, K.W. Alt, and J. Burger. (2005). Ancient DNA from the first European farmers in 7500-year-old Neolithic sites, Science, 310 (5750), 1016-1018.

http://www.sciencemag.org/content/310/5750/1016.short

Melchior, L., N. Lynnerup, H.R. Siegismund, T. Kivisild, J. Dissing. (2010). Genetic diversity among ancient Nordic populations, PLoS ONE, 5(7): e11898

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0011898#pone-0011898-g002

Mishmar, D., E. Ruiz-Pesini, P. Golik, V. Macaulay, A.G. Clark, S. Hosseini, M. Brandon, K. Easley, E. Chen, M.D. Brown, R.I. Sukernik, A. Olckers, and D.C. Wallace. (2003). Natural selection shaped regional mtDNA variation in humans, Proceedings of the National Academy of Sciences (USA), 100 (1), 171-176.

http://www.pnas.org/content/100/1/171.full

Richards, M., V. Macaulay, E. Hickey, E. Vega, B. Sykes, et al. (2000). Tracing European founder lineages in the Near Eastern mtDNA pool, American Journal of Human Genetics, 67, 1251-1276.

http://www.sciencedirect.com/science/article/pii/S0002929707629541

Rowley-Conwy, P. (2011). Westward ho! The spread of agriculturalism from Central Europe to the Atlantic, Current Anthropology, 52 (S4), S431-S451.

http://arkeobotanika.pbworks.com/w/file/fetch/48307263/Rowley-Conwy%2011%20CA%20Farming%20westward.pdf

Semino, O., G. Passarino, P.J. Oefner, A.A. Lin, S. Arbuzova, et al. (2000). The genetic legacy of Paleolithic Homo sapiens sapiens in extant Europeans: A Y chromosome perspective, Science, 290, 1155-1159.

http://fboekelo.tripod.com/boekelo/GP/semino.pdf

Speth, J.D. (1983). Energy source, protein metabolism, and hunter-gatherer subsistence strategies, Journal of Anthropological Archaeology, 2, 1-31.

http://faculty.ksu.edu.sa/archaeology/Publications/Hearths/Energy%20source,%20protein%20metabolism,%20and%20hunter-gatherer%20subsistence%20strategies.pdf

Zvelebil, M. and P. Dolukhanov. (1991). The transition to farming in Eastern and Northern Europe, Journal of World Prehistory, 5, 233-278.

http://link.springer.com/article/10.1007/BF00974991

Burakumin, Paekchong, and Cagots

This is the first of a series of ebooks. You can access an Epub version here or a PDF here. Below is the foreword.

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Foreword

 

The Burakumin of Japan, the Paekchong of Korea, and the Cagots of France … What do they have in common? All three were despised castes—closed groups of people who married among themselves. A despised caste is not just a low class. Otherwise, it would always be gaining and losing members, with some moving up and out and others down and in. As Gregory Clark has shown, the English lower class is descended largely from people who were middle or even upper class a few centuries before. This may seem strange if you equate the middle class with voluntary childlessness, but until the late 19th century they were the ones who had the most children—even more so if we look only at children who lived to adulthood. The resulting demographic overflow continually spilled over into the lower class.

In contrast, not much new blood flows into a despised caste, at least not on an ongoing basis. Social stigma discourages people from marrying out or marrying in. Nor does one enter simply by virtue of being poor, since the fear of losing caste keeps out most of the downwardly mobile. Despite this lack of new blood, a despised caste can perpetuate itself indefinitely because its members usually have enough resources—through their monopoly over equally despised occupations—to get married, form families, and have enough children to replace themselves. This was not the case with urban lower classes of pre-industrial times, which typically had large numbers of childless single men.

Because a caste is closed and self-perpetuating, it may preserve genetic traits that disappear everywhere else. It thus becomes more and more different not because it is changing but because its host population is changing.

But how can a population change over a few centuries? Didn’t human nature assume its present form back in the Pleistocene when cultural evolution took over from genetic evolution? In reality, these two evolutionary processes have reinforced each other. Human genetic evolution actually accelerated 40,000 years ago and even more so 10,000 years ago, apparently in response to a growing diversity of cultural environments.

What about Richard Lewontin’s finding that human genes vary much more within populations than between populations? Isn’t that proof that genetic evolution stagnated while humans were spreading over the earth and forming the many populations we see today? Lewontin’s finding is correct but does not mean what it seems to mean. Indeed, the same genetic overlap has been found between many species that are nonetheless distinct anatomically, morphologically, and behaviorally. Genetic variation between populations differs qualitatively from genetic variation within populations. In the first case, genes vary across a boundary that separates different environments and, thus, different selection pressures. This kind of genetic variation is shaped by selection and gives rise to real phenotypic differences. The situation is something else entirely when genes vary among individuals who belong to the same population and face similar selection pressures. That kind of variation matters much less, the actual phenotypic differences often being trivial or nonexistent.

Human evolution is a logarithmic curve where most of the interesting changes have happened since the advent of farming and complex societies. Homo sapiens was not a culmination but rather a beginning … of gene-culture co-evolution. There are many ways to study this co-evolution, but one way is to look at the different evolutionary trajectories followed by castes and their host populations.

A little less brown and not necessarily blue-eyed

 

 

The skin color is about right. Not so sure about the eyes (source: Spanish National Research Council (CSIC)). There seems to have been a succession of changes to hair, eye, and skin color within a relatively restricted area of Europe. These changes then spread outward, the changes to eye color being apparently the earliest.

Ancient DNA has been retrieved from another Mesolithic hunter-gatherer, who is dated to 7,000 years ago and comes from La Braña-Arintero, Spain. We again see a strange combination of dark skin and light eyes. If we look at the three genes that produce white skin, only one of them, TYRP1, had the derived ‘European’ allele. The other two had the ancestral allele. So this Mesolithic individual was a bit lighter-skinned than the one from Luxembourg, dated to 8,000 BP, who had ancestral alleles at all three loci:

 

Of the ten variants, the Mesolithic genome carried the ancestral and non-selected allele as a homozygote in three regions: C12orf29 (a gene with unknown function), SLC45A2 (rs16891982) and SLC24A5 (rs1426654). The latter two variants are the two strongest known loci affecting light skin pigmentation in Europeans and their ancestral alleles and associated haplotypes are either absent or segregate at very low frequencies in extant Europeans (3% and 0% for SLC45A2 and SLC24A5, respectively). We subsequently examined all genes known to be associated with pigmentation in Europeans, and found ancestral alleles in MC1R, TYR and KITLG, and derived alleles in TYRP1, ASIP and IRF4. (Olalde et al., 2014)

Media reports describe the two Mesolithic individuals from Spain and Luxembourg as blue-eyed, although this is not what either study actually found. All we know is that their eyes were not brown. They had blue, gray, hazel, or green eyes:

 

[The individual had] the associated homozygous haplotype spanning the HERC2–OCA2 locus that is strongly associated with blue eye colour. Moreover, a prediction of eye colour based on genotypes at additional loci using HIrisPlex24 produced a 0.823 maximal and 0.672 minimal probability for being non-brown-eyed (Supplementary Information). The genotypic combination leading to a predicted phenotype of dark skin and non-brown eyes is unique and no longer present in contemporary European populations. Our results indicate that the adaptive spread of light skin pigmentation alleles was not complete in some European populations by the Mesolithic, and that the spread of alleles associated with light/blue eye colour may have preceded changes in skin pigmentation. (Olalde et al., 2014)

These findings seem to conflict with previous estimates of the time frame when European skin became white: 11,000 to 19,000 years ago according to Beleza et al. (2013) and 7,600 to 19,200 years ago according to Canfield et al. (2014). I would argue that this was indeed the time frame when European skin became white; however, white skin was initially confined to a geographic area that covered only part of Europe, essentially the plains of the north and east.

It also appears that the changes to hair, eye, and skin color did not happen simultaneously. First came the diversification of eye color and then the diversification of hair color. Parallel to these changes, and extending over a longer time, was the whitening of skin color. 

The most surprising—though least commented on—finding is that this Mesolithic hunter-gatherer had the ancestral allele for KITLG. According to Beleza et al. (2013), this gene was involved in the first stage of skin lightening that affected the common ancestors of Europeans and East Asians some 30,000 years ago. It looks like this first stage, like the second stage over 10,000 years later, affected Europeans only within part of Europe. The Mesolithic hunter-gatherers from Spain and Luxembourg thus seem to have belonged to a population that was peripheral to the evolution of white skin and multi-hued hair and eyes.

References

 

Beleza, S., Murias dos Santos, A., McEvoy, B., Alves, I., Martinho, C., Cameron, E., Shriver, M.D., Parra E.J., and Rocha, J. (2013). The timing of pigmentation lightening in Europeans. Molecular Biology and Evolution, 30, 24-35.

http://mbe.oxfordjournals.org/content/30/1/24.short 

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, 2059-2067.
http://www.g3journal.org/content/3/11/2059.full 

Lazaridis, I., Patterson, N., Mittnik, A., Renaud, G., Mallick, S., et al. (2013). Ancient human genomes suggest three ancestral populations for present-day Europeans, BioRxiv, December 23.

http://biorxiv.org/content/early/2013/12/23/001552.full-text.pdf+html

Olalde, I., M.E. Allentoft, F. Sanchez-Quinto, G. Saintpere, C.W.K. Chiang, et al. (2014).  Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European, Nature, early view

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12960.html

SLC24A5: Reply to Greg Cochran

 

Ethiopian manuscript paintings (source: A. Davey). Ethiopians have a self-image that is lighter-skinned than their actual selves. If the prevalence of SLC24A5 is higher in Ethiopia than the degree of admixture from lighter-skinned peoples across the Red Sea, this discrepancy may be explained by social selection for lighter skin.

 

Greg Cochran has been asking why the “European” allele for SLC24A5 has been so successful, not only in Europe but also elsewhere. He seems to be hinting that this allele has a selective advantage that is unrelated to skin color.

I posted the following comments at his website. References have been inserted for this post.

 

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SLC24A5 is one of three genes involved in the whitening of European skin, the other two being SLC45A2 and TYRP1. This whitening occurred over a relatively short time and long after the entry of modern humans into Europe some 40,000 years ago. Sandra Beleza’s team estimates that all three genes “went white” between 11,000 and 19,000 years ago (Beleza et al, 2013). Victor Canfield’s team, working only with SLC24A5, estimates between 7,600 and 19,200 years ago (Canfield et al., 2014).

So, yes, white European skin is not an adaptation to weaker sunlight; otherwise, it would have evolved much earlier. Are we looking at some pleiotropic effect then? In that case, this effect would involve not only SLC24A5 but the other two genes as well. Perhaps, but I’d like to see the evidence.

No, I’m not going to invoke sexual selection to explain the apparent success of the “European” allele for SLC24A5. Sexual selection, especially sexual selection of women, occurs under very limited circumstances.  I believe that there was an episode of very intense sexual selection of women, but this episode was confined to northern and eastern Europe during the time frame of 10,000 to 20,000 years ago, i.e., the last ice age (Frost, 2006; Frost, 2008).

How then do we explain the much greater geographic and historical success of this allele? The answer is part of the larger one of why lighter-skinned folks have done better than darker-skinned folks. Mean temperature is inversely correlated with technological complexity, even going back to the hunter-gatherer stage of cultural evolution. This is partly because colder environments created a greater need for heat conservation (by means of tailored clothing and insulated shelters) and partly because their food resources were more dispersed and typically available for short periods of time. There was thus strong selection in such environments for time budgeting, forward planning, and the ability to manage storage technologies (ice cellars) and untended facilities (traps, snares). Northern hunters were pre-adapted for technological complexity and thus better able to exploit the sort of complex cultural environments that developed much later in time (Hoffecker, 2002, pp. 6-12).

Is this the whole story? You point to Ethiopians as an example where SLC24A5 seems to be present at a higher frequency than Caucasian admixture. This discrepancy can probably be explained by social selection for lighter skin, as indicated by an Israeli study that found light-skin preference among Falasha children (Munitz et al., 1987).  This preference may be learned, although there is evidence that people are predisposed to associate lighter skin with certain good qualities (as a result of a mental algorithm that uses skin tone for identification of women and young infants) (Frost, 2011). For whatever reason, social selection for lighter skin is a reality in Ethiopia, and it probably has had some impact on the prevalence of SLC24A5.

 

Question from ‘RS’: Sexual selection, especially sexual selection of women, occurs under very limited circumstances. So you are equally attracted to all women?

 

Sexual selection is not the same thing as sexual preference. In other words, what you prefer is not necessarily what you will get. It all comes down to the law of supply and demand.

Sexual selection occurs when too many of one sex have to compete for too few of the other. Normally, the males have to compete for the females. The reverse is rare in nature. It happens when (1) males die much earlier than females do and (2) the costs of providing for a second mate are too high for almost all males. In human hunter-gatherers, the ratio of men to women on the mate market declines as one moves away from the equator. It reaches its lowest point in open steppe-tundra environments, where almost all food is obtained by men through hunting and where male mortality is high because men have to pursue mobile herds of game over long distances in a cold environment that offers few alternate sources of food.

Steppe-tundra covered most of northern and eastern Europe during the Late Pleniglacial (25,000 to 10,000 BP). I use the term “last ice age” in order to be better understood. There was a series of ice ages during the Pleniglacial (70,000 to 10,000 BP), but modern humans had to adapt only to the last one. To date, it looks like the most visible features of Europeans (white skin, multi-hued hair and eyes) evolved during the time window of the Late Pleniglacial. If the estimates by Beleza and Canfield are to be believed, the time window is somewhere between 20,000 and 10,000 BP.

These estimates seem to conflict with the recent findings of brown-skinned Mesolithic Europeans from Spain (7,000 BP) and Luxembourg (8,000 BP). I would argue that the changes to hair, eye, and skin color took place within a relatively restricted geographic area (essentially the plains of northern and eastern Europe) and later spread outward. It’s silly to argue that these changes must have originated in the Middle East, since the Middle East was inhabited by an African-like population until at least 12,000 BP. This population (the Natufians) shows no biological continuity with later Middle Easterners.

 

References

 

Beleza, S., Murias dos Santos, A., McEvoy, B., Alves, I., Martinho, C., Cameron, E., Shriver, M.D., Parra E.J., and Rocha, J. (2013). The timing of pigmentation lightening in Europeans. Molecular Biology and Evolution, 30, 24-35.

http://mbe.oxfordjournals.org/content/30/1/24.short 

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, 2059-2067.

http://www.g3journal.org/content/3/11/2059.full 

Cochran, G. (2014). Shades of pale, January 27, West Hunter

http://westhunt.wordpress.com/2014/01/27/shades-of-pale/

Frost, P. (2011). Hue and luminosity of human skin: a visual cue for gender recognition and other mental tasks, Human Ethology Bulletin, 26(2), 25-34. http://media.anthro.univie.ac.at/ISHE/index.php/bulletin/bulletin-contents 

Frost, P. (2008). Sexual selection and human geographic variation, Special Issue: Proceedings of the 2nd Annual Meeting of the NorthEastern Evolutionary Psychology Society. Journal of Social, Evolutionary, and Cultural Psychology, 2(4), pp. 169-191.

http://www.jsecjournal.com/articles/volume2/issue4/NEEPSfrost.pdf

Frost, P. (2006). European hair and eye color - A case of frequency-dependent sexual selection? Evolution and Human Behavior, 27, 85-103.

Hoffecker, J.F. (2002). Desolate Landscapes. Ice-Age Settlement in Eastern Europe. New Brunswick: Rutgers University Press.

Munitz, S., B. Priel, and A. Henik. (1987). Color, skin color preferences and self color identification among Ethiopian and Israeli born children, in M. Ashkenazi and A. Weingrod (eds.), Ethiopian Jews and Israel. (pp. 74-84). New Brunswick (U.S.A.): Transaction Books.