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THE POSITIVE SELECTION AND THE INCREASE OF THE POLYGENIC SCORE DO NOT EQUATE WITH THE INCREASE OF THE COMPLEX TRAIT (III)

An extremely interesting study of Zhang & Huang (2017) demonstrated that only 29% of the human genome can freely accommodate mutations. It means on average 50 of the 70 de novo mutations (SNP) of a new-born are deleterious.

Another study (Hernandez, 2017) found that singletons alone contribute ~23% of all cis-heritability across genes, and 50.9% of cis-heritability is contributed by globally very rare variants (MAF<0.1%).

A recent study of Young (2017) found that MAF > 0.1% explain only 17% of educational attainment. Although, the heritability of educational attainment is estimated at 35-40%. It means very rare variants (MAF<0.1%) explain at least 50% of this heritability. Also Hill (2017) found 64% of the heritability of educational attainment and 58% of the heritability of intelligence are explained by rare variants (MAF<1%).

All these results demonstrate that the mutational pressure on complex traits and diseases is much higher than predicted by the neutral theory. If more than two third of de novo mutations are deleterious, it is necessary that polygenic score on common polymorphism increases to maintain the actual level of a complex trait. It means the increase of a polygenic score does not equate with the increase of the complex trait. If the increase of the polygenic score does not (over)compensate the accumulation of de novo mutations, the complex trait will even decrease, despite a positive selection on the trait and on the polygenic score.

Furthermore, not only de novo mutations and rare variants are deleterious for complex traits, but probably even many SNP that reach the frequency of common polymorphism are deleterious too. There are studies that demonstrated the total number of MAF > 1% (common variants) in the individual genome positively correlates with the risk of schizophrenia (He, 2017), Parkinson disease (Zhu, 2015) and type 2 diabetes (Lei, 2017). I expect the total number of MAF > 1% in the individual genome negatively correlates with complex traits too.

 

REFERENCES

He, P. et al (2017) Accumulation of minor alleles and risk prediction in schizophrenia. Sci Rep 7(1): 11661.

Hernandez, R.D. et al (2017) Singleton Variants Dominate the Genetic Architecture of Human Gene Expression. bioRxiv doi: http://dx.doi.org/10.1101/219238 .

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

Lei, X. & Huang, S. (2017) Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population. PLoS One 12(11): e0187644.

Young, A.I. et al (2017) Estimating heritability without environmental bias. bioRxiv doi: http://dx.doi.org/10.1101/218883 .

Zhang, Y. & Huang, S. (2017) De novo mutations in autism spectrum disorders and an empirical test of the neutral DNA model. bioRxiv doi: http://dx.doi.org/10.1101/231944 .

Zhu, Z. et al (2015) Enrichment of Minor Alleles of Common SNPs and Improved Risk Prediction for Parkinson’s Disease. PLoS One 10(7): e0133421.

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THE POSITIVE SELECTION AND THE INCREASE OF THE POLYGENIC SCORE DO NOT EQUATE WITH THE INCREASE OF THE COMPLEX TRAIT (II)

A recent meta-analysis found that individuals with an additional 1 Mb of copy-altered interval (be it duplication or deletion) was associated with a 0.132 cm shorter stature, but for each Mb of total deletion burden the decrease of height was 0.41 cm (Mace, 2017). Also, Mace (2017) found an increase of BMI and WHR by the total burden on CNV. I expect there is even a higher effect on intelligence and educational attainment of the total burden on CNV, because the mutational target of both of them must be much higher than the mutational target of height.

De novo structural changes affect on average 4.1kbp of genomic sequence per generation (Kloosterman, 2015). It means 4.1 Mb by 1,000 generations. The effect on genotypic height is 0.54 cm decrease during 25,000-30,000 years. Also Kloosterman (2015) found 66% of structural variants are deletions, and it means 2.71 Mb deleted by 1,000 generations, equating with 1,11 cm shorter stature after 25,000-30,000 years.

Another study found the total burden on CNV in a Han Chinese sample is 44.86 Mb, and the total burden on CNV of an Yoruban sample is 37.75 Mb (Chaisson, 2017). It means a difference of 7.11 MB between Han Chinese and Yoruba produced during 60,000 years, since the Out of Africa. This 7.1 Mb higher burden equates with a 0.94 cm relative decrease of Han Chinese by rapport to Yoruba. The burden on deletions is 25.47 Mb for Han Chinese, and 21.16 Mb for Yoruba (Chaisson, 2017). The difference of 4.31 Mb equates with a 1.77 cm shorter stature of Han Chinese. Differences of total burden of CNV between Han and Yoruba demonstrates that mutational load was not be efficiently eliminated by purifying selection, at least during last 60,000 years in Eurasians. Also, Mace (2017) found rare CNV with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/m 2 ), that were not eliminated by the purifying selection in Europeans, despite the positive selection on height and BMI found by some studies. The higher burden on CNV of Han Chinese than Yorubans is in line with the study of Henn (2016), that found the mutational load increases with distance from Africa. Also, the mutational load increases during population growth (Gazave, 2013) and during range expansion (Peischl, 2013).

The higher polygenic score on height of Mesolithic hunter gatherers and even of Neolithic farmers than Early Upper Paleolithic Europeans (Berg, 2017) despite their shorter stature than Upper Paleolithic hunter gatherers (Formicola, 1999) could be partially explained by the accumulation of height-decreasing rare CNV, and by the increase of the total burden on CNV after Upper Paleolithic period.

The evolution of height and of POLY_HEIGHT since Early Upper Paleolithic demonstrates than a genotypic trait could decrease even if there is positive selection on this trait, reflected by the increase of the polygenic score on common polymorphism.

 

REFERENCES

Berg, J.J. et al. (2017) Polygenic Adaptation has Impacted Multiple Anthropometric Traits. bioRxiv doi: http://dx.doi.org/10.1101/167551 .

Chaisson, M.J.P. et al. (2017) Multi-platform discovery of haplotype-resolved structural variation in human genomes. bioRxiv doi: http://dx.doi.org/10.1101/193144 .

Formicola, V. & Giannecchini, M. (1999) Evolutionary trends of stature in Upper Palaeolithic and Mesolithic Europe. J. Hum. Evol., 36: 319–333.

Gazave, E., Chang, D., Clark, A.G. & Keinan, A. (2013). Population growth inflates the per-individual number of deleterious mutations and reduces their mean effect. Genetics 195: 969-978.

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

Kloosterman, W.P., Francioli, L.C., Hormozdiari, F., Marschall, T., & Guryev, V. (2015). Characteristics of de novo structural changes in the human genome. Genome Research 25: 792-801.

Mace, A. et al. (2017) CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits. Nature Communications DOI: 10.1038/s41467-017-00556-x

Peischl, S., Dupanloup, I., Kirkpatrick, M. & Excoffier, L. (2013). On the accumulation of deleterious mutations during range expansions. Molecular Ecology 22: 5972-5982.

 

 

 

THE POSITIVE SELECTION AND THE INCREASE OF THE POLYGENIC SCORE DO NOT EQUATE WITH THE INCREASE OF THE COMPLEX TRAIT (I)

Woodley & Piffer (2017) published the most important study concerning the evolution of genotypic intelligence during last thousands years. On 130 SNP found by the GWAS of Okbay (2016), they found an increase of POLY_EDU of 2% in Europe since Bronze Age. I sustained the genotypic intelligence could decrease last 3,000-4,000 years, despite the positive selection on education/intelligence found by the increase of polygenic score on educational attainment.

Today, the decrease of the complex trait despite the positive selection and the raise of the polygenic score is evident for brain size and height.

Beiter (2017) found positive selection for increased total intracranial volume during last 2,000 years, despite the intracranial capacity significantly decreased during this period (Henneberg, 1988).

Also, Berg (2017) found an increase of 4% of the polygenic score on height between Early Upper Paleolithic and Mesolithic in Europe, despite the phenotypic height decreased more than 10 cm during this period (Formicola, 1999). At least partially, the decrease of height is due of genetic conditions. Probably, the increase of the POLY_HEIGHT did not compensate the decrease of genotypic height by the accumulation of de novo mutations, even if there was a positive selection on height. Even Neolithic farmers of Near East and Europe have slightly higher POLY_HEIGHT than Early Upper Paleolithic hunter gatherers, despite their shorter stature.

The common polymorphism explains 0.430 of the phenotypic variation, and 0.489 of the genotypic variation of the height (Xia, 2016). The common polymorphism explains 0.156 of the phenotypic variation, and 0.357 of the genotypic variation of the educational attainment (Hill, 2017). Also, the effect of each height-increase SNP is much stronger than the effect of each EDU-increasing SNP (Okbay, 2016). Furthermore, the mutational target for educational attainment probably is much larger than for height, and the generational decrease of genotypic EDU by de novo mutations must be much higher than the generational decrease of height due of new mutations. Hence, it is expected that an increase of POLY_HEIGHT positively influences the trait more than a similar increase of POLY_EDU. If a 4% increase of POLY_HEIGHT (during 20,000-30,000 years) is associated with a 10 cm decrease of height, it is probable that a 2% increase of POLY_EDU (during 3,000-4,000 years) could be associated with a decrease of the genotypic educational attainment or, at least, with a decrease of the genotypic intelligence, that have a correlation of 0.7 with genotypic education.

I sustained polygenic score must increase to maintain the actual level of a complex trait. But even if the polygenic score increases, the trait could decrease. In this moment, my assumption is demonstrated for brain size and height. Due of enormous mutational target of intelligence/educational attainment, my assumption must be valid for IQ/EDU also.

 

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.

Berg, J.J. et al. (2017) Polygenic Adaptation has Impacted Multiple Anthropometric Traits. bioRxiv doi: http://dx.doi.org/10.1101/167551 .

Formicola, V. & Giannecchini, M. (1999) Evolutionary trends of stature in Upper Palaeolithic and Mesolithic Europe. J. Hum.
Evol., 36: 319–333.

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

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

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

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

Xia C, et al. (2016) Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation. PLoS Genet 12(2): e1005804. doi:10.1371/journal.pgen.1005804

 

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

 

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