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NEW EVIDENCE FOR THE DECREASE OF THE GENOTYPIC INTELLIGENCE DURING HOLOCENE: POSITIVE SELECTION OF MORE „MACAQUE-LIKE” SMALLER BRAINS SINCE MESOLITHIC PERIOD

A very interesting study of Reardon (2017) compared larger and smaller brains of humans, and found that larger brains have even relatively larger associative-integrative areas (default mode, dorsal attentional), but smaller brains have relatively larger sensory-motor areas. Associative-integrative areas (prefrontal, medial parietal and lateral parieto-temporal cortices) have higher metabolic rates than sensory-motor areas. Smaller brains are more „infant-like” and „macaque-like”. The results of this study are more in line with the correlation of 0.4 between brain size and IQ found by Gignac & Bates (2017) than with the correlation of 0.24 found by Pietschnig (2015).

Also, the result of Reardon (2017) is in line with some studies published past years. Estimating the cerebral blood flow by the diameter of carotid foramina, Seymour (2015) found brain perfusion rate increases with brain size much faster in primates than in marsupials. Also, Seymour (2016) found that blood flow rate to the brain increased faster than brain volume during human evolution, and this demonstrates a higher cerebral metabolic rate in humans than in their ancestors. Quing & Gong (2016) found a robust linear correlation between the whole brain size and intrinsic brain activity (that consumes over 95% of brain’s energy) of healthy humans.

If these correlations are valid across today individuals and across extinct and extant species, they must be valid for humans that lived some thousands years ago too. But human brain size decreased worldwide during Holocene. For example, Mesolithic European women had an average brain size of 1502 ml, and today European women have a brain size of only 1241 ml (Henneberg, 1988). It means a 0.5 ml decrease of brain size by generation. It means there were selected smaller brains, and more „macaque-like” brains during each generation. It means there was a decrease of the brain volume, but also a relative decrease of associative areas related to cognition, and a relative increase of primary sensory-motor areas during Holocene. The theory of the increase of „brain efficiency” and the raise of genotypic intelligence despite the decrease of brain size during Holocene have no biological support. The decrease of „brain efficiency” could be even higher than the decrease of brain volume since Mesolithic period.

Also, the relative cerebellar volume of today humans is more „ape-like” than those of Late Pleistocene humans. Cerebellum of today humans is both absolutely and relatively larger than cerebellum of Neanderthals and Cro-Magnons, that had the largest brains, and also that had the largest cerebral hemispheres relative to cerebellum volume of any primates (Weaver, 2005).

An interesting recent study of Beiter (2017) found positive selection for increased total intracranial volume during last 2,000 years, despite 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 Ages. Even if the frequency of the intracranial capacity-increasing common alleles increased last 2,000 years, the genotypic intracranial capacity decreased during this period, probably because of the accumulation of de novo mutations favoring the decrease of 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. Woodley & Piffer (2017) found a 2% increase of POLY_EDU since Bronze Age.  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 due of de novo mutations that produce ADHD (Cretan, 2017).

Although, the decrease of the genotypic intelligence during Holocene is in line with Cold Winters Theory of Richard Lynn: the warming of climate predicts a decrease of human intelligence.

 

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/

 

Gignac, G.E. & Bates, T.C. (2017)  Brain volume and intelligence: The moderating role of intelligence measurement quality. Intelligence 64(C): 18-29

 

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

 

Pietschnig, J. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience & Biobehavioral Review 57: 411-432.

 

Qing, Z., Gong, G., (2016) Size matters to function: Brain volume correlates with intrinsic brain activity across healthy individuals. NeuroImage doi: 10.1016/j.neuroimage.2016.06.046

 

Reardon, P.K. et al (2017) Title: Normative Brain Size Variation and the Remodeling of Brain Shape in Humans. bioRxiv doi: http://dx.doi.org/10.1101/205930.

 

Seymour, R.S. et al (2015) Scaling of cerebral blood perfusion in primates and marsupials. The Journal of Experimental Biology 218: 2631-2640 doi:10.1242/jeb.124826

 

Seymour, R.S. et al (2016) Fossil skulls reveal that blood flow rate to the brain increased faster than brain volume during human evolution. R. Soc. open sci. 3: 160305. http://dx.doi.org/10.1098/rsos.160305

 

Weaver, A.H. (2005) Reciprocal evolution of the cerebellum and neocortex in fossil humans. PNAS 102(10): 3576 –3580

 

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

 

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HUMAN MIGRATIONS SELECTED RISK ALLELES FOR BIPOLAR DISORDER AND ADHD

The frequencies of common SNP that favor bipolar disorder parallel distances from Africa of populations and super-populations. I calculated the average frequency of 22 SNP that favor bipolar disorder (Stahl, 2017) of populations in 1000 GENOMES:

AFR=0.4695, FIN=0.4750, TSI=0.4841, PUR=0.4850, IBS=0.4873, EUR=0.4873, GBR=0.4914, CEU=0.4973, AMR=0.5077, CLM=0.5155, ITU=0.5177, PJL=0.5232, CHS=0.5277, SAS=0.5291, BEB=0.5332, GIH=0.5336, STU=0.5355, CHB=0.5345, MXL=0.5364, EAS=0.5367, JPT=0.5373, KHV=0.5450, CDX=0.5464, PEL=0.5677.

Also, the average frequencies of common variants that favor ADHD parallel distances from Africa. The scores for the 12 risk-SNP, found by the GWAS on ADHD of Demontis (2017) are:

AFR=0.3975, TSI=0.4117, IBS=0.4200, FIN=0.4267, GIH=0.4267, EUR=0.4275, ITU=0.4300, GBR=0.4358, STU=0.4367, PUR=0.4367, SAS=0.4367, PJL=0.4417, CEU=0.4508, BEB=0.4567, CLM=0.4575, JPT=0.4775, AMR=0.4783, CHB=0.4892, MXL=0.4950, EAS=0.5008, CHS=0.5025, KHV=0.5167, CDX=0.5175, PEL=0.5350.

A combined score POLY_BD+ADHD is even better associated with distance from Africa: AFR=0.4441, FIN=0.4579, TSI=0.4585, IBS=0.4635, EUR=0.4662, PUR=0.4679, GBR=0.4718, CEU=0.4809, ITU=0.4868, PJL=0.4944, CLM=0.4947, GIH=0.4959, SAS=0.4965, AMR=0.4974, STU=0.5005, BEB=0.5072, JPT=0.5162, CHB=0.5185, CHS=0.5188, MXL=0.5218, EAS=0.5238, KHV=0.5350, CDX=0.5362, PEL=0.5562

It could be an explanation for the persistence and even for the increase of risk-variants for two common psychiatric disorders.

 

REFERENCES

Demontis, D. et al (2017) Discovery of the first genome-wide significant risk loci for ADHD. bioRxiv doi: http://dx.doi.org/10.1101/145581 .

Stahl, E.A. et al (2017) Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder. bioRxiv doi: http://dx.doi.org/10.1101/173062 .

EVIDENCE FOR THE INCREASE OF THE GENETIC RISK FOR PSYCHIATRIC DISORDERS IN EURASIA DURING NEOLITHIC PERIOD, BRONZE AGE AND AFTER IRON AGE

I revisited the very interesting paper of Berens (2017) concerning the genomic health of ancient humans and I found:

1. Between 2 non-human Upper Paleolithic hominins that lived between 50,300 and 40,000 BP (Neanderthal and Denisovan), only one (the Neanderthal) had a POLY_PSY above average of today humans. It means 50%.

2. Between 5 European Upper Paleolithic hunter-gatherers (37,500-13,200), only one (the most recent, Satsurblia, 13,200 BP) had a POLY_PSY above average of today humans. It means 20%.

3. Between 11 European Mesolithic hunter-gatherers (9700-7600 BP), only 2 had a POLY_PSY above average of today humans. It means 18%.

4. Between all the 20 Paleolithic and Mesolithic hunter-gatherers (excepting Neanderthal and Denisovan), 5 had a POLY_PSY above average of today humans. It means 25%.

5. Between 20 Anatolian Farmers, one farmer from Levant and one farmer from Iran that lived between 9000 and 8000 BP, 5 had a POLY_PSY above average of today humans. It means 23%.

6. Between the other 33 Eurasian Neolithic farmers, that lived before Bronze Age (between 8000BP and 5000 BP), 12 had a POLY_PSY above average of today humans. It means 36.4%.

7. Between 63 Eurasian farmers and pastoralists that lived during Bronze Age (between 5000 BP and 2800 BP), 23 had a POLY_PSY above average of today humans. It means 36.5%.

8. Between 6 Eurasians that lived during Iron Age (after 2800 BP), only one had a POLY_PSY above average of today humans. It means 17%.

9. The only Early modern period sample (I1955, 430 BP) had a POLY_PSY above average of today humans. It means 100%.

10. Between 144 modern humans that lived before Middle Ages, 47 had a POLY_PSY above average of today humans. It means 33%.

It is evident it was an increase of POLY_PSY because it was a positive selection for high POLY_PSY in Eurasia during Neolithic period. It means rare alleles that favor psychiatric disorders increased during Neolithic period too. Also, the genetic risk for psychiatric disorders increased during Bronze Age too, due of de novo mutations, because there was not selection against POLY_PSY. Probably many of these rare alleles and de novo mutations favor low intelligence too, like many of common alleles of POLY_PSY.

During Iron Age it seems there was a selection against the genetic risk for psychiatric disorders, and POLY_PSY reached the low Mesolithic levels.

Since Iron Age, there was a positive selection for high POLY_PSY, that is higher today even than during Neolithic period and Bronze Age.

It is possible the real percentages of Mesolithic and Iron Age Eurasian populations were higher than for these samples, because the samples for both periods lived at higher latitudes, in colder climates, and it is expected they had higher intelligence and better mental health than their contemporaries more Southern populations.

 

PS. The samples of Berens et al. (2017) can be downloaded here:  http://digitalcommons.wayne.edu/humbiol_preprints/115/

 

REFERENCE

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

 

THE POLYGENIC RISK SCORE FOR SCHIZOPHRENIA PREDICTS THE MEASURED IQ OF EUROPEAN POPULATIONS BETTER THAN POLYGENIC SCORES FOR IQ AND EDUCATIONAL ATTAINMENT

I calculated the mean frequencies of 112 SNP that favor schizophrenia in European populations from 1000 GENOMES, and I found these frequencies better predict the measured IQ of populations than POLY_IQ and POLY_EDU:

POP     IQ     POLY_SCZ      POLY_IQ     POLY_EDU

FIN     101         0.2821             0.4621          0.5160

GBR   100         0.2887             0.4654          0.5060

CEU     99         0.2933             0.4747          0.5030

TSI       99          0.2965            0.4579          0.5130

IBS       97          0.2959            0.4535          0.5130

Furthermore, a simple empirical formula could predict the (genotypic) IQ of European populations in 1000 GENOMES:

IQ_POP = POLY_SCZ_GBR : POLY_SCZ_POP x 100

Here are the results: FIN=102.34, GBR=100, CEU=98.43, TSI=97.37, IBS=97.57

Although, a very recent study (Alloza, 2017) found a correlation of 0.508 between intelligence and POLY_SCZ at individual level. At population level, the correlation seems be even higher.

 

Also, POLY_SCZ on 122 SNP predicts the measured IQ of super-populations better than other polygenic scores:

POP     IQ      POLY_SCZ     POLY_IQ     POLY_EDU

AFR     69         0.3040              0.4714          0.4804

SAS      82         0.2926              0.4439          0.5038

AMR    85         0.2915              0.4515          0.4890

EUR     99         0.2886              0.4627          0.5102

EAS    104         0.2776              0.5129          0.5234

 

REFERENCES

Alloza, C. et al (2017) Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia. Human Brain Mapping doi: 10.1002/hbm.23798.

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

Ripke, S. et al (2014) Biological Insights From 108 Schizophrenia-Associated Genetic Loci. Nature 511(7510): 421-427 doi:  10.1038/nature13595

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

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)

 

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