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Population genomics of Bronze Age Eurasia


Abstract and Figures

The Bronze Age of Eurasia (around 3000-1000 BC) was a period of major cultural changes. However, there is debate about whether these changes resulted from the circulation of ideas or from human migrations, potentially also facilitating the spread of languages and certain phenotypic traits. We investigated this by using new, improved methods to sequence low-coverage genomes from 101 ancient humans from across Eurasia. We show that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia. Our findings are consistent with the hypothesized spread of Indo-European languages during the Early Bronze Age. We also demonstrate that light skin pigmentation in Europeans was already present at high frequency in the Bronze Age, but not lactose tolerance, indicating a more recent onset of positive selection on lactose tolerance than previously thought.
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ARTICLE doi:10.1038/nature14507
Population genomics of Bronze Age
Morten E. Allentoft
*, Martin Sikora
*, Karl-Go
¨ran Sjo
, Simon Rasmussen
, Morten Rasmussen
, Jesper Stenderup
Peter B. Damgaard
, Hannes Schroeder
, Torbjo
¨rn Ahlstro
, Lasse Vinner
, Anna-Sapfo Malaspinas
, Ashot Margaryan
Tom Higham
, David Chivall
, Niels Lynnerup
, Lise Harvig
, Justyna Baron
, Philippe Della Casa
, PawełDa˛browski
Paul R. Duffy
, Alexander V. Ebel
, Andrey Epimakhov
, Karin Frei
, Mirosław Furmanek
, Tomasz Gralak
, Andrey Gromov
Stanisław Gronkiewicz
, Gisela Grupe
, Tama
´s Hajdu
, Radosław Jarysz
, Valeri Khartanovich
, Alexandr Khokhlov
´ria Kiss
, Jan Kola
, Aivar Kriiska
, Irena Lasak
, Cristina Longhi
, George McGlynn
, Algimantas Merkevicius
Inga Merkyte
, Mait Metspalu
, Ruzan Mkrtchyan
, Vyacheslav Moiseyev
, La
, Gyo
¨rgy Pa
, Dalia Pokutta
Łukasz Pospieszny
, T. Douglas Price
, Lehti Saag
, Mikhail Sablin
, Natalia Shishlina
´clav Smrc
, Vasilii I. Soenov
Vajk Szevere
, Guszta
, Synaru V. Trifanova
, Liivi Varul
, Magdolna Vicze
, Levon Yepiskoposyan
Vladislav Zhitenev
, Ludovic Orlando
, Thomas Sicheritz-Ponte
, Søren Brunak
, Rasmus Nielsen
, Kristian Kristiansen
& Eske Willerslev
The Bronze Age of Eurasia (around 3000–1000 BC) was a period of major cultural changes. However, there is debate about
whether these changes resulted from the circulation of ideas or from human migrations, potentially also facilitating the
spread of languages and certain phenotypic traits. We investigated this by using new, improved methods to sequence
low-coverage genomes from 101 ancient humans from across Eurasia. We show that the Bronze Age was a highly
dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of
present-day demographic structure in both Europe and Asia. Our findings are consistent with the hypothesized spread
of Indo-European languages during the Early Bronze Age.We also demonstrate that light skin pigmentation in Europeans
was already present at high frequency in the Bronze Age, but not lactose tolerance, indicating a more recent onset of
positive selection on lactose tolerance than previously thought.
The processes that created the genetic landscape of contemporary
human populations of Europe and Asia remain contentious. Recent
studies have revealed that western Eurasians and East Asians diverged
outside Africa between 45 and 36.2 thousand years before present (45
and 36.2 kyr BP)
and that East Asians, but not Europeans, received
subsequent gene flow from remnants of an earlier migration into Asia
of Aboriginal Australian ancestors at some point before 20 kyr BP
There is evidence that the western Eurasian branch constituted a
meta-population stretching from Europe to Central Asia
and that
it contributed genes to both modern-day western Eurasians
and early
indigenous Americans
. The early Europeans received gene flow
from the Middle East during the Neolithisation (transition from hunt-
ing-gathering to farming) around 8–5 kyr BP
and possibly also
from northern Asia
. However, what happened hereafter, during
the Bronze Age, is much less clear.
The archaeological record testifies to major cultural changes in
Europe and Asia after the Neolithic period. By 3000 BC, the
Neolithic farming cultures in temperate Eastern Europe appear to
be largely replaced by the Early Bronze Age Yamnaya culture, which
is associated with a completely new perception of family, property and
11 JUNE 2015 | VOL 522 | NATURE | 167
*These authors contributed equally to this work.
Centre for GeoGenetics, Natural History Museum, University of Copenhagen, Øster Voldgade 5-7, 1350 Copenhagen K, Denmark.
Department of Historical Studies, University of Gothenburg, 405 30
Gothenburg, Sweden.
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Kgs Lyngby, Denmark.
Faculty of Archaeology, Leiden University,
2300 Leiden, The Netherlands.
Department of Archaeology and Ancient History, Lund University, 221 00 Lund, Sweden.
Oxford Radiocarbon Accelerator Unit, University of Oxford, Oxford OX1 3QY, UK.
Unit of Forensic Anthropology, Department of Forensic Medicine, University of Copenhagen, 2100 Copenhagen, Denmark.
Institute of Archaeology, University of Wrocław, 50-139 Wrocław, Poland.
Archaeological Institute, University of Zurich, CH-8006, Zurich, Switzerland.
Department of Anatomy, Wrocław Medical University, 50-368 Wrocław, Poland.
Department of Anthropology, University of
Toronto, Toronto ONM5S 2S2, Canada.
Department of Archeology and General History, Gorno-Altaisk State University, 649000 Gorno-Altaisk, Russia.
Institute of History and Archaeology RAS (South
Ural Department), South Ural State University, 454080 Chelyabinsk, Russia.
Environmental Research and Material Science and Centre for Textile Research, The National Museum of Denmark, 1471
Copenhagen K, Denmark.
Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) RAS, 199034 St Petersburg, Russia.
Department of Anthropology, Polish Academy of Sciences, 50–
449 Wrocław, Poland.
Biocentre of the Ludwig-Maximilian-University Mu
¨nchen, 82152 Munich, Germany.
Department of Biological Anthropology, Institute of Biology, Eo
¨s Lora
´nd University, H-1117
Budapest, Hungary.
Department of Anthropology, Hungarian Natural History Museum, H-1083 Budapest, Hungary.
The Archaeological Museum of Wrocław, 50-077 Wrocław, Poland.
Samara State
Academy of Social Science and Humanities, 443099 Samara, Russia.
Institute of Archaeology of the Hungarian Academy of Sciences, Research Center for the Humanities, H-1250 Budapest, Hungary.
Institute of Archaeology and Museology, Faculty of Arts, Masaryk University, CZ-602 00 Brno, Czech Republic.
Department of Vegetation Ecology, Institute of Botany of the Czech Academy of Sciences,
CZ-602 00 Brno, Czech Republic.
Department of Archaeology, University of Tartu, 51003 Tartu, Estonia.
Archaeological Superintendence of Lombardy, 20123 Milano, Italy.
Department of
Archaeology, University of Vilnius, LT-01513 Vilnius, Lithuania.
The SAXO Institute, University of Copenhagen, 2300 Copenhagen S, Denmark.
Department of Evolutionary Biology, Estonian Biocentre
and University of Tartu, 51010 Tartu, Estonia.
Department of History, Yerevan State University, 0025 Yerevan, Armenia.
Hungarian National Museum, H-1083 Budapest, Hungary.
Department of
Biological Anthropology, University of Szeged, H-6726 Szeged, Hungary.
Institute of Archaeology and Ethnology of the Polish Academy of Sciences, 61-612 Poznan
´, Poland.
Laboratory for
Archaeological Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.
Zoological Institute of the Russian Academy of Sciences, 199034 St Petersburg, Russia.
Department of
Archaeology, State Historical Museum, 109012 Moscow, Russia.
Institute for History of Medicine and Foreign Languages of the First Faculty of Medicine, Charles University, 121 08 Prague, Czech
Research Center for the History and Culture of the Turkic Peoples, Gorno-Altaisk State University, 649000 Gorno-Altaisk, Russia.
Department of Pre- and Early History, Institute of
Archaeological Sciences, Faculty of Humanities, Eo
¨s Lora
´nd University, H-1088 Budapest, Hungary.
Matrica Museum, 2440 Sza
´zhalombatta, Hungary.
Laboratory of Ethnogenomics, Institute of
Molecular Biology, National Academy of Sciences, 0014 Yerevan, Armenia.
Department of Archaeology, Faculty of History, Moscow State University, 119991 Moscow, Russia.
Novo Nordisk Foundation
Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark.
Center for Theoretical Evolutionary Genetics, University of California, Berkeley, California 94720-3140, USA.
G2015 Macmillan Publishers Limited. All rights reserved
, rapidly stretching from Hungary to the Urals
. By
2800 BC a new social and economic formation, variously named
Corded Ware, Single Grave or Battle Axe cultures developed in tem-
perate Europe, possibly deriving from the Yamnaya background, and
culturally replacing the remaining Neolithic farmers
(Fig. 1). In
western and Central Asia, hunter-gatherers still dominated in Early
Bronze Age, except in the Altai Mountains and Minusinsk Basin
where the Afanasievo culture existed with a close cultural affinity to
(Fig. 1). From the beginning of 2000 BC, a new class of
master artisans known as the Sintashta culture emerged in the Urals,
building chariots, breeding and training horses (Fig. 1), and pro-
ducing sophisticated new weapons
. These innovations quickly
spread across Europe and into Asia where they appeared to give rise
to the Andronovo culture
(Fig. 1). In the Late Bronze Age around
1500 BC, the Andronovo culture was gradually replaced by the
Mezhovskaya, Karasuk, and Koryakova cultures
. It remains debated
if these major cultural shifts during the Bronze Age in Europe and
Asia resulted from the migration of people or through cultural dif-
fusion among settled groups
, and if the spread of the Indo-
European languages was linked to these events or predates them
Archaeological samples and DNA retrieval
Genomes obtained from ancient biological remains can provide
information on past population histories that is not retrievable from
contemporary individuals
. However, ancient genomic studies have
so far been restricted to single or a few individuals because of the
degraded nature of ancient DNA making sequencing costly and time
. To overcome this, we increased the average output of
authentic endogenous DNA fourfold by: (1) targeting the outer
cementum layer in teeth rather than the inner dentine layer
(2) adding a ‘pre-digestion’ step to remove surface contaminants
and (3) developing a new binding buffer for ancient DNA extraction
(Supplementary Information, section 3). This allowed us to obtain
low-coverage genome sequences (0.01–7.43average depth, overall
average equal to 0.73) of 101 Eurasian individuals spanning the entire
Bronze Age, including some Late Neolithic and Iron Age individuals
(Fig. 1, Supplementary Information, sections 1 and 2). Our data set
includes 19 genomes, between 1.1–7.43average depth, thereby doub-
ling the number of existing Eurasian ancient genomes above 13
coverage (ref. 27).
Bronze Age Europe
By analysing our genomic data in relation to previously published
ancient and modern data (Supplementary Information, section 6),
we find evidence for a genetically structured Europe during the
Bronze Age (Fig. 2; Extended Data Fig. 1; and Supplementary Figs 5
and 6). Populations in northern and central Europe were composed of
a mixture of the earlier hunter-gatherer and Neolithic farmer
groups, but received ‘Caucasian’ genetic input at the onset of the
Bronze Age (Fig. 2). This coincides with the archaeologically well-
defined expansion of the Yamnaya culture from the Pontic-Caspian
steppe into Europe (Figs 1 and 2). This admixture event resulted in the
formation of peoples of the Corded Ware and related cultures, as
supported by negative ‘admixture’ f
statistics when using Yamnaya
as a source population (Extended Data Table 2, Supplementary Table
12). Although European Late Neolithic and Bronze Age cultures such
Early/mid second
Early/mid second
Corded Ware
Corded Ware
Third millennium BC
Third millennium BC
0 1,000 km
01,000 km
3400 BC 2600 BC 1800 BC 1000 BC 200 BC 600 AD
Iron Age
Afontova Gora
Nordic Late BA
Late BA
Middle BA
Nordic LN-EBA
Nordic LN
Bell Beaker
Stalingrad Q.
Nordic MN B
Remedello, CA
Stalingrad quarry,
Yamnaya, CA-
Afanasievo, EBA
Battle Axe and
Corded Ware,
Nordic MN B
Bell Beaker, CA-
Okunevo, EBA
Unetice, EBA
Maros, MBA
Sintashta, EBA
Nordic LN
Vatya, MBA
Nordic LN-EBA
Andronovo, BA
Middle BA
Karasuk, LBA
Late BA
Nordic Late BA
Afontova Gora,
Velika Gruda,
Iron Age
01,000 km
Figure 1
Distribution maps of ancient samples. Localities, cultural
associations, and approximate timeline of 101 sampled ancient individuals
from Europe and Central Asia (left). Distribution of Early Bronze Age cultures
Yamnaya, Corded Ware, and Afanasievo with arrows showing the Yamnaya
expansions (top right). Middle and Late Bronze Age cultures Sintashta,
Andronovo, Okunevo, and Karasuk with the eastward migration indicated
(bottom right). Black markers represent chariot burials (2000–1800 BC) with
similar horse cheek pieces, as evidence of expanding cultures. Tocharian is the
second-oldest branch of Indo-European languages, preserved in Western
China. CA, Copper Age; MN, Middle Neolithic; LN, Late Neolithic; EBA, Early
Bronze Age; MBA, Middle Bronze Age; LBA, Late Bronze Age; IA, Iron Age;
BAC, Battle Axe culture; CWC, Corded Ware culture.
168 | NATURE | VOL 522 | 11 JUNE 2015
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as Corded Ware, Bell Beakers, Unetice, and the Scandinavian cultures
are genetically very similar to each other (Fig. 2), they still display a
cline of genetic affinity with Yamnaya, with highest levels in Corded
Ware, lowest in Hungary, and central European Bell Beakers being
intermediate (Fig. 2b and Extended Data Table 1). Using D-statistics,
we find that Corded Ware and Yamnaya individuals form a clade to
the exclusion of Bronze Age Armenians (Extended Data Table 1)
showing that the genetic ‘Caucasus component’ present in Bronze
Age Europe has a steppe origin rather than a southern Caucasus
origin. Earlier studies have shown that southern Europeans received
substantial gene flow from Neolithic farmers during the Neolithic
Despite being slightly later, we find that the Copper Age Remedello
culture in Italy does not have the ‘Caucasian’ genetic component and
is still clustering genetically with Neolithic farmers (Fig. 2; Extended
Data Fig. 1 and Supplementary Fig. 6). Hence this region was either
unaffected by the Yamnaya expansion or the Remedello pre-dates
such an expansion into southern Europe. The ‘Caucasian’ component
is clearly present during Late Bronze Age in Montenegro (Fig. 2b).
The close affinity we observe between peoples of Corded Ware and
Sintashta cultures (Extended Data Fig. 2a) suggests similar genetic
sources of the two, which contrasts with previous hypotheses placing
the origin of Sintastha in Asia or the Middle East
. Although we
cannot formally test whether the Sintashta derives directly from an
eastward migration of Corded Ware peoples or if they share common
ancestry with an earlier steppe population, the presence of European
Neolithic farmer ancestry in both the Corded Ware and the Sintashta,
combined with the absence of Neolithic farmer ancestry in the earlier
Yamnaya, would suggest the former being more probable (Fig. 2b and
Extended Data Table 1).
Bronze Age Asia
We find that the Bronze Age in Asia is equally dynamic and char-
acterized by large-scale migrations and population replacements. The
Early Bronze Age Afanasievo culture in the Altai-Sayan region is
genetically indistinguishable from Yamnaya, confirming an eastward
expansion across the steppe (Figs 1 and 3b; Extended Data Fig. 2b and
Extended Data Table 1), in addition to the westward expansion into
Europe. Thus, the Yamnaya migrations resulted in gene flow across
vast distances, essentially connecting Altai in Siberia with Scandinavia
in the Early Bronze Age (Fig. 1). The Andronovo culture, which arose
in Central Asia during the later Bronze Age (Fig. 1), is genetically
closely related to the Sintashta peoples (Extended Data Fig. 2c), and
clearly distinct from both Yamnaya and Afanasievo (Fig. 3b and
Extended Data Table 1). Therefore, Andronovo represents a temporal
and geographical extension of the Sintashta gene pool. Towards the
end of the Bronze Age in Asia, Andronovo was replaced by the
Karasuk, Mezhovskaya, and Iron Age cultures which appear multi-
ethnic and show gradual admixture with East Asians (Fig. 3b and
Extended Data Table 2), corresponding with anthropological and
biological research
. However, Iron Age individuals from Central
Asia still show higher levels of West Eurasian ancestry than contem-
porary populations from the same region (Fig. 3b). Intriguingly, indi-
viduals of the Bronze Age Okunevo culture from the Sayano-Altai
region (Fig. 1) are related to present-day Native Americans (Extended
Data Fig. 2d), which confirms previous craniometric studies
. This
finding implies that Okunevo could represent a remnant population
related to the Upper Palaeolithic Mal’ta hunter-gatherer population
from Lake Baikal that contributed genetic material to Native
0.05 0.00 0.05 0.10
Europe S
Europe NE
0.05 0.00 0.05
West Asia
0.05 0.00 0.05
Bronze Age
Bell Beaker
Corded Ware
Contemporary Eurasians
6000 BC 1000 BC 2900 BC 800 BC
Bronze Age
Bronze Age
steppe / Caucasus
Figure 2
Genetic structure of ancient Europe and the Pontic-Caspian
steppe. a, Principal component analysis (PCA) of ancient individuals (n593)
from different periods projected onto contemporary individuals from Europe,
West Asia, and Caucasus. Grey labels represent population codes showing
coordinates for individuals (small) and population median (large). Coloured
circles indicate ancient individuals b, ADMIXTURE ancestry components
(K516) for ancient (n593) and selected contemporary individuals. The
width of the bars representing ancient individuals is increased to aid
visualization. Individuals with less than 20,000 SNPs have lighter colours.
Coloured circles indicate corresponding group in the PCA. Probable
Yamnaya-related admixture is indicated by the dashed arrow.
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Spread of the Indo-European languages
Historical linguists have argued that the spread of the Indo-European
languages must have required migration combined with social or
demographic dominance, and this expansion has been supported by
archaeologists pointing to striking similarities in the archaeological
record across western Eurasia during the third millennium BC
Our genomic evidence for the spread of Yamnaya people from the
Pontic-Caspian steppe to both northern Europe and Central Asia
during the Early Bronze Age (Fig. 1) corresponds well with the
hypothesized expansion of the Indo-European languages. In contrast
to recent genetic findings
, however, we only find weak evidence for
admixture in Yamnaya, and only when using Bronze Age Armenians
and the Upper Palaeolithic Mal’ta as potential source populations
(Z522.39; Supplementary Table 12). This could be due to the
absence of eastern hunter-gatherers as potential source population
for admixture in our data set. Modern Europeans show some genetic
links to Mal’ta
that has been suggested to form a third European
ancestral component (Ancestral North Eurasians (ANE))
. Rather
than a hypothetical ancient northern Eurasian group, our results
reveal that ANE ancestry in Europe probably derives from the spread
of the Yamnaya culture that distantly shares ancestry with Mal’ta
(Figs 2b and 3b and Extended Data Fig. 3).
Formation of Eurasian genetic structure
It is clear from our autosomal, mitochondrial DNA and Y chro-
mosome data (Extended Data Fig. 6) that the European and Central
Asian gene pools towards the end of the Bronze Age mirror
present-day Eurasian genetic structure to an extent not seen in
the previous periods (Figs 2 and 3; Extended Data Fig. 1 and
Supplementary Fig. 6). Our results imply that much of the basis
of the Eurasian genetic landscape of today was formed during the
complex patterns of expansions, admixture and replacements dur-
ing this period. We find that many contemporary Eurasians show
lower genetic differentiation (F
) with local Bronze Age groups
than with earlier Mesolithic and Neolithic groups (Extended Data
Figs 4 and 5). Notable exceptions are contemporary populations
from southern Europe such as Sardinians and Sicilians, which
show the lowest F
with Neolithic farmers. In general, the levels
of differentiation between ancient groups from different temporal
and cultural contexts are greater than those between contempor-
ary Europeans. For example, we find pairwise F
50.08 between
Mesolithic hunter-gatherers and Bronze Age individuals from
Corded Ware, which is nearly as high as F
between contem-
porary East Asians and Europeans (Extended Data Fig. 5). These
results are indicative of significant temporal shifts in the gene
pools and also reveal that the ancient groups of Eurasia were
genetically more structured than contemporary populations.
The diverged ancestral genomic components must then have dif-
fused further after the Bronze Age through population growth,
combined with continuing gene flow between populations, to
generate the low differentiation observed in contemporary west
0.02 0.00 0.02
East Asia
West Eurasia
0.02 0.00 0.02
Early / Middle Bronze Age
Europe NE
0.02 0.00 0.02
Late Bronze Age / Iron Age
Afontova Gora
Contemporary Eurasians
22000 BC 2900 BC 200 AD
Bronze Age
Stalingrad Quarry
Afontova Gora
Early Middle Late Iron Age
Figure 3
Genetic structure of Bronze Age Asia. a, Principal component
analysis (PCA) of ancient individuals (n540) from different periods projected
onto contemporary non-Africans. Grey labels represent population codes
showing coordinates for individuals (small) and population median (large).
Coloured circles indicate ancient individuals. b, ADMIXTURE ancestry
components (K516) for ancient (n540) and selected contemporary
individuals. The width of the bars representing ancient individuals is increased
to aid visualization. Individuals with less than 20,000SNPs have lighter colours.
Coloured circles indicate corresponding group in the PCA. Shared ancestry of
Mal’ta with Yamnaya (green component) and Okunevo (grey component) is
indicated by dashed arrows.
170 | NATURE | VOL 522 | 11 JUNE 2015
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Temporal dynamics of selected SNPs
The size of our data set allows us to investigate the temporal dynamics
of 104 genetic variants associated with important phenotypic traits or
putatively undergoing positive selection
(Supplementary Table 13).
Focusing on four well-studied polymorphisms, we find that two single
nucleotide polymorphisms (SNPs) associated with light skin pig-
mentation in Europeans exhibit a rapid increase in allele frequency
(Fig. 4). For rs1426654, the frequency of the derived allele increases
from very low to fixation within a period of approximately 3,000 years
between the Mesolithic and Bronze Age in Europe. For rs12913832, a
major determinant of blue versus brown eyes in humans, our results
indicate the presence of blue eyes already in Mesolithic hunter-gath-
erers as previously described
. We find it at intermediate frequency in
Bronze Age Europeans, but it is notably absent from the Pontic-
Caspian steppe populations, suggesting a high prevalence of brown
eyes in these individuals (Fig. 4). The results for rs4988235, which is
associated with lactose tolerance, were surprising. Although tolerance
is high in present-day northern Europeans, we find it at most at low
frequency in the Bronze Age (10% in Bronze Age Europeans; Fig. 4),
indicating a more recent onset of positive selection than previously
. To further investigate its distribution, we imputed all
SNPs in a 2 megabase (Mb) region around rs4988235 in all ancient
individuals using the 1000 Genomes phase 3 data set as a reference
panel, as previously described
. Our results confirm a low frequency
of rs4988235 in Europeans, with a derived allele frequency of 5% in
the combined Bronze Age Europeans (genotype probability.0.85)
(Fig. 4b). Among Bronze Age Europeans, the highest tolerance fre-
quency was found in Corded Ware and the closely-related
Scandinavian Bronze Age cultures (Extended Data Fig. 7).
Interestingly, the Bronze Age steppe cultures showed the highest
derived allele frequency among ancient groups, in particular the
Yamnaya (Extended Data Fig. 7), indicating a possible steppe origin
of lactase tolerance.
It has been debated for decades if the major cultural changes that
occurred during the Bronze Age resulted from the circulation of peo-
ple or ideas and whether the expansion of Indo-European languages
was concomitant with these shifts or occurred with the earlier spread
of agriculture
. Our findings show that these transformations
involved migrations, but of a different nature than previously sug-
gested: the Yamnaya/Afanasievo movement was directional into
Central Asia and the Altai-Sayan region and probably without much
local infiltration, whereas the resulting Corded Ware culture in
Europe was the result of admixture with the local Neolithic people.
The enigmatic Sintashta culture near the Urals bears genetic resemb-
lance to Corded Ware and was therefore likely to be an eastward
migration into Asia. As this culture spread towards Altai it evolved
into the Andronovo culture (Fig. 1), which was then gradually
admixed and replaced by East Asian peoples that appear in the later
cultures (Mezhovskaya and Karasuk). Our analyses support that
migrations during the Early Bronze Age is a probable scenario for
the spread of Indo-European languages, in line with reconstructions
based on some archaeological and historical linguistic data
. In the
light of our results, the existence of the Afanasievo culture near Altai
around 3000 BC could also provide an explanation for the mysterious
presence of one of the oldest Indo-European languages, Tocharian in
the Tarim basin in China
. It seems plausible that Afanasievo, with
their genetic western (Yamnaya) origin, spoke an Indo-European
language and could have introduced this southward to Xinjang and
. Importantly, however, although our results support a cor-
respondence between cultural changes, migrations, and linguistic pat-
terns, we caution that such relationships cannot always be expected
but must be demonstrated case by case.
Online Content Methods, along with any additional Extended Data display items
and SourceData, are available in theonline version of the paper;references unique
to these sections appear only in the online paper.
Received 14 February; accepted 1 May 2015.
1. Fu, Q. et al. Genome sequence of a 45,000-year-old modern human from western
Siberia. Nature 514, 445–449 (2014).
2. Seguin-Orlando, A. et al. Genomic structure in Europeans dating back at least
36,200 years. Science 346, 1113–1118 (2014).
3. Rasmussen, M. et al. An Aboriginal Australian genome reveals separate human
dispersals into Asia. Science 334, 94–98 (2011).
4. Raghavan, M. et al. Upper Palaeolithic Siberian genome reveals dual ancestry of
Native Americans. Nature 505, 87–91 (2014).
5. Raghavan, M. et al. The genetic prehistory of the New World Arctic. Science 345,
1255832 (2014).
6. Rasmussen, M. et al. The genome of a Late Pleistocenehuman from a Clovis burial
site in western Montana Nature 506, 225–229 (2014).
7. Bramanti, B. et al. Genetic discontinuity between local hunter-gatherers and
Central Europe’s first farmers. Science 326, 137–140 (2009).
8. Malmstro
¨m, H. et al. Ancient DNA reveals lack of continuity between Neolithic
hunter-gatherers and contemporary Scandinavians. Curr. Biol. 19, 1758–1762
9. Skoglund, P. et al. Origins and genetic legacy of Neolithic farmers and hunter-
gatherers in Europe. Science 336, 466–469 (2012).
10. Lazaridis,I. et al. Ancient human genomes suggest three ancestral populations for
present-day Europeans. Nature 513, 409–413 (2014).
11. Haak, W. et al. Ancient DNA from European early Neolithic farmers reveals their
Near Eastern affinities. PLoS Biol. 8, e1000536 (2010).
12. Gamba, C. et al. Genome flux and stasis in a five millennium transect of European
prehistory. Nature Commun. 5, 5257 (2014).
13. Kristiansen, K. in The World System and theEarth System. Global Socioenvironmental
Change and Sustainability Since the Neolithic (eds Hornborg,B. & Crumley, C.) (Left
Coast Press, 2007).
14. Shishlina,N. Reconstruction of the BronzeAge of the Caspian Steppes. LifeStyles and
Life Ways of Pastoral Nomads. Vol. 1876 (Archaeopress, 2008).
15. Anthony, D. The Horse, The Wheel and Language. How Bronze-Age Riders from the
Eurasian Steppes Shaped the Modern World (Princeton Univ. Press, 2007).
Ancient Modern
Derived allele frequency
BA Europe
BA Steppe
BA Asia
IA Asia
South Europe
North Europe
South Asia
East Asia
Derived allele frequency
BA Europe
BA Steppe
BA Asia
IA Asia
South Europe
North Europe
South Asia
East Asia
5 10 15 20+
rs4988235 (LCT)
rs1426654 (SLC24A5)
rs16891982 (SLC45A2)
rs12913832 (OCA2HERC2)
N Chromosomes
Figure 4
Allele frequencies for putatively positively selected SNPs.
a, Coloured circles indicate the observed frequency of the respective SNP in
ancient and modern groups (1000 Genomes panel). The size of the circle is
proportional to the number of samples for each SNP and population. b, Allele
frequency of rs4988235 in the LCT (lactase) gene inferred from imputation of
ancient individuals. Numbers indicate the total number of chromosomes for
each group. BA, Bronze Age; IA, Iron Age.
11 JUNE 2015 | VOL 522 | NATURE | 171
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16. Harrison, R. & Heyd, V. The Transformation ofEurope in the third millennium BC:
the exampleof ‘Le Petit-Chasseur I1III’ (Sion, Valais, Switzerland). Praehistorische
Zeitschrift. 82, 129–214 (2007).
17. Vandkilde, H. Culture and Change in the Central European Prehistory, 6th to 1st
millennium BC (Aarhus Univ. Press, 2007).
18. Kristiansen, K. & Larsson, T. The Rise of Bronze Age Society. Travels, Transmissions
and Transformations (Cambridge Univ. Press, 2005).
19. Hanks, B.K., Epimakhov, A. V. &Renfrew, A. C. Towardsa refined chronologyfor the
Bronze Age of the southern Urals, Russia. Antiquity 81, 353–367 (2007).
20. Kuznetsov,P. F. The emergenceof Bronze Age chariots inEastern Europe. Antiquity
80, 638–645 (2006).
21. Koryakova, L. & Epimakhov, A. V. The Urals and Western Siberia in the Bronze and
Iron Ages (Cambridge Univ. Press, 2007).
22. Rasmussen, M. et al. Ancient human genome sequence of an extinct Palaeo-
Eskimo. Nature 463, 757–762 (2010).
23. Carpenter, M. L. et al. Pulling out the 1%: whole-genome capture for the targeted
enrichment of ancient DNA sequencing libraries.Am. J. Hum. Genet. 93, 852–864
24. Barros Damgaard, P. d. et al. Improving access to endogenous DNA in ancient
bones and teeth. Preprint at bioRxiv (2015).
25. Adler, C. J., Haak, W., Donlon, D., Cooper, A. & The Genographic Consortium.
Survival and recovery of DNA from ancient teeth and bones. J. Archaeol. Sci. 38,
956–964 (2011).
26. Orlando, L. et al. True single-molecule DNA sequencing of a Pleistocene horse
bone. Genome Res. 21, 1705–1719 (2011).
27. Olalde, I. & Lalueza-Fox, C. Modern humans’ paleogenomics and the new
evidences on the European prehistory. Science and Technology of Archaeological
Research 1, (2015).
28. Grigoriev, S. Ancient Indo-Europeans (Charoid, 2002).
29. Bendezu-Sarmiento, J. De l’A
ˆge du Bronze et l’A
ˆge du Fer au Kazakkstan, gestes
´raires et parame
`tres biologiques. Identite
´s culturelles des population Andronovo
et Saka (De Boccard, 2007).
30. Kozintsev, A. G., Gromov, A. V. & Moiseyev, V. G. Collateral relatives of American
Indians amongthe Bronze Age populationsof Siberia? Am. J. Phys. Anthropol. 108,
193–204 (1999).
31. Kristiansen, K. in Becoming European. The transformation of third millennium
Northernand Western Europe (edsPrescott, C. & Glørstad, H.) (OxbowBooks, 2012).
32. Haak, W. et al. Massive migration from the steppe was a source for Indo-European
languagesin Europe. Nature (this issue).
33. Olalde,I. et al. Derivedimmune and ancestral pigmentationalleles in a 7,000-year-
old Mesolithic European. Nature 507, 225–228 (2014).
34. Itan, Y.,Powell, A., Beaumont,M. A., Burger, J. & Thomas,M. G. The origins of lactase
persistence in Europe. PLoS Computational Biol. 5, e1000491 (2009).
35. Mallory, J. In Search of the Indo-Europeans. Language, Archaeology and Myth
(Thames & Hudson, 1987).
36. Renfrew, A. C. Archaeology and Language. The Puzzle of Indo-European Origins
(Penguin, 1987).
37. Mallory, J. & Mair, V. The Tarim Mummies. Ancient China and the Mystery of the
Earliest People from the West (Thames & Hudson, 2000).
38. Keyser, C. et al. Ancient DNA provides new insights into the history of south
Siberian Kurgan people. Hum. Genet. 126, 395–410 (2009).
Supplementary Information is available in the online version of the paper.
Acknowledgements We thank K. Magnussen, L. A. Petersen, C. D.Mortensen and
A. Seguin-Orlando at the Danish National Sequencing Centre for help with the
sequencing. Wethank C. G.Zacho fortechnicalassistance. The projectwas funded by The
European ResearchCouncil(FP/2007-2013,grant no. 269442, The Rise), The University
of Copenhagen (KU2016 programme), MarieCurie Actionsof the European Union (FP7/
2007-2013, grantno. 300554),The Villum Foundation (YoungInvestigatorProgramme,
grantno. 10120),Frederik Paulsen, The Miller Institute, University of California, Berkeley,
The Lundbeck Foundation, and The Danish National Research Foundation.
Author Contributions E.W. and K.K. initiated and led the study. M.E.A., J.S., L.V., H.S.,
P.B.D., A.M., M.R., L.S. performed the DNA laboratory work. M.Si., S.R., M.E.A., A.-S.M.,
P.B.D., A.M.analysed the genetic data. K.-G.S., T.A., N.L.,L.H., J.B., P.D.C., P.D.,P.R.D., A.E.,
A.V.E., K.F.,M.F., G.G., T.G., A.G., S.G., T.H., R.J., J.K., V.K., A.K., V.K., A.K., I.L., C.L.,A.M., G.M.,
I.M., M.M., R.M.,V.M., D.Po., G.P., L.P., D.Pr.,L.P., M.Sa., N.S., V.Sm., V.Sz., V.I.S., G.T., S.V.T.,
L.V., M.V., L.Y., V.Z. collected the samples and/or provided input to the archaeological
interpretations. T.H. and D.C. conducted radiocarbon dating. T.S.-P., L.O., S.B., R.N.
provided input to the genetic analyses. E.W., K.K., M.E.A., M.Si., K.-G.S. wrote the paper
with input from all co-authors.
Author Information DNA sequence alignments are available from the European
Nucleotide Archive ( under accession number PRJEB9021.
Reprints and permissions information is available at The
authors declare no competing financialinterests. Readers arewelcome to comment on
the online version of the paper. Correspondence and requests for materials should be
addressed to E.W. (
172 | NATURE | VOL 522 | 11 JUNE 2015
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DNA extraction and library preparation. A total of 603 human Bronze Age
samples from across Eurasia were selected for initial molecular ‘screening’ to
assess DNA preservation and hence the potential for genome-scale analyses.
The samples consisted almost exclusively of teeth, but also a few bone and hair
samples were included. All the molecular work (pre-library amplification) was
conducted in dedicated ancient DNA clean laboratory facilities at the Centre for
GeoGenetics, Natural History Museum, University of Copenhagen.
Preferentially targeting the outer cementum layer in teeth rather than the
dentine allowed us to maximize access to endogenous DNA
tary Information, section 3). The amount of starting material varied, but was
generally 100–600 mg. We also added a ‘pre-digestion’ step to the extraction
protocol, where the drilled bone or tooth powder is incubated in an EDTA-based
buffer before complete digestion to facilitate the removal of surface contami-
(Supplementary Information, section 3). Additionally, we developed
a new DNA binding buffer for extraction that proved more efficient in recov-
ering short DNA fragments compared to previous protocols (Supplementary
Information, section 3). DNA libraries for sequencing were prepared using
NEBNext DNA Sample Prep Master Mix Set 2 (E6070) and Illumina-specific
following established protocols
. The libraries were ‘shot-gun’
sequenced in pools using Illumina HiSeq2500 platforms and 100-bp single-read
chemistry (Supplementary Information, section 3).
Molecular screening. For the molecular screening phase we generally generated
between 5 and 20 million reads per library and these were used to evaluate the
state of molecular preservation. Candidate samples were selected for further
sequencing if they displayed a .10% C–T misincorporation damage signal in
the 59ends as an indication of authentic ancient DNA
, and a human DNA
content .0.5% (Supplementary Information, section 3).
Genomic capture. We selected 24 samples with relatively low human DNA
content (0.5–1.1%) for a whole-genome capture experiment
to enrich for the
low human DNA fraction in these samples. The capture was performed using
the MYbait Human Whole Genome Capture Kit (MYcroarray, Ann Arbor, MI),
following the manufacturer’s instructions (
MYbaits-manual.pdf). After amplification, the libraries were purified using
Agencourt AMPure XP beads, quantified using an Agilent 2100 bioanalyzer,
pooled in equimolar amounts, and sequenced on Illumina HiSeq2500, as described
above. Methods and results are found in Supplementary Information, section 3.
Bioinformatics. The Illumina data was basecalled using Illumina software
CASAVA 1.8.2 and sequences were de-multiplexed with a requirement of full
match of the 6 nucleotide index that was used for library preparation. Adaptor
sequences and leading/trailing stretches of Ns were trimmed from the reads and
additionally bases with quality 2 or less were removed using AdapterRemoval-
1.5.4. Trimmed reads of at least 30 bp were mapped to the human reference
genome build 37 using bwa-0.6.2 (ref. 44) with the seed disabled to allow for
higher sensitivity
. Mapped reads were filtered for mapping quality 30 and sorted
using Picard ( and SAMtools
. Data was merged to
library level and duplicates removed using Picard MarkDuplicates (http://picard. and hereafter merged to sample level. Sample level BAMs were
re-aligned using GATK-2.2-3 and hereafter had the md-tag updated and extended
BAQs calculated using SAMtools calmd
. Read depth and coverage were deter-
mined using pysam ( and BEDtools
. Statistics
of the read data processing are shown in Supplementary Table 6.
DNA authentication. DNA contamination can be problematic in samples
from museum collections that may have been handled extensively. To secure
authenticity, we used the Bayesian approach implemented in mapDamage 2.0
(ref. 48) and recorded the following three key damage parameters for each sam-
ple: (1) the frequency of CRT transitions at the first position at the 59end of
reads, (2) l, the fraction of bases positioned in single-stranded overhangs, and
(3) ds, the estimated CRT transition rate in the single-stranded overhangs
(Supplementary Information, section 5). For further sequencing and down-
stream analyses we only considered individuals displaying at least 10% CRT
damage transitions at position 1. MapDamage outputs are summarized in
Supplementary Table 7.
We also estimated the levels of mitochondrial DNA contamination. We used
contamMix 1.0–10 (ref. 49) that generates a moment-based estimate of the error
rate and a Bayesian-based estimate of the posterior probability of the contam-
ination fraction. We conservatively removed individuals with indications of
contamination .5% (Supplementary Information, section 5). For males with
sufficient depth of coverage we also estimated contamination based on the X
as implemented in ANGSD
(Supplementary Information, sec-
tion 5). Results are shown in Supplementary Table 8. After implementing
the 0.5% cut-off for human DNA content, combined with these ancient DNA
authentication criteria, our final sample consisted of 101 individuals (Supple-
mentary Information, section 1).
Data sets. We constructed two data sets for population genetic analysis by mer-
ging ancient DNA data generated in this as well as previous studies with two
reference panels of modern individual genotype data (Supplementary
Information, section 6). For both data sets, genotypes for all ancient individuals
were obtained at all variant positions in the reference panel, discarding variants
where alleles for the ancient individuals did not match either of the alleles
observed in the panel. Genotypes for low-coverage samples (including all data
generated in this study) were obtained by randomly sampling a single read with
both mapping and base quality $30. Genotypes for high-coverage samples were
called using the ‘call’ command of bcftools (
bcftools) and filtering for quality score (QUAL) $30. Error rates and inclusion
thresholds for low coverage samples were obtained by performing PCA and
model-based clustering (described below) on subsampled data sets of higher
coverage individuals. For population genetic analyses (Dand fstatistics, F
we obtained sample allele frequencies for the ancient groups (Supplementary
Table 9) at each SNP by counting the total number of alleles observed, treating
the low coverage individuals as haploid. See Supplementary Information, section
6 for more details.
PCA and model-based clustering. We performed principal component analysis
, projecting ancient individuals onto the components inferred
from sets of modern individuals by using the ‘lsqproject’ option of smartpca. The
data set was converted to all homozygous genotypes before the analysis, by
randomly sampling an allele at each heterozygote genotype of modern and
high-coverage ancient individuals. See Supplementary Information, section 6
for more details.
Model-based clustering analysis was carried out using the maximum-like-
lihood approach implemented in ADMIXTURE
. We used an approach where
we first infer the ancestral components using modern samples only, and then
‘project’ the ancient samples onto the inferred components using the ancestral
allele frequencies inferred by ADMIXTURE (the ‘P’ matrix). We ran
ADMIXTURE on an LD-pruned data set of all 2,345 modern individuals in the
Human Origins SNP array data set, assuming K52 to K520 ancestral compo-
nents, selecting the best of 50 replicate runs for each value of K. See
Supplementary Information, section 6 for more details. Genotypes where the
ancient individuals showed the damage allele at C .T and G .A SNPs were
excluded for each low coverage ancient individual.
- and f-statistics and population differentiation. We used the Dand fstatistic
to investigate patterns of admixture and shared ancestry in our data
set. All statistics were calculated from allele frequencies using the estimators
described previously
, with standard errors obtained from a block jackknife with
5 Mb block size. We investigated population differentiation by estimating F
all pairs of ancient and modern groups from allele frequencies using the sample-
size corrected moment estimator of Weir and Hill
, restricting the analysis to
SNPs where a minimum two alleles were observed in each population of the pair.
See Supplementary Information, section 6 for more details.
Phenotypes and positive selection. To investigate the temporal dynamics of
SNPs associated with phenotypes or putatively under positive selection, we esti-
mated allele frequencies for a catalogue of 104 SNPs
in all ancient and modern
groups in the 1000 Genomes data set. Genotypes for the LCT region were imputed
from genotype likelihoods with the 1000 Genomes Phase 3 reference panel
using BEAGLE
. See Supplementary Information, section 6 for more details.
Data reporting. No statistical methods were used to predetermine sample size.
The experiments were not randomized. The investigators were not blinded to
allocation during experiments and outcome assessment.
Code availability. Source code with R functions used in the analysis for this
study is available as an R package at GitHub
39. Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly
multiplexed target capture and sequencing. Cold Spring Harb. Protocols (2010).
40. Orlando, L. et al. Recalibrating Equus evolution using the genome sequence of an
early Middle Pleistocene horse. Nature 499, 74–78 (2013).
41. Malaspinas, A.-S. et al. Two ancient human genomes reveal Polynesian ancestry
among the indigenous Botocudos of Brazil. Curr. Biol. 24, R1035–R1037 (2014).
42. Willerslev, E. & Cooper, A. Ancient DNA. Proc. Royal Soc. B 272, 3–16 (2005).
43. Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a
Neandertal. Proc. Natl Acad. Sci. USA 104, 14616–14621 (2007).
44. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler
transform. Bioinformatics 25, 1754–1760 (2009).
45. Schubert, M. et al. Improving ancient DNA read mapping against modern
reference genomes. BMC Genomics 13, 178 (2012).
46. Li, H. et al. The Sequence Alignment/Map formatand SAMtools. Bioinformatics 25,
2078–2079 (2009).
G2015 Macmillan Publishers Limited. All rights reserved
47. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing
genomic features. Bioinformatics 26, 841–842 (2010).
48. Jo
´nsson, H., Ginolhac, A., Schubert, M.,Johnson, P. & Orlando, L. mapDamage2.0:
fast approximate Bayesian estimates of ancient DNA damage parameters.
Bioinformatics (2013).
49. Fu, Q. et al. DNA analysis of an early modern human from Tianyuan Cave, China.
Proc. Natl Acad. Sci. USA 110, 2223–2227 (2013).
50. Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next
generation sequencing data. BMC Bioinformatics 15, (2014).
51. Patterson, N., Price, A. L. & Reich, D. Population structure and Eigenanalysis. PLoS
Genet. 2, e190 (2006).
52. Alexander,D. H., Novembre, J. & Lange,K. Fast model-based estimationof ancestry
in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
53. Patterson,N. et al. Ancient admixture in human history.Genetics 192, 1065–1093
54. Weir, B. S. & Hill, W. Estimating F-statistics . Annu. Rev. Genet. 36, 721–750
55. Nystro
¨m, V. et al. Microsatellite genotyping reveals end-Pleistocene decline in
mammoth autosomal genetic variation. Mol. Ecol. 21, 3391–3402 (2012).
56. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and
missing-data inference for whole-genome association studies by use of localized
haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).
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Extended Data Figure 1
Principal component analysis of ancient genomes.
a,b, Principal component analysis of ancient individuals projected onto
contemporary individuals from non-African populations (a), Europe, West
Asia and the Caucasus (b). Grey labels represent population codes indicating
coordinates for individuals (small) and median of the population (large).
Coloured labels indicate positions for ancient individuals (small) and median
for ancient groups (large). Ancient individuals within a group are connected to
the respective median position by coloured lines.
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Extended Data Figure 2
Pairwise outgroup
statistics. Panels depict
pairwise plots of outgroup f
statistics of the form f
, Population
), showing the correlation of the amount of
shared genetic drift for a pair of ancient groups (Population
) with all modern
populations (Population
) in the Human Origins data set (panel A). Closely
related ancient groups are expected to show highly correlated statistics.
a, Sintashta/Corded Ware. b, Yamnaya/Afanasievo. c, Sintashta/Andronovo.
d, Okunevo/Mal’ta. Coloured circles indicate modern populations; error bars
indicate 61 standard error from the block jackknife.
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Extended Data Figure 3
Yamnaya ancestry mirrors Mal’ta ancestry in
present-day Europeans and Caucasians. Panels show pairwise plots of
D-statistics D(Outgroup, Ancient)(Bedouin, Modern), contrasting Mal’ta
(MA1) and Hunter-gatherers (a), and MA1 and Yamnaya (b). Coloured labels
indicate modern populations, with lines corresponding to 61 standard error of
the respective D-statistic from block jacknife. Text away from the diagonal line
indicates an ancient group with relative increase in allele sharing with the
respective modern populations.
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Extended Data Figure 4
Genetic differentiation between ancient and
modern groups in Human Origins data set. Panels show F
between pairs of
modern and ancient groups (coloured lines) for subsets of ancient groups, with
results for the remaining groups in the background (grey). Top, early
Europeans. Middle, Bronze Age Europeans and steppe/Caucasus. Bottom,
Bronze Age Asians. Results based on Human Origins data set (panel A).
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Extended Data Figure 5
Genetic differentiation between ancient and modern groups in 1000 Genomes data set. Matrix of pairwise F
values between
modern and ancient groups in the 1000 Genomes data set (panel B).
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Extended Data Figure 6
Distribution of uniparentallineages in Bronze Age Eurasians. a,b, Barplots showing the relative frequencyof Y chromosome (a) and
mitochondrial DNA lineages (b) in different Bronze Age groups. Top row shows overall frequencies for all individuals combined.
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Extended Data Figure 7
Derived allele frequencies for lactase persistence in modern and ancient groups. Derived allele frequency of rs4988235 in the LCT
gene inferred from imputation of ancient individuals. Numbers indicate the total number of chromosomes for each group.
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Extended Data Table 1
Selected D-test results from 1000 Genomes data set (panel B)
*Results are shown for Karasuk as group X, which is the only ancient group with Z.3 for D(Yoruba, X)(Yamnaya, Afanasievo)
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Extended Data Table 2
statistic results for ancient groups
*Human origins data set (panel A); {1000 Genomes data set (panel B); {group with single individual; 1pair with lowest f
reported for groups with negative f
without significant Z-score after correcting for multiple
hypothesis tests (24.1 ,min(Z),0; 1,260 tests per group); jjtoo few markers with data from more than one chromosome.
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... During the late Neolithic, steppe populations, characterised by the Yamnaya culture, appear as a mix of EHG and CHG/Iranian ancestries [39,49,61]. At the start of the Bronze Age after 5000 years ago, migrants with this "Yamnaya" ancestry moved west into Europe and had a profound impact on the genetic landscape [39], with steppe ancestry appearing first in individuals from central-eastern Europe [74] and spreading rapidly into central and northern Europe [4]. By 4,500 years ago, steppe ancestry appears in the British Isles and Ireland, brought by a population associated with the Corded Ware people and who replaced approximately 90% of Britain's gene pool within a few hundred years [89,16]. ...
... Many examples of selection on European alleles have been demonstrated. In particular, selection signals on the lactase persistence allele have been found using multiple methods [4,75,93]. Finding signals of positive selection on relevant genes in the MesoNeo dataset is of particular interest since the dataset covers a big transition in lifestyle from hunter gathering to farming. It is hypothesized that such a change would result in selective pressures on features relating to diet and metabolism and may therefore be relevant to certain chronic diseases today. ...
... 3. Simulate chromosomes from the model. 4. Run RELATE on the simulated data. ...
Recently, two new approaches have transformed our understanding of human population history. Firstly, the sequencing of ancient genomes which gives us a snapshot of past genetic variation. We can therefore make inferences from observed genetic signatures present before historical events such as population bottlenecks and natural selection have obscured them from the modern gene pool. Ancient DNA has thus revealed what cannot be determined from modern genomes alone. Secondly, the development of methods that aim to reconstruct population genealogies from genetic variation data. Together with an understanding of how evolutionary processes alter genealogies, this has allowed inference of historical and ongoing processes in real world populations. The latest updates in these approaches now allow us to combine the two and infer genealogies involving both present-day and ancient individuals. In this thesis I present a new method to infer local ancestry along sample chromosomes. The method applies machine learning to tree sequences built from ancient and present-day genomes and is based on a deterministic model of population structure, within which I introduce the concept of ‘path ancestry’. I show with extensive simulation that the method is robust to a variety of demographic scenarios and generalises over model misspecification. Subsequent downstream analyses include estimating past effective population size, timing of population specific selection and the time since admixture for individuals. I apply the method to a large ancient DNA dataset covering Europe and West Eurasia to paint all sample chromosomes. I show that the inferred admixture ages are a better metric than sample ages alone for understanding movements of people across Europe in the past.
... This period, from 3000 to 2000 BCE, was characterized by major gene flow events, where 'Steppe-related' ancestry had a substantial genetic impact throughout Europe [e.g. Haak et al., 2015, Allentoft et al., 2015, leading to widespread genetic admixtures as far west as Britain and Iberia . Applying ancIBD to the relevant published aDNA record of 304 ancient Western Eurasians organized into 24 archaeological groups (Supp. ...
... Anthony, 2010]. A genetic link has already been evident from genomic similarity and Y haplogroups [Allentoft et al., 2015, Narasimhan et al., 2019; however, the time depth of this connection remained unclear. We find IBD signals across all length scales, importantly including several shared IBD segments even longer than . ...
... Moreover, there are several intriguing observations regarding individuals associated with the Corded Ware culture, who are the earliest Central and Northern Europeans to carry high amounts of Steppe-like ancestry. Previous aDNA research has shown them to be a genetic mix of previous Final Neolithic farmer cultures as well as Steppe-like ancestry [Haak et al., 2015, Allentoft et al., 2015, Papac et al., 2021. Using IBD, we find that individuals from diverse Corded Ware cultural groups, including from Sweden (associated with the Battle Axe culture), Russia (Fatyanovo), and East/Central Europe share high amounts of long IBD with each other, and also have IBD sharing up to 20 cM with various Yamnaya groups (Fig. 5, Fig. E2). ...
Full-text available
Long DNA sequences shared between two individuals, known as Identical by descent (IBD) segments, are a powerful signal for identifying close and distant biological relatives because they only arise when the pair shares a recent common ancestor. Existing methods to call IBD segments between present-day genomes cannot be straightforwardly applied to ancient DNA data (aDNA) due to typically low coverage and high genotyping error rates. We present ancIBD, a method to identify IBD segments for human aDNA data implemented as a Python package. Our approach is based on a Hidden Markov Model, using as input genotype probabilities imputed based on a modern reference panel of genomic variation. Through simulation and downsampling experiments, we demonstrate that ancIBD robustly identifies IBD segments longer than 8 centimorgan for aDNA data with at least either 0.25x average whole-genome sequencing (WGS) coverage depth or at least 1x average depth for in-solution enrichment experiments targeting a widely used aDNA SNP set (‘1240k’). This application range allows us to screen a substantial fraction of the aDNA record for IBD segments and we showcase two downstream applications. First, leveraging the fact that biological relatives up to the sixth degree are expected to share multiple long IBD segments, we identify relatives between 10,156 ancient Eurasian individuals and document evidence of long-distance migration, for example by identifying a pair of two approximately fifth-degree relatives who were buried 1410km apart in Central Asia 5000 years ago. Second, by applying ancIBD, we reveal new details regarding the spread of ancestry related to Steppe pastoralists into Europe starting 5000 years ago. We find that the first individuals in Central and Northern Europe carrying high amounts of Steppe-ancestry, associated with the Corded Ware culture, share high rates of long IBD (12-25 cM) with Yamnaya herders of the Pontic-Caspian steppe, signaling a strong bottleneck and a recent biological connection on the order of only few hundred years, providing evidence that the Yamnaya themselves are a main source of Steppe ancestry in Corded Ware people. We also detect elevated sharing of long IBD segments between Corded Ware individuals and people associated with the Globular Amphora culture (GAC) from Poland and Ukraine, who were Copper Age farmers not yet carrying Steppe-like ancestry. These IBD links appear for all Corded Ware groups in our analysis, indicating that individuals related to GAC contexts must have had a major demographic impact early on in the genetic admixtures giving rise to various Corded Ware groups across Europe. These results show that detecting IBD segments in aDNA can generate new insights both on a small scale, relevant to understanding the life stories of people, and on the macroscale, relevant to large-scale cultural-historical events.
... Bronze Age globalization led to large-scale archaeological and cultural changes in much of Eurasia (de Barros Damgaard et al., 2018;Jeong et al., 2018;Jeong et al., 2019). The widespread spread of Indo-Europeans and related populations has been accompanied by strong replacement and mixing of population in many regions (Allentoft et al., 2015), such as South Siberia (Yu et al., 2020), Central Asia (Ning et al., 2019;Zhang et al., 2021;Kumar et al., 2022), South Asia (Narasimhan et al., 2019), the Middle East (Lazaridis et al., 2016), and Europe (Haak et al., 2015;Damgaard et al., 2018;Mathieson et al., 2018;Olalde et al., 2018). However, the Bronze Age globalization does not necessarily lead to the large-scale replacement of the population. ...
... As, discussed in the Introduction section, Bronze age globalization has led to mass replacement and mixing of populations in multiple parts of Eurasia (Allentoft et al., 2015). In East Asia, however, the situation is quite different. ...
Full-text available
Objectives: Previous studies of archaeology and history suggested that the rise and prosperity of Bronze Age culture in East Asia had made essential contribution to the formation of early state and civilization in this region. However, the impacts in perspective of genetics remain ambiguous. Previous genetic researches indicated the Y-chromosome Q1a1a-M120 and N1a2a-F1101 may be the two most important paternal lineages among the Bronze Age people in ancient northwest China. Here, we investigated the 9,000-years history of haplogroup N1a2a-F1101 with revised phylogenetic tree and spatial autocorrelation analysis. Materials and Methods: In this study, 229 sequences of N1a2a-F1101 were analyzed. We developed a highly-revised phylogenetic tree with age estimates for N1a2a-F1101. In addition, we also explored the geographical distribution of sub-lineages of N1a2a-F1101, and spatial autocorrelation analysis was conducted for each sub-branch. Results: The initial differentiation location of N1a2a-F1101 and its most closely related branch, N1a2b-P43, a major lineage of Uralic-speaking populations in northern Eurasia, is likely the west part of northeast China. After ~4 thousand years of bottleneck effect period, haplgroup N1a2a-F1101 experienced continuous expansion during the Chalcolithic age (~ 4.5 kya to 4 kya) and Bronze age (~ 4 kya to 2.5 kya) in northern China. Ancient DNA evidence supported that this haplogroup is the lineage of ruling family of Zhou Dynasty (~ 3 kya-2.2 kya) of ancient China. Discussion: In general, we proposed that the Bronze Age people in the border area between the eastern Eurasian steppe and northern China not only played a key role in promoting the early state and civilization of China, but also left significant traces in the gene pool of Chinese people.
... There has been extensive research on the population history of Europeans [2,16,17] 69 and the broad picture is well established. Therefore, we used a large dataset of present 70 day and high quality shotgun sequenced, imputed ancient genomes (MesoNeo dataset) 71 to test the method. ...
... During the late Neolithic, steppe populations, characterised by the Yamnaya (Yam) 139 culture, appear as a mix of EHG and CHG/Iranian ancestries [17,19,23] Europeans [16,20,[23][24][25]. ...
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It has become apparent from ancient DNA analysis, that the history of many human populations from across the globe are often complex, involving multiple population split, admixture, migration and isolation events. Local ancestry inference (LAI) aims to identify from which ancestral population chromosomal segments in admixed individuals are inherited. However, ancestry in existing LAI tools is characterised by a discrete population identity, a definition which is limited in the context of a complex demographic history involving multiple admixture events at different times. Moreover, many LAI tools rely on a reference panel of present day genomes that act as proxies for the ancestral populations. For ancient admixture events, these proxy genomes are likely only distantly related to the true ancestral populations. Here we present a new method that leverages advances in ancient DNA sequencing and genealogical inference to address these in issues in LAI. The method applies machine learning to tree sequences inferred for ancient and present day genomes and is based on a deterministic model of population structure, within which we introduce the concept of path ancestry. We show that the method is robust to a variety of demographic scenarios, generalises over model misspecification and that it outperforms a leading local ancestry inference tool. We further describe a downstream method to estimate the time since admixture for individuals with painted chromosomes. We apply the method to a large ancient DNA dataset covering Europe and West Eurasia and show that the inferred admixture ages are a better metric than sample ages alone for understanding movements of people across Europe in the past.
... The Bronze Age in Europe was without a doubt a time of increased human mobility. At the core of this lies the westward migration of herders, mostly men, from the Black Sea steppes [1][2][3]. In addition to this, long-distance travels were undertaken for the acquisition of tin and copper, finished bronze items and other objects of prestige necessary for accelerating social and political competition [4][5][6]. ...
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European Bronze Age societies are generally characterised by increased mobility and the application of isotopic methods to archaeology has allowed the rate and range of human travels to be quantified. However, little is known about the mobility of the people inhabiting East-Central Europe in the late Early and Middle Bronze Age (1950-1250 BC) whose primary subsistence strategy was herding supported by crop cultivation. This paper presents the results of strontium (87Sr/86Sr) and oxygen (δ18O) isotope analyses in the enamel of people buried in collective graves at the cemeteries in Gustorzyn and Żerniki Górne. These sites are located in Kujawy and the Nida Basin, a lowland and an upland region with clearly different environmental conditions, respectively. Both sites are classified as belonging to the Trzciniec cultural circle and were used between 16th and 13th centuries BC. Among the 34 examined individuals only an adult female from Gustorzyn can be assessed as non-local based on both 87Sr/86Sr and δ18O signatures in her first molar. This may indicate the practice of exogamy in the studied population but more generally corresponds with the hypothesis of limited mobility within these societies, as has previously been inferred from archaeological evidence, anthropological analysis, and stable isotope-based diet reconstruction. New and existing data evaluated in this paper show that the 87Sr/86Sr variability in the natural environment of both regions is relatively high, allowing the tracking of short-range human mobility. A series of oxygen isotope analyses (conducted for all but one individuals studied with strontium isotopes) indicates that δ18O ratios measured in phosphate are in agreement with the predicted modern oxygen isotope precipitation values, and that this method is useful in detecting travels over larger distances. The challenges of using both 87Sr/86Sr and δ18O isotopic systems in provenance studies in the glacial landscapes of temperate Europe are also discussed.
... The geographic patterns of the cultural and population diversities in the modern world are deeply rooted in large-scale human migrations and cultural interactions since the Neolithic age. For instance, the expansion of the Yamnaya population during the middle Holocene, as revealed by ancient DNA study, profoundly impacted the Europe's cultural landscape and helped give rise to the formation of the Corded Ware culture (Allentoft et al., 2015;Haak et al., 2010). More extensive migrations and cultural interactions occurred during the subsequent Eurasian Bronze Age, which helped to facilitate large settlements in Central Asia's arid regions and create pathways for prehistoric transcontinental cultural exchange (Anthony & Brown, 2011;Frachetti, 2012;Frachetti et al., 2017;Li, 2002). ...
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Objective: Mid- to late-Holocene large-scale population migration profoundly impacted the interaction of ethnic groups and cultures across Eurasia, notably in Central Asia. However, due to a lack of thorough historical documents, distinctive burial items, and human remains, the process of population migration during this historical era in the area is still unclear. Using an interdisciplinary approach at the Lafuqueke (LFQK) cemetery, this study investigates the spatiotemporal processes and explores the factors that influenced human migration in the eastern Tianshan Mountains between the 7th and 12th centuries. Materials and methods: In this study, tooth enamel from 56 human remains found in the LFQK cemetery in Hami Basin, eastern Tianshan Mountains, is examined for strontium and lead isotopes. Results: The early, middle, and late phases of migration might potentially be represented by a three-phase migration model, according to the isotopic study. The highest proportion of the early phase (ca. 7th-mid 7th century) comprised non-locals (54.55%), although this percentage decreased in the middle phase (mid 7th-mid 8th centuries, 30.77%). After the 10th century, the proportion of non-locals again fell (16.13%). Conclusion: In this study, the interdisciplinary approach was employed to propose a new model for the diachronic changes that accompanied human migration and cultural interaction in the eastern Tianshan Mountains and identified geopolitics as a significant factor influencing the migratory behavior of LFQK population in this region between the 7th and 12th centuries.
... From ~5 ka, steppe pastoralists of the Yamnaya culture expanded east-and westward, linking Asia and Europe (Allentoft et al., 2015;Lazaridis et al., 2022). This created a geographic corridor which, aided by the development of horse-riding and chariotry (Anthony, 2007;Anthony and Brown, 2011;Wilkin et al., 2021), led to the dispersal of crops, herds, and commensal species from one continent to another. ...
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The Punta Lucero III cave is a natural trap where abundant vertebrate remains were accumulated during the Meghalayan (Late Holocene). To better understand the paleoenvironmental conditions in which this record was accumulated, the micromammal assemblage, comprising a minimum number of 1396 individuals belonging to 19 taxa, was studied using the Mutual Ecogeographic Range and the Habitat Weighting Method. Throughout ~2600 years, the micromammal community's quick turnover reflected a shift from patchy forests and humid meadows to open, shrubbier grasslands. The Late Holocene Thermal Maximum's humid and mild climatic conditions underwent a cooling and aridification phase, coeval with the Iron Age Cold Epoch. These concluded in a slight temperature rising, coeval with the Roman Warm Period. Macromammals experienced a shift from wild populations to domestic herds. Therefore, this work discusses a broader context for this mammalian turnover from a human cultural perspective.
... In the present study, we wanted to address the difficulties of low-coverage aDNA data and dissect the main factors that affect kinship calculations. To overcome typing bias, random sampling of one allele per site (pseudo-haploid calling) was used successfully in aDNA studies [13][14][15][16][17][18][19][20][21][22]. In order to compare diploid and pseudo-haploid datasets, heterozygous alleles of diploid data need to be random pseudo-haploidized (RpsH) by randomly assigning heterozygote alleles as either homozygote reference (REF) or homozygote alternative (ALT). ...
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Kinship analysis from very low-coverage ancient sequences has been possible up to the second degree with large uncertainties. We propose a new, accurate, and fast method, correctKin, to estimate the kinship coefficient and the confidence interval using low-coverage ancient data. We perform simulations and also validate correctKin on experimental modern and ancient data with widely different genome coverages (0.12×–11.9×) using samples with known family relations and known/unknown population structure. Based on our results, correctKin allows for the reliable identification of relatedness up to the 4th degree from variable/low-coverage ancient or badly degraded forensic whole genome sequencing data.
Ancient DNA has revealed multiple episodes of admixture in human prehistory during geographic expansions associated with cultural innovations. One important example is the expansion of Neolithic agricultural groups out of the Near East into Europe and their consequent admixture with Mesolithic hunter-gatherers.1,2,3,4 Ancient genomes from this period provide an opportunity to study the role of admixture in providing new genetic variation for selection to act upon, and also to identify genomic regions that resisted hunter-gatherer introgression and may thus have contributed to agricultural adaptations. We used genome-wide DNA from 677 individuals spanning Mesolithic and Neolithic Europe to infer ancestry deviations in the genomes of admixed individuals and to test for natural selection after admixture by testing for deviations from a genome-wide null distribution. We find that the region around the pigmentation-associated gene SLC24A5 shows the greatest overrepresentation of Neolithic local ancestry in the genome (|Z| = 3.46). In contrast, we find the greatest overrepresentation of Mesolithic ancestry across the major histocompatibility complex (MHC; |Z| = 4.21), a major immunity locus, which also shows allele frequency deviations indicative of selection following admixture (p = 1 × 10-56). This could reflect negative frequency-dependent selection on MHC alleles common in Neolithic populations or that Mesolithic alleles were positively selected for and facilitated adaptation in Neolithic populations to pathogens or other environmental factors. Our study extends previous results that highlight immune function and pigmentation as targets of adaptation in more recent populations to selection processes in the Stone Age.
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MUTYH plays an essential role in preventing oxidation-caused DNA damage. Pathogenic germline variations in MUTYH damage its function, causing intestinal polyposis and colorectal cancer. Determination of the evolutionary origin of the variation is essential to understanding the etiological relationship between MUTYH variation and cancer development. In this study, we analyzed the origins of pathogenic germline variants in human MUTYH. Using a phylogenic approach, we searched pathogenic MUTYH variants in modern humans in the MUTYH of 99 vertebrates across eight clades. We did not find pathogenic variants shared between modern humans and the non-human vertebrates following the evolutionary tree, ruling out the possibility of cross-species conservation as the origin of human pathogenic variants in MUTYH. We then searched the variants in the MUTYH of 5031 ancient humans and extinct Neanderthals and Denisovans. We identified 24 pathogenic variants in 42 ancient humans dated between 30,570 and 480 years before present (BP), and three pathogenic variants in Neanderthals dated between 65,000 and 38,310 years BP. Data from our study revealed that human pathogenic MUTYH variants mostly arose in recent human history and were partially inherited from Neanderthals.
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Lactase persistence (LP) is common among people of European ancestry, but with the exception of some African, Middle Eastern and southern Asian groups, is rare or absent elsewhere in the world. Lactase gene haplotype conservation around a polymorphism strongly associated with LP in Europeans (-13,910 C/T) indicates that the derived allele is recent in origin and has been subject to strong positive selection. Furthermore, ancient DNA work has shown that the -13,910*T (derived) allele was very rare or absent in early Neolithic central Europeans. It is unlikely that LP would provide a selective advantage without a supply of fresh milk, and this has lead to a gene-culture coevolutionary model where lactase persistence is only favoured in cultures practicing dairying, and dairying is more favoured in lactase persistent populations. We have developed a flexible demic computer simulation model to explore the spread of lactase persistence, dairying, other subsistence practices and unlinked genetic markers in Europe and western Asia's geographic space. Using data on -13,910*T allele frequency and farming arrival dates across Europe, and approximate Bayesian computation to estimate parameters of interest, we infer that the -13,910*T allele first underwent selection among dairying farmers around 7,500 years ago in a region between the central Balkans and central Europe, possibly in association with the dissemination of the Neolithic Linearbandkeramik culture over Central Europe. Furthermore, our results suggest that natural selection favouring a lactase persistence allele was not higher in northern latitudes through an increased requirement for dietary vitamin D. Our results provide a coherent and spatially explicit picture of the coevolution of lactase persistence and dairying in Europe.
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Poor DNA preservation is the most limiting factor in ancient genomic research. In the majority of ancient bones and teeth, endogenous DNA molecules represent a minor fraction of the whole DNA extract, rendering shot-gun sequencing inefficient for obtaining genomic data. Based on ancient human bone samples from temperate and tropical environments, we show that an EDTA-based enzymatic 'pre-digestion' of powdered bone increases the proportion of endogenous DNA several fold. By performing the pre-digestion step between 30 min and 6 hours on five bones, we observe an asymptotic increase in endogenous DNA content, with a 2.7-fold average increase reached at 1 hour. We repeat the experiment using a brief pre-digestion (15 or 30 mins) on 21 ancient bones and teeth from a variety of archaeological contexts and observe an improvement in 16 of these. We here advocate the implementation of a brief pre-digestion step as a standard procedure in ancient DNA extractions. Finally, we demonstrate on 14 ancient teeth that by targeting the outer layer of the roots we obtain up to 14 times more endogenous DNA than when using the inner dentine. Our presented methods are likely to increase the proportion of ancient samples that are suitable for genome-scale characterization.
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The modern human settlement of Europe is a complex process with overimposed migrations and regionalisation episodes, as well as adaptive processes that shaped the genome of modern populations. New developments in massive sequencing techniques allow now the generation of an unprecedented amount of genomic data, including the genotyping of a large number of single nucleotide polymorphisms (SNPs) and the generation of complete ancient genomes. These paleogenomic data will have much more resolution for studying the past than genomic data from present-day individuals because migration, admixture and population replacement can confound the interpretation of current genetic diversity. However, their interpretation will require a real multidisciplinary effort, involving population geneticists, archaeologists and anthropologists. Here, we explain how paleogenomics is providing new insights into a wide range of topics such as demography, undetected migrations, mating strategies or adaptive processes of prehistoric European populations. Statement of significance The study of the prehistoric modern humans' settlement of Europe has been until recently restricted to fields such as archaeology and physical anthropology. However, with the advent of the new sequencing technologies in the last few years, it is increasingly possible to retrieve massive genomic data from these past populations, and to directly test previous hypothesis on human population movements and affinities. We provide here a guideline of the main areas of study of the past that will likely be impacted by paleogenomic studies in the next years. Data availability The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are contained within the paper.
Poor DNA preservation is the most limiting factor in ancient genomic research. In the vast majority of ancient bones and teeth, endogenous DNA molecules only represent a minor fraction of the whole DNA extract, rendering traditional shot-gun sequencing approaches cost-ineffective for whole-genome characterization. Based on ancient human bone samples from temperate and tropical environments, we show that an initial EDTA-based enzymatic 'pre-digestion' of powdered bone increases the proportion of endogenous DNA several fold. By performing the pre-digestion step between 30 min and 6 hours on five bones, we identify the optimal pre-digestion time and document an average increase of 2.7 times in the endogenous DNA fraction after 1 hour of pre-digestion. With longer pre-digestion times, the increase is asymptotic while molecular complexity decreases. We repeated the experiment with n=21 and t=15-30', and document a significant increase in endogenous DNA content (one-sided paired t-test: p=0.009). We advocate the implementation of a short pre-digestion step as a standard procedure in ancient DNA extractions from bone material. Finally, we demonstrate on 14 ancient teeth that crushed cementum of the roots contains up to 14 times more endogenous DNA than the dentine. Our presented methodological guidelines considerably advance the ability to characterize ancient genomes.
This book is the first synthesis of the archaeology of the Urals and Western Siberia. It presents a comprehensive overview of the late prehistoric cultures of these regions, which are of key importance for the understanding of long-term changes in Eurasia. At the crossroads of Europe and Asia, the Urals and Western Siberia are characterized by great envIronmental and cultural diversity which is reflected in the variety and richness of their archaeological sites. Based on the latest achievements of Russian archaeologists, this study demonstrates the temporal and geographical range of its subjects starting with a survey of the chronological sequence from the late fourth millennium BC to the early first millennium CE. Recent discoveries made in different regions of the area contribute to an understanding of several important issues, such as development of Eurasian metallurgy, technological and ritual innovations, the emergence and development of pastoral nomadism and its role in Eurasian interactions, and major sociocultural fluctuations of the Bronze and Iron Ages. © Ludmila Koryakova and Andrej Vladimirovich Epimakhov 2007 and Cambridge University Press, 2010.
Roughly half the world's population speaks languages derived from a shared linguistic source known as Proto-Indo-European. But who were the early speakers of this ancient mother tongue, and how did they manage to spread it around the globe? Until now their identity has remained a tantalizing mystery to linguists, archaeologists, and even Nazis seeking the roots of the Aryan race.The Horse, the Wheel, and Languagelifts the veil that has long shrouded these original Indo-European speakers, and reveals how their domestication of horses and use of the wheel spread language and transformed civilization. David Anthony identifies the prehistoric peoples of central Eurasia's steppe grasslands as the original speakers of Proto-Indo-European, and shows how their innovative use of the ox wagon, horseback riding, and the warrior's chariot turned the Eurasian steppes into a thriving transcontinental corridor of communication, commerce, and cultural exchange. He explains how they spread their traditions and gave rise to important advances in copper mining, warfare, and patron-client political institutions, thereby ushering in an era of vibrant social change. Anthony describes his discovery of how the wear from bits on ancient horse teeth reveals the origins of horseback riding. And he introduces a new approach to linking prehistoric archaeological remains with the development of language. The Horse, the Wheel, and Languagesolves a puzzle that has vexed scholars for two centuries--the source of the Indo-European languages and English--and recovers a magnificent and influential civilization from the past.