www.nature.com/jhg

ARTICLEOPEN

Inferring intelligence of ancient people based on modern
genomic studies
Kaisar Dauyey

1,2

and Naruya Saitou1,2✉

1234567890();,:© The Author(s) 2022

Quantiﬁcation of ancient human intelligence has become possible with recent advances in polygenic prediction. Intelligence is acomplex trait that has both environmental and genetic components and high heritability. Large-scale genome-wide association
studies based on ~270,000 individuals have demonstrated highly signiﬁcant single-nucleotide polymorphisms (SNPs) associatedwith intelligence in present-day humans. We utilized those previously reported 12,037 SNPs to estimate a genetic component ofintelligence in ancient Funadomari Jomon individual from 3700 years BP as well as four individuals of Afanasievo nuclear family
from about 4100 years BP and who are considered anatomically modern humans. We have demonstrated that ancient individuals
could have been not inferior in intelligence compared to present-day humans through assessment of the genetic component ofintelligence. We have also conﬁrmed that alleles associated with intelligence tend to spread equally between ancestral and derived
origin suggesting that intelligence may be a neutral trait in human evolution.
Journal of Human Genetics; https://doi.org/10.1038/s10038-022-01039-8

INTRODUCTION
Intelligence is a complex phenotypic trait that has a large genetic
component with high heritability [1]. Large-scale genome-wide
association studies (GWAS) indicate that prediction of intelligence
may be possible using genetic data [2]. The most powerful metaanalysis of GWAS based on 269,867 individuals identiﬁed several
hundred genetic markers explaining up to 5.2% of variance inintelligence [3]. The assessment of cognition in that study was UK
biobank ﬂuid intelligence score derived from 13 questions
focusing on memory, logic, verbal and numeric reasoning [4].
The genetic markers have been reported in the form of singlenucleotide polymorphisms (SNPs) spread across the whole humangenome in high association with bidirectional effect in form of Zscore obtained through METAL software [5]. We have used those
SNPs in our analysis based on classical assumption of singlenucleotide polymorphisms being the most common type ofgenetic variation in human genome and present in at least 1% of
the global population [6].
Assessment of human intelligence and cognitive abilities is achallenging task. One of the challenges is lack of clear-cut deﬁnitionof intelligence. Intelligence includes genetic and environmental
components providing humans with multiple capacities of processing new information, linguistic as well as mathematical abilities,
creativity that has been deﬁned by more than two dozen
deﬁnitions by American Psychological Association [7].
Quantiﬁcation of cognitive abilities is a historically controversial
issue. Intelligence quotient (IQ) tends to be a universal metric ofneuropsychological abilities like mental speed, decision making,and problem-solving required for educational planning [8]. A large
cohort of other tests exist like Peabody Picture Vocabulary test (ameasure of children’s verbal intelligence) as well as scholarly

aptitude tests like Scholastic Aptitude Test for colleges or
Graduate Record Examination utilized by graduate schools in
the United States. Previous research has demonstrated that
performance on a subset of a particular test correlated withresults on other parts of the test [9].
Intelligence assessment in ancient people like Neanderthal andAustralopithecus has been attempted via medical imaging offossilized skulls. Medical imaging used to reconstruct brain volumesof Australopithecus individuals living 3–4 million years (Myr) ago
conﬁrmed that anatomically modern humans (AMHs) have on
average three times larger brain size [10]. Comparison of brain
volumes in early and late AMHs (living 300,000–10,000 years ago)
did not demonstrate signiﬁcant difference in terms of total volume,
but rather conﬁrmed more complex structure in cortical areas withlarger parietal lobe and cerebellum in more recent specimens [11].
The cortical areas more prominent in late AMH fossils compared toearly samples are largely responsible for development of social
tasks likely contributing to population growth and development oflanguage as early as 50,000 years ago [12].
An alternative approach to fossil analysis termed “neuroarcheology” has utilized brain imaging of modern humans engaged inprocess of creating stone age tools in attempt to mimic ancienthuman brain activity [13]. Brain activity recorded during knappingof Acheulian handaxes dated ~1.75 Myr ago was signiﬁcantly
different from brain activity involved in making Oldowan tools
dated about 2.6 Myr. Making more skillfully shaped Acheulian
tools required activation of complex neural circuitry similar tobrain activity observed during playing piano [14].
Anatomical measurements of brain structures, modeling
cerebellar blood ﬂow, mathematical modeling of brain size—
body physiological parameters in ancient human species overall

1
Department of Genetics, School of Life Science, Graduate University for Advanced Studies (SOKENDAI), Mishima, Shizuoka 411-8540, Japan. 2SAITOU Laboratory, National
Institute of Genetics, Mishima, Shizuoka 411-8540, Japan. ✉email: saitounr@nig.ac.jp

Received: 20 February 2022 Revised: 18 April 2022 Accepted: 20 April 2022

K. Dauyey and N. Saitou

2
Table 1. Ancient genomes used for polygenic estimation of
intelligence
Sample name

Averagecoverage

Table 2.

Ancestral state analysis of SNPs associated with intelligence
Ancestral state

Reference

Funadomari Jomon

48x

Kanzawa-Kiriyama
et al. [16]Afanasievo mother

21.2x

Wohns et al. [17]Afanasievo father

25.3x

Wohns et al. [17]Afanasievo son1

10.8x

Wohns et al. [17]Afanasievo son2

25.8x

Wohns et al. [17]have demonstrated a trend of increasing brain sizes as well as
higher metabolic neuronal activity suggesting an upward rise ofhuman cognitive abilities in AMHs compared to Neanderthal andAustralopithecus [15, 16]
The aim of this study was to assess a genetic component ofintelligence in ancient individuals who are considered AMHs
through genome analysis. Intelligence as a complex trait has
likely been shaped by genetic variation in the course ofevolution. We utilized 12,037 SNPs distributed across entire
genome. Sex chromosomes weren’t included in our analysis sincenone of the X-chromosome and Y-chromosome SNPs have notreached the p value <5 × 10−8 threshold in the GWAS discovery
phase [3]. We applied the derived polygenic scores (PGS) to 5
ancient genomes from ancient individuals (Table 1) including
Funadomari Jomon individual discovered in Hokkaido, Japan
with high sequencing coverage and peak depth of 48x(estimated age about 3700 years BP) [17]. Four other ancientgenomes data originated from a nuclear family of four—amother, a father, and their two sons from Afanasievo Culture
discovered in modern Russia, who lived about 4100 thousand
years BP (https://reich.hms.harvard.edu/datasets) [18].
Our analysis was aimed at elucidating a genetic component ofintelligence in late AMHs (largely originating within 10,000 yearsago) such as individuals from Jomon and Afanasievo cultures. The
reason for that is the GWAS summary statistics obtained frompresent-day humans around the globe aligned to more archaic
genomes like Denisovans and Neanderthal has a high likelihood of
non-interpretable results due to considerable population divergence time (up to 170,000–700,000 years between Denisovans
and present-day humans) [19]. We compared intelligence PGS
derived from genomic data of ancient individuals (considered asAMHs) to 2504 present-day humans from the 1000 Genome
Project Phase 3 [20]. We also inferred absolute IQ scores forancient individuals compared to general population based on a
genetic component of intelligence.
METHODS
Selection of SNPs for polygenic scoring
Genetic markers of intelligence were obtained from a large-scale meta-analysis
of GWAS on cognitive abilities with 269,867 participants from 14 European
epidemiological cohorts [3]. Genome-wide signiﬁcance (p < 5 × 10−8) inassociation with intelligence was conﬁrmed for total number of 12,110 SNPs.
Polygenic risk score prediction demonstrated that around 5.2% variation in
intelligence can be explained by those SNPs. We estimated ancestral state of
the majority of SNPs associated with intelligence by multiple alignment ofreference genome of modern human (GRCH37) to bonobo, chimpanzee,
gorilla, orangutan, gibbon, and macaque using “Ortheus” method implemented in ENSEMBLE database [21]. Fisher’s exact test of independence was used
to assess any nonrandom association between ancestral state and effect on
intelligence (Table 2).

Calculating intelligence polygenic scores
We built PGS using 12,037 SNPs that reached genome-wide signiﬁcance(p < 5 × 10−8) in the GWAS summary statistics. We utilized publicly

Ancestral

Total
DerivedRegression direction
Positive

2542

3333

5875

Negative

2490

3262

5752

5032

6595

11,627

Total

available datasets from 1000 genome project phase 3 data (2504individuals across global 26 populations) to construct PGS for eachindividual. The Funadomari Jomon genome sequence was selected foranalysis due to high sequencing coverage (peak depth of 48x) considered
as “the reference Jomon genome”. Four ancient European genomes withhigh-quality sequence belonging to Afanasievo culture were used forcomparison as well (Table 1). Although the intelligence-associated SNPs
have been identiﬁed in European populations we proceeded withevaluation of genetic component of intelligence in above mentioned
ancient high-quality genomes in spite of estimated age of 3900–4100 BP.
We used PLINK version 1.9 [22] and R version 4.0.2 [23] to computeintelligence PGS. Data visualization was done through ggplot2 implemented in R [24]. Genetic intelligence scores were obtained by summing up the
GWAS meta-analysis output beta regression coefﬁcients identiﬁed for
effective alleles in independent UK Biobank data subset for educational
attainment replication. Each subject score was calculated as a sum of SNP
effects considering number of effect allele presence (0, 1, or 2) multipliedby reported beta regression coefﬁcients using polygenic risk score
calculation [25].
We calculated genetic component of intelligence by constructing PGS
through a linear model for each individual of the study cohort. Intelligence
PGS PGS for each individual was deﬁned in the form of:
PGS ¼ β1 x1 þ β2 x2 þ ¼ þ βk xk þ βn xn , where βk represents per-allele
beta coefﬁcient of logistic regression for intelligence at SNP k, and xk basedon allele dosage of 0, 1, or 2 for SNP k with total n number of SNPs
included in PGS.
We used two subsets of SNPs from total 12,037 SNPs for PGS derivation:
one set comprised of 9128 SNPs (p value threshold p < 5 × 10−8) as well asa smaller set of 1402 SNPs (p value threshold p < 4 × 10−11) replicated in an
independent UK Biobank cohort and having top association in the original
GWAS study. We decided to build two PGS based on different threshold of
p values for SNPs reported in the original GWAS study according to
generally accepted guidelines on performing PGS analysis throughcomparison of PGS and absolute IQ scores for ancient genomes topresent-day humans [26]. Functions for calculating PGS and datavisualization are available as R scripts on GitHub (https://github.com/
Kays3/Ancient_intelligence.git).

Statistical inference
The overall PGS of the intelligence data was tested for normality(Shapiro–Wilks’s test) and plotted assuming normal distribution. PLINK
version 1.9 was used to extract the genotype data calculating eigenvalues for principal component analysis (PCA), and building matrixes forcomputing genetic intelligence scores for each subject. Population
structure demonstrated by PCA was built based on subset of 9128 SNPs
and 1402 SNPs shared by modern and ancient human genomes. We alsoinferred absolute values of IQ for ancient individuals based on PGS results
and compared them to a general human population mean of 100
and standard deviation (SD) of 15 [27] using open-access software
designed for translating PGS into relevant absolute values of phenotypical traits [28].

RESULTS
We have built genotypes based on 12,037 SNPs highly associatedwith intelligence from genome sequences of 2504 individuals
from 1000 Genome Project Phase 3. We also constructed two setsof genotypes comprised of 9148 SNPs and 1402 SNPs for genomesof Funadomari Jomon individual, Afanasievo family of fourindividuals as well as 1000 Genome Project subjects. Intelligence
PGS constructed for total 2509 individuals were tested for
Journal of Human Genetics

K. Dauyey and N. Saitou

3

Fig. 1 Intelligence polygenic scores in ancient individuals compared to modern humans. A PGS based on top signiﬁcant 1402 SNPs out of
12,110 total SNPs (p value threshold p < 4 × 10−11) Afanasievo Mother z = −2.94, Afanasievo Son1. z = −2.36, Afanasievo Son2 z = −2.88,
Afanasievo Father z = −1.77. Intelligence polygenic scores in Afanasievo individuals observed within 3 standard deviations (SD) from themean of 1000 Genome Populations while Funadomari Jomon individual is above the mean, z = 0.33. B Funadomari Jomon individual,
Afanasievo individuals PGS based on 9148 out of 12,110 total SNPs (p value threshold p < 5 × 10−8) extracted in common with 1000 Genome
Populations. Afanasievo Mother z = −3.59, Afanasievo Son1 z = −2.45, Afanasievo Son2 z = −2.99, Afanasievo Father z = −2.04. Intelligencepolygenic scores in Afanasievo individuals fell within 3 SD from the mean of 1000 Genome Populations while Funadomari Jomon individual iswithin 2 SD below the mean, z = −1.29

normality using Shapiro–Wilk test. Intelligence PGS were indeed
normally distributed (W = 0.99964, p value = 1.22 × 10−5).
The PGS of intelligence based on highly signiﬁcant 1402 SNPs
(p value threshold p < 4 × 10−11) demonstrated Funadomari
Jomon individual’s value within 1 SD above population meanof 1000 Genome Project (z = 0.34), while Afanasievo mother hada score lower than 2 SD of the mean (z = −2.94), Afanasievo
father score was also below 2 SD from the mean (z = −1.77).
Afanasievo sons scores were located between the maternal andpaternal values where Son1 had a score of z = −2.36 and Son2z = −2.88 (Fig. 1A).
The PGS of intelligence based on 9128 SNPs (p value thresholdp < 5 × 10−8) placed Funadomari Jomon individual within 1 SD
below population mean of 1000 Genome Project (z = −1.29),
while Afanasievo mother had score lower than 2 SD of the mean(z = −3.59), Afanasievo father score was below 1 SD from themean (z = −2.03). Afanasievo sons scores were located between
the maternal and paternal scores where Son1 score z = −2.44 andSon2 with z = −2.99 (Fig. 1B).
Absolute IQ score inference based on variance explained byintelligence PGS (5.2%) with mean of the trait of 100 and SD of 15
demonstrated following scores for ancient individuals: for the1402 SNPs PGS Funadomari Jomon individual IQ = 101 (95% CI =72.58–129.74), while Afanasievo mother’s IQ = 89 (95% CI =60.96–118.12), Afanasievo father IQ = 94 (95% CI = 65.2–122.36).
Afanasievo sons scores were located between the maternal andpaternal scores where Son1’s IQ = 92 (95% CI = 63.17–120.3) and
Son2’s IQ = 90 (95% CI = 61.54–118.7) (Fig. 2A).
Journal of Human Genetics

Absolute IQ score estimates for PGS based on 9128 SNPs
demonstrated Funadomari Jomon individual’s IQ = 95 (95% CI =66.88–124.04), while Afanasievo mother’s IQ = 87 (95% CI =57.96–115.12), Afanasievo father IQ = 93 (95% CI = 64.3–121.46).
Afanasievo sons scores were similarly located between thematernal and paternal scores where Son1 had IQ = 91 (95%CI = 62.87–120.02) and for Son2: IQ = 89 (95% CI = 60.96–118.12)(Fig. 2B).
PCA based on 1402 SNPs demonstrated close genetic relationships between ancient Funadomari Jomon individual to modern
East Asian populations while Afanasievo family individuals
clustered with modern European populations from the 1000
Genome Project (Fig. 3A). PCA based on 9128 SNPs did not reveal
any clear population structure with Funadomori Jomon individual
clustered in proximity to East Asian populations compared to
Afanasievo family (Fig. 3B).
We assessed ancestral state of 12,037 SNPs used for PGS
construction in 1000 Genome Populations through alignment with
six primate genomes including bonobo, chimpanzee, gorilla,orangutan, gibbon, and macaque using “Ortheus” method implemented in ENSEMBLE database [21]. The alignment results between
ancestral species and GWAS summary statistics used for polygenic
scoring was available only for 11,627 SNPs (96.6%) accessible fromENSEMBLE database. SNPs contributing to positive effect andnegative effect on intelligence demonstrated no association with
derived state (p value = 0.985, OR with 95% CI = 1.00 [0.93–1.10])
based on Fisher exact test (Table 2). Ancestral state analysis of SNPs
highly associated with intelligence did not demonstrate any

K. Dauyey and N. Saitou

4Fig. 2 Absolute IQ values inferred from intelligence polygenic scores in ancient individuals compared to modern humans. A Absolute IQ
values inference based on variance explained by intelligence polygenic score build from 1402 SNPs (p value threshold p < 4 × 10−11)
demonstrated Funadomari Jomon individual’s IQ = 101 (95% CI = 72.58–129.74), while Afanasievo mother’s IQ = 89 (95% CI = 60.96–118.12),
Afanasievo father IQ = 94 (95% CI = 65.2–122.36). Afanasievo sons scores were located between the maternal and paternal scores where
Son1 score had IQ = 92 (95% CI = 63.17–120.3) and Son2 IQ = 90 (95% CI = 61.54–118.7). Variance explained by intelligence polygenic score(R2 = 5.2%), mean IQ = 100 with SD = 15 (95 % CI = 71.42–128.58) in modern human general population. B Absolute IQ values inference basedon variance explained by intelligence polygenic score build from 9128 SNPs (p value threshold p < 5 × 10−8) demonstrated Funadomari Jomon
individual’s IQ = 95 (95% CI = 66.88–124.04), while Afanasievo mother’s IQ = 87 (95% CI = 57.96–115.12), Afanasievo father IQ = 93 (95% CI =64.3–121.46). Afanasievo sons scores were similarly located between the maternal and paternal scores where Son1 had IQ = 91 (95% CI =62.87–120.02) and for Son2: IQ = 89 (95% CI = 60.96–118.12)

signiﬁcant differences suggesting that genomic regions contributing to intelligence PGS are present not only in modern humans, but
in primates as well.
DISCUSSION
We have demonstrated ﬁrst ever insight into genetic componentof intelligence through PGS in ancient individuals from around3700–4100 BP. The performed calculations indicate a possibility
that people living on the territory of modern Hokkaido and Russia
in that period being not less intelligent than modern humans.
Absolute IQ values inferred from PGS in Afanasievo individuals
and Funadomari Jomon individual tends to be within the 95%
range of mean general human population suggesting similarity ofintelligence of humans living 3700 BP and modern humans.
Although intelligence PGS of Afanasievo family tend to ﬂuctuateon the lower tail of normal distribution of the scores of 1000
Genome project these scores translate to absolute IQ values
within mean of general population given the low varianceexplained by intelligence PGS (R2 = 5.2%).
We used two different p value thresholds for constructing PGS
of intelligence, since there is no clear consensus on how selectionof SNPs may affect the predictive power of the analysis. Previous
work on PGS of intelligence demonstrated that different thresholds may actually have association with particular aspects ofcognition like memory or verbal intelligence [29].

Previous studies on PGS prediction conﬁrmed lower applicability and reproducibility of the majority of GWAS reported in
global populations due to the fact that most data used in the
discovery phase came from people of European descent [30, 31].
However, recent development in derivation of absolute trait
values from PGS conﬁrmed potential clinical utility and rationaleof polygenic prediction in context of complex traits and clinicaldecision making [32, 33].
Even though there is a possibility that SNPs associated withcognition may have lower predictive abilities when applied tonon-Europeans, there have not been any other studies reporting
intelligence prediction of ancient individuals through genetic data
to our knowledge. This analysis is an example of application of
GWAS ﬁndings toward assessment of cognitive abilities inindividuals living around 4000 years ago. Previous studies on
polygenic prediction of height as well disease risk in ancient DNA
conﬁrmed similar predictive power in ancient humans to modern
individuals [34, 35].
Modern concept on intelligence measured by IQ holds onprinciple of dual contribution of genetic and environmental
components (socioeconomic aspects, medical care) forming
essential cognitive functions. IQ measures have been implicatedwith survival, adaptation to environment, and mental functioning[8]. Digital genomic biobank DNA.Land as well as various genetic
applications like GenePlaza, 23andMe previously reported polygenic prediction of a number of complex traits including

Journal of Human Genetics

K. Dauyey and N. Saitou

5genetic factors suggests a need for alternative intelligence
prediction tools incorporating environment and socioeconomic
factors.
A common approach in studying quantitative traits like
intelligence in humans has been based on monozygotic anddizygotic twins [39]. Previous studies on three-dimensional brain
mapping in twins supported correlation between gray-mattervolumes in genetically identical twins and high heritability forbrain areas responsible for IQ, speech, and language [40, 41]. High
heritability of intelligence has also been criticized due to overlapof cognitive ability measurements with various factors like
presence of IQ statistics, socioeconomic inﬂuence, and otherenvironmental inﬂuences [42].
Intelligence as a phenotypic trait with underlying effects of DNApolymorphism has been likely shaped by evolutionary processes.
Majority of mutations in genes affecting underlying cognition
used in this study tend to interact in extremely complex networks
with higher activity in hippocampal as well as somatosensory
neurons [3]. Since not only humans, but primates have active
neurogenesis in those brain areas [43] we hypothesize thatgenetic contribution to intelligence through mutations are shared
to some extent with human ancestral species. High abundance ofshared SNPs related to intelligence in primates and humans
observed in our study may suggest that most mutations ingenomic regions associated with intelligence of ancient humans
and their ancestors are in line with neutral theory of evolution[44]. We have demonstrated conserved state of half of causative
SNPs in primates and humans (Table 2). Since the ancestral stateinference was done in relation to primates, there is no clear
boundary between alleles contributing to higher intelligence
being more common in modern humans than in ancestral species.
We demonstrated that genomic data from ancient individuals
can be used to evaluate a genetic component of intelligence.
Funadomari Jomon as well as Afanasievo family individuals
demonstrated intelligence PGS as well as IQ scores in line withmodern humans. DNA evidence may indicate a possibility ofintelligence being a neutral trait in human evolution suggestingthat ancient individuals living 3700–4100 years BP could havebeen as intelligent as modern humans.

Fig. 3 Intelligence-associated genetic relationship based on principal component analysis between ancient and modern humans.
A Principal component analysis based on 1402 top signiﬁcant SNPs
out of 12,110 SNPs (p value threshold p < 4 × 10−11) shared byancient individuals and subjects from 1000 Genomes Project.
Ancient individuals including Funadomari Jomon (F23), Afanasievo
family—Mother (AM), Father (AF), Son1 (AS1), Son2 (AS2) andmodern humans deﬁned in accordance with the groupings in the1000 Genomes Project: European (EUR), admixed American (AMR),
East Asian (EAS), African (AFR) and South Asian (SAS); B principal
component analysis based on 9128 SNPs out of 12,110 SNPs (p value
threshold p < 5 × 10−8) in high association with intelligence

intelligence based on GWAS ﬁndings [36, 37]. Although thepredictions have the potential to elucidate individual traits incomparison to massive digital databanks, a small percent ofgenetic contribution to the traits is still the most important
limiting factor in wider applicability of any predictions [38].
DNA.Land platform has previously demonstrated evaluation of
intelligence PGS using GWAS ﬁndings based on 72 SNPs [2]. The
variance of intelligence explained by polygenic scoring in DNA.
Land study was about 4.8%. Although 12,110 SNPs we used in ourstudy only explain about 5.2% variance in intelligence through
PGS, large number of those SNPs have been mapped to protein
coding and non-coding DNA elements highly associated withcognitive functions and mental disorders [3]. Likely such a modest
increase in predictive power of intelligence based solely on
Journal of Human Genetics

REFERENCES
1. Zabaneh D, Krapohl E, Gaspar HA, Curtis C, Lee SH, Patel H, et al. A genome-wide
association study for extremely high intelligence. Mol Psychiatry. 2018;23:1226–32.
2. Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al.
Genome-wide association meta-analysis of 78,308 individuals identiﬁes new lociand genes inﬂuencing human intelligence. Nat Genet. 2017;49:1107–12.
3. Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, de Leeuw CA, et al.
Genome-wide association meta-analysis in 269,867 individuals identiﬁes new
genetic and functional links to intelligence. Nat Genet. 2018;50:912–9.
4. Fawns-Ritchie C, Deary IJ. Reliability and validity of the UK Biobank cognitive
tests. PLoS ONE. 2020;15:e0231627.
5. Willer CJ, Li Y, Abecasis GR. METAL: fast and efﬁcient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.
6. Arias TD, Jorge LF, Barrantes R. Uses and misuses of deﬁnitions of genetic
polymorphism. A perspective from population pharmacogenetics. Br J Clin
Pharm. 1991;31:117–20.
7. Sternberg RJ, Detterman DK. What is intelligence?: Contemporary viewpoints on
its nature and deﬁnition. Norwood, NJ: Ablex Pub. Corp.; 1986.
8. Braaten EB, Norman D. Intelligence (IQ) testing. Pediatr Rev. 2006;27:403–8.
9. Lyall DM, Cullen B, Allerhand M, Smith DJ, Mackay D, Evans J, et al. Cognitive testscores in UK Biobank: data reduction in 480,416 participants and longitudinalstability in 20,346 participants. PLoS ONE. 2016;11:e0154222.
10. Holloway RL. The evolution of the hominid brain. In: Henke W, Tattersall I, editors.
Handbook of paleoanthropology. Berlin, Heidelberg: Springer; 2015. p. 1961–87.
11. Montgomery S. Hominin brain evolution: the only way is up? Curr Biol. 2018;28:
R788–90.
12. Shultz S, Nelson E, Dunbar RIM. Hominin cognitive evolution: identifying patterns
and processes in the fossil and archaeological record. Philos Trans R Soc B Biol
Sci. 2012;367:2130–40.

K. Dauyey and N. Saitou

6
13. Renfrew C, Frith C, Malafouris L, Stout D, Toth N, Schick K, et al. Neural correlatesof Early Stone Age toolmaking: technology, language and cognition in human
evolution. Philos Trans R Soc B Biol Sci. 2008;363:1939–49.
14. Putt SS, Wijeakumar S, Franciscus RG, Spencer JP. The functional brain networks
that underlie Early Stone Age tool manufacture. Nat Hum Behav. 2017;1:1–8.
15. Pearce E, Stringer C, Dunbar RIM. New insights into differences in brain organization between Neanderthals and anatomically modern humans. Proc R Soc B
Biol Sci. 2013;280:20130168.
16. Seymour RS, Bosiocic V, Snelling EP, Chikezie PC, Hu Q, Nelson TJ, et al. Cerebral
blood ﬂow rates in recent great apes are greater than in Australopithecus species
that had equal or larger brains. Proc R Soc B Biol Sci. 2019;286:20192208.
17. Kanzawa-Kiriyama H, Jinam TA, Kawai Y, Sato T, Hosomichi K, Tajima A, et al. Late
Jomon male and female genome sequences from the Funadomari site in Hokkaido, Japan. Anthropol Sci. 2019;127:83–108.
18. Wohns AW, Wong Y, Jeffery B, Akbari A, Mallick S, Pinhasi R, et al. A uniﬁed
genealogy of modern and ancient genomes: uniﬁed, inferred tree sequences of1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples. 2021. https://doi.org/10.5281/zenodo.5512994.
19. Meyer M, Kircher M, Gansauge M-T, et al. A high-coverage genome sequence
from an Archaic Denisovan Individual. Science. 2012;338:222–6.
20. The 1000 Genomes Project Consortium. A global reference for human genetic
variation. Nature. 2015;526:68–74.
21. Paten B, Herrero J, Fitzgerald S, Beal K, Flicek P, Holmes I, et al. Genome-wide
nucleotide-level mammalian ancestor reconstruction. Genome Res. 2008;18:1829–43.
22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: atool set for whole-genome association and population-based linkage analyses.
Am J Hum Genet. 2007;81:559–75.
23. R Core Team. R: A language and environment for statistical computing. Vienna,
Austria: R Found Stat Comput, R-Proj Org; 2017.
24. Wickam H. ggplot2: elegant graphics for data analysis. NY: Springer; 2014.
25. Liu JZ, Erlich Y, Pickrell JK. Case–control association mapping by proxy using
family history of disease. Nat Genet. 2017;49:325–31.
26. Choi SW, Mak TSH, O’Reilly PF. A guide to performing Polygenic Risk Score
analyses. Nat Protoc. 2020;15:2759–72.
27. Lynn R, Meisenberg G. National IQs calculated and validated for 108 nations.
2010.
28. Pain O, Gillett AC, Austin JC, Folkersen L, Lewis CM. A tool for translating polygenic scores onto the absolute scale using summary statistics. Eur J Hum Genet.
2022;30:339–48.
29. Genç E, Schlüter C, Fraenz C, Arning L, Metzen D, Nguyen HP, et al. Polygenic
scores for cognitive abilities and their association with different aspects ofgeneral intelligence—a deep phenotyping approach. Mol Neurobiol. 2021;58:
4145–56.
30. Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, et al. Analysis ofpolygenic risk score usage and performance in diverse human populations. Nat
Commun. 2019;10:3328.
31. Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. Variableprediction accuracy of polygenic scores within an ancestry group. ELife. 2020;9:e48376.
32. Sud A, Turnbull C, Houlston R. Will polygenic risk scores for cancer ever beclinically useful? Npj Precis Oncol. 2021;5:1–5.
33. Lewis CM, Vassos E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 2020;12:44.
34. Berens AJ, Cooper TL, Lachance J. The genomic health of ancient hominins. Hum
Biol. 2017;89:7–19.
35. Cox SL, Ruff CB, Maier RM, Mathieson I. Genetic contributions to variation inhuman stature in prehistoric Europe. Proc Natl Acad Sci. 2019;116:21484–92.

36. Burki T. Genetic apps: raising more questions than they answer? Lancet Digit
Health. 2020;2:e13–4.
37. Yuan J, Gordon A, Speyer D, Aufrichtig R, Zielinski D, Pickrell J, et al. DNA. Land isa framework to collect genomes and phenomes in the era of abundant genetic
information. Nat Genet. 2018;50:160.
38. Plomin R, von Stumm S. The new genetics of intelligence. Nat Rev Genet.
2018;19:148–59.
39. Neale MC, Cardon LR. Methodology for genetic studies of twins and families.
Dordrecht; London: Springer; 2011.
40. Posthuma D, De Geus EJC, Baaré WFC, Pol HEH, Kahn RS, Boomsma DI. The
association between brain volume and intelligence is of genetic origin. Nat
Neurosci. 2002;5:83–4.
41. Thompson PM, Cannon TD, Narr KL, van Erp T, Poutanen V-P, Huttunen M, et al.
Genetic inﬂuences on brain structure. Nat Neurosci. 2001;4:1253–8.
42. Visscher PM, Hill WG, Wray NR. Heritability in the genomics era—concepts andmisconceptions. Nat Rev Genet. 2008;9:255–66.
43. Gould E, Reeves AJ, Fallah M, Tanapat P, Gross CG, Fuchs E. Hippocampal neurogenesis in adult Old-World primates. Proc Natl Acad Sci USA. 1999;96:5263–7.
44. Kimura M. The neutral theory of molecular evolution. Cambridge, New York:
Cambridge University Press; 1983.

AUTHOR CONTRIBUTIONS
Kaisar Dauyey conducted all the data analyses, and Naruya Saitou supervised hisanalyses.

COMPETING INTERESTS
The authors declare no competing interests.

ADDITIONAL INFORMATION
Correspondence and requests for materials should be addressed to Naruya Saitou.
Reprints and permission information is available at http://www.nature.com/
reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claimsin published maps and institutional afﬁliations.

Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unlessindicated otherwise in a credit line to the material. If material is not included in thearticle’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directlyfrom the copyright holder. To view a copy of this license, visit http://creativecommons.
org/licenses/by/4.0/.

© The Author(s) 2022

Journal of Human Genetics