Genetic and climatic factors in the dispersal of
Anatomically Modern Humans Out of Africa
University of Adelaide
University of Adelaide https://orcid.org/0000-0001-7543-4864
University of Adelaide https://orcid.org/0000-0002-5351-3104
UNSW Sydney https://orcid.org/0000-0001-6733-0993
South Australian Museum
Shane Grey ( firstname.lastname@example.org )
Garvan Institute of Medical Research https://orcid.org/0000-0003-2160-1625
Biological Sciences - Article
Keywords: Anatomically Modern Humans, rapid genetic adaptation, genomic analyses
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Genetic and climatic factors in the dispersal of Anatomically Modern
Humans Out of Africa
Raymond Tobler1,9,*, Yassine Souilmi1,2,*, Christian D. Huber1,10,*, Nigel Bean3,4, Chris S.M.
Turney5, Alan Cooper6,†, Shane T. Grey7,8,†
1Australian Centre for Ancient DNA, The University of Adelaide, Adelaide, SA 5005,
2Environment Institute, The University of Adelaide, Adelaide, SA 5005, Australia
3ARC Centre of Excellence for Mathematical and Statistical Frontiers, The University of
Adelaide, Adelaide, SA 5005, Australia
4School of Mathematical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
5Chronos 14Carbon-Cycle Facility and Earth and Sustainability Science Research Centre
(ESSRC), University of New South Wales, Sydney, NSW 2052, Australia
6South Australian Museum, Adelaide, SA 5005, Australia and BlueSky Genetics, PO 287,
Ashton, SA 5137, Australia
7St Vincent's Clinical School, Faculty of Medicine, University of New South Wales,
Darlinghurst 2010, NSW, Australia
8Transplantation Immunology Group, Immunology Division, Garvan Institute of Medical
Research, Darlinghurst, NSW 2010, Australia
9Current address: Evolution of Cultural Diversity Initiative, Australian National University,
Canberra 2601, ACT, Australia
10 Current address: Department of Biology, Pennsylvania State University, University Park,
*Equal first co-authors
†Equal senior co-authors
Corresponding authors: R.T. (email@example.com), Y.S.
(firstname.lastname@example.org), AC. (email@example.com), S.T.G.
The evolutionarily recent dispersal of Anatomically Modern Humans (AMH) out of Africa
and across Eurasia provides an opportunity to study rapid genetic adaptation to multiple new
environments. Genomic analyses of modern human populations have detected limited signals
of strong selection such as hard sweeps1, but genetic admixture between populations2,3 is
capable of obscuring these patterns and is well known in recent human history, such as during
the Bronze Age4. Here we show that ancient human genomic datasets contain multiple
genetic signatures of strong selection including 57 hard sweeps, many with strong
associations with cold adaptation. Similar genetic signatures of adaptation are also observed
in adaptively-introgressed archaic hominin loci, as well as modern Arctic human groups.
Consistent targets include the regulation of fat storage, skin physiology, cilia function and
neural development; with multiple associations to modern western diseases. The
spatiotemporal patterns of the hard sweeps allow reconstruction of early AMH population
dispersals, and reveal a prolonged period of genetic adaptation (~80-50,000 years) following
their initial out of Africa movement, before a rapid spread across Eurasia reaching as far as
When Anatomically Modern Human populations moved Out of Africa (OoA) they
encountered a range of environments that were markedly different from their African past.
Despite this, modern human populations show few classical genetic signatures of strong
selection such as hard sweeps, where new or rare beneficial alleles have been driven to
high-frequency by selection. This has led to the suggestion that most recent human genetic
adaptation may have instead involved alternate modes of selection that leave less pronounced
signatures in genomes5,6 (e.g. ‘soft’ sweeps and polygenic selection; Supplementary
To examine whether recent population admixture may have obscured signatures of past
genetic adaptation we constructed a dataset containing genomic information and curated
metadata from 1,162 ancient western Eurasian genome datasets including both low-coverage
genomes and high-density Single Nucleotide Polymorphism (SNP) scans7, which are
concentrated mostly in western Eurasia between the early Holocene (~12ka) and Bronze Age8
(Fig. 1, Extended Data Figs. 1, 2, Extended Data Table 1).We were able to group 18 distinct
ancient populations based on their archaeological and genetic relationships. The genomic
sequences within each population were aligned and scanned for evidence of distorted allele
frequency patterns characteristic of hard selective sweeps (e.g. anomalously low diversity)
using SweepFinder29, which uses a dynamic sliding window approach to control for
demographic history and population structure (Supplementary Information 1). For
comparison, we also analysed five modern human populations (three from European
ancestry: Utah residents with Northern and Western European ancestry (CEU); Finnish in
Finland (FIN); Toscani in Italy (TSI); one Asian: Han Chinese in Beijing (CHB); and one
African: Yoruba in Ibadan, Nigeria (YRI); ref. 10).
Ancient human genomes reveal a hidden history of hard sweeps
In direct contrast to studies of modern human genomes11, we were able to identify 57 hard
sweeps (Extended Data Fig. 3, Extended Data Table 2) in the ancient populations with
high-confidence (study-wide false positive rate <11%; ref. 7), all of which were limited to
Eurasian populations and absent in the YRI African population. While some of the 56
detected hard sweeps were very common, none were present in all of the ancient Eurasian
populations, and they were almost entirely absent amongst other contemporary African
populations. This suggests that the sweeps most likely arose after the separation of the
founding AMH OoA population from African groups, but were probably not fixed prior to
the subsequent dispersal of this population across Eurasia. The SNP frequency differences
between ancient Eurasians and the Yoruba population were used to determine a set of
divergent marker alleles that characterize each sweep haplotype. These allowed the
ascertainment of 56 sweep haplotypes in ancient and modern human samples, after discarding
one (LINCO1153) with too few marker SNPs to make accurate measurements
(Supplementary Information 2).
The spatiotemporal distribution of the 56 hard sweeps provides a novel genetic marker for
early AMH population movements out of Africa and across Eurasia, analogous to the ~2%
Neandertal genomic content observed globally in modern non-African populations12,13. The
Neandertal admixture signal14 has been used to trace the dispersal of the ancestors of modern
populations across Eurasia and Island Southeast Asia (ISEA) as far as Australia and to date
this movement to 60-50ka12, and potentially as late as ~53-50ka (Supplementary Information
2). This timing is concordant with a sudden proliferation of early archaeological dates
reliably associated with AMH presence across Asia and Australia around 50ka15,16, and
molecular clock dating of mitochondrial, Y chromosome, and autosomal DNA which all
indicates the last common genetic ancestors of global non-African populations existed around
45-55ka17,18. Together, this suggests a major dispersal of AMH across Eurasia around 50-55ka
and indicates that any earlier AMH movements OoA did not measurably contribute to
subsequent human groups12.
The Eurasian dispersal appears to have occurred a considerable period (~50,000 years) after
the estimated ~100ka genetic separation of the OoA population from other African
populations12. This timing is consistent with widespread evidence of early AMH groups from
around 125ka throughout the Arabian Peninsula, from the Levant to the Gulf of Oman19. We
refer to this apparent prolonged delay as the Arabian Standstill (Supplementary Information
2), and during this period previous genetic studies have suggested the OoA population split
into the now extinct Basal Eurasians, and the Main Eurasians which subsequently admixed
with Neandertals and dispersed globally12 (Fig. 1).
Genetic selection in Paleolithic Eurasia
To examine potential genetic selection during these events we reconstructed the
spatiotemporal pattern of the hard sweep haplotypes using moderate- to high-coverage
genomic sequences of Late Pleistocene western Eurasian individuals up to ~45ka in age, as
well as indigenous Oceanic groups, such as Aboriginal Australians, whose genetic ancestry
stems from the initial Main Eurasian dispersal and who have remained largely isolated since
(Supplementary Information 2). We used the oldest point at which the sweep haplotype was
observed or inferred within the genetically reconstructed Eurasian dispersal process13 as
evidence that the selection pressure was likely to have been present at that time point, even if
the locus was potentially not yet fixed in all individuals. The sweeps appear to persist through
the complex series of population admixture events in late Pleistocene Europe20, suggesting
the selective pressure remained ongoing (Fig. 2). In Europe, the highest sweep frequencies
occurred prior to the onset of the Holocene before decreasing markedly, most notably during
the Bronze Age (from 5ka) which is a known period of extensive population admixture4.
Around half of the hard sweeps (31/56) appear to have reached relatively high-frequencies
during the Arabian Standstill phase as they are distributed very broadly across Eurasia in the
descendant ancient and modern populations, including distant Oceanic populations in very
different selective environments (Fig. 1, Extended Data Tables 2, 3). This large number of
sweeps suggests the ancestral OoA population had experienced selection over an extended
period of isolation, and model-based and linear regression analyses suggest this originated
around 80ka (Extended Data Fig. 4). A marked period of AMH population movements
occurred within the Arabian Peninsula around this time, associated with a brief moist climatic
The genomic data records the subsequent appearance of several additional groups of hard
sweeps as Main Eurasian populations pushed into the colder northern latitudes of Eurasia
around 53-51ka (Fig. 1; Supplementary Information 2). Genomes from the first (Initial Upper
Paleolithic) European and Asian AMH populations (~45-40ka; ref. 21) contain the earliest
observations of eight sweeps, while early West Eurasian individuals dated between 38-18ka
record a further ten sweeps (Fig. 1, Extended Data Table 3). The sweeps in western Eurasian
specimens appear to group into four distinct time bins, which correlate with early European
archaeological cultures (Fig. 1). After the Initial Upper Paleolithic, nine further sweeps were
detected in two specimens (Kostenki14, 38ka, and GoyetQ116-1, 35ka) associated with the
Aurignacian Culture (~43-35ka), often referred to as the first pan-European technocomplex22.
Further single sweeps appear in individuals associated with the subsequent Gravettian
Culture (35-25ka; represented by the Sunghir 1-4, 35-33ka, and Věstonice16, 31ka,
individuals; refs. 22,23), and towards the end of the Last Glacial Maximum in the Magdalenian
cultures as represented by the El Mirón specimen (19ka). The pattern of shared sweep signals
are consistent with previously recognized genetic replacements between the IUP,
Aurignacian, and Gravettian populations (Extended Data Table 4; Supplementary Information
2), which also occur close in time to two major geomagnetic events (the Laschamps and
Mono Lake excursions, respectively) suggested to have caused rapid environmental shifts24.
Individuals from late-glacial/Epigravettian cultures (e.g. Villabruna and the Azilian Bichon,
both ~14ka) contain a further six sweeps which appear to have originated earlier in
populations to the east, largely outside the sampling area, but spread geographically westward
into view of this study around this time.
It is notable that 14 of the hard sweeps (~25%) overlap with known regions of introgressed
archaic hominin DNA that have previously been identified as putative targets of selection
(Extended Data Figs. 5, 6), raising the possibility that some of the 56 sweeps may have been
driven by adaptively-introgressed (AI) hominin variants. This is consistent with suggestions
that Neandertal genetic adaptation to colder northern environments may have provided
beneficial alleles to the early Eurasian populations, and known AI variants associated with
immune 25, dietary, and climate adaptation26 (Supplementary Information 3). However, most
of these putative AI loci lie on the periphery of our sweep regions, and introgressed hominin
regions were actually underrepresented near the peak sweep signal (Fig. 3, Extended Data
Fig. 6). This suggests that the beneficial sweep variants were most likely AMH-derived,
removing introgressed hominin loci lying near to the beneficial variant while bringing linked
introgressed loci to higher frequencies through genetic hitchhiking, producing false positive
signals of adaptive introgression in previous studies.
Sustained adaptation to cold Eurasian environments
We applied iSAFE a recently developed method for localizing the adaptive locus27 to the 56
sweep regions observed in ancient Eurasians, and in 32 we were able to identify single driver
genes as the putative target for selective pressure, permitting functional analyses (Extended
Data Tables 4, 5; Supplementary Information 3). Surprisingly, the 32 ancient Eurasian driver
genes revealed a pattern of gene classes and biological functions strongly reminiscent of loci
previously identified as being under population-specific selection in multiple present-day
Arctic human populations (Supplementary Information 3). Both the ancient Eurasian driver
genes and a set of 49 high-confidence selected (i.e. candidate) genes from modern Arctic
human populations grouped with marked concordance around three functional categories:
neurological (31% and 33%, respectively); developmental (both 31%); and metabolic (28%
and 16%) (Tables 1, Extended Data 5). Furthermore, a similar level of functional
concordance was also observed with a set of 54 adaptively-introgressed Neandertal and
Denisovan candidate loci identified in modern OoA populations (neurological 35%,
developmental 33% and metabolic 22%).
A closer examination of the functions across the 32 ancient Eurasian driver genes, the 49
candidate selected genes in modern Arctic human populations28, and the 54 archaic hominin
AI loci revealed a number of layers of concordant biological connectivity, including multiple
biological processes known to be involved in human cold adaptation29 (Supplementary
Information 3). For instance, three ancient Eurasian driver genes play roles in fat metabolism
(Fig. 4), a key metabolic nexus for mammalian cold adaptation30. Namely, PPARD, a
metabolically-sensitive transcription factor that regulates fatty acid oxidation for the
generation of ATP or heat and is involved in adipogenesis, and SMCO and TMCC1 which
have been linked to variation in body fat. Within archaic hominin AI loci, PPARG (a
PPAR-family nuclear receptor like PPARD) and WDFY are required for formation of white
and brown adipocytes, which provide fuel storage as triglycerides or heat generation from
oxidative phosphorylation, respectively. Similarly, FADS1,2and 3within the selected genes
in modern Arctic groups also regulate fatty acid synthesis. Remarkably, most of these
selected genes are also directly linked in regulatory networks (Fig. 4). PPARD is a
transcription factor that regulates the expression of PPARG, which in turn is also a
transcription factor that regulates the expression of FADS1 and FADS2, as well as the archaic
hominin AI metabolism gene AGL26,31.
A third of the Eurasian single gene sweeps were associated with development
(Supplementary Information 3). DNAH6 and FBN1 are associated with body pattern and
body size (Extended Data Table 5), and cold temperature has been identified as a major
selective pressure for increased body size in humans32. There was also an unexpected
enrichment of genes involved in both the developmental formation and function of cilia
within the ancient Eurasian driver genes. This was mirrored within the putatively adaptive
genes in both modern Arctic populations and archaic hominin AI loci, and a similar pattern
previously identified in Arctic mammalian populations26,30 (Fig. 4; Supplementary
Information 3). Cilia are evolutionarily-conserved hair-like cell structures that can function as
cellular environmental sensors or provide locomotion, but are also important for lung and
airway health in cold and dry environments.
Genes associated with neuronal functions comprised 31% (10/32 genes) of the Eurasian
driver genes, 33% of selected loci in cold-adapted modern humans, and 35% of AI genes
from archaic hominins (Supplementary Information 3). The dominance of signals for
neurology-associated genes was not necessarily expected, but neural tissues play a central
role in coordinating environmental information into physiological and behavioural responses
necessary to navigate new environments33. Human cognitive performance is also impaired in
cold conditions34, and, intriguingly, eight of the ten selected Eurasian driver genes associated
with neuronal function are associated with severe retardation and developmental delay
phenotypes in humans (Tables 1, Extended Data Table 5). Collectively, the neuronal selected
genes highlight fundamental neurological processes of vesicle trafficking, growth of neurites
and cerebral cortex formation, suggesting that there has been selection on the maintenance of
environmental perception and cognitive functions in cold environments (Extended Data Table
5). In this regard, the driver gene MPP6 is required for nerve myelination, which changes in
response to environmental cues throughout life and may represent a plastic neural response to
environmental challenges (Supplementary Information 3).
Human genetic adaptation through time
Based on the temporal patterns, the genetic signals appear to reflect a consistent selective
pressure for cold adaptation, which had started by the middle of the Arabian Standstill period
and continued through the colonization of Eurasia and into the Last Glacial Maximum (20ka).
While the marked cold conditions characterizing much of late Pleistocene Eurasia are well
known, the Arabian Standstill was also characterized by a pronounced and sustained cooling
phase from 80ka, associated with the termination of Marine Isotope Stage 5, during which the
mean annual temperatures in the Arabian Peninsula are estimated to have decreased ~4˚C
(likely greater during the boreal winter)35. The limited ability of AS populations to migrate in
response to this major climatic change is likely to have exacerbated selective pressure,
consistent with the ~80ka estimated age for the earliest sweeps26 (Supplementary Information
2). Further major environmental challenges potentially included the Mt. Toba supervolcanic
eruption at 74ka, which heavily impacted the Indian Ocean and Arabian Peninsula area36. The
impacts of Mt. Toba on low latitude sites has been suggested to have been severe, with a
pronounced cooling period of centuries to millennia, associated with extreme aridity and
The large amount of genomic information now available from ancient and modern AMH
specimens, along with extensive databases of human functional and disease genetics,
provides a unique opportunity to use the global spread of AMH as a model system to study
the tempo and mode of evolutionary selection from a genomic perspective. Our analyses have
revealed the prevalence of hard sweeps in recent human history, many of which appear to
have been subsequently eroded by recent population admixture, with only around half
previously identified as potential soft, partial, or hard sweeps in modern population data7.
The hard sweeps provide an unexpected view of evolution, with the majority of gene targets
concentrated around evolutionarily-conserved intracellular machinery (Table 1, Extended
Data Table 5), dominated by enzymes, components of intracellular protein signaling
complexes, and transcription regulators as opposed to cell surface receptors and ligands,
which might seem more obviously associated with sensing and responding to new
environments. We also find that neurological processes appear to be under-appreciated
candidates for selection in adaptive responses. The latter observation most likely relates to the
critical role the nervous system and brain play in regulating homeostasis of peripheral
physiology in response to environmental cues including body temperature, circadian clock
responses, pregnancy, as well as the cardiovascular, metabolic and immune systems26,37–39
(Supplementary Information 3). Thus, recalibration of neurological processes towards new
physiological optima may represent a critical mechanism for rapid adaptation to changed
environmental conditions (e.g. cold). In contrast, the absence of genes involved in the
immune system stands out, especially as these appear to have been repeat targets for Arctic
human populations and archaic hominin AI loci after the initial Neandertal admixture
~53ka40,41. The latter raises the question of whether immune selection may have been
promoted by hominin admixture itself.
The large number and broad function of the selected loci detected in ancient Eurasians raises
the possibility that the speed of AMH movement Out of Africa and across Eurasia may have
been limited by the need for genetic adaptation to new environments (e.g. during the AS), as
much as the existing occupation of areas by archaic hominin groups. For example, the
Eurasian dispersal moved very rapidly eastwards through Asia and down as far as southern
Australia following a familiar savannah ecozone15,42 despite the presence of multiple
Denisovan and other ISEA hominin groups along this route (where admixture occurred), as
well as significant marine barriers through ISEA15,42. The contrasting delay before AMH
groups start to spread northwestwards throughout Europe from 47ka has often been explained
by the presence of Neandertal populations in the area43, but here we show this delay was
potentially associated with a distinct phase of genetic adaptation to cold northern
environments, first seen in our dataset as the set of sweeps in the Initial Upper Paleolithic
This study also highlights the importance of evolutionary history for understanding modern
disease. Haploinsufficiency in over half of the Eurasian driver genes causes Mendelian
disease phenotypes while 25% are associated with premature lethality (Table 1,
Supplementary Information 3), and their medical relevance is further indicated by the lack of
loss-of-function mutations in human lineages26,44 (Extended Data Fig. 7, Extended Data Table
5). Importantly, a number of the loci involve genes or functions associated with major
modern diseases including: the ciliopathies (e.g. DNAH6;RCBTB2), a recently recognized
severe disease class that includes sensory, immunological, reproductive as well as
developmental abnormalities45 (Extended Data Table 5); metabolic syndrome, including
obesity and diabetes (e.g. PPARD); and neurodegenerative diseases including dementia and
autism (TAF15;AMBRA1), all of which represent increasing or significant medical maladies
in present-day populations46,47. Our study highlights how understanding the evolutionary
history and specific environmental pressures shaping modern population genetic structure can
aid our understanding of the genetic basis of disease.
1. Pritchard, J. K., Pickrell, J. K. & Coop, G. The genetics of human adaptation: hard sweeps, soft
sweeps, and polygenic adaptation. Curr. Biol. 20, R208–15 (2010).
2. Huber, C. D., Nordborg, M., Hermisson, J. & Hellmann, I. Keeping it local: evidence for positive
selection in Swedish Arabidopsis thaliana. Mol. Biol. Evol. 31, 3026–3039 (2014).
3. Harris, R. B., Sackman, A. & Jensen, J. D. On the unfounded enthusiasm for soft selective
sweeps II: Examining recent evidence from humans, flies, and viruses. PLoS Genet. 14,
4. Haak, W. et al. Massive migration from the steppe was a source for Indo-European languages in
Europe. Nature 522, 207–211 (2015).
5. Schrider, D. R. & Kern, A. D. Soft Sweeps Are the Dominant Mode of Adaptation in the Human
Genome. Mol. Biol. Evol. 34, 1863–1877 (2017).
6. Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification
in genome-wide association studies. Elife 8, (2019).
7. Souilmi, Y. et al. Ancient human genomes reveal a hidden history of strong selection in Eurasia.
Cold Spring Harbor Laboratory 2020.04.01.021006 (2020) doi:10.1101/2020.04.01.021006.
8. Marciniak, S. & Perry, G. H. Harnessing ancient genomes to study the history of human
adaptation. Nat. Rev. Genet. 18, 659–674 (2017).
9. Huber, C. D., DeGiorgio, M., Hellmann, I. & Nielsen, R. Detecting recent selective sweeps while
controlling for mutation rate and background selection. Mol. Ecol. 25, 142–156 (2016).
10. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature
526, 68–74 (2015).
11. Szpak, M., Xue, Y., Ayub, Q. & Tyler-Smith, C. How well do we understand the basis of classic
selective sweeps in humans? FEBS Lett. 593, 1431–1448 (2019).
12. Bergström, A., Stringer, C., Hajdinjak, M., Scerri, E. M. L. & Skoglund, P. Origins of modern
human ancestry. Nature 590, 229–237 (2021).
13. Lipson, M. & Reich, D. A Working Model of the Deep Relationships of Diverse Modern Human
Genetic Lineages Outside of Africa. Mol. Biol. Evol. 34, 889–902 (2017).
14. Bergström, A. et al. Insights into human genetic variation and population history from 929
diverse genomes. Science 367, 674986 (2020).
15. O’Connell, J. F. et al. When did Homo sapiens first reach Southeast Asia and Sahul? Proc. Natl.
Acad. Sci. U. S. A. 115, 8482–8490 (2018).
16. Sun, X.-F. et al. Ancient DNA and multimethod dating confirm the late arrival of anatomically
modern humans in southern China. Proc. Natl. Acad. Sci. U. S. A. 118, e2019158118 (2021).
17. Hallast, P., Agdzhoyan, A., Balanovsky, O., Xue, Y. & Tyler-Smith, C. A Southeast Asian origin
for present-day non-African human Y chromosomes. Hum. Genet. 140, 299–307 (2021).
18. Posth, C. et al. Pleistocene Mitochondrial Genomes Suggest a Single Major Dispersal of
Non-Africans and a Late Glacial Population Turnover in Europe. Curr. Biol. 26, 827–833 (2016).
19. Groucutt, H. S. et al. Homo sapiens in Arabia by 85,000 years ago. Nat Ecol Evol 2, 800–809
20. Fu, Q. et al. The genetic history of Ice Age Europe. Nature 534, 200–205 (2016).
21. Hajdinjak, M. et al. Initial Upper Palaeolithic humans in Europe had recent Neanderthal ancestry.
Nature 592, 253–257 (2021).
22. Higham, T. et al. Precision dating of the Palaeolithic: a new radiocarbon chronology for the Abri
Pataud (France), a key Aurignacian sequence. J. Hum. Evol. 61, 549–563 (2011).
23. Banks, W. E. et al. An application of hierarchical Bayesian modeling to better constrain the
chronologies of Upper Paleolithic archaeological cultures in France between ca. 32,000–21,000
calibrated years before present. Quat. Sci. Rev. 220, 188–214 (2019).
24. Cooper, A. et al. A global environmental crisis 42,000 years ago. Science 371, 811–818 (2021).
25. Dannemann, M., Andrés, A. M. & Kelso, J. Introgression of Neandertal- and Denisovan-like
Haplotypes Contributes to Adaptive Variation in Human Toll-like Receptors. Am. J. Hum. Genet.
98, 22–33 (2016).
26. Materials and methods are available as supplementary materials.
27. Akbari, A. et al. Identifying the favored mutation in a positive selective sweep. Nat. Methods 15,
28. Fumagalli, M. et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation.
Science 349, 1343–1347 (2015).
29. Saltykova, M. M. The Main Physiological Mechanisms of Cold Adaptation in Humans.
Neuroscience and Behavioral Physiology vol. 48 543–550 (2018).
30. Yudin, N. S., Larkin, D. M. & Ignatieva, E. V. A compendium and functional characterization of
mammalian genes involved in adaptation to Arctic or Antarctic environments. BMC Genet. 18,
31. Fang, L. et al. PPARgene: A Database of Experimentally Verified and Computationally Predicted
PPAR Target Genes. PPAR Res. 2016, 6042162 (2016).
32. Will, M., Krapp, M., Stock, J. T. & Manica, A. Different environmental variables predict body
and brain size evolution in Homo. Nat. Commun. 12, 4116 (2021).
33. Corbetta, M., Patel, G. & Shulman, G. L. The reorienting system of the human brain: from
environment to theory of mind. Neuron 58, 306–324 (2008).
34. Mäkinen, T. M. Human cold exposure, adaptation, and performance in high latitude
environments. Am. J. Hum. Biol. 19, 155–164 (2007).
35. Tierney, J. E., deMenocal, P. B. & Zander, P. D. A climatic context for the out-of-Africa
migration. Geology 45, 1023–1026 (2017).
36. Williams, M. A. J. et al. Reply to the comment on ‘Environmental impact of the 73ka Toba
super-eruption in South Asia’ by M. A. J. Williams, S. H. Ambrose, S. van der Kaars, C.
Ruehlemann, U. Chattopadhyaya, J. Pal, P. R. Chauhan [Palaeogeography, Palaeoclimatology,
Palaeoecology 284 (2009) 295–314]. Palaeogeogr. Palaeoclimatol. Palaeoecol. 296, 204–211
37. Brunton, P. J. & Russell, J. A. The expectant brain: adapting for motherhood. Nat. Rev. Neurosci.
9, 11–25 (2008).
38. Krashes, M. J., Lowell, B. B. & Garfield, A. S. Melanocortin-4 receptor--regulated energy
homeostasis. Nat. Neurosci. 19, 206–219 (2016).
39. Nakamura, K. & Morrison, S. F. A thermosensory pathway that controls body temperature. Nat.
Neurosci. 11, 62–71 (2008).
40. Zammit, N. W. et al. Denisovan, modern human and mouse TNFAIP3 alleles tune A20
phosphorylation and immunity. Nat. Immunol. 20, 1299–1310 (2019).
41. Gittelman, R. M. et al. Archaic Hominin Admixture Facilitated Adaptation to Out-of-Africa
Environments. Curr. Biol. 26, 3375–3382 (2016).
42. Teixeira, J. C. & Cooper, A. Using hominin introgression to trace modern human dispersals.
Proc. Natl. Acad. Sci. U. S. A. 116, 15327–15332 (2019).
43. Hublin, J.-J. et al. Initial Upper Palaeolithic Homo sapiens from Bacho Kiro Cave, Bulgaria.
Nature 581, 299–302 (2020).
44. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456
humans. Nature 581, 434–443 (2020).
45. Reiter, J. F. & Leroux, M. R. Genes and molecular pathways underpinning ciliopathies. Nat. Rev.
Mol. Cell Biol. 18, 533–547 (2017).
46. Saklayen, M. G. The Global Epidemic of the Metabolic Syndrome. Curr. Hypertens. Rep. 20, 12
47. Hebert, L. E., Weuve, J., Scherr, P. A. & Evans, D. A. Alzheimer disease in the United States
(2010-2050) estimated using the 2010 census. Neurology 80, 1778–1783 (2013).
48. Peter, B. M. 100,000 years of gene flow between Neandertals and Denisovans in the Altai
mountains. bioRxiv 2020.03.13.990523 (2020) doi:10.1101/2020.03.13.990523.
49. Rasmussen, S. O. et al. A stratigraphic framework for abrupt climatic changes during the Last
Glacial period based on three synchronized Greenland ice-core records: refining and extending
the INTIMATE event stratigraphy. Quaternary Science Reviews vol. 106 14–28 (2014).
Acknowledgments: We thank the many colleagues have provided valuable support and
suggestions; Funding: We thank the ARC (A.C., R.T., Y.S., C.D.H., N.B., C.S.M.T) and
NHMRC (S.T.G.) for research funding and Fellowships, and acknowledge the U. Adelaide
Environment Institute for support; Author contributions: R.T., Y.S., A.C., S.T.G., and
C.D.H. designed and performed the research, all authors analysed the data; A.C., R.T., Y.S.,
and S.T.G. wrote the paper with input from all authors; Competing interests: The authors
declare no competing interests; Data and materials availability: All data are available in the
main text or the supplementary materials.
Extended Data Figures 1-12
Extended Data Tables 1-8
Data Files 1 to 56
Figure 1. Dispersal of Anatomically Modern Humans Out of Africa. Simplified
reconstruction of the movement Out of Africa (125-80ka) into Arabia, and subsequent rapid
expansion across Eurasia 60-50ka (or potentially 53-51ka26,44; see Supplementary Information
2), based on the spatiotemporal distribution of the 56 hard sweeps and archaeological data.
Initial AMH movement into the Arabian Peninsula (~125ka) was followed by an extended
period of genetic isolation starting around ~100ka, termed here the Arabian Standstill, during
which Basal and Main Eurasians split and hard sweeps accumulate from ~80-70ka. Shortly
after a major phase of Neandertal gene flow (dark blue arrow) ~53-50ka, the Main Eurasian
lineage rapidly dispersed across Eurasia as far as Australia by 50ka. Discrete spatiotemporal
groupings of the 56 hard sweeps are shown (boxes 1-4), with an undated group (box 5)
appearing to originate outside the sampling range. Early European movements are simplified
into 3 time bins (boxes 2-4) for clarity, with an oval representing the Aurignacian. Areas of
inferred admixture with archaic hominins are indicated (i-iv; Denisovans: N; Neandertals).
Key ancient specimens/sites: U=Ust’-Ishim, T=Tianyuan, K=Kostenki, S=Sunghir, G=Goyet,
A=Andaman Islands. The function of identified driver genes is indicated by colour (key,
along with an approximate timescale. brown = reproduction, orange = cardiovascular).
Underlining indicates sweeps identified as overlapping with adaptively-introgressed archaic
Figure 2. The accumulation of sweep haplotypes through time. Presence of each of the 56
sweep haplotypes in ancient samples (circles) and modern populations (triangles; averages
and standard deviations shown; data obtained from the 1000 Genomes Project [1KGP] and
Human Genome Diversity Panel [HGDP]). By fitting a local regression (LOESS) of sweep
count (y-axis) as a function of sample age (x-axis; samples >10ka are individually labelled)
in ancient West Eurasians, we observe that the number of sweeps steadily increases
throughout the Upper Paleolithic (~50ka to ~12ka), before plateauing in the early Mesolithic
(~15ka) and undergoing a sharp decline that was most pronounced in the Bronze Age (~5ka
to ~2.5ka), coincident with high population admixture in Europe. Sweep numbers increase
again in European populations across the past few thousand years, potentially as a result of
the underlying sweep pressure persisting until recent times. Mean sweep counts for ancient
samples (dashed lines; with Moroccan Iberomaurusian samples [i.e. TAF10-14] omitted) are
consistent with their modern counterparts, suggesting that modern Oceanians should provide
reasonable proxies for estimating ancestral sweep presence at the time of population
separation from Main Eurasian lineages.
Figure 3. Introgressed archaic hominin loci in the vicinity of ancient hard sweeps. To
determine the distribution of introgressed hominin loci around each sweep, we used
admixfrog software 48 to directly infer these loci in ancient genomes prior to the Holocene
admixture events. The inferred loci are shown for each of the 27 Anatolian EF individuals
(black lines) for three sweeps (labelled panels), with the resulting allele frequencies at each
position being shown as a purple line. For comparison, we also show the SweepFinder2 CLR
scores (blue lines), with the maximum score indicating the most likely location of the
underlying causal allele. Each gene in the region is shown as a coloured rectangle, with the
colour indicating the genescore used to identify sweeps (see key). Notably, introgressed loci
tend to occur at negligible frequencies beneath the peak CLR score and at higher frequencies
when moving further away from the peak. This pattern that was more generally borne out
across all sweeps (bottom right panel; black line = mean frequency in 25kb bins either side of
peak, grey shading = 2 standard errors, red dashed line = mean frequency near peak) – with
introgressed loci being significantly more common >150kb from the peak than at the peak –
consistent with introgressed loci hitchhiking on a beneficial AMH-derived variant.
Figure 4. Convergent signals of selection in ancient Eurasian (Green), archaic hominin
(Red), modern cold-adapted human groups (Blue); both ancient Eurasian and cold-adapted
humans (Green*); both archaic hominin and cold-adapted humans (Purple) in genes that: (A),
regulate metabolism through adipogenesis, as well as fat synthesis and fat distribution.
Arrows indicate gene regulatory networks; (B) genes involved in cilia function, particularly
formation of the basal body complex and dynein motor complexes; and (C) genes that control
skin physiology including the ‘woolly’ phenotype, wound healing, and skin formation; as
well as (D) pigmentation through the formation of melanosomes, melanin synthesis within
melanosomes and melanosome transport to the cell periphery. See S5 for detailed gene
characteristics and functions.
Figure 5. Environmental reconstruction for the Arabian Standstill. AMH groups on the
Arabian Peninsula experienced severe cold conditions with the onset of Marine Isotope Stage
4 (~79ka), potentially further exacerbated by the Toba Eruption (~74ka). (A) NGRIP δ18O
record reported on the GICC05 timescale Before Present (CE 1950) 49; Greenland Stadial 13
(GS-13) and Heinrich 5 (H5), and Mt. Toba eruption 36 are shown. (B) Mean annual sea
surface temperatures (SSTs) from the Gulf of Aden marine core MD90-963 35. Late Holocene
(last 2.5ka) temperature range shown for comparison. (C) Hydroclimate changes in northeast
Africa reconstructed from stable hydrogen isotopic composition of leaf waxes corrected for
ice volume contributions from MD90-963 35. Horizontal bars define age ranges for key AMH
events across the Arabian Peninsula and Eurasia, including potential for earlier Neandertal
gene flow (dashed line) during Arabian Standstill 26,44.
Table 1. (A) Biological role of genes identified as under strong selection in Ancient Eurasians,
cold-adapted modern human groups and archaic hominin introgressed loci. Frequency [%] is
calculated from the total number of genes annotated for each respective data set (Extended Data
Table 5). (B) Key biological impacts and functions of genes identified as under strong selection in
Ancient Eurasians. Frequency [%] is calculated from the total number of genes annotated for each
respective data set (Extended Data Table 5). Lethal phenotype defined by spontaneous embryonic
lethality or premature lethality post-partum in humans or animals. Constrained genes identified by
LOUEF score ≤0.5. Major physiology impact defined as a loss-of-function mutation in human
subjects and or from gene targeting studies in animal models which cause at least one of;
premature lethality; physical malformations; or, developmental delay. Gene functions defined as
Membrane Proteins (receptors, ion pumps, transporters, tethered proteins), Extracellular Proteins
(secreted or otherwise released), or Intracellular Proteins (defined as either Enzymes;
Transcription Regulators, Signalling molecules etc). Signalling molecules were able to be further
This is a list of supplementary les associated with this preprint. Click to download.