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THE GENOTYPIC INTELLIGENCE OF EUROPEANS DECREASED SINCE BRONZE AGE (IV)

Woodley & Piffer (2017) found an increase of three polygenic scores for educational attainment since Eurasian Bronze Age: for 130, 11 and 9 SNP. They believe this increase demonstrates the increase of the genotypic intelligence of Europeans since Bronze Age. Here are their results: https://osf.io/xwucp/

For the 9 SNP, the frequencies of EDU-increasing alleles are: TSI=39%, Bronze Age=39.27%, IBS=40%, GBR=41%, CEU=43.33%, FIN=47.56%. We can notice Ancient Eurasians outperformed today Tuscans. But today Tuscans have higher measured IQ (99) than Iberians (97), and the same measured IQ as CEU (99). But CEU have lower measured IQ than Britons (100), even if they have higher POLY_EDU_9SNP. Also, CEU have 2.33% higher frequency than GBR, and GBR have only 1.73% higher frequency than Bronze Age Eurasians. Also, 5 of the 9 SNP have lower frequencies in IBS, and 4 of the 9 SNP have lower frequencies in TSI, GBR and CEU than in Bronze Age Eurasians. Furthermore, if we eliminate one SNP (the 5th), the POLY_EDU_8SNP will be: TSI=36%, IBS=36.37%, GBR=38.13%, Bronze Age=39.13%, CEU=41%, FIN=44.88%. We can notice that scores with 9 SNP and 8 SNP give the same order of European populations of 1000 GENOMES, and only Ancient Eurasians change their position. Furthermore, if we eliminate Finns, that are not very representative Europeans, POLY_EDU_8SNP_EUR=37.88%, lower than Bronze Age Eurasians.

But even if we admit there was selection favoring high-EDU, or even high-IQ, this does not imply that genotypic EDU/IQ increased, because, if the selection pressure was not strong enough to (over)compensate the decrease of genotypic EDU/IQ by de novo mutations, these complex traits could decrease. A study published today demonstrated this possibility for another complex trait: intracranial capacity (Beiter, 2017).

Concerning the brain size, Woodley & Piffer (2017) wrote: „…declining brain volume during the Holocene may have been a consequence of enhanced brain efficiency stemming from increased corticalization and neuronal connectivity, with more bioenergetically optimized brains simply requiring less mass to achieve greater processing power. It may therefore be this process that the increase in POLY COG is tracking in our samples. A prediction stemming from Hawks (2011) is that variants that predict brain volume and not GCA should show the opposite trend in time to POLY COG when examined in the context of the present samples.” This is an erroneous prediction. The study of Beiter (2017) found positive selection for increased total intracranial volume during last 2,000 years, even if the intracranial capacity decreased during this period. The classical study of Henneberg (1988) found a 53 ml decrease of men’s intracranial capacity since Roman Period to Modern Period. Concerning women, the decrease is even higher: 150 ml since Iron Age, and 111 ml since Early Middle Age. Even if the frequency of the intracranial capacity-increasing alleles increased, the genotypic intracranial capacity decreased because of the accumulation of de novo mutations favoring the decrease of the brain size. But the mutational target for intelligence is significantly larger than for brain size, and this implies that the selection pressure on intelligence must be much higher than selection on brain size to maintain the actual level of the trait.

Although, the polygenic score on common polymorphism of a complex trait must increase to maintain the actual level of the this trait, because of de novo mutations that decrease the trait. The increase of the polygenic score equates with selection for the complex trait, but does not equate with the genotypic increase of the trait. If the selection is not strong enough, we can expect a genotypic decrease of the trait. The result of the study of Beiter (2017) concerning intracranial capacity sustains the true of this reasoning. We must keep always in our minds that selection on a complex trait does not signify the genotypic increase of the trait.

Also, Woodley & Piffer (2017) wrote: „Furthermore, studies utilizing POLY COG have found that it does not predict variation in brain volume, despite both POLY COG and brain volume making independent contributions to GCA (Deary et al., 2016).” But… absence of evidence is not evidence of absence. A study published few days ago (Savage, 2017) demonstrated that POLY_IQ predicts (0.263) intracranial volume. Furthermore, another very recent study (Lam, 2017) demonstrated that only half of SNP found by Okbay’s GWAS (and probably half of the 130 SNP of Woodley & Piffer) are IQ-increasing alleles.

The other half probably are (self)domestication-increasing alleles that have higher frequencies in populations that have a more ancient entrance in Neolithic and Complex Civilization. (For example, TSI and IBS have higher POLY_EDU on 161 SNP of Okbay (Piffer, 2016) than GBR and CEU, despite their lower measured IQ and their lower POLY_IQ (Piffer, 2017) on 15 lead SNP found by the GWAS on IQ of Sniekers (2017). Also, SAS have roughly the same POLY_EDU as EUR on 161 SNP (Piffer, 2016), despite a gap of more than 15 point between their measured IQ.) Most of Eurasian Bronze Age samples of Woodley & Piffer (2017) were Central Asians and Norhern Europeans that had a later entrance in Neolithic, and many of them were pastoralists that never entered in Neolithic. It is expected they had fewer (self)domestication-increasing alleles even than their contemporary Southern Europeans, not only than the today Europeans. Although, POLY_EDU on 130 SNP do not reflect differences of measured IQ or differences of POLY_IQ on 15 SNP of Sniekers of today European populations: Bronze Age=44.77%, TSI=46.88%, CEU=46.94%, GBR=47.07%, IBS=47.44%, FIN=47.48%.

But, even if there was selection favoring high-IQ, the selection pressure was not strong enough to compensate barely the decrease of the genotypic intelligence produced by de novo mutations that produce ADHD (Cretan, 2017).

Unfortunately, Beiter (2017) did not search polygenic selection for EDU or IQ. Concerning psychiatric diseases, Beiter (2017) found selection only against schizophrenia during last 2,000 years. It means the genotypic risk for the other psychiatric disorders increased, by the accumulation of de novo mutations favoring these diseases, and this fact probably produced a decrease of the genotypic intelligence during this period.

 

REFERENCES

 

Beiter, E.R. et al. (2017) Polygenic selection underlies evolution of human brain structure and behavioral traits. bioRxiv doi: http://dx.doi.org/10.1101/164707.

 

Cretan, C. (2017) https://constantincretan.wordpress.com/2017/03/19/the-genotypic-intelligence-of-europeans-decreased-since-bronze-age/

 

Henneberg, M. (1988). Decrease of human skull size in the Holocene. Human Biology 60: 395-405.

 

Lam, M. et al. (2017) Large-scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets. bioRxiv doi: http://dx.doi.org/10.1101/176842 .

 

Piffer, D. (2016). Polygenic selection on educational attainment: a replication. https://figshare.com/article/Polygenic_selection_on_educational_attainment_a_replication/3381439

 

Piffer, D. (2017)  2017 Intelligence GWAS: Group-level polygenic scores http://rpubs.com/Daxide/279148

 

Savage, J.E. et al. (2017) GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence. bioRxiv doi: http://dx.doi.org/10.1101/184853.

 

Sniekers, S. et al (2017) Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics doi:10.1038/ng.3869

 

Woodley, M.A. et al. (2017) Holocene selection for variants associated with cognitive ability: Comparing ancient and modern genomes. bioRxiv http://dx.doi.org/10.1101/109678

Anunțuri

ARE PSYCHIATRIC DISORDERS THE RESULT OF POSITIVE SELECTION OF VARIANTS THAT FAVOR SELF-DOMESTICATION?

A very recent large-scale cognitive GWAS meta-analysis (Lam, 2017) found 70 independent genomic loci associated with intelligence. Even more exciting, this meta-analysis found that only half of the EDU-increasing SNP found by the largest GWAS on EDU (Okbay, 2016) are IQ-increasing variants. Probably the other half of the EDU-increasing SNP are (self-)domestication-increasing variants. Perhaps these variants were under strong positive selection especially after the entrance in Neolithic and in Complex Civilization of populations. It could explain why Piffer (2016) found the lowest POLY_EDU in Africans and Amerindians, the last populations that entered in Neolithic and complex civilization. Also, if two populations have the same POLY_EDU, it is expected that the population that firstly started agriculture has more variants associated with self-domestication, and fewer variants associated with intelligence. Also, the higher POLY_EDU of today Europeans than Bronze Age Europeans, found by Woodley & Piffer (2017), could be explained by the increase of frequency of variants associated with self-domestication, and not by the raise of frequency of IQ-increasing variants.

Another very recent study (Davies, 2017) found 99 genomic loci associated with intelligence. Also, this study found important negative genetic correlations between intelligence and psychiatric disorders: Alzheimer’s disease (-0.38), ADHD (-0.36), major depressive disorder (-0.30), schizophrenia (-0.25), neuroticism (-0.16), bipolar disorder (-0.09). These correlations are stronger that correlations found by the GWAS on IQ of Sniekers (2017), that found also negative genetic correlations of intelligence with depressive symptoms (-0.27), anxiety (-0.19) and insomnia (-0.14). Furthermore, a recent study (Mullins, 2017) found a positive correlation between fertility and POLY_ADHD (0.15) and POLY_MDD (0.04) in today healthy Icelanders, that is in line with selection against high IQ in today Europeans. Also, Mullins (2017) found a negative correlation between fertility and POLY_ASD (-0.25) even in healthy individuals, but ASD is the only psychiatric disorder that is positively correlated with intelligence at genetic level.

Probably the selection on intelligence was associated with selection on neuropsychiatric disorders in the past too. A very interesting recent paper (Berens, 2017) evaluated the genotypic health of ancient humans by polygenic risk scores for diseases. All ancient populations had lower risk scores than today humans for neuropsychiatric diseases. It means there were not selection against the genetic risk of mental disorders or even this polygenic risk was positively selected. It is possible a high polygenic risk for neuropsychiatric disorders favored the self-domestication of humans, because the domestication is, in fact, a disease of the neural crest (Wilkins, 2014). The GWAS on EDU of Okbay (2016) found positive genetic correlations between educational attainment and bipolar disorder (0.28) and schizophrenia (0.10), despite the negative correlations of both diseases with intelligence.  Furthermore, polygenic scores for schizophrenia and educational attainment are associated with behavioural problems in early childhood in the general population (Jansen, 2017). Benitez-Burraco (2017) demonstrated that people with schizophrenia exhibit more marked domesticated traits at the morphological, physiological, and behavioral levels. Also, he found that genes involved in domestication and neural crest development and function comprise nearly 20% of schizophrenia candidates, most of which exhibit altered expression profiles in the brain of patients, specifically in areas involved in language processing.

If the selection on intelligence was (negatively) associated with selection on mental disorders during last thousands of years, and if POLY_PSY increased since Palaeolithic period, I expect POLY_IQ did not significantly increase or even decreased during the same period. Although, the frequency of 122 SNP that favor schizophrenia predicts better the measured IQ of superpopulations in 1000 GENOMES (Cretan, 2017) than the frequency of 15 lead SNP found by the GWAS of Sniekers (Piffer, 2017).

 

REFERENCES

 

Benitez-Burraco, A. (2017) Schizophrenia and Human Self-Domestication: An Evolutionary Linguistic Approach. Brain Behav Evol. 89(3):162-184. doi: 10.1159/000468506.

 

Berens, A.J. et al. (2017) The genomic health of ancient hominins. bioRxiv doi: http://dx.doi.org/10.1101/145193 .

 

Cretan, C. (2017) https://constantincretan.wordpress.com/2017/07/22/new-evidence-for-the-decrease-of-genotypic-intelligence-since-the-palaeolithic-period/

 

Davies, G. et al. (2017) Ninety-nine independent genetic loci influencing general cognitive function include genes
associated with brain health and structure (N = 280,360). bioRxiv doi: http://dx.doi.org/10.1101/176511 .

 

Jansen, P.R. et al. (2017) Polygenic scores for schizophrenia and educational attainment are associated with behavioural problems in early childhood in the general population. The Journal of Child Psychology and Psychiatry DOI: 10.1111/jcpp.12759

 

Lam, M. et al. (2017) Large-scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets. bioRxiv doi: http://dx.doi.org/10.1101/176842 .

 

Mullins, N. et al. (2017) Reproductive fitness and genetic risk of psychiatric
disorders in the general population. NATURE COMMUNICATIONS | 8:15833 | DOI: 10.1038/ncomms15833.

 

Okbay, A. et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533: 539-542.

 

Piffer, D. (2016). Polygenic selection on educational attainment: a replication. https://figshare.com/article/Polygenic_selection_on_educational_attainment_a_replication/3381439

 

Piffer, D. (2017)  2017 Intelligence GWAS: Group-level polygenic scores http://rpubs.com/Daxide/279148

 

Sniekers, S. et al (2017) Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics doi:10.1038/ng.3869

 

Wilkins, A.S. et al. (2014) The “Domestication Syndrome” in Mammals: A Unified Explanation Based on Neural Crest Cell Behavior and Genetics. 

 

Woodley, M.A. et al. (2017) Holocene selection for variants associated with cognitive ability: Comparing ancient and modern genomes. bioRxiv http://dx.doi.org/10.1101/109678

 

 

NEW EVIDENCE FOR THE DECREASE OF THE GENOTYPIC INTELLIGENCE SINCE THE PALAEOLITHIC PERIOD

In my article published in Mankind Quarterly (Cretan, 2016), I predicted that selection for high intelligence must parallel selection against psychiatric disorders: „Apart from intelligence, a good indicator for brain functioning in a population is the prevalence of mental disorders. A study of the prevalence of psychiatric disorders in the USA, in the non-institutionalized population aged 15-54, found that nearly 50% of respondents reported at least one lifetime disorder and 30% reported at least one 12-month disorder. More than half of lifetime disorders occurred in the 14% of the population who had a history of at least three comorbid disorders (Kessler et al., 1994). Even if the 14% (of those with disorders) representing alcohol-related pathology are excluded, the prevalence remains very high. A meta-analysis of 27 studies with a combined sample size of 150,000 subjects aged 18 to 65, from 16 European countries, found that 27% had been affected by at least one mental disorder in the last 12 months (Wittchen & Jacobi, 2005). Personality disorders, being life-long, have an over 9% prevalence in the US population (Lezenweger, 2007). However, a Finnish study reveals a total prevalence of mental disorders of only 17.4% (Lethinen et al., 1990). The heritability for each of these disorders is at least 40% (Burmeister, McInnis & Zöllner, 2008).
Should natural selection have managed to increase intelligence in the last 10,000 years, then it should have also decreased the prevalence of mental disorders in the same period. In this case, the Mesolithic population must have had a high rate of psychiatric disorders in addition to lower intelligence. However, it is the Finns who, being genetically closest to European Mesolithic hunter-gatherers and most distant from the Neolithic farmers, were the last to abandon the hunter-gatherer lifestyle and had the least time available to adapt to modern life, who have the lowest prevalence of mental disorders.”

Although, the GWAS on IQ of Sniekers (2017) found a negative genetic correlation between intelligence and Alzheimer’s disease (-0.36), depressive symptoms (-0.27), ADHD (-0.27), schizophrenia (-0.20), anxiety (-0.19), neuroticism (-0.19), insomnia (-0.14), major depressive disorder (-0.11), Parkinson’s disease (-0.01) and bipolar disorder (-0.01). Positive correlations were found only with ASD (0.21) and anorexia nervosa (0.08).

At least concerning schizophrenia, my prediction seems be correct. Ohi (2017) published the frequencies of 122 SNP that favor schizophrenia in continental populations of 1000 GENOMES. I calculated a risc score on schizophrenia (POLY_SCZ) for these populations, and I found a negative correlation between POLY_SCZ and measured IQ. (I added the frequencies of each allele in a population and I divided by 122, the number of SNP. I obtained an average frequency of a SNP in each population.) Here are the results:

AFR=0.3040 (IQ=69)

SAS=0.2926 (IQ=82)

AMR=0.2915 (IQ=85)

EUR=0.2886 (IQ=99)

EAS=0.2776 (IQ=104)

The average frequency in populations of an IQ-increasing SNP is also around 0.3, for the 15 lead SNP of Sniekers (Piffer, 2017):

BEB=0.29

ACB=0.30

CDX=0.31

CHB=0.335

For comparison, here are POLY_IQ on 15 SNP (Piffer, 2017; Sniekers, 2017) and POLY_EDU on 161 SNP (Piffer, 2016; Okbay, 2016):

AFR – 0.4714 – 0.4804

SAS – 0.4439 – 0.5038

AMR – 0.4515 – 0.4890

EUR – 0.4627 – 0.5102

EAS – 0.5129 – 0.5234

We can observe that POLY_SCZ predicts better the measured IQ than POLY_IQ and POLY_EDU.

Only my scores, based on POLY_IQ/EDU and the number of rare alleles per genome, predicts the measured IQ of populations (excepting AMR) like POLY_SCZ (https://constantincretan.wordpress.com/2017/07/01/quantifying-the-differences-of-genotypic-intelligence-and-genotypic-education-between-superpopulations-in-1000-genomes-two-simple-empirical-formulas-based-on-the-results-of-david-hill/).

Srinivasan (2016) found that Neanderthals had a lower „POLY_SCZ” than today humans. This fact suggests that Neanderthals could have a higher intelligence than today humans. Also, Cro-Magnons, that had higher amounts of Neanderthal admixture than today humans (Fu, 2016), could have a higher genotypic intelligence than today humans.

But, concerning today populations, the amount of Neanderthal admixture (Sankararaman, 2016) does not parallel the POLY_SCZ and the measured IQ of continental populations. It means that after the admixture of humans and Neanderthals there were different selection pressures on intelligence and on psychiatric disorder risk of continental populations. Although, Wong (2017) observed variation in allelic differentiation between populations at tissue-specific expression quantitative trait loci (eQTL), with greatest effects found for genes expressed in a region of the brain that has been linked to schizophrenia and bipolar disorder. Consistent with this, genome-wide association study regions also showed high levels of population differentiation for these diseases. The most parsimonious explanation for this high differentiation found by Wong (2017) and by different GWAS is a relaxed selection on intelligence and psychiatric disorder risk, at least for some of continental populations. This is in line with the study of Racimo (2017), that found selection on educational attainment only for East Asians and only before Holocene period. Also, POLY_SCZ is part of mutational load. The mutational load is due 90% of common polymorphism (Henn, 2016). Furthermore, the mutational load increases with distance from Africa (Henn, 2016). But East Asians do not have the the highest POLY_SCZ, but the lowest. Hence, in East Asians the selection against psychiatric disorders (and favoring high intelligence) was the strongest between all continental populations.

In my opinion, all these facts represent new evidence for the decrease of the genotypic intelligence of humans since Palaeolithic period.

 

REFERENCES

Cretan, C. (2016) Was the Cro-Magnon the Most Intelligent Modern Human? MANKIND QUARTERLY 57:2 158-195

Cretan, C. (2017) https://constantincretan.wordpress.com/2017/07/01/quantifying-the-differences-of-genotypic-intelligence-and-genotypic-education-between-superpopulations-in-1000-genomes-two-simple-empirical-formulas-based-on-the-results-of-david-hill/

Fu, Q. et al (2016) The genetic history of Ice Age Europe. Nature 534: 200–205 doi:10.1038/nature17993

Henn, B. et al (2016) Distance from su-Saharan Africa predicts mutational load in diverse human genomes. PNAS 113(4): E440-449 doi:  10.1073/pnas.1510805112

Ohi, K. et al (2017) Variability of 128 schizophrenia-associated gene variants
across distinct ethnic populations. Transl Psychiatry 7, e988; doi:10.1038/tp.2016.260

Okbay, A. et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533: 539-542.

Piffer, D. (2016). Polygenic selection on educational attainment: a replication. https://figshare.com/article/Polygenic_selection_on_educational_attainment_a_replication/3381439

Piffer, D. (2017)  2017 Intelligence GWAS: Group-level polygenic scores http://rpubs.com/Daxide/279148

Sniekers, S. et al (2017) Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics doi:10.1038/ng.3869

Racimo, F. et al (2017) Detecting polygenic adaptation in admixture graphs. bioRxiv doi: http://dx.doi.org/10.1101/146043

Sankararaman, S. et al (2016) The Combined Landscape of Denisovan and Neanderthal Ancestry in Present-Day Humans. Current Biology, http://dx.doi.org/10.1016/j.cub.2016.03.037

Srinivasan, S. et al (2016) Genetic Markers of Human Evolution Are
Enriched in Schizophrenia. Biological Psychiatry 80:284–292

Wong, E.S. & Powel, J.E. (2017) Allelic differentiation of complex trait loci across human populations. bioRxiv doi: http://dx.doi.org/10.1101/126888 .

 

 

 

QUANTIFYING THE DIFFERENCES OF GENOTYPIC INTELLIGENCE AND GENOTYPIC EDUCATION BETWEEN (SUPER)POPULATIONS IN 1000 GENOMES: TWO SIMPLE EMPIRICAL FORMULAS BASED ON THE NEW RESULTS OF DAVID HILL

Polygenic scores on intelligence do not reflect differences on genotypic intelligence between populations and superpopulations (Piffer, 2017; Sniekers, 2017). Polygenic scores on educational attainment (Piffer, 2016; Okbay, 2016) work slightly better, but British and Utah Whites have the same scores as South Asians, for example.

Based on the last revised study of David Hill (5 june 2017), that found MAF 0.001-0.01 account for 45% of genotypic IQ (G) and for 33% of genotypic EDU (E), I created two scores of populations and superpopulations of 1000 GENOMES for G and E:

G_POP = (POLY_IQ_POP : POLY_IQ_GBR x (1 + gdGBR) x 55% + MAF0.000-0.005_GBR : MAF0.000-0.005_POP x 45%) x IQ_GBR

E_POP = (POLY_EDU_POP : POLY_EDU_GBR x (1+ gdGBR) x 67% + MAF0.000-0.005_GBR : MAF0.000-0.005_POP x 33%) x IQ_GBR

I used MAF 0.000-0.005 (1000 GENOMES, 2015 – Extended Data, Figure 3b) as a proxy for MAF 0.001-0.01.

gdGBR is the genetic distance on SNP from British: 0 for Europeans, 0.060 for South Asians, 0.112 for East Asians and 0.176 for Africans. I used these genetic distances because not all SNP of found by GWAS on Europeans are IQ-increasing and EDU-increasing variants in other populations. Also, the other populations have IQ-increasing and EDU-increasing SNP that are neutral for Europeans. Higher is the genetic distance of a population, higher is this number of SNP that are not captured by POLY-IQ and POLY-EDU of Europeans.

These scores work much better than POLY-IQ and POLY-EDU, excepting populations that have Native American admixture. The explanation is simple. MAF 0.00-0.005 (1000 GENOMES, 2015) arose during the severe bottleneck (23,000-16,000 years ago) and the fast grow (since 16,000 years ago) of Amerindian populations. In Native Americans, many SNP that arose during this period reached high frequencies, hence they have higher than 0.005 frequency even in 1000 GENOMES total samples, and are not captured by my two formulas.

I used POLY-EDU and POLY-IQ found by Piffer (2016, 2017) using the results of GWAS of Okbay (2016) and Sniekers (2017), 161 SNP and 15 SNP respectively.

Here are the results for G, E, and measured IQ of populations and superpopulations. The correlations between G, E and measured IQ are much higher than for POLY-IQ and POLY-EDU:

 

 

POP        G           E         IQ

 

CHB – 105.27 – 104.56 – 105

CHS  – 104.98 – 105.17 – 105

JPT  –  104.42 – 103.29 – 105

KHV – 100.74 – 102.05 –  99

CDX – 100.26 – 102.24

EAS  – 103.06 – 103.42 – 103.50

 

FIN – 101.37 – 102.62 – 101

GBR   –  100 – 100 – 100

CEU  – 97.94 – 97.29 – 99

IBS   –  95.44 – 98.61 – 97

TSI   –  94.54 – 97.57 – 99

EUR  – 97.72 – 99.13 – 99.2

 

GIH – 91.72 – 94.98

PJL  – 86.91 – 93.89 – 84

STU – 85.05 – 91.91 – 79

BEB  – 84.59 – 93.02 – 81

ITU  – 83.74 – 92.60

SAS – 86.34 – 93.25 – 81.33

 

ASW – 81.98 – 87.53 – 85

ACB  – 81.64 – 86.95 – 83

YRI   – 81.27 – 86.71 – 71

ESN   – 80.70 – 86.02 – 71

MSL  – 80.51 – 82.91 – 64

GWD – 80.37 – 86.23 – 62

LWK  – 75.70 – 83.25 – 74

AFR   – 80.08 – 85.52 – 68.5

 

REFERENCES

1000 GENOMES (2015) https://www.nature.com/nature/journal/v526/n7571/full/nature15393.html

Hill, D.W. et al. (2017) Genomic analysis of family data reveals additional genetic effects on intelligence and personality. bioRxiv http://dx.doi.org/10.1101/106203

Okbay, A. et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533: 539-542.

Piffer, D. (2016). Polygenic selection on educational attainment: a replication. https://figshare.com/article/Polygenic_selection_on_educational_attainment_a_replication/3381439

Piffer, D. (2017)  2017 Intelligence GWAS: Group-level polygenic scores http://rpubs.com/Daxide/279148

Sniekers, S. et al (2017) Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics doi:10.1038/ng.3869

NEW EVIDENCE FOR THE DECREASE OF THE GENOTYPIC INTELLIGENCE DURING HOLOCENE: A RECENT STUDY OF DAVID HILL

David Hill republished, in 5 june 2017, an extremly interesting revised study in bioRxiv:

In Table 3 page 21, David Hill presents the variance of intelligence and education explained by minor alleles with different frequencies, that demonstrates different genetic architectures for the two complex traits, despite many authors use educational attainment as a proxy for intelligence.

The most interesting fact is that MAF 0.001-0.01 account for 44% of genotypic IQ and only for 33% of genotypic EDU. The MAF 0.001-0.01 have arose between 25,000 and 5,500 years ago (Zwick, 2001). There are much more MAF 0.001-0.01 that decrease than increase IQ and EDU. The very high percentage of variance explained by both of them could indicate a relaxed selection on the two traits since Last Glacial Maximum, and a relatively weaker selection on genotypic IQ than on genotypic EDU. This decrease of the selection parallels the decrease of brain size last 25,000 years (Ruff, 1997).

Another very interesting fact is that MAF 0.1-0.2 account for significantly less variance of IQ than all other MAF. MAF 0.1-0.2 arose between 325,000 and 200,000 years ago (Zwick, 2001), and their uniformly distribution in humans suggests this period was the period with the strongest selection on intelligence in humans. This is the period of emergence of Homo Sapiens. Also, brain size increased at 1450 cmc, larger than today brains. The penultimate place in explaining differences of IQ is occupied by MAF 0.01-0.1 (that arose between 200,000 and 25,000 years ago (Zwick, 2001)) suggesting during this period the selection on intelligence also was strong, and purifying selection uniformised the distribution of these MAF in humans.

Also, these MAF 0.01-0.1 and MAF 0.4-0.5 (that arose between 550,000 and 475,000 years ago (Zwick, 2001)) account for significantly less variance of EDU than all other MAF, suggesting between 200,000 and 25,000 years ago and between 550,000 and 475,000 years ago the selection for EDU was stronger than during other periods. Upper Palaeolithic selection on EDU is in line with the results of Racimo (2017), that found selection in East Asians before Holocene. Also, around 500,000 years ago emerged archaic humans, and brain sizes expanded from 900 cmc to 1300 cmc. Between 200,000 and 25,000 years ago there was strong selection on both EDU and IQ. During this period the brain size of humans peaked at 1600 cmc.

The lower decrease of selection on EDU than on IQ during Holocene could be explained if we admit that there are two types of variants that favor high-EDU: the first are shared variants that favor high-IQ, and the second are variants that favor high (self-)domestication of humans. Many of variants that favor high domestication favor low-IQ too. During Holocene variants that favor high-IQ decreased, and variants that favor low-IQ but high domestication increased. By this way, genotypic IQ decreased more than genotypic EDU during Holocene.

Concerning MAF younger than MAF studied by the article of David Hill, an older paper estimated that 81.4% of the rare protein-altering SNVs found in Americans with European origins originated in the last 5,000 years, and 14.4% of these SNVs are deleterious. 91.2% of the deleterious alleles originated in the last 5,000 years (Fu et al., 2013).

Although, Spain (2015) found fewer rare SNP in exome of people with very high IQ. Also, the IQ of superpopulations in 1000 GENOMES parallel the proportion of individuals that carry uncommon minor SNP (MAF frequency lower than 5%) in genes (Rao, 2017).

Probably future studies of David Hill will produce even stronger proofs for the decrease of genotypic intelligence since Palaeolithic period.

REFERENCES

Fu, W., O’Connor, T.D., Jun, G., Kang, H.M., Abecasis, G., Leal, S.M.,… & Akey, J.M. (2013). Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493: 216-220.

Hill, D.W. et al. (2017) Genomic analysis of family data reveals additional genetic effects on intelligence and personality. bioRxiv http://dx.doi.org/10.1101/106203

Racimo, F. et al (2017) Detecting polygenic adaptation in admixture graphs. bioRxiv doi: http://dx.doi.org/10.1101/146043

Rao, A.R. & Nelson, S.F. (2017) Calculating the statistical significance of rare variants causal for Mendelian and complex disorders. bioRxiv doi: http://dx.doi.org/10.1101/103218

Ruff, C.B., Trinkaus, E. & Holliday, T.V. (1997). Body mass and encephalisation in
Pleistocene Homo. Nature 387: 173-176.

Spain, S.L., Pedroso, I., Kadeva, N., Miller, M.B., Plomin, R. & Simpson, M.A. (2015). A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence. Molecular Psychiatry 21: 1145-1151.

Zwick, M.E. et al (2001) Genetic Variation Analysis of Neuropsychiatric Traits. in Methods in Genomic Neuroscience Chin, H.R., Moldin, S.O. (editors) CRC Press LLC

 

THE GENOTYPIC INTELLIGENCE OF EUROPEANS DECREASED SINCE BRONZE AGE (III)

Between the 70 de novo mutations (DNM) 1.4% are in exome, 1% are nonsynonymous and 0.4% are synonymous. For 70 de novo SNP (and 5 de novo indels), an individual will have 75×1.4%=1.05 total de novo mutations in exome, 75×1%=0,75 de novo nonsynonymous mutations, and 0,30 de novo synonymous mutations.
The percentages of nonsynonymous, synonymous and total SNP in exome between total SNP in genome are around: 0.28%, 0,32% and 0,6% respectively (very slightly higher in non-Africans than in Africans), significantly lower than percentages of de novo mutations (1000 GENOMES, 2015). At an accumulation rate of 70 SNP per generation, the oldest SNP of human genome have at least 1.5 millions years.
It means 74% of de novo nonsynonymous mutations, 20% of de novo synonymous mutations and 47% of all de novo mutations in exome were eliminated each generation by natural selection. But it means that at least 47% of de novo mutations in exome (in one DNA base by two!) are deleterious enough to be eliminated by selection. At this strength of selection pressure on exome, only 53% of individuals in each generation could reproduce, and this individuals must have 4 surviving offspring to maintain the actual number of population. Clark & Hamilton (2006) found between 2.2 and 3.2 surviving children for all economic and social classes in pre-Industrial England, and this is far to strength of selection that operated during last 1.5 millions years.
Also, al least 8.2% of the human genome sequence is functionally significant and selectively constrained (Rands, 2014). Hence, each individual will have, on average, 8.2% x 75 =6.1 deleterious DNM.
According with The Omnigenic Theory of Boyle, Li & Pritchard (2017), it means that 35% of people could have one de novo mutation detrimental for intelligence in the exome, because 15,000 genes (75% of all genes) are expressed in the brain. Although, Spain (2015) found fewer rare SNP in exome of people with very high IQ. Also, the IQ of superpopulations in 1000 GENOMES parallel the proportion of individuals that carry uncommon minor SNP (MAF frequency lower than 5%) in genes (Rao, 2017). Furthermore, according to Omnigenic Theory, all humans have 6 IQ-decreasing DMN in non-coding genome. This signify that polygenic score on IQ/EDU must increase to maintain the genotypic IQ/EDU at the actual level, and even POLY IQ/EDU can increase and genotypic IQ/EDU can decrease, despite selection for high-IQ.
During 100 generations, the accumulation of the IQ-decreasing DMN in non-coding genome will be 600 SNP. If the average effect of an IQ-decreasing DMN equates the average effect of an IQ-increased SNP found by GWAS, the POLY IQ must increase with 600 SNP after 100 generations to maintain the actual level of genotypic IQ. If POLY IQ/EDU contains 30,000 SNP, this signifies a 2% increase of POLY IQ/EDU.
Woodley & Piffer (2017) found a 2% increase of POLY EDU during 114 generations, since Bronze Age.
REFERENCES

 

Boyle, E.A. et al (2017) An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell 169(7): 1177-1186 DOI: http://dx.doi.org/10.1016/j.cell.2017.05.038

Clark, G. & Hamilton, G. (2006) Survival of the Richest: The Malthusian Mechanism in Pre-Industrial England. The Journal of Economic History 66(3): 1-30

Rands, C.M. et al (2014) 8.2% of the Human Genome Is Constrained: Variation in Rates of Turnover across Functional Element Classes in the Human Lineage. PLoS Genet 10(7): e1004525. doi:10.1371/journal.pgen.1004525

Rao, A.R. & Nelson, S.F. (2017) Calculating the statistical significance of rare variants causal for Mendelian and complex disorders. bioRxiv doi: http://dx.doi.org/10.1101/103218

Spain, S.L., Pedroso, I., Kadeva, N., Miller, M.B., Plomin, R. & Simpson, M.A. (2015). A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence. Molecular Psychiatry 21: 1145-1151.

Woodley, M.A. et al. (2017) Holocene selection for variants associated with cognitive ability: Comparing ancient and modern genomes. bioRxiv http://dx.doi.org/10.1101/109678

 

THE EASTERN ASIAN CRO-MAGNON THAT LIVED DURING THE LAST GLACIAL MAXIMUM WAS THE MOST INTELLIGENT HUMAN

Davide Piffer published the polygenic scores resulted from 15 leading SNP found by the most recent GWAS on intelligence (Sniekers, 2017):  http://rpubs.com/Daxide/279148

These scores suggest no significant increase of the intelligence of Western Eurasians since Out of Africa. Today differences in intelligence between Africans and Europeans are due rather of a higher decrease of intelligence of Africans than Europeans since Out of Africa.

The POLY IQ of super-populations from 1000 GENOMES are: AFR-0.4714, SAS-0.4439, EUR-0.4627, EAS-0.5129, AMR-0.4515.

All of these 15 SNP increase the intelligence of Europeans. It is expected fewer of them increase the intelligence of SAS, even fewer of EAS and the fewest of AFR, because genetic distances and times since divergence between these populations.

We can suppose that in the moment of Out of Africa, the Africans and the future non-Africans have roughly the same polygenic scores on these 15 SNP. We can suppose the polygenic score increase during last 60,000 years in all populations. If non-Africans ancestors are also the ancestors of LWK (today LWK have the lowest polygenic score of today Africans: 0.4567), the increase of the polygenic score in EUR is only 0.01 higher than the increase of LWK during last 60,000 years. But if the ancestors of the non-Africans are not the ancestors of LWK, the frequency of these 15 variants increased more in Africans than in Europeans since the Out of Africa, because all the other today Africans (excepting LWK) have higher polygenic scores than the average of Europeans. Also, the frequency increased more in Africans than in South Asians. This results are in line with polygenic scores on EDU, obtained with the 74 SNP found by the GWAS of Okbay (2016): ASW have the same score with IBS and TSI, and higher score than CEU and GBR. Also, CEU have lower score than all Africans, excepting LWK. (The GWAS on EDU find not only IQ-increasing SNP, but also SNP that favor „domestication”, and these common variants spread due of civilization. For example, peoples that entered faster in Neolithic and complex civilization, like IBS and TSI, have 0.01-0.02 higher scores on EDU-SNP and 0.02 lower scores on IQ-SNP than peoples that had latter entrance in sedentary civilizations, like CEU, GBR, FIN.)

Althought, Racimo (2017) found selection on polygenic score on educational attainment only for EAS, and not for other populations or super-populations of 1000 GENOMES, and this selection in EAS produced before 10,000 years ago. If there was not positive selection on EDU, the genotypic EDU decreased due of the increase of rare variants detrimental to EDU, produced by de novo mutations. It is necessary a positive selection on a complex trait (and an increase of polygenic score) to maintain this trait at the actual level. Hence, the result of Racimo (2017) demonstrates that genotypic EDU decreased in all populations during Holocene, and decreased in all (super)populations excepting EAS between Out of Africa and Holocene. But it is probable there were some periods of selection on EDU in Europeans during last 60,000 years, because all GWAS did not find any common SNP that decreases barely 1 or to 2 IQ points the genotypic EDU, hence the IQ-decreasing SNP did not reach the frequency of common polymorphism in Europeans. The alternation of periods of positive selection and periods of negative selection could explain too the fact, noticed by Piffer (2016), that there are 45% ancestral alleles between IQ-increasing SNP found by the GWAS of Okbay (2016). During periods of selection against high intelligence, IQ-decreasing variants could reach the frequency of common polymorphism. Also, EUR have fewer IQ-increasing ancestral alleles than EAS, and this fact could be due of the spread of IQ-decreasing derived alleles during longer (or stronger) periods of negative selection on IQ in EUR than in EAS.

Although, the selection against high-IQ did not produce only after Industrial Revolution. De la Croix (2017) found Upper class (presumed also the most intelligent social class) had the lowest fertility in pre-industrial England.

It would be very strange a higher increase of the frequency of some neutral alleles in Africans, and a lower increase of the same beneficial alleles in Europeans during 2,000 generations. It is more probable these 15 SNP (and the other IQ-increasing SNP) were under a soft selection in Europeans, and partially in other populations, the most of last 60,000 years. It is more probable too these IQ-increasing common SNP were selected against in all the populations (mostly in Europeans, South Asians and Native Americans) during some periods that favored selection against high-intelligence: Neolithic transition, entrance in complex civilizations and Industrial Revolution. This selection against high intelligence decreased the polygenic scores more in Europeans, South Asians and Amerindians than in Africans, because many of these alleles are not related to intelligence in Africans and are not influenced by the selection against high-IQ. During periods of soft selection for high-IQ, like in European Metal Ages and Medieval Age (Woodley & Piffer, 2017), the polygenic score could increase even if genotypic intelligence remains constant or even decreases, because the detrimental effect of de novo mutations is partially compensated by the increase of polygenic score, and only partially compensated by elimination of deleterious de novo mutations and other rare alleles. The frequency of these common SNP could increase even if the selection on intelligence is zero, due of pleiotropy of some of them, and of selection on other complex traits.

Even if not all these 15 SNP increase the IQ of East Asians, their polygenic score is 0.05 higher than in Europeans. This is in line with all polygenic scores on educational attainment, resulting after the counts of SNP found by the GWAS of Rietveld (2013), Davies (2016) and Okbay (2016). All of these counts found 0.05 higher POLY EDU of East Asians than of Europeans and other Eurasians. Probable many (or even mostly) of the IQ-increasing mutations in linkage with SNP found by different GWAS are older than the divergence of Europeans and East Asians, and these mutations increase the intelligence of both populations. The 0.05 higher polygenic score of all East Asians (in 1000 GENOMES and in ALFRED too) than Europeans demonstrates a strong selection on intelligence of East Asians. It is near zero probability that the same very strong selection pressure operated on Eastern Siberian hunter-gatherers and on Vietnamese and Han farmers during Holocene. It is more probable this high selection on intelligence operated before Holocene, before the separation of different populations of East Asians, but after the split of Native Americans, that have 0.06 lower polygenic score than East Asians, and that diverged from East Asians 23,000 years ago (Raghavan, 2015). Also, Northern East Asians have higher POLY IQ than Southern East Asians, due of the dilution of Northern Han Chinese during Southward migration. But Japanese have the highest POLY IQ of EAS, even higher than Northern Han Chinese, and it means the increase of the strong selection on IQ of East Asians produced before the divergence of Jomons, estimated 22,000-23,000 years ago (Kanazawa-Kiriyama, 2017). The most probable this strong selection on intelligence of East Asians produced during Last Glacial Maximum.

After this high increase of the genotypic intelligence of East Asians, the selection pressure relaxed and probably their intelligence decreased during Holocene, due of warmer climate and of civilizations, mostly by accumulation of IQ-decreasing rare variants.

The Eastern Asian Cro-Magnon living during Last Glacial Maximum had the highest genotypic intelligence of all humans ever living on the Earth.

Finally, I would like to draw attention to some proof, which I find indisputable,
of the superiority of Palaeolithic man to modern man in terms of visual-spatial
intelligence and memory. A study that analyzed how accurately quadrupedal
walking was rendered in 1000 works of art from the Palaeolithic and modern times
found an error rate of 46.2% in Palaeolithic artists, of 83.5% in artists before 1887,
and of 57.9% in artists after 1887, the year when Eadweard Muybridge published
a series of 20,000 photographs investigating the stages of animal locomotion
(Horvath et al., 2012). Furthermore, Cro-Magnons had an error rate even lower
than the rate of those illustrating animal anatomy books, of 63.6%, and they were
very close to the error rate of taxidermists of natural history museums, of 41.1-
43.1% (Horvath et al., 2009).

The loss of (visuo-spatial) intelligence of modern man compared to Paleolithic man should not surprise us. Today, untrained and even trained adult man has a working memory for numbers lower than that of a young trained chimpanzee (Inoue, 2007; Matsuzawa, 2009; Cook, 2010). Also, chimpanzee choice rates in competitive games match equilibrium game theory predictions. Chimpanzee’s choices are close to the equilibrium predictions and are more responsive than human choices to past history and to payoff changes (Martin, 2014). Also, baboons but not modern humans break cognitive set in a visuo-motor task, indicating greater mental flexibility (Pope, 2015). Furthermore, Koko the gorilla, at 43 to 65 month, scored between 85.2 and 91.7 IQ points on the Stanford-Binet intelligence test (Patterson, 1993), surpassing many living humans who already benefited by Lynn-Flynn effect at that time, in 1975-1976.

 

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Davies, G., Marioni, R.E., Liewald, D.C., Hill, W.D., Hagenaars, S.P.,… & Deary, I.J. (2016). Genome-wide association study of cognitive functions and educational attainment
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De la Croix, D. et al (2017) “Decessit sine prole” – Childlessness, Celibacy, and Survival of the Richest in Pre-Industrial England. CEPR Discussion Paper No. DP11752. Available at SSRN: https://ssrn.com/abstract=2896042

Horvath, G., Csapo, A., Nyeste, A., Gerics, B., Csorba, G. et al. (2009). Erroneous quadruped walking depictions in natural history museums. Current Biology 19: 61-62.

Horvath, G., Farkas, E., Boncz, I., Blaho, M. & Kriska, G. (2012). Cavemen were better at depicting quadruped walking than modern artists: Erroneous walking illustrations in the fine arts from prehistory to today. PLoS ONE 7(12): e49786

Inoue, Sana & Matsuzawa, Tetsuro (2007) Working memory of numerals in chimpanzees. Current Biology 17(23): 1004-1005

Kanazawa-Kyryiama, H. (2017) A partial nuclear genome of the Jomons who lived
3000 years ago in Fukushima, Japan. Journal of Human Genetics 62: 213–221

Martin, Cristopher F. et al (2014) Chimpanzee choice rates in competitive games match equilibrium game theory predictions. Scientific Reports 4: 5182 doi: 10.1038/srep05182

Matsuzawa, Tetsuro (2009) Symbolic representation of number in chimpanzees. Current Opinion in Neurobiology 19: 92–98

Okbay, A., Beauchamp, J.P., Fontana, M.A., Lee, J.J., Pers, T.H., Rietveld, C., & Benjamin, D.J. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533: 539-542.

Patterson, Francine & Gordon, Wendy (1993) The Case for the Personhood of Gorillas.In Paola Cavalieri& Peter Singer (eds.) The Great Ape Project. New York: St. Martin’s Griffin, pp. 58-77

Piffer, D. (2017)  2017 Intelligence GWAS: Group-level polygenic scores http://rpubs.com/Daxide/279148

Pope, Sarah M. et al (2015) Baboons (Papio papio), but not humans, break cognitive set in a visuomotor task. Animal Cognition 18:1339–1346

Raghavan, M. (2015) Genomic evidence for the Pleistocene and recent population history of Native Americans. Science Vol. 349, Issue 6250, aab3884 DOI: 10.1126/science.aab3884

Racimo, F. et al (2017) Detecting polygenic adaptation in admixture graphs. bioRxiv doi: http://dx.doi.org/10.1101/146043

Rietveld, C.A., Medland, S.E., Derringer, J., Yang, J., Esko, T., & Koellinger, P.D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340: 1467-1471.

Sniekers, S. et al (2017) Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics doi:10.1038/ng.3869

Woodley, M.A. et al. (2017) Holocene selection for variants associated with cognitive ability: Comparing ancient and modern genomes. bioRxiv http://dx.doi.org/10.1101/109678