Population genomics of Bronze Age Eurasia

Abstract

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.

Full text

ARTICLE doi:10.1038/nature14507
Population genomics of Bronze Age
Eurasia
Morten E. Allentoft
1
*, Martin Sikora
1
*, Karl-Go
¨ran Sjo
¨gren
2
, Simon Rasmussen
3
, Morten Rasmussen
1
, Jesper Stenderup
1
,
Peter B. Damgaard
1
, Hannes Schroeder
1,4
, Torbjo
¨rn Ahlstro
¨m
5
, Lasse Vinner
1
, Anna-Sapfo Malaspinas
1
, Ashot Margaryan
1
,
Tom Higham
6
, David Chivall
6
, Niels Lynnerup
7
, Lise Harvig
7
, Justyna Baron
8
, Philippe Della Casa
9
, PawełDa˛browski
10
,
Paul R. Duffy
11
, Alexander V. Ebel
12
, Andrey Epimakhov
13
, Karin Frei
14
, Mirosław Furmanek
8
, Tomasz Gralak
8
, Andrey Gromov
15
,
Stanisław Gronkiewicz
16
, Gisela Grupe
17
, Tama
´s Hajdu
18,19
, Radosław Jarysz
20
, Valeri Khartanovich
15
, Alexandr Khokhlov
21
,
Vikto
´ria Kiss
22
, Jan Kola
´r
ˇ
23,24
, Aivar Kriiska
25
, Irena Lasak
8
, Cristina Longhi
26
, George McGlynn
17
, Algimantas Merkevicius
27
,
Inga Merkyte
28
, Mait Metspalu
29
, Ruzan Mkrtchyan
30
, Vyacheslav Moiseyev
15
, La
´szlo
´Paja
31,32
, Gyo
¨rgy Pa
´lfi
32
, Dalia Pokutta
2
,
Łukasz Pospieszny
33
, T. Douglas Price
34
, Lehti Saag
29
, Mikhail Sablin
35
, Natalia Shishlina
36
,Va
´clav Smrc
ˇka
37
, Vasilii I. Soenov
38
,
Vajk Szevere
´nyi
22
, Guszta
´vTo
´th
39
, Synaru V. Trifanova
38
, Liivi Varul
25
, Magdolna Vicze
40
, Levon Yepiskoposyan
41
,
Vladislav Zhitenev
42
, Ludovic Orlando
1
, Thomas Sicheritz-Ponte
´n
3
, Søren Brunak
3,43
, Rasmus Nielsen
44
, Kristian Kristiansen
2
& Eske Willerslev
1
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)
1,2
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
3
.
There is evidence that the western Eurasian branch constituted a
meta-population stretching from Europe to Central Asia
2,4
and that
it contributed genes to both modern-day western Eurasians
4
and early
indigenous Americans
4–6
. 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
7–12
and possibly also
from northern Asia
10
. 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.
1
Centre for GeoGenetics, Natural History Museum, University of Copenhagen, Øster Voldgade 5-7, 1350 Copenhagen K, Denmark.
2
Department of Historical Studies, University of Gothenburg, 405 30
Gothenburg, Sweden.
3
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Kgs Lyngby, Denmark.
4
Faculty of Archaeology, Leiden University,
2300 Leiden, The Netherlands.
5
Department of Archaeology and Ancient History, Lund University, 221 00 Lund, Sweden.
6
Oxford Radiocarbon Accelerator Unit, University of Oxford, Oxford OX1 3QY, UK.
7
Unit of Forensic Anthropology, Department of Forensic Medicine, University of Copenhagen, 2100 Copenhagen, Denmark.
8
Institute of Archaeology, University of Wrocław, 50-139 Wrocław, Poland.
9
Archaeological Institute, University of Zurich, CH-8006, Zurich, Switzerland.
10
Department of Anatomy, Wrocław Medical University, 50-368 Wrocław, Poland.
11
Department of Anthropology, University of
Toronto, Toronto ONM5S 2S2, Canada.
12
Department of Archeology and General History, Gorno-Altaisk State University, 649000 Gorno-Altaisk, Russia.
13
Institute of History and Archaeology RAS (South
Ural Department), South Ural State University, 454080 Chelyabinsk, Russia.
14
Environmental Research and Material Science and Centre for Textile Research, The National Museum of Denmark, 1471
Copenhagen K, Denmark.
15
Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) RAS, 199034 St Petersburg, Russia.
16
Department of Anthropology, Polish Academy of Sciences, 50–
449 Wrocław, Poland.
17
Biocentre of the Ludwig-Maximilian-University Mu
¨nchen, 82152 Munich, Germany.
18
Department of Biological Anthropology, Institute of Biology, Eo
¨tvo
¨s Lora
´nd University, H-1117
Budapest, Hungary.
19
Department of Anthropology, Hungarian Natural History Museum, H-1083 Budapest, Hungary.
20
The Archaeological Museum of Wrocław, 50-077 Wrocław, Poland.
21
Samara State
Academy of Social Science and Humanities, 443099 Samara, Russia.
22
Institute of Archaeology of the Hungarian Academy of Sciences, Research Center for the Humanities, H-1250 Budapest, Hungary.
23
Institute of Archaeology and Museology, Faculty of Arts, Masaryk University, CZ-602 00 Brno, Czech Republic.
24
Department of Vegetation Ecology, Institute of Botany of the Czech Academy of Sciences,
CZ-602 00 Brno, Czech Republic.
25
Department of Archaeology, University of Tartu, 51003 Tartu, Estonia.
26
Archaeological Superintendence of Lombardy, 20123 Milano, Italy.
27
Department of
Archaeology, University of Vilnius, LT-01513 Vilnius, Lithuania.
28
The SAXO Institute, University of Copenhagen, 2300 Copenhagen S, Denmark.
29
Department of Evolutionary Biology, Estonian Biocentre
and University of Tartu, 51010 Tartu, Estonia.
30
Department of History, Yerevan State University, 0025 Yerevan, Armenia.
31
Hungarian National Museum, H-1083 Budapest, Hungary.
32
Department of
Biological Anthropology, University of Szeged, H-6726 Szeged, Hungary.
33
Institute of Archaeology and Ethnology of the Polish Academy of Sciences, 61-612 Poznan
´, Poland.
34
Laboratory for
Archaeological Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.
35
Zoological Institute of the Russian Academy of Sciences, 199034 St Petersburg, Russia.
36
Department of
Archaeology, State Historical Museum, 109012 Moscow, Russia.
37
Institute for History of Medicine and Foreign Languages of the First Faculty of Medicine, Charles University, 121 08 Prague, Czech
Republic.
38
Research Center for the History and Culture of the Turkic Peoples, Gorno-Altaisk State University, 649000 Gorno-Altaisk, Russia.
39
Department of Pre- and Early History, Institute of
Archaeological Sciences, Faculty of Humanities, Eo
¨tvo
¨s Lora
´nd University, H-1088 Budapest, Hungary.
40
Matrica Museum, 2440 Sza
´zhalombatta, Hungary.
41
Laboratory of Ethnogenomics, Institute of
Molecular Biology, National Academy of Sciences, 0014 Yerevan, Armenia.
42
Department of Archaeology, Faculty of History, Moscow State University, 119991 Moscow, Russia.
43
Novo Nordisk Foundation
Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark.
44
Center for Theoretical Evolutionary Genetics, University of California, Berkeley, California 94720-3140, USA.
G2015 Macmillan Publishers Limited. All rights reserved

personhood
13,14
, rapidly stretching from Hungary to the Urals
15
. 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
16,17
(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
Yamnaya
15
(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
18
. These innovations quickly
spread across Europe and into Asia where they appeared to give rise
to the Andronovo culture
19,20
(Fig. 1). In the Late Bronze Age around
1500 BC, the Andronovo culture was gradually replaced by the
Mezhovskaya, Karasuk, and Koryakova cultures
21
. 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
15–17
, and if the spread of the Indo-
European languages was linked to these events or predates them
15
.
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
4,22
. 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
consuming
23
. 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
24,25
,
(2) adding a ‘pre-digestion’ step to remove surface contaminants
24,26
,
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
10
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
3
statistics when using Yamnaya
as a source population (Extended Data Table 2, Supplementary Table
12). Although European Late Neolithic and Bronze Age cultures such
Okunevo
Okunevo
Karasuk
Karasuk
Andronovo
Andronovo
Early/mid second
Early/mid second
millennium
BC
millennium
BC
Sintashta
Sintashta
Corded Ware
Corded Ware
Afanasievo
Afanasievo
Third millennium BC
Third millennium BC
0 1,000 km
01,000 km
Tocharian
Tocharian
Yamnaya
Yamnaya
N
N
3400 BC 2600 BC 1800 BC 1000 BC 200 BC 600 AD
Iron Age
Afontova Gora
Nordic Late BA
Mezhovskaya
Karasuk
Late BA
Andronovo
Vatya
Middle BA
Sintashta
Nordic LN-EBA
Nordic LN
Maros
Unetice
Okunevo
Bell Beaker
Stalingrad Q.
Nordic MN B
BAC, CWC
Yamnaya
Afanasievo
Remedello
Remedello, CA
Stalingrad quarry,
CA-EBA
Yamnaya, CA-
EBA
Afanasievo, EBA
Battle Axe and
Corded Ware,
CA-EBA
Nordic MN B
Bell Beaker, CA-
EBA
Okunevo, EBA
Unetice, EBA
Maros, MBA
Sintashta, EBA
Nordic LN
Vatya, MBA
Nordic LN-EBA
Andronovo, BA
Middle BA
Karasuk, LBA
Mezhovskaya,
LBA
Late BA
Nordic Late BA
Afontova Gora,
LBA-IA
Velika Gruda,
LBA-IA
Iron Age
N
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.
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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
9
.
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
28
. 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
29
. 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
30
. 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
Americans
4
.
−0.10
−0.05
0.00
0.05
0.10
−0.05 0.00 0.05 0.10
PC2
Mesolithic
Europe S
Europe NE
a
b
−0.05 0.00 0.05
PC1
Neolithic
West Asia
Sardinia
−0.05 0.00 0.05
Siberia
Caucasus
Bronze Age
West
Scandinavia
Hungary
Central
Scandinavia
Remedello
Hungary
Bell Beaker
Corded Ware
Unetice
Scandinavia
Baltic
Monenegro
Yamnaya
Sintashta
Armenia
Sardinian
Hungarian
Norwegian
Lithuananian
Armenian
Contemporary Eurasians
6000 BC 1000 BC 2900 BC 800 BC
Mesolithic
hunter-gatherers
Neolithic
farmers
Bronze Age
Europe
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.
11 JUNE 2015 | VOL 522 | NATURE | 169
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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
15,18,31
.
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
32
, 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
4
that has been suggested to form a third European
ancestral component (Ancestral North Eurasians (ANE))
10
. 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
ST
) 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
ST
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
ST
50.08 between
Mesolithic hunter-gatherers and Bronze Age individuals from
Corded Ware, which is nearly as high as F
ST
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
Eurasians.
a
b
−0.075
−0.050
−0.025
0.000
0.025
0.050
−0.02 0.00 0.02
PC2
Paleolithic
East Asia
West Eurasia
America
Mal’ta
−0.02 0.00 0.02
PC1
Early / Middle Bronze Age
Europe NE
−0.02 0.00 0.02
Late Bronze Age / Iron Age
Siberia
Mal’ta
Afontova Gora
Contemporary Eurasians
22000 BC 2900 BC 200 AD
Paleolithic
Bronze Age
Yamnaya
Afanasievo
Stalingrad Quarry
Okunevo
Sintashta
Andronovo
Mezhovskaya
Karasuk
Afontova Gora
Altai
Kalash
Tubular
Mansi
Altaian
Dai
Nganasan
Chukchi
Chipewyan
Karitiana
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.
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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
33
(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
33
. 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
estimated
34
. 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
12
. 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.
Implications
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
13,15,35,36
. 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
15,31
. 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
37
. 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
Tarim
38
. 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.
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1.00
Paleolithic
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IA Asia
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rs1426654 (SLC24A5)
rs16891982 (SLC45A2)
rs12913832 (OCA2−HERC2)
N Chromosomes
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|
Allele frequencies for putatively positively selected SNPs.
a, Coloured circles indicate the observed frequency of the respective SNP in
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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.
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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 (http://www.ebi.ac.uk/ena) under accession number PRJEB9021.
Reprints and permissions information is available at www.nature.com/reprints. 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. (ewillerslev@snm.ku.dk).
172 | NATURE | VOL 522 | 11 JUNE 2015
RESEARCH ARTICLE
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METHODS
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
24,25
(Supplemen-
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-
nants
24,26
(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
adapters
39
following established protocols
39–41
. 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
42,43
, 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
23
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 (http://www.mycroarray.com/pdf/
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
45
. Mapped reads were filtered for mapping quality 30 and sorted
using Picard (http://picard.sourceforge.net) and SAMtools
46
. Data was merged to
library level and duplicates removed using Picard MarkDuplicates (http://picard.
sourceforge.net) 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
46
. Read depth and coverage were deter-
mined using pysam (http://code.google.com/p/pysam/) and BEDtools
47
. 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
chromosome
3
as implemented in ANGSD
50
(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 (https://github.com/samtools/
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
ST
)
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
with EIGENSOFT
51
, 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
52
. 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.
D
- and f-statistics and population differentiation. We used the Dand fstatistic
framework
53
to investigate patterns of admixture and shared ancestry in our data
set. All statistics were calculated from allele frequencies using the estimators
described previously
53
, with standard errors obtained from a block jackknife with
5 Mb block size. We investigated population differentiation by estimating F
ST
for
all pairs of ancient and modern groups from allele frequencies using the sample-
size corrected moment estimator of Weir and Hill
54
, 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
33
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
55
using BEAGLE
56
. 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 https://github.com/martinsikora/
admixr.
39. Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly
multiplexed target capture and sequencing. Cold Spring Harb. Protocols (2010).
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early Middle Pleistocene horse. Nature 499, 74–78 (2013).
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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).
ARTICLE RESEARCH
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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
(2012).
54. Weir, B. S. & Hill, W. Estimating F-statistics . Annu. Rev. Genet. 36, 721–750
(2002).
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).
RESEARCH ARTICLE
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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
f
3
statistics. Panels depict
pairwise plots of outgroup f
3
statistics of the form f
3
(Ju’hoan
North;Population
1
, Population
2
), showing the correlation of the amount of
shared genetic drift for a pair of ancient groups (Population
1
) with all modern
populations (Population
2
) 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
ST
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
ST
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.
ARTICLE RESEARCH
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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
|
f
3
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
3
reported for groups with negative f
3
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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