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Patrilocality and hunter-gatherer-related ancestry of populations in East-Central Europe during the Middle Bronze Age

Authors:
Maciej Chyleński at Adam Mickiewicz University
Maciej Chyleński
Przemysław Makarowicz at Adam Mickiewicz University
Przemysław Makarowicz
Anna Juras at Adam Mickiewicz University
Anna Juras
Maja Krzewińska at Stockholm University
Maja Krzewińska
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Abstract and Figures

The demographic history of East-Central Europe after the Neolithic period remains poorly explored, despite this region being on the confluence of various ecological zones and cultural entities. Here, the descendants of societies associated with steppe pastoralists form Early Bronze Age were followed by Middle Bronze Age populations displaying unique characteristics. Particularly, the predominance of collective burials, the scale of which, was previously seen only in the Neolithic. The extent to which this re-emergence of older traditions is a result of genetic shift or social changes in the MBA is a subject of debate. Here by analysing 91 newly generated genomes from Bronze Age individuals from present Poland and Ukraine, we discovered that Middle Bronze Age populations were formed by an additional admixture event involving a population with relatively high proportions of genetic component associated with European hunter-gatherers and that their social structure was based on, primarily patrilocal, multigenerational kin-groups.
The geographical and temporal context and genetic affinities of the analysed Bronze Age individuals A Maps showing the locations of samples published in this study and the geographical range of their associated cultural entities; the size of the marker corresponds to the number of samples from each site. The map was created using QGIS 2.12.2⁴⁹ and basemap from NOAA National Geophysical Data Center. 2009: ETOPO1 1 Arc-Minute Global Relief Model. NOAA National Centers for Environmental Information. Accessed 2013. B The age of the newly generated genomes (calculated as an average of 2σ BCE dates) corresponding to the temporal range of the archaeological cultures they are associated with. C The results of unsupervised admixture analysis (K = 7) on the selected populations. D PCA plot of ancient individuals projected onto contemporary individuals from West Eurasia from the Human Origins reference panel (not shown). The symbols in both the PCA and admixture analysis correspond to individuals associated with the following cultures: IC - Iwno Culture, KC – Komarów Culture, MC – Mierzanowice Culture, SC – Strzyżów Culture, TC – Trzciniec Culture (from this study and reference populations), AFN – Afanasievo Culture, ALP – Alföld Linear Pottery Culture, AN – Anatolian Neolithic, AND – Andronovo Culture, BABL – Bronze Age Baltic, BAC – Battle Axe Culture, BACz Bronze Age Czechia, BAHU – Bronze Age Hungary, BAP – Bronze Age Poland, BASC – Bronze Age Scandinavia, BBC – Bell Beaker Culture (Poland, Czechia and Germany, respectively), BKG – Brześć Kujawski Group, CAT – Catacomb Culture, CWC – Corded Ware Culture, EBG – Early Bronze Age Germany, EHG – Eastern Hunter Gatherers, ENSt – Eneolithic Steppe, FBC – Funnel Beaker Culture, GAC – Globular Amphora Culture, HGBL – Hunter Gatherer Baltic, LBK – Linear Pottery Culture, LNBG – Late Neolithic/Bronze Age Germany, MNG – Middle Neolithic Germany, NBL – Neolithic Baltic, NEU – Neolithic Ukraine, POL – Poltavka Culture, PWC – Pitted Ware Culture, SHG – Scandinavian Hunter Gatherers, SNT – Sintashta Culture, SRB – Srubnaya Culture, UNC – Únětice Culture, WHG – Western Hunter Gatherers, YAM – Yamnaya Culture.
The geographical and temporal context and genetic affinities of the analysed Bronze Age individuals A Maps showing the locations of samples published in this study and the geographical range of their associated cultural entities; the size of the marker corresponds to the number of samples from each site. The map was created using QGIS 2.12.2⁴⁹ and basemap from NOAA National Geophysical Data Center. 2009: ETOPO1 1 Arc-Minute Global Relief Model. NOAA National Centers for Environmental Information. Accessed 2013. B The age of the newly generated genomes (calculated as an average of 2σ BCE dates) corresponding to the temporal range of the archaeological cultures they are associated with. C The results of unsupervised admixture analysis (K = 7) on the selected populations. D PCA plot of ancient individuals projected onto contemporary individuals from West Eurasia from the Human Origins reference panel (not shown). The symbols in both the PCA and admixture analysis correspond to individuals associated with the following cultures: IC - Iwno Culture, KC – Komarów Culture, MC – Mierzanowice Culture, SC – Strzyżów Culture, TC – Trzciniec Culture (from this study and reference populations), AFN – Afanasievo Culture, ALP – Alföld Linear Pottery Culture, AN – Anatolian Neolithic, AND – Andronovo Culture, BABL – Bronze Age Baltic, BAC – Battle Axe Culture, BACz Bronze Age Czechia, BAHU – Bronze Age Hungary, BAP – Bronze Age Poland, BASC – Bronze Age Scandinavia, BBC – Bell Beaker Culture (Poland, Czechia and Germany, respectively), BKG – Brześć Kujawski Group, CAT – Catacomb Culture, CWC – Corded Ware Culture, EBG – Early Bronze Age Germany, EHG – Eastern Hunter Gatherers, ENSt – Eneolithic Steppe, FBC – Funnel Beaker Culture, GAC – Globular Amphora Culture, HGBL – Hunter Gatherer Baltic, LBK – Linear Pottery Culture, LNBG – Late Neolithic/Bronze Age Germany, MNG – Middle Neolithic Germany, NBL – Neolithic Baltic, NEU – Neolithic Ukraine, POL – Poltavka Culture, PWC – Pitted Ware Culture, SHG – Scandinavian Hunter Gatherers, SNT – Sintashta Culture, SRB – Srubnaya Culture, UNC – Únětice Culture, WHG – Western Hunter Gatherers, YAM – Yamnaya Culture.
… 
The hunter-gatherer ancestry in the Middle Bronze Age populations from East-Central Europe A The shared genetic drift between the newly published individuals and WHG hunter-gathers was estimated with the use of the f3-statistic, shown separately for autosomal and (B) X-chromosome data. C the WHG ancestry estimated for new (outlined in black) and reference individuals from the Final Neolithic to the Middle Bronze Age. The degree of ancestry was estimated from three-way admixture models including the WHG, AN and YAM, the points represent coefficient for WHG ancestry calculated using qpAdm (only values with p value for nested models <0.05). Error bars in (A–C) correspond to one standard error for the f3-statistics or qpAdm values (vertical) and 2σ for the dates (horizontal). The fit lines in (A–C) display smoothed conditional means for all individual (blue) and after removal of outliers (red) with corresponding 95% confidence intervals (light blue and yellow respectively). D Outgroup f3 statistics values of form f3_Xchr(YRI, TC, popX) and f3_Autosomes(YRI, TC, popX) plotted against each other with error bars representing one standard deviation for each value, red line represents linear regression inserted for visualisation purpose. E The patrilocal character of Żerniki Górne cemetery as shown by the difference in the D-statistic in the form D(YRI, Żerniki individual; Żerniki, TC) for the two sexes. The boxplots show the median (middle horizontal line), interquartile range (25th and 75th percentile) indicated with lower (25th percentile) and upper (75th percentile) hinges of the box, and whiskers extending to the lowest (highest) value that is within 1.5 times the interquartile range of the upper (lower) hinge. The labels in all panels are as follows: IC – Iwno Culture, KC – Komarów Culture, MC – Mierzanowice Culture, SC – Strzyżów Culture, TC – Trzciniec Culture, AN – Anatolia Neolithic, BAC – Battle Axe Culture, BAHu – Bronze Age Hungary, BAP – Bronze Age Poland, BASC – Bronze Age Scandinavia, BBC – Bell Beaker Culture, BKG – Brzesc Kujawski Group, CWC – Corded Ware Culture, EBG – Early Bronze Age Germany, FBC – Funnel Beaker Culture, GAC – Globular Amphora Culture, LNBG – Late Neolithic/Bronze Age Germany, MNG Middle Neolithic Germany, NBL Neolithic Baltic, NCHu Neolithic/Chalcolithic Hungary.
The hunter-gatherer ancestry in the Middle Bronze Age populations from East-Central Europe A The shared genetic drift between the newly published individuals and WHG hunter-gathers was estimated with the use of the f3-statistic, shown separately for autosomal and (B) X-chromosome data. C the WHG ancestry estimated for new (outlined in black) and reference individuals from the Final Neolithic to the Middle Bronze Age. The degree of ancestry was estimated from three-way admixture models including the WHG, AN and YAM, the points represent coefficient for WHG ancestry calculated using qpAdm (only values with p value for nested models <0.05). Error bars in (A–C) correspond to one standard error for the f3-statistics or qpAdm values (vertical) and 2σ for the dates (horizontal). The fit lines in (A–C) display smoothed conditional means for all individual (blue) and after removal of outliers (red) with corresponding 95% confidence intervals (light blue and yellow respectively). D Outgroup f3 statistics values of form f3_Xchr(YRI, TC, popX) and f3_Autosomes(YRI, TC, popX) plotted against each other with error bars representing one standard deviation for each value, red line represents linear regression inserted for visualisation purpose. E The patrilocal character of Żerniki Górne cemetery as shown by the difference in the D-statistic in the form D(YRI, Żerniki individual; Żerniki, TC) for the two sexes. The boxplots show the median (middle horizontal line), interquartile range (25th and 75th percentile) indicated with lower (25th percentile) and upper (75th percentile) hinges of the box, and whiskers extending to the lowest (highest) value that is within 1.5 times the interquartile range of the upper (lower) hinge. The labels in all panels are as follows: IC – Iwno Culture, KC – Komarów Culture, MC – Mierzanowice Culture, SC – Strzyżów Culture, TC – Trzciniec Culture, AN – Anatolia Neolithic, BAC – Battle Axe Culture, BAHu – Bronze Age Hungary, BAP – Bronze Age Poland, BASC – Bronze Age Scandinavia, BBC – Bell Beaker Culture, BKG – Brzesc Kujawski Group, CWC – Corded Ware Culture, EBG – Early Bronze Age Germany, FBC – Funnel Beaker Culture, GAC – Globular Amphora Culture, LNBG – Late Neolithic/Bronze Age Germany, MNG Middle Neolithic Germany, NBL Neolithic Baltic, NCHu Neolithic/Chalcolithic Hungary.
… 
The kinship structure of the Middle Bronze Age populations from East-Central Europe The proposed pedigrees of detected kin groups from Bronze Age cemeteries associated with the Trzciniec culture, asterisk (*) marks instances where more than one interpretation of the detected kinship is possible. The colours correspond to either the collective burials on the plan of the site in which the individuals were interred (in the case of Żerniki Górne) or the skeletal remains within collective burials. The burial photographs were reprinted with permission from Wiley from Juras, A. et al. Mitochondrial genomes from Bronze Age Poland reveal genetic continuity from the Late Neolithic and additional genetic affinities with the steppe populations. Am. J. Phys. Anthropol. 172, 176–188 (2020); © 2020 Wiley Periodicals, Inc All Rights Reserved.
The kinship structure of the Middle Bronze Age populations from East-Central Europe The proposed pedigrees of detected kin groups from Bronze Age cemeteries associated with the Trzciniec culture, asterisk (*) marks instances where more than one interpretation of the detected kinship is possible. The colours correspond to either the collective burials on the plan of the site in which the individuals were interred (in the case of Żerniki Górne) or the skeletal remains within collective burials. The burial photographs were reprinted with permission from Wiley from Juras, A. et al. Mitochondrial genomes from Bronze Age Poland reveal genetic continuity from the Late Neolithic and additional genetic affinities with the steppe populations. Am. J. Phys. Anthropol. 172, 176–188 (2020); © 2020 Wiley Periodicals, Inc All Rights Reserved.
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Article https://doi.org/10.1038/s41467-023-40072-9
Patrilocality and hunter-gatherer-related
ancestry of populations in East-Central
Europe during the Middle Bronze Age
Maciej Chyleński
1
,Przemysław Makarowicz
2
, Anna Juras
1
,
Maja Krzewińska
3,4
,Łukasz Pospieszny
5,6
,EdvardEhler
7
,AgnieszkaBreszka
1
,
Jacek Górski
8,9
, Halina Taras
10
,AnitaSzczepanek
11
,MartaPolańska
12
,
Piotr Włodarczak
11
, Anna Lasota-Kuś
11
, Irena Wójcik
9
, Jan Romaniszyn
2
,
Marzena Szmyt
2,13
, Aleksander Kośko
2
, Marcin Ignaczak
2
, Sylwester Sadowski
10
,
Andrzej Matoga
9
, Anna Grossman
14
, Vasyl Ilchyshyn
15
,MarynaO.Yahodinska
16
,
Adriana Romańska
17
, Krzysztof Tunia
11
,MarcinPrzybyła
18
,RyszardGrygiel
19
,
Krzysztof Szostek
20
,MiroslawaDabert
21
, Anders Götherström
3,4
,
Mattias Jakobsson
22,23,24
&HelenaMalmström
22,23
The demographic history of East-Central Europe after the Neolithic period
remains poorly explored, despite this region being on the confluence of var-
ious ecological zones and cultural entities. Here, the descendants of societies
associated with steppe pastoralists form Early Bronze Age were followed by
Middle Bronze Age populations displaying unique characteristics. Particularly,
the predominance of collective burials, the scale of which, was previously seen
only in the Neolithic. The extent to which this re-emergence of older traditions
is a result of genetic shift or social changes in the MBA is a subject of debate.
Here by analysing 91 newly generated genomes from Bronze Age individuals
from present Poland and Ukraine, we discovered that Middle Bronze Age
populations were formed by an additional admixture event involving a
population with relatively high proportions of genetic component associated
with European hunter-gatherers and that their social structure was based on,
primarily patrilocal, multigenerational kin-groups.
Currently, it is well established that the European gene pool has been
shapedby several major demographic events, including the postglacial
spread of hunter-gatherers1; subsequent migrations of early farmers,
which marked the beginning of the Neolithic in Europe2,3;andthelater
arrival of Pontic-Caspian steppe pastoralists4–6. However, there is still
extensive debate surrounding the scale and exact nature of these
demographic events and how they affected the genetic makeup of
different regions across Europe7.
East-Central Europe, in particular, is a region often on the frontier
of these events, resulting in a mosaic of genetically distinct popula-
tions associated with a variety of cultural entities. By the turn of the
Bronze Age, this region was dominated by populations associated with
the Corded Ware Culture (CWC) and Bell Beaker Culture (BBC) and
characterised by high levels of steppe ancestry4–6,8. Descendants of
steppe pastoralists are thought to have replaced and admixed with the
late Neolithic populations, which were characterised by large amounts
of Anatolian farmer ancestry with additional low to medium levels of
hunter-gatherer ancestry9,10. However, the Funnel Beaker Culture
(FBC) and Globular Amphora Culture (GAC), major entities predating
the arrival of steppe pastoralists associated with the later Neolithic in
the region, are thought to have been long lasting, with some of their
local variants continuing well into the Early Bronze Age (up to 2000
Received: 7 July 2022
Accepted: 7 July 2023
Check for updates
A full list of affiliations appears at the end of the paper. e-mail: maciej.ch@amu.edu.pl;annaj@amu.edu.pl;helena.malmstrom@ebc.uu.se
Nature Communications | (2023) 14:4395 1
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Content courtesy of Springer Nature, terms of use apply. Rights reserved
BCE, in the case of the GAC)11. As no ancient DNA (aDNA) data are
available from individuals associated with later populations, their
genetic makeup can only be inferred from their 3rd millennium-BC
counterparts. The northern and eastern parts of East-Central Europe
followed slightly different trajectories, with populations associated
with the sub-Neolithic forest zone12,13, characterised by high levels of
hunter-gatherer ancestry (and, to a large extent, lifestyle) throughout
the Neolithic. In these regions, Anatolian farmer ancestry was intro-
duced along with steppe ancestry during the onset of the Bronze
Age14,15. Similar patterns have been observed in other regions sur-
rounding the Baltic Sea, such as southern Scandinavia and Gotland,
wherehunter-gathererindividuals(bothintermsoflifestyleand
genetic composition) of the Pitted Ware Culture (PWC) coexisted in
close proximity (but without significant gene flow) with the FBC in the
4th millennium BCE and the Battle Axe Culture (BAC), the Scandina-
vian variant of the CWC, in the first half of the 3rd millennium BCE16,17.
The cultural landscape of Early Bronze Age (EBA) in East-Central
Europe (2400–1800 BCE) is widely believed to be a direct continuati on
of processes that started during the onset of the epoch. For example,
the cultural entitiespresent in the region, such as those associated with
the Mierzanowice, Iwno and Strzyżów archaeological cultures (MC, IC
and SC, respectively)18–20,are largely seen as continuations of groups
associated with the CWC and BBC21–23. In addition, steppe24,25 or the
northern forest zone26 cultures have been suggested to have influ-
enced, to some extent, the SC.
The Middle Bronze Age (MBA) in the region (1800–1200 BCE)
was in turn dominated by the Trzciniec Cultural Circle (TCC). This
cultural phenomenon extended from the Oder River drainage basin
to the Desna and Seym River basins (ca. 1200 km) and from the Baltic
seashore to the Prut basin (ca. 750 km), exhibiting several territorial
variants27. This study focuses on MBA individuals associated with
two of these variants (Fig. 1A): the Trzciniec Culture (TC), which
occupied the lands belonging to modern-day Poland and central-
western Ukraine, and the Komarów Culture (KC), found in modern-
day southwestern Ukraine and neighbouring parts of Romania and
Moldova27. These MBA cultures retained many cultural aspects of
their EBA counterparts, such as styles of pottery and bronze arte-
facts as well as funeral practices including under-barrow graves and
cremation27. This cultural similarity have been shown to coincide
with genetic continuity between CWC and both EBA and MBA
populations as seen in mitochondrial genome data28. However , some
elements of the TCC are unique to the EBA or MBA, particularly the
predominance and scale of collective burials. Collective burials of
multiple individuals were also prevalent among Middle and Late
Neolithic populations in Central and East-Central Europe associated
with local variants of the FBC or GAC29,30. Recent studies have shown
that these Neolithic collective burials often contain remains of
multiple individuals who belonged to, in most cases, patrilocal kin
groups31–34. The biological relatedness among individuals found in
these MBA collective burials associated with the TCC, remains
unexplored. The presence or absence of kinship among these
populations would, however, greatly increase our understanding of
the social organization and structure of these societies. The re-
emergence of collective burials is the subject of another debate
concerning whether this predominant burial custom in the TCC was
a result of genetic shifts or social changes within the Bronze Age
populations30.
This study, explores genetic affinities between individuals from
various EBA and MBA cultures and their genetic relations to popu-
lations of preceding cultural complexes as well as possible kinship
structures within MBA societies. To achieve this we conducted
population genetic analyses using 91 newly generated genomes from
Bronze Age individuals associated with EBA and MBA cultures from
modern-day southern and south-eastern Poland and western
Ukraine.
Results and discussion
Out of the 175 Bronze Age individuals screened, 92 produced enough
data (>0.018 genome coverage) tobe retained for further analysis and/
or deeper sequencing. An additional 100 libraries, including 37 uracil-
DNA-glycosylase (UDG)-treated libraries, were created and sequenced
for the selected individuals. With the exception of two libraries from
two different individuals, all libraries displayed characteristic post-
mortem damage at the 5′and 3′ends of aDNA fragments and low
contamination levels, as estimated by mitochondrial sequences and X
chromosome sequences in males, where sufficient coverage was
obtained (Supplementary Data1 and Supplementary Data 2). Only one
library for individual poz751, which displayed very low levels of post-
mortem damage, and one of two libraries for individual poz664, in
which high levels of mitochondrial DNA (mtDNA) contamination were
detected, were excluded from further analysis. Therefore, the final
dataset used for the kinship and population genetic analyses consisted
of 91 individuals with a median genome coverageof 0.2× (ranging from
0.019× to 2.29×), representing the IC (n= 3), MC (n=15),SC(n=6),TC
(n=62)andKC(n= 5) cultures.
Genetic affinities of Early Bronze Age populations from East-
Central Europe indicate the continuation of processes initiated
in the onset of the epoch
The majority of the EBA individuals in this study (2200–1850 BCE)
associated with the MC, IC and SC are genetically similar to their direct
cultural predecessors (such as the BBC and CWC), as indicated by the
principal component analysis (PCA) plot (Fig. 1D). The results of
unsupervised admixture analysis (K = 7) of the selected populations
(Fig. 1C) support these relationships as they indicate similar levels of
admixture components (Fig. 1C). These findings are consistent with the
general archaeological consensus and previous analyses of mito-
chondrial data28 Although the IC is believed to have the greatest cul-
tural similarity to groups associated with the BBC, the one individual
analysed here (poz929) exhibited closer affinity with individuals
associated with various CWC groups rather than the BBC populations
according to the f3 and Dstatistics (Supplementary Data 7, Supple-
mentary Data 12 and Supplementary Fig. 2). A similar trend was
observed in the case of individuals attributed to the MC, which dis-
played closer affinity to CWC individuals from Estonia over other EBA
groups. However, the division between the BBC and CWC (and their
definition as independent cultural entities associated with distinct
populations) is contested7, and the majority of the above D statistics
have low Zscores. Further studies are needed to fully understand the
regional, cultural and genetic complexity of Central and Eastern Eur-
ope after the arrival of steppe pastoralists.
One IC-associated male from Łojewo (poz502) deviated from the
general pattern, as he was genetically closest to the Middle and Late
Neolithic populations, which occupied the same space on the PCA plot
and displayed similar admixture proportions. The results of f3 statis-
tics indicate that the population sharing the most genetic drift withthis
individual was the GAC, followed closely by the FBC (Supplementary
Data 7). Such seemingly Neolithic individuals have occasionally been
observed in populations postdating the arrival of steppe pastoralists
and have been hypothesised to be foreigners who were incorporated
into Bronze Age societies from isolated populations that retained a
Middle Neolithic genetic makeup up to the end of the 3rd millennium
BCE35. If this interpretation is applied to poz502, radiocarbon dated to
the border between the EBA and MBA (2008-1750 BCE), it might
indicate that such isolated populations lasted far longer than pre-
viously reported. This hypothesis is supported by the archaeological
record, which shows that some Neolithic cultures, most notably the
GAC, lasted well into the Bronze Age21.
Similar to poz502, two of the SC-associated males (poz794 and
poz758) differed genetically from other EBA individuals. These two
males were closer to the hunter-gatherer space in the PCA plot (Fig. 1D)
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 2
Content courtesy of Springer Nature, terms of use apply. Rights reserved
and showed an increased proportion of genetic components that were
maximised in various European hunter-gatherer populations in the
admixture analysis (Fig. 1C). However, direct radiocarbon dating for
one of these individuals (poz794, 1921–1697 BCE) as well as their
genetic similarity to the MBA populations analysed in this study indi-
cate thatthese two males should be discussed as a part of the genetic
shifts observed in the MBA. Notably, the SC is generally thought to be a
regional cultural phenomenon with mixed cultural traits, leading to
frequent dispute over the association of individual burials or sites with
this culture24–26. Therefore, the definition and associations of the SC
with any genetically distinct population warrants further exploration
targeting a broader selection of individuals attributed to this culture.
Increase in hunter-gatherer ancestry in East-Central Europe in
the Middle Bronze Age
The MBA individuals analysed here were dated to a range between
1750 and 1200 BCE and were associated with the TCC, representing
both the TC and KC. The majority of these individuals clustered
together in PCA space and shared similar admixture proportions
(Fig. 1C and D). This apparent genetic relation is further highlighted by
f3 and Dstatistics, which indicate that when analysed separately, KC
and TC individuals do not, in majority of cases, display any statistically
significant closer genetic affinity to either of the two populations
(Supplementary Data 7 and Supplementary Data 12, Supplementary
Figs. 3C and 4B). These results are in accordance with the
5
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57⁰
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NN
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PC2
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Avg. pairwise similarity: 0.999
THIS STUDY
Late Neolithic - Early Bronze Age
Early and Middle Neolithic steppe pastoralists
hunter-gatherers
Late BA
AB
CD
EHG
SHG
WHG
HGBL
NBL
NEU
PWC
AN
ALP
LBK
BKG
FBC
GAC
MNG
YAM
ENSt
SRB
POL
CAT
AFN
AND
SNT
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►
►
CWC
BAC
BBCge
BBCcz
BBCpl
BAP
LNBG
EBG
BACz
BAHu
BASC
IC
MC
SC
KC
TC
UNC
BABL
Fig. 1 | The geographical and temporal context and genetic affinities of the
analysed Bronze Age individuals. A Maps showing the locations of samples
published in this study and the geographical range of their associated cultural
entities; the size of the marker corresponds to the number of samples from each
site. The map was created using QGIS 2.12.249 and basemap from NOAA National
Geophysical Data Center. 2009: ETOPO1 1 Arc-Minute Global Relief Model. NOAA
National Centers for Environmental Information. Accessed 2013. BThe age of the
newly generated genomes (calculated as an average of 2σBCE dates) corre-
sponding to the temporal range of the archaeologicalcultures they are associated
with. CThe results of unsupervised admixture analysis (K= 7) on the selected
populations. DPCA plot of ancient individuals projected onto contemporary
individuals from West Eurasia from the Human Origins reference panel (not
shown). The symbols in both the PCA and admixture analysis correspond to indi-
viduals associated with the following cultures: IC - Iwno Culture, KC –Komarów
Culture, MC –Mierzanowice Culture,SC –Strzyżów Culture, TC –Trzciniec Culture
(from thisstudy and referencepopulations), AFN –Afanasievo Culture, ALP–Alföld
LinearPottery Culture,AN –Anatolian Neolithic, AND –Andronovo Culture,BABL –
Bronze Age Baltic, BAC –Battle Axe Culture, BACz Bronze Age Czechia, BAHU –
Bronze Age Hungary, BAP –Bronze Age Poland, BASC –Bronze Age Scandinavia,
BBC –Bell Beaker Culture (Poland, Czechia and Germany, respectively), BKG –
Brześć Kujawski Group, CAT –Catacomb Culture, CWC –Corded Ware Culture,
EBG –Early Bronze Age Germany, EHG –Eastern Hunter Gatherers, ENSt –Eneo-
lithicSteppe, FBC –FunnelBeaker Culture,GAC –Globular Amphora Culture,HGBL
–Hunter Gatherer Baltic, LBK –Linear Pottery Culture, LNBG –Late Neolithic/
Bronze Age Germany, MNG –Middle Neolithic Germany, NBL –Neolithic Baltic,
NEU –Neolithic Ukraine, POL–Poltavka Culture,PWC –Pitted Ware Culture, SHG –
Scandinavian Hunter Gatherers, SNT –Sintashta Culture, SRB –Srubnaya Culture,
UNC –Únětice Culture, WHG –Western HunterGatherers,YAM –Yamnaya C ulture.
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved
archaeological interpretation that questions the separation of the TC
and KC, arguing in favour of treating them as regional variants of the
same phenomenon27,36.
Interestingly compared to EBA populations, the MBA individuals
were closer in the PCA space to various hunter-gatherer populations
from Europe (Fig. 1D), something that previously was not detected in
analyses of mitochondrial genome data alone28.Moreover,admixture
analysis indicated elevated amounts of genetic components max-
imised in hunter-gatherers (Fig. 1C). This suggests an additional
admixture event at the beginning of the MBA involving a population
with relatively high proportions of this genetic component. However,
there were notable deviations to this trend, with three individuals
associated with TC from Pielgrzymowice site and poz643, a relatively
early KC male from Beremiany, clustering closer to EBA populations in
PCA space and displaying the lowest levels of shared geneticdrift with
both TC and hunter-gatherer populations (Supplementary Data 7).
When using qpAdm to test for possible two-way admixture models
that resulted in the formation of MBA populations, several models
were determined to be plausible (Supplementary Data 13) with the
highest pvalue (p= 0.21) obtained for pair consisting of IC and Neo-
lithic Baltic hunter-gatherers (NBL). Similarly high pvalues were found
for other pairs including IC and other hunter-gatherer populations:
Western Hunter Gatherers (WHG), PWC hunter-gatherers from Got-
land, hunter-gatherer buried in BKG context (BKGout) and hunter-
gatherer populations predating the NBL (HGBL) (p= 0.207, 0.204,
0.164, 0.160 respectively). These results are in accordance with the
archaeological hypothesis that the IC greatly contributed to the
emergence of the TC27.However,thisfinding should be interpreted
with caution, as only one individual associated with the IC had suffi-
cient coverage for inclusion in qpAdm. High fit values were also
obtained for pair of populations, containing individuals from modern
day Estonia associated with the CWC, as the EBA predecessorand PWC,
as the source of hunter-gatherer ancestry (p= 0.11).
The process of admixture was likely more complex, as indicated
by the three-way admixture model with the addition of a Neolithic
population. These models yielded even better fits than the two-way
models. Multiple plausible scenarios were found, all displaying high fit
values (p> 0.9) including CWCes in addition to various hunter-
gatherer and Neolithic populations (Supplementary Data 14). The
more discriminatory rotating outgroup approach to qpAdm37 used for
both two- and three- way models helped to narrow down the number
of plausible scenarios. After excluding the models containing sources
consisting of one individual, only three-way scenarios including
CWCes and either GAC or FBC as Neolithicand NBL or HGBL as hunter-
gatherer population were found to be plausible (Supplementary
Data 15 and Supplementary Data 16). Based on geographical and
temporal proximity as well as results of D statistics directly comparing
the potential ancestry sources for each MBA individual (Supplemen-
tary Data 12, Supplementary Fig. 3) we find CWCes, NBL and GAC to be
the best proxies for populations involved in the admixture process.
However high fit values and close genetic affinity to the individual with
hunter-gatherer ancestry buried in BKG context, indicate that further
studies might help to better define the populations involved in the
process.
The most likely hypothesis is that these admixed MBA popula-
tions originated in the confluence of the sub-Neolithic forest zone,
associated with populations with dominant WHG ancestry14,15 as well as
post-CWC groups characterised by a large proportion of steppe
ancestry. The sub-Neolithic forest zone”is a broad term that includes
various archaeological cultures from north-eastern Europe, char-
acterised by long-lasting preservation of a predominantly hunter-
gatherer lifestyle, and the incorporation of cultural elements of Neo-
lithic and Bronze Age origin38. These populations remained genetically
distinct from the Neolithic and post-Neolithic populations, although
they maintained some level of long lasting cultural and economic
exchange14 It is possible that this lead to some degree of gene flow
between those populations, similar to the one observed in the case of
PWC in Gotland6,16 followed by subsequent contacts with EBA des-
cendants of steppe pastoralists. Moreover, the TCC and sub-Neolithic
forest zone exhibited similar cultural traits, mostly in the form of
pottery and technologies12,13,39–41. These similarities have often been
interpreted as signs of primarily cultural exchange. Our results,
showing an increase inWHG ancestry during the MBA, indicate that at
least some level of admixture occurred during these interactions.
Notably, the two EBA individuals (poz794 and poz758) associated
with the SC, that displayed closer genetic affinity to the MBA popula-
tions both came from the south-eastern part of modern-day Poland
(Supplementary Text); of these two, the individual with a direct date
(poz794) predated the MBA samples analysed here. This observation
suggests that the contact zone described above is not the only place
where admixture took place and/or that the process was more geo-
graphically diffused. Eitheris plausible,given the rangeand duration of
exchange networks seen in the EBA40. Individual poz794 might even
signal the beginning of the observed gene flow, which would date it to
approximately 1800 BCE.
The SC is usually seen as a continuation of CWC traditions with
additional elements from steppe cultures such as the Catacomb
Culture24,25. However, the genetic shift toward an increase in WHG
ancestry cannot be explained byadditional migration from the steppe,
a notion that we previously proposed based solely on mitochondrial
data28. This idea is not supported by any indication of increase of
steppe ancestry as calculated by three-way qpAdm modelling includ-
ing WHG, AN and YAM as best proxies of major ancestries in European
gene pool (Supplementary Data 17, Supplementary Fig. 4A). Moreover,
two-way qpAdm models that explored the scenarios resulting in the
emergence of the MBA populations, including EBA individuals and
hunter-gatherer populations, yielded higher probabilities than models
including additional steppe populations such as Andronovo, Afana-
sievo, Sintashta, Poltavka, Karasuk, or Srubnaya (Supplementary
Data 13).
The process of admixture, which began around1800 BCE, appears
to have been a continuous rather than a result of a single migratory
event, as evidenced by the presence of individuals with very high or
very low proportions of hunter-gatherer ancestry throughout the
whole temporal range of MBA samples analysed here (Fig. 2C, Sup-
plementary Data 17). However, gene flow was likely more extensive in
the beginning, as both shared genetic drift (as identified by f3 statis-
tics) and admixture proportions (calculated with qpAdm) show that
the proportion of hunter-gatherer ancestry decreased slightly over
time (Fig. 2AandFig.2C). The results of this event must ha ve been long
lasting, as Late Bronze Age individuals from modern-day Latvia and
Lithuania14 retainthe same genetic composition as our MBA individual
despite living nearly half a century later and were found, based on f3
statistics, to have the closest genetic affinity to the MBA individuals
presented here out of all Bronze Age populations (Supplementary
Data 7 and Supplementary Fig. 3C).
Furthermore, several lines of reasoning support the idea that this
admixture event was dominated by males originating in a population
characterised by a high level of hunter-gatherer ancestry. First, as
shown by our direct kinship analyses below, the resulting population
was primarily patrilocal. Second, the MBA composition of Y-DNA
haplogroups differed significantly from the predating populations, as
dominance of I2a1a and I2a1b haplogroups was previously seen only
sporadically in various hunter-gatherer populations, including two
Narva Culture individuals14 and interestingly in high frequency,
although for different sub-haplogroups,in GAC collective burials10,34,42.
The I2a1 haplogroups were found in 75% of TC-associated MBA indi-
viduals, even after selecting only one individual from each detected kin
group. Moreover, this shift was not apparent when looking at the
mitochondrial haplogroups28. Finally, direct analysis of genetic
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 4
Content courtesy of Springer Nature, terms of use apply. Rights reserved
distances in X-chromosome data, as determined by f3 statistics using
the approach suggested by Saag et al.15 showed that on autosomes TC
was relatively more similar to hunter-gatherer populations than on X
chromosome (Fig. 2D, Supplementary Data 5 and Supplementary
Data 6). In addition, when looking at the temporal changes in X-based
f3 values we did not observe increased amounts of genetic drift shared
with WHG individuals between 1800 and 1500 BCE, as seen in the
autosomal data (Fig. 2B, Supplementary Data 8).
The exact trajectory of events leading to the genetic shift in the
MBA cannot be reconstructed with current knowledge. The Eastern
Baltic hunter-gatherer populations were associated with multiple
archaeological cultures that engaged in direct contact with Neolithic
farmers for millennia. It cannot be excluded that at some points of this
coexistence, migratory events occurred, leading to the emergence of
admixed populations that, in turn, later mixed with steppe pastoralists
or their Central European descendants, resulting in the formation of
MBA populations analysed here. The lack of more diverse genetic data
from East-Central Europe prevents us from pinpointing the exact
populations that took part in this admixture. As the archaeological
record shows that contact between culturally distinct groups of
farmers and hunter-gatherers were long lasting, leading to substantial
cultural changes38, it is possible that the practices of collective burials
and patrilocal residence were some of those changes. This could be
reflected in the high frequency of I2a1b Y haplogroup in some collec-
tive burials associated with middle Neolithic GAC culture10,34,42.The
observed changes could have resulted from several processes invol-
ving multiple populations; our observations represent the sum of
those processes.
-0.020
-0.015
-0.010
-0.005
0.000
XX XY
A
B
C
D
AN
BAC
BAHu
BBCbr
BBCge
BBCpl
BKG
BKGout
CWCes
CWCge
CWCpl
CWCplout FBC
GAC
IC
LBK
MNG
NBL
NCHu
PWC
WHG
YAM
0.160
0.165
0.170
0.175
0.180
0.35 0.40 0.45
f3 X
Neolithic farmers
Early Bronze Age
Hunter-Gatherers
3 autosoms
E
0.160
0.165
0.170
0.175
0.180
1500
2000
2500
population
BAHu
BASC
BBCge
BBCpl
CWCes
IC
KC
LNBG
MC
SC
TC
f3 (ind, WHG, Y RI)
Cal BC
0.28
0.30
0.32
0.34
15002000
2500 Cal BC
f3 X (ind, WHG, Y RI)
population
BAHu
BASC
BBCge
BBCpl
CWCes
IC
KC
LNBG
MC
SC
TC
0.1
0.2
0.3
1500
2000
2500 Cal BC
BBCcz
BBChu
EBG
qpAdm WHG coeff.
population
BASC
BBCge
CWCes
IC
KC
LNBG
MC
SC
TC
n=6 n=6
Fig. 2 | The hunter-gatherer ancestry in the Middle Bronze Age populations
from East-Central Europe. A The shared genetic drift between the newly pub-
lished individuals and WHG hunter-gatherswas estimated with the use of the f3-
statistic, shown separately for autosomal and (B) X-chromosome data. Cthe WHG
ancestry estimated for new (outlined in black) and reference individuals from the
Final Neolithic to the Middle Bronze Age. The degree of ancestry was estimated
from three-way admixture models including the WHG, AN and YAM, the points
represent coefficientfor WHG ancestry calculated using qpAdm (only values with p
value for nested models <0.05). Error bars in (A–C) correspond to one standard
error for the f3-statistics or qpAdm values (vertical) and 2σfor the dates (hor-
izontal). The fit lines in (A–C)display smoothed conditionalmeans for all individual
(blue) and after removal of outliers (red) with corresponding 95% confidence
intervals (light blue and yellow respectively). DOutgroup f3 statistics values of
form f3_Xchr(YRI, TC,popX) and f3_Autosomes(YRI,TC, popX) plottedagainst each
other with error bars representing one standard deviation for each value, red line
represents linear regression inserted for visualisation purpose. EThe patrilocal
character of Żerniki Górne cemetery as shown by the difference in the D-statistic in
the form D(YRI, Żerniki individual; Żerniki, TC) for the two sexes. The boxplots
show the median (middle horizontal line), interquartile range (25th and 75th per-
centile)indicated with lower (25th percentile) andupper (75th percentile) hingesof
the box,and whiskers extending to the lowest (highest)value that iswithin 1.5 times
the interquartile range of the upper (lower) hinge. The labels in all panels are as
follows: IC –Iwno Culture, KC –Komarów Culture, MC –Mierzanowice Culture, SC
–Strzyżów Culture, TC –Trzciniec Culture, AN –Anatolia Neolithic, BAC –Battle
Axe Culture, BAHu –Bronze Age Hungary, BAP –Bronze Age Poland, BASC –
Bronze Age Scandinavia, BBC –Bell Beaker Culture, BKG –Brzesc Kujawski Group,
CWC –Corded Ware Culture, EBG –Early Bronze Age Germany, FBC –Funnel
Beaker Culture, GAC –Globular Amphora Culture, LNBG –Late Neolithic/Bronze
Age Germany, MNG Middle Neolithic Germany, NBL Neolithic Baltic, NCHu Neo-
lithic/Chalcolithic Hungary.
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 5
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Paternal kinship structure among the Bronze Age collective
burials
The Trzciniec Cultural Circle stands out from the other Bronze Age
populations in East-Central Europe due to the high number of TC and
KC-associated individuals buried in collective burials. In this study, we
analysed 62 individuals from 12 archaeological sites, 52 of which were
buried within structures containing remains of at least two people. In
addition, as in the case of two individuals from the Dacharzów site,
single graves were often in close proximity to collective burials; for
example, beneath the burial mounds constructed over them30.Our
data clearly show that MBA collective burials associated with the TCC
contained numerous genetically related individuals, with multiple first-
and second-degree kinships found within those structures (Supple-
mentaryFig. 5). The largest numberof close relationships wasdetected
among individuals from the Żerniki Górne cemetery, which displayed
the best overall aDNA preservation. Out of 28 analysed individuals
interred in 9 structures, 17 individuals were found to belong to kin
groups that, in some cases, had reconstructed pedigrees spanning at
least 4 generations (Fig. 3). Interestingly, direct genetic kinship was
also found between individuals buried in different, although adjacent,
burial chambers. This shows that not only did the graves themselves
represent kin groups within the population, but also that the spatial
relations of graves within the cemetery represented kin relations. The
prevalence of close kinship among adult male descendants compared
to adult females suggests that patrilocality was the dominant marriage
arrangement. This notion is further supported by higher mitochon-
drial diversity compared to Y-DNA diversity and larger average genetic
distancesbetweenfemalesthanmales.ThelatterissupportedbyD
statistics that showed that males displayed greater tendency to form a
cladewithotherŻerniki Górne individuals over the general TCC-
associated population (Fig. 2E, Supplementary Data 10).
However, not all analysed individuals within the same collective
grave were genetically related. This finding could reflect the inability to
sample all individuals or the inability to characterise them due to
insufficient DNA preservation. Moreover, those without detectable
kinship in the cemetery were mostly females (2 males and 9 females
without detectable kin at Żerniki Górne), which further supports the
notionof patrilocality. In somecases, such as Pielgrzymowice grave no.
9, the burial pit/chamber was used for an extended period43 and pos-
sibly spanning multiple generations; therefore, detecting multiple
first- and second-degree kinships was less likely. That said, two out of
five analysed individuals shared first-degree kinship and were likely a
mother and her adult son. The collective grave at the Brodzica site
seems to contain the remains of a nuclear family, and a sufficient
684
H11b
I2a1a1b1a1~
671
J1c1b
H
H
1
1
1
1
b
b
667
J1c1b
I2a1a1b1a~
675
W3a1
I2a1a1b1a1~
746
H2a1
I2a1a
682
H11b
670
U5a2a2a
I2a1a1b1a1~
?
?
U
U
5
5
a
a
2
2
a
a
2
2
a
a
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J
1
1
c
c
1
1
b
b
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W
3
3
a
a
1
1
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H2a1
747
U4b1a1a1
I2a1a1b1a1~
U
U
4
4
b
b
1
1
a
a
1
1
a
a
1
1
I2a1a1b1a1~
719
U5b3b1
I2a1b1a1b1~
716
U5b3b1
554
I1a1a
R1a1a1~
I2a1b1a1b1~
555
J1c2
R1a1a1b~
552
I1a1a
sample
mitochondrial
Y-chrom
sub-aadult
Adult
Female
Male
sample
mitochondrial
Pielgrzymowice
662
V
I2a~
665
U5a1a1+152
I2a1a1b1a1~
659
N1b1a5
663
V
660
N1b1a5
I2a1a1b1a1~
62
10
12 86
69
71
3
99
98
5
72
73
102
117
54
N
05m
Żerniki Górne Brodzica
I
I
1
1
a
a
1
1
a
a
651
H11b
I2a(1a1b1a1~)
655
H11b
I2a1a1b1a1~
649
U5a1j
I(2a1a1b~)
658
H13a1a1d
I2a1a1b1a1~
U
U
5
5
a
a
1
1
j
j
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H
1
1
3
3
a
a
1
1
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d
d
*
*
*
Fig. 3 | The kinship structure of the Middle Bronze Age populations from East-
Central Europe. The proposed pedigrees of detected kin groups from Bronze Age
cemeteries associated with the Trzciniec culture,asterisk (*) marksinstances where
more than one interpretation of the detected kinship is possible. The colours
correspond to either the collective burials on the plan of the site in which the
individuals were interred (in the case of Żerniki Górne) or the skeletal remains
within collective burials. The burial photographs were reprinted with permission
from Wiley from Juras, A. et al. Mitochondrial genomes from Bronze Age Poland
reveal genetic continuity from the Late Neolithic and additional genetic affinities
with the steppe populations. Am. J. Phys. Anthropol. 172, 176–188 (2020); © 2020
Wiley Periodicals, Inc All Rights Reserved.
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 6
Content courtesy of Springer Nature, terms of use apply. Rights reserved
amount of data was available for three out of the four individuals, all of
whom were related. These individuals most likely represent a father
with his two children. The fourth individual, interpreted as an adult
women (although the remains did not yield enough nuclear data for
kinship analysis), was found to belong to the same mitochondrial
haplogroup as the two children28 and thus could potentially be their
mother or an additional sibling. The MBA population associated with
the TC appeared to be slightly less diverse than its EBA predecessors,
according to within-group pairwise f3 statistics used for diversity
estimation. This result was not driven by sites with multiple related
individuals, as a similar f3 distances distribution was found in pairs of
individuals from different sites (Supplementary Fig. 4C).
The idea that collective burials represent patrilineal kin groups is
in accordance with previous observations of earlier Neolithic popula-
tions in Europe31,33–35. The prevalence of collective kin-based burials
was interrupted by the arrival of steppe pastoralists in the turn of the
Neolithic and Bronze Age, leading to the development of more indi-
vidualised societies, such as those associated with the BBC and CWC.
The practice of collective burials never disappeared completely as
collective burials occurred throughout the EBA30;thesegraveshave
been shown to contain the remains of kin groups44.Weidentified one
such example at the Hrebenne cemetery and another possibly at the
Zubowice site (although the low single-nucleotide polymorphism
[SNP] overlap makes this estimation uncertain); both of these collec-
tive graves were associated with the MC. The scale of this phenomenon
in the TC is, however, much more similar to what was present in
Neolithic societies and could be interpreted as a proof of the re-
emergence of older traditions.
Patrilocal social structure and male-dominated migrations have
been linked with populations and events associated with steppe pas-
toralists and their descendants15,45,46. Hunter-gatherer societies, on the
other hand, are commonly thought to have been much more fluid in
their postmarital residence preferences, with the majority of modern
and historic hunter-gatherer groups displaying bilateral practices47.
Due to the scarcity of samples, we could not assess the postmarital
residence preferred by ancient European hunter-gatherers. On the
other hand, recent data obtained on Middle Neolithic farmers from
Western and Central Europe show that collective burials in these
populations usually comprised related individuals of patrilocal
descent31,33,48, supporting the notion that those populations or their
descendants also played a role inthe events that resulted in the genetic
shift in the MBA.
The results presented here indicate that EBA people in East-
Central Europe buried in the MC, IC and SC contexts were most likely
the direct descendants of preceding populations associated with the
CWC. In addition, the MBA populations were dominated by patrilocal
lineages of apparent hunter-gatherer origin, practising burial customs
that, while displaying some elements associated with steppe pastor-
alists, were most analogous to those practised in the Middle and Late
Neolithic cultures, predating the arrival of steppe pastoralists into
Central Europe. We conclude that after the introduction of steppe
ancestry into European populations, hunter-gatherers and farmers
remained genetically distinct in some regions and influenced later
demographic and cultural processes, as seen in the genetic composi-
tion of MBA populations.
Methods
Samples
For all samples. collected for the study, appropriate permits, required
by Polish and Ukrainian law, were acquired from the institutions pro-
viding the access to the specimen. We sampled teeth and/or the pet-
rous parts of the temporal bone from 176 human skeletal remains for
aDNA analyses. The samples originated from the broad range of
Bronze Age populations that lived in modern-day Poland and Ukraine.
The geographical origin for all the individuals is presented in Fig. 1A
created with the use of QGIS 2.12.249.TheyincludedTC-,KC-,MC-and
IC-associated individuals (Supplementary Data 1). We generated
genomic data from 91 individuals, including individuals associated
with the TC (n=62), KC (n= 5), MC (n=15),SC(n= 6) and IC (n=3)
(Table S2). Detailed information about each individual sampled can be
found in Supplementary Data 1 and Supplementary Information Text.
Laboratory methods
Sample preparation, DNA extraction and genomic library preparation
were conducted in a dedicated aDNA laboratory at Adam Mickiewicz
University in Poznan, Poland. The laboratory followed established
guidelines for aDNA facilities and utilised UV lamps (254nm), positive
air pressure, and high efficiency particulate air (HEPA)-filtered laminar
flow hoods. The laboratories, equipment and nonbiological reagents
were regularly decontaminated using bleach and/or DNA-away (Ther-
moScientific) and UV irradiation.
Prior to DNA extraction, bones and teeth were cleaned with 5%
NaOCl, rinsed with sterile water and finally decontaminated with UV
irradiation (254-nm wavelength, 12 V) for 60 min on each side in a
cross-linker. A Dremel® drill with diamond cutting wheels was used to
slice the petrous parts of the temporal bone in half, exposing the
structures of the otic capsule. We then drilled these structures and
tooth roots to obtain 50–150 mg of bone powder for DNA extraction.
DNA was extracted using a silica-spin column protocol50 but with the
sodium dodecyl sulfate (SDS) in the extraction buffer exchanged for
1Murea
51.Thefinal elution was performed in 100 µl of elution buffer
(EB) (Qiagen).
Twenty microlitres of DNA extract was converted into single-
indexed blunt-end Illumina genomic libraries using P5 and P7
adapters52,53, omitting the initial nebulization step due to the frag-
mented nature of aDNA. In addition, for the selected samples (see
Dataset S1 for the library types per sample), UDG-treated libraries were
also prepared using UDG and endonuclease VIII (endo VIII) treatment
to cut postmortem deaminated sites54. From each DNA extract, 1–5
genomiclibraries were prepared, and one negative library control was
processed for every 8–12 aDNA libraries. Each library was then ampli-
fied using five to seven polymerase chain reactions (PCRs) for 12–16
cycles. The amplifications were used to introduce single indices and
performed in 25 µl containing a mix of 3 µloftheDNAlibrarytemplate
with 12.5 µl of 1 × AmpliTaq Gold® 360 Master Mix (Life Technologies),
0.5 µl of PCR primer IS4 (10 mM) and 0.5 µl of indexing primer
(10 µM)55. Negative controls were included in both the library pre-
paration and PCR steps. All five to seven PCRs per individual were
pooled and purified with AMPure® XP Reagents (Agencourt-Beckman
Coulter) according to the manufacturer’s protocol. Pooled libraries
were then quantified using the High Sensitivity D1 000 Screen Tape
assay on a 2200 TapeStation system (Agilent). The genomic libraries
were sequenced at the SciLifeLab SNP & SEQ Technology platform in
Uppsala or at the National Genomics Infrastructure (NGI) in Stockholm
using (in both locations) the Illumina HiSeq 2500 with v2 paired-end,
125 bp chemistry or HiSeq X Ten with v2.5 paired-end, 150bp chem-
istry. The processed negative controls did not yield any DNA and were
not sequenced.
Seven genomic libraries (see Dataset S1 for the library types per
sample) underwent an enrichment procedureby hybridisation capture
using biotinylated probes supplied by MYcroarray (Ann Arbor, MI,
USA; www.mycroarray.com). The capture utilised a subset of probes
targeting 15,000 SNPs with the highest average per-sample coverage
from the 1240k panel used in56. Prior to hybridisation, the DNA libraries
(each ≈100 ng) were concentrated to dryness using a Speedvak con-
centrator (Savant) and resuspended in 6.8 µl of double-distilled water
(ddH
2
O). Two rounds of enrichment were conducted according to the
manufacturer’s protocol (v2.3.1) with minor changes57. Primers and IS5
and IS6 from53 as well as PISI and AIS4 from57, were used in the post-
capture amplification of the libraries. The second pair was used to
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved
allow sequencing of one blunt-end Illumina library on an Ion Torrent
Personal Genome Machine (Ion PGM) system (Ion Torrent, Thermo-
FisherScientific Inc.). The library was then sequenced with the Ion PGM
system at the Molecular Biology Techniques Laboratory, Faculty of
Biology, Adam Mickiewicz University, using the Ion Torrent One Touch
System II and the Ion One Touch 200 template kit v2 DL according to
the manufacturer’s recommendations. Sequencing was performed on
the Ion 318TM Chip Kit v2 using 520 flows and the Ion PGM Hi-Q
sequencing kit v2.
Processing of raw DNA sequence data and read mapping
The obtained paired-end Illumina sequence reads were first merged
(with the minimum required overlap set to 11) and the adapters were
removed using AdapterRemoval v2.1.758 or MergeReadsFastQ_cc.py59.
The reads acquired byIon PGM sequencing were processed following a
custom pipeline57.
Finally, trimmed and merged reads were mapped to the hs37d5
version of the human reference genome using Burrows-Wheeler
Aligner v0.7.17-r118860 with the following nondefault parameter set-
tings: -l 16500 -n 0.01 -o 22,61. Duplicate reads were detected and col-
lapsed using a modified version of FilterUniqueSAMCons.py59.In
addition, reads with reference identity <90% or read length shorter
than 35 bp were removed.
Basic statistics
The number of reads, the proportion of reads that mapped to the
human genome, average read length, clonality, and mean depth of
coverage were calculated to assess each library’squality
52.Theratioof
reads that mapped to sex chromosomes was used to determine the
genetic sex of each individual62. For samples that underwent multiple
rounds of sequencing, the mapped bam files from each library were
merged with the use of the merge option in samtools v1.563.The
merged bam files underwent duplicate read removal and bas ic statis tic
calculations were performed as in the case of single libraries.
Contamination estimates
To assess the authenticity of the data, several methods were applied
both for the individual and merged bam files. The signature of dea-
mination at the 5′and 3′read ends64 and the read length distribution
were calculated using MapDamage v2.0.865.
ContamMix v1.0.10 66 was used with default parameters to cal-
culate the posterior probability of mtDNA contamination using a
Bayesian approach. For this purpose, the reads were remapped against
the rcrs mitochondrial sequence67, following the same procedure as in
the case of the whole human genome, and consensus mtDNA
sequences were called with the use of the doFasta tool of the ANGSD
v0.910 package68; reads were accepted only if they had a mapping
score of 30, a minimum base quality of 20, and positions with a
minimum coverage of 3.
For samples determined to derive from males, X-chromosome-
based contamination estimation was performed using the con-
tamination R script included in ANGSD package. Any individual
libraries and merged datasets for which contamination estimates
exceeded 0.2 and/or the frequency of postmortem damage fell below
25% at the 5′-and3′-ends were excluded from further analyses.
Mitochondrial and Y-chromosome analyses
Consensus mtDNA sequences were called as described above for
mtDNA contamination estimates. The obtained fasta files were used to
assign mtDNA haplogroups utilising the Haplogrep69 online tool based
on the PhyloTree phylogenetic tree build 1770.
For the individuals determined to be genetic males, the most
likely Y-chromosome haplogroups (Y-DNA) were assigned based on
the genotype calls from 69 391 no-indel branch determining SNPs
obtained from the International Society of Genetic Genealogy
collection (obtained in December 2021 from https://isogg.org/). The
genotypes were called based on the genotype likelihood calculated
with the use of the aDNA_GenoCaller.py script71, which takes into
account the post-mortem damage estimated with MapDamage. The
genotypes in low-quality (genetic quality [GQ] < 50) transition sites
were used only when C or Galleles were found. When a T or A allele was
found in transversion sites that did not have those alleles according to
the reference dataset, they were assumed to be a result of post-
mortem deamination and labelled C and G, respectively. In addition,
where damage-repaired libraries were available, the transitions were
also called with aDNA_GenoCaller.py script and merged with the
genotype data.
Individuals were assigned to the lowest level haplogroup sup-
ported by the highest number of derived mutations linked to the most
likely lineage starting from the Y-DNA tree root (Supplementary
Data 4). The derived mutations not connected tothis lineage were not
taken into consideration.
Datasets
Several modern datasets were used for various analyses:
Human Origins (HO):616 938 autosomal SNP sites from the Affy-
metrix Human Origins/The Human Origins SNP Array complete data-
set containing genotypes for 2583 individuals representing 214
populations worldwide72 narrowed down to 1172 individuals repre-
senting 89 populations in Europe and northwestern Asia.
Simons Genome Diversity Project (SGDP): 1,001,613 autosomal
nonfunctional SNPs with minor allele frequency (MAF) < 0.005 from
the Simons Genome Diversity Project73 whole genome set matching
1,240,000 capture data available for a large portion of comparative
ancient samples.
1k: 6,864,699 autosomal SNPs from the 1000 Genomes Project74
1k_trv_YRI: 1,622,524 autosomal transversions from the 1000
Genomes Project74, of which the minor allele frequency in the Yoruba
(YRI) population was at least 0.155.
1k_X:3,357,504 SNP from outside the pseudoautosomal region of
the X chromosome from the 1000 Genomes Project74.
Kinship analyses
Both READ75 and NgsRelate76 were used to estimate relatedness
between pairs of individuals.
The genotype likelihoods for the SNPs from the 1k and 1k_X panels
for NgsRelate analysis were calculated with the use of aDNA_Gen-
oCaller.py for transversions in the merged bam files and aDNA_Gen-
oCaller.py script for transitions if damage-repaired libraries were
present71.
To maximise the number of overlapping SNPs while filtering them
for linkage disequilibrium (LD), all possible combinations of individual
pairs were merged and thinned separately with the vcftools77 (0.1.16)-
thin 2500 option. Then, NgsRelate was used on each pair after anno-
tating the vcf file with allele frequencies from the West Eurasia popu-
lations from the reference panel (Supplementary Data 18 and
Supplementary Data 19).
The genotypes for the SNPs from the 1k panel for READ analysis
were acquired from genotype likelihoods obtained as above and fil-
tered similar to the Y chromosome genotypes. Then, READ was used
on the haploidized final dataset after filteringoutSNPswithaminimal
allele frequency below 0.1 (Supplementary Data 20).
Pedigree reconstruction
Only the kinship estimates based on at least 10,000 (or 1000, for the
1k_X panel) overlapping SNPs were considered. Age at death; genetic
sex; R0, R1 and R2 values for autosomal data; the difference between θ
values for autosomal and X-chromosome data; and mitochondrial and
Y-DNA haplogroups were used to determine the exact kin relation
where possible. In case of conflicting reconstructed degree of kinship
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved
between the two used methods the results of NGSrelate were found to
be more reliable. The parent-offspring and sibling pairs were first
distinguished with the uniparental markers and age at death (e.g. if
both individuals were children or males sharing the same mitochon-
drial haplogroup, they were interpreted as siblings). Then, where
available, the degrees of kinship to other related individuals were
compared. For example, if an individual was found to share 1st-degree
kinship with one individual from a pair of known siblings and 2nd-
degree with the other, the individual in question was determinedto be
child of the first sibling and niece/nephew of the second. Similarly, if
2nd-degree kinship was detected with only one individual from a pair
of known siblings, it was interpreted as a grandchild of this individual,
as the grandparent, uncle or aunt, and niece or nephew would all be
equallyrelated to both siblings. Finally, to verify assumptions based on
the abovementioned factors, or in the case of their absence, X chro-
mosomal θand autosomal k1 were used The summaryof all the pairs of
individuals found to be related can be found in Supplementary Data 21.
Population genetics
Principal component analysis. The PCA was carried out as an initial
assessment of the genetic affinities of the analysed populations. The
smartpca program in the EIGENSOFT package78 was used to estimate
eigenvectors in the HO dataset. The ancient samples that overlapped
with at least 98% of the reference panel SNPs were projected onto the
first two principal components inferred from modern samples with the
following nondefault settings: altnormstyle: NO, numoutlieriter: 0,
numoutlierevec: 0, lsqproject: YES, shrinkmode: YES.
To assess, whatever the use of capture data in the five cases where
it was merged with shotgun data could cause any bias, in three cases
where sufficient amount of both types of data were present, they were
analysed both separately and combined (see results in Supplementary
Information text).
Admixture analysis
Unsupervised model-based clustering was performed with the use of
ADMIXTURE (v1.3) software79 on the same set of individuals as inclu-
ded in the PCA. Prior to the analysis, the dataset was first limited to
transversions and then pruned for LD with the use of the–indep-pair-
wise 200 25 0.4 option from PLINK toolset (v1.90b5)80.Finally,clus-
tering was performed on samples that overlapped with at least 85% of
the final set of 70 149 SNPs for K = 3 to K = 14 in 10 replicate runs with
different random seeds for each K. The results were visualised with the
use of pong v1.4.981 to bundle together the membership coefficient
matrices (Q) from different replicates and the different numbers of
clusters. The K values with the smallest standard error of the cross-
validation error estimate for the selected set of individuals (K = 7,
CV =0.559698) were then selected and discussed in detail; the results
for all K values of all samples can be found in the Supplementary
Materials (Supplementary Data 11 and Supplementary Fig. 6).
F3 and D statistics
Various analyses were performed to quantify shared drift between
individuals and/or populations as their divergence from an outgroup
population with the use of outgroup f3- and D-statistics, implemented
with the qp3pop and qpDstat tools from ADMIXTOOLS82 software,
respectively. All analyses were performed on data genotyped to the
1k_trv_YRI reference dataset, using the African Yoruba population as an
outgroup.
Individuals determined to be genetic outliers, based on their
position on PCA space and admixture analysis results, were not
included in the determination of populations based on individuals’
association with archaeological cultures or horizons. In addition,
only one individual (with the highest genome coverage) was selected
from each group of individuals determined to share direct genetic
kinship.
Before running the analyses, variants found in only one ancient
individual and individuals with <85% genotyped SNPs were filtered out.
First, qp3pop with default settings was used to test individuals’
assignment to populations in the form of f3(YRI, ancient population,
ancient population) and f3(YRI, tested individual, ancient population).
Then, shared drift between pairs of individuals, except for 1st- and 2nd-
degree relatives, was measured in each population to assess within-
group genetic diversity83 in the form of f3(YRI, tested individual, tested
individual). The results (Supplementary Data 9) were displayed as
boxplots of 1-f3 values for all individual representative populations
analysed here and several Bronze Age reference populations (Fig. 2C).
To determine how data from TC sites with a high number of directly
related individuals affected the obtained results, the sites yielding the
most individuals (Żerniki Górne, Pielgrzymowice and Gustorzyn) were
plotted separately from the rest of the TC individuals.
To determine if sex bias was present in the gene flow from the
putative source of hunter-gatherer ancestry during the formation of
the TC, the data was also genotyped to the 1k_X dataset, which
represented the X chromosome of the 1 000 Genome Project and was
filtered similar to the autosomal data. Then, qp3pop was run on both
autosomal and X-chromosome data in the form of f3(YRI,TCindivi-
dual, WHG) to track the temporal changes, and f3(YRI, TC, pop X) for
direct comparison on population level (where various populations
with dominating Hunter-Gatherer, steppe or farmer ancestry were
used as pop X)15. The temporal changes were displayed on graphs for
which trend lines were obtained with geom smooth tool from ggplot2
package in R. The conditional means were calculated both for all the
individuals and after removing outliers and displayed with 95% con-
fidence intervals.
qpDstat with default settings was applied to verify various
demographic scenarios by testing the analysed individuals against
pairs of potential ancestry sources in the form of D(YRI, tested indi-
vidual,population1, population2). This analysis was used todetermine
which ancient population out of all the analysed populations was the
best proxy for the source of hunter-gather ancestry in the TC and post-
CWC populations (Supplementary Data 12). In addition, to confirm the
predominance of patrilocality in TC groups, data from individuals
from the Żerniki site were tested in the form of D(YRI, Żerniki indivi-
dual, Żerniki population, other TC individuals), and the results were
plotted separately for each sex (Supplementary Data 10, Fig. 2D).
qpAdmixture
Based on the f3 and Dstatistic results, various demographic scenarios
of TC origin were tested with the use of qpAdm tool from the
ADMIXTOOLS82 package with the following nondefault parameters:
details: YES, summary: YES, allsnps: YES, and maxrank: 7. The SGDP
reference dataset was used, and additional ancient individuals were
included as outgroups. The right file with the outgroup population
comprised the Onge, Papuan, Han, Mixe, Karitiana, Natufian, and
Chukchi populations from the dataset and 25 ancient individuals from
13 hunter-gatherer populations worldwide with addition of Neolithic
Anatolians (Supplementary Data 3). Two- and three-way admixture
models were tested. The models on population levels included the TC
and KC as test populations. The two-way models additionally included
one EarlyBronze Age post-CWC and one hunter-gather population; the
three-way models also included a late Neolithic population from
Eastern Europe, characterised by a mixture of Anatolian farmer and
hunter-gatherer ancestry. All results are presented in Supplementary
Data 13 and Supplementary Data 14; only cases where the full model
could be separated from the nested models were considered and
discussed further.Based on the results 16 populations were chosen to
tun the analysis in more discriminative rotation outgroup approach as
suggested by37. The models in which the source populations were
comprised of only single individuals or had a nested pvalue > 0.05
were reported however approached with caution and not considered
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved
plausible (Supplementary Data 15 and Supplementary Data 16). In
addition, individual levels of admixture were calculated with the use
the three-way models (Supplementary Data 17). The degree of WHG
ancestry for individuals published here and individuals from selected
Neolithic and Bronze-Age reference populations was calculated using a
three-way model including the WHG, Anatolian Neolithic (AN) and
Yamnaya Culture (YAM) populations as source populations. In cases
where the model had a nested pvalue > 0.05. A separate analysis was
performed for the corresponding two-way model. When the nested
model did not include WHG, the ancestry was set to 0 (Supplementary
Data 17). To track the temporal changes in the amount of hunter-
gatherer ancestry the coefficients values for WHG, for which radio-
carbon dates were available, were plotted in R together with condi-
tional means similarly to the f3 values.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The aligned sequencing data have been deposited in the European
Nucleotide Archive database under accession number PRJEB53670.
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Acknowledgements
The study was possible thanks to the Polish National Science Center
grants (nos. 2015/17/B/HS3/00114 and 2018/31/HS3/01326) lead by P.M.
M.J. and A.Gö were supported by Riksbankens Jubileumsfond (grant no.
M13-0904:1) and Knut and Alice Wallenberg foundation. Polish National
Science Center supported M.C. (grant no. 2017/24/T/HS3/00511), Ł.P.
(grant no. 2014/15/D/HS3/01304), M.S. (grant no. 2017/27/B/HS3/
01444) and A.J. (grant no 2017/01/X/NZ8/01472). A.J. and E.E. were
supported by the mobility grant project awarded by NAWA (grant no.
PPN/BCZ/2019/1/00010 and 8J20PL063). E.E. was supported by ELIXIR
CZ research infrastructure project (MEYS Grant No: LM2023055)
including access to computing and storage facilities and H.M. was
supported by the Swedish Research Council (grant no. 2017-02503) and
by Riksbankens Jubileumsfond (grant. no. P21-0266). Ancient DNA
libraries were sequenced at National Genomics Infrastructure (NGI)
Uppsala, Sweden. Computations were conducted with the support of
PL-Grid Infrastructure and the data handling was enabled by resources
provided by the Swedish National Infrastructure for Computing (SNIC) at
Uppmax partially funded by the Swedish Research Council through
grant agreement no. 2018-05973. The analyses were performed under a
number of computing and storage projects including SNIC 2022/2-11,
SNIC 2022-/22-299 and SNIC 2022/23-163.
Author contributions
M.C., A.J., P.M. and H.M. designed and supervised the study. J.G., H.T.,
A.S., M.Po., P.W., A.L., I.W., J.R., M.S., A.K., M.I., S.S., A.M., A.Gr., V.I.,
M.O.Y.,A.R.,K.T.,M.Pr.,R.G.andK.S.,excavatedorcuratedsamplesand
provided archaeological contextualisation. M.C., A.B. and A.J. per-
formed or supervised laboratory work.H.M.andM.K.supervisedDNA
sequencing and sequencing data curation. M.C. analysed the data. M.C.
wrote the paper with contributions from A.J., P.M., E.E., M.K, Ł.P., M.D.,
A.Gö., M.J. and H.M.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41467-023-40072-9.
Correspondence and requests for materials should be addressed to
Maciej Chyleński, Anna Juras or Helena Malmström.
Peer review information Nature Communications thanks David Car-
amelli and Martin Sikora for their contribution to the peer review of this
work.
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© The Author(s) 2023
1
Institute of Human Biology and Evolution, Faculty of Biology, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 6, 61-614 Poznań,Poland.
2
Faculty of Archaeology, Adam Mickiewicz U niversity in Poznań, Uniwersytetu Poznańskiego 7, 61- 614 Poznań,Poland.
3
Archaeological Research Laboratory,
Department of Archaeology and Classical Studies, Stockholm University, Lilla Frescativägen 7, SE-106 91 Stockholm, Sweden.
4
Centre for Palaeogentics,
Svante Arrhenius väg 20C, SE-106 91 Stockholm, Sweden.
5
Institute of Archaeology, University of Gdańsk, ul. Bielańska 5, 80-851 Gdańsk, Poland.
6
Department of Anthropology and Archaeology, University of Bristol, 43 Woodland Road, Bristol BS8 1UU, UK.
7
Laboratory of Genomics and Bioinformatics,
Institute of Molecular Genetics of the Czech Academy of Sciences, Vídeňská 1083, 142 20 Prague 4, Prague, Czech Republic.
8
Department of History and
Cultural Heritage, University of Pope Jan PawełII, Kanonicza 9, 31-002 Cracow, Poland.
9
Archaeological Museum in Cracow, Senacka 3, 31-002
Cracow, Poland.
10
Institute of Archaeology, Maria Curie-Skłodowska University, M.C.-Skłodowska sq. 4, 20-031 Lublin, Poland.
11
Institute of Archaeology and
Ethnology, Polish Academy of Science, Sławkowska 17, 31-016 Cracow, Poland.
12
Department of Material and Spiritual Culture, Lublin Museum, Zamkowa 9,
20-117 Lublin, Poland.
13
Archaeological Museum in Poznań, Wodna 27, 61-781 Poznań,Poland.
14
Muzeum Archeologiczne w Biskupinie, Biskupin 17, 88-410
Gąsawa, Poland.
15
Zaliztsi Museum of Local Lore, Schevchenka 51, Zalizhtsi, 47243 Ternopil reg, Ukraine.
16
Ternopil Regional Center for Protection and
Research of Cultural Heritage Sites, Kyyivsʹka 3а, 46016 Ternopil, Ukraine.
17
Wojewódzki Urząd Ochrony Zabytków, Gołębia 2, 61-840 Poznań,Poland.
18
Archaeological company “Dolmen Marcin Przybyła, MichałPodsiadłos.c.”, Serkowskiego Sq. 8/3, 30-512 Cracow, Poland.
19
Museum of Archaeology and
Ethnography in Łódź,PlacWolności 14, 91-415 Łódź,Poland.
20
Institute of Biological Sciences, Cardinal Stefan Wyszynski University in Warsaw, Wóycickiego
1/3, 01-938 Warsaw, Poland.
21
Molecular Biology Techniques Laboratory, Faculty of Biology, Adam Mickiewicz University in Poznań,UniwersytetuPoznańs-
kiego 6, 61-614 Poznań,Poland.
22
Human Evolution, Department of Organismal Biology, Uppsala University, Norbyvägen 18C, SE-752 36 Uppsala, Sweden.
23
Centre for Anthropological Research, University of Johannesburg, Auckland Park, 2006 Johannesburg, South Africa.
24
SciLifeLab, Stockholm and
Uppsala, Sweden. e-mail: maciej.ch@amu.edu.pl;annaj@amu.edu.pl;helena.malmstrom@ebc.uu.se
Article https://doi.org/10.1038/s41467-023-40072-9
Nature Communications | (2023) 14:4395 12
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... Consistent with the hypothesis of a rapid spread of patrilocality after the Neolithic transition, ancient DNA studies show that male relatedness is often higher than female relatedness within archaeological sites from the Neolithic and the Bronze Age (mostly from Europe). In particular, males tend to share more often the same haplogroup of the Y chromosome, whereas there is a greater diversity of haplogroups for mtDNA [67][68][69][70][71][72][73][74][75][76][77] . In addition, reconstructed pedigrees show more first and second-degree related males than females 67,69,70,72,[74][75][76][77] . ...
... In particular, males tend to share more often the same haplogroup of the Y chromosome, whereas there is a greater diversity of haplogroups for mtDNA [67][68][69][70][71][72][73][74][75][76][77] . In addition, reconstructed pedigrees show more first and second-degree related males than females 67,69,70,72,[74][75][76][77] . These observations have been interpreted as the result of patrilocal residence (with varying degrees of compliance with the rule), with males more likely to remain in their birthplace and females more likely to migrate between sites. ...
... This result is consistent with previous studies indicating that polygyny alone is expected to have a reduced effect on genetic diversity 22,87 , and would probably not be sufficient on its own to account for the male effective population size bottleneck reported by Karmin et al. 1 . In addition, kinship analyses based on ancient DNA data from the Neolithic and Bronze Age (mostly Europe) showed low frequency of half-brothers and half-sisters [67][68][69][70][71][73][74][75][76][77] , with the exception of Hazleton North long cairn 72 . This latter case has been interpreted as evidence for polygyny or serial monogamy. ...
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... A final important question involves the relationship between individuals interred in collective graves, particularly the children buried with the female in the collective grave as well as individuals interred as pairs and sequentially. In light of the current knowledge about mass burials in the TCC region, we know that related individuals have been found buried in one grave and between adjacent graves (Chyleński et al. 2023). Future aDNA research will provide an answer to the question about the possible kinship of individuals buried in the Kordyshiv barrows, and especially those who were buried in collective graves. ...
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Full-text available
  • Jun 2021
Ancient human remains have the potential to explain a great deal about the prehistory of humankind. Due to recent technological and bioinformatics advances, their study, at the palaeogenomic level, can provide important information about population dynamics, culture changes, and the lifestyles of our ancestors. In this study, mitochondrial and nuclear genome data obtained from human bone remains associated with the Neolithic Globular Amphorae culture, which were recovered in the Megalithic barrow of Kierzkowo (Poland), were reanalysed to gain insight into the social organisation and use of the archaeological site and to provide information at the individual level. We were able to successfully estimate the minimum number of individuals, sex, kin relationships, and phenotypic traits of the buried individuals, despite the low level of preservation of the bone samples and the intricate taphonomic conditions. In addition, the evaluation of damage patterns allowed us to highlight the presence of “intruders”—that is, of more recent skeletal remains that did not belong to the original burial. Due to its characteristics, the study of the Kierzkowo barrow represented a challenge for the reconstruction of the biological profile of the human community who exploited it and an excellent example of the contribution that ancient genomic analysis can provide to archaeological reconstruction.
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Variable kinship patterns in Neolithic Anatolia revealed by ancient genomes
Article
Full-text available
  • Apr 2021
  • CURR BIOL
The social organization of the first fully sedentary societies that emerged during the Neolithic period in Southwest Asia remains enigmatic,1 mainly because material culture studies provide limited insight into this issue. However, because Neolithic Anatolian communities often buried their dead beneath domestic buildings,2 household composition and social structure can be studied through these human remains. Here, we describe genetic relatedness among co-burials associated with domestic buildings in Neolithic Anatolia using 59 ancient genomes, including 22 new genomes from Aşıklı Höyük and Çatalhöyük. We infer pedigree relationships by simultaneously analyzing multiple types of information, including autosomal and X chromosome kinship coefficients, maternal markers, and radiocarbon dating. In two early Neolithic villages dating to the 9th and 8th millennia BCE, Aşıklı Höyük and Boncuklu, we discover that siblings and parent-offspring pairings were frequent within domestic structures, which provides the first direct indication of close genetic relationships among co-burials. In contrast, in the 7th millennium BCE sites of Çatalhöyük and Barcın, where we study subadults interred within and around houses, we find close genetic relatives to be rare. Hence, genetic relatedness may not have played a major role in the choice of burial location at these latter two sites, at least for subadults. This supports the hypothesis that in Çatalhöyük,3-5 and possibly in some other Neolithic communities, domestic structures may have served as burial location for social units incorporating biologically unrelated individuals. Our results underscore the diversity of kin structures in Neolithic communities during this important phase of sociocultural development.
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THE ABSOLUTE CHRONOLOGY OF COLLECTIVE BURIALS FROM THE 2ND MILLENNIUM BC IN EAST CENTRAL EUROPE
Article
Full-text available
  • Jan 2021
  • RADIOCARBON
This article discusses the absolute chronology of collective burials of the Trzciniec Cultural Circle communities of the Middle Bronze Age in East Central Europe. Based on Bayesian modeling of 91 accelerator mass spectrometry radiocarbon (AMS 14C) dates from 18 cemeteries, the practice of collective burying of individuals was linked to a period of 400–640 (95.4%) years, between 1830–1690 (95.4%) and 1320–1160 (95.4%) BC. Collective burials in mounds with both cremation and inhumation rites were found earliest in the upland zone regardless of grave structure type (mounded or flat). Bayesian modeling of 14C determinations suggests that this practice was being transmitted generally from the southeast to the northwest direction. Bayesian modeling of the dates from the largest cemetery in Z· erniki Górne, Lesser Poland Upland, confirmed the duration of use of the necropolis as ca. 140–310 (95.4%) years. Further results show the partial contemporaneity of burials and allow formulation of a spatial and temporal development model of the necropolis. Based on the investigation, some graves were used over just a couple of years and others over nearly 200, with up to 30 individuals found in a single grave.
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Assessing the Performance of qpAdm: A Statistical Tool for Studying Population Admixture
Article
Full-text available
  • Jan 2021
  • GENETICS
qpAdm is a statistical tool for studying the ancestry of populations with histories that involve admixture between two or more source populations. Using qpAdm, it is possible to identify plausible models of admixture that fit the population history of a group of interest and to calculate the relative proportion of ancestry that can be ascribed to each source population in the model. Although qpAdm is widely used in studies of population history of human (and non-human) groups, relatively little has been done to assess its performance. We performed a simulation study to assess the behavior of qpAdm under various scenarios in order to identify areas of potential weakness and establish recommended best practices for use. We find that qpAdm is a robust tool that yields accurate results in many cases, including when data coverage is low, there are high rates of missing data or ancient DNA damage, or when diploid calls cannot be made. However, we caution against co-analyzing ancient and present-day data, the inclusion of an extremely large number of reference populations in a single model, and analyzing population histories involving extended periods of gene flow. We provide a user guide suggesting best practices for the use of qpAdm.
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Mobility and Social Change: Understanding the European Neolithic Period after the Archaeogenetic Revolution
Article
Full-text available
  • Jan 2021
  • J Archaeol Res
This paper discusses and synthesizes the consequences of the archaeogenetic revolution to our understanding of mobility and social change during the Neolithic period in Europe (6500–2000 BC). In spite of major obstacles to a productive integration of archaeological and anthropological knowledge with ancient DNA data, larger changes in the European gene pool are detected and taken as indications for large-scale migrations during two major periods: the Early Neolithic expansion into Europe (6500–4000 BC) and the third millennium BC “steppe migration.” Rather than massive migration events, I argue that both major genetic turnovers are better understood in terms of small-scale mobility and human movement in systems of population circulation, social fission and fusion of communities, and translocal interaction, which together add up to a large-scale signal. At the same time, I argue that both upticks in mobility are initiated by the two most consequential social transformations that took place in Eurasia, namely the emergence of farming, animal husbandry, and sedentary village life during the Neolithic revolution and the emergence of systems of centralized political organization during the process of urbanization and early state formation in southwest Asia.
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A dynastic elite in monumental Neolithic society
Article
Full-text available
  • Jun 2020
  • NATURE
The nature and distribution of political power in Europe during the Neolithic era remains poorly understood¹. During this period, many societies began to invest heavily in building monuments, which suggests an increase in social organization. The scale and sophistication of megalithic architecture along the Atlantic seaboard, culminating in the great passage tomb complexes, is particularly impressive². Although co-operative ideology has often been emphasised as a driver of megalith construction¹, the human expenditure required to erect the largest monuments has led some researchers to emphasize hierarchy³—of which the most extreme case is a small elite marshalling the labour of the masses. Here we present evidence that a social stratum of this type was established during the Neolithic period in Ireland. We sampled 44 whole genomes, among which we identify the adult son of a first-degree incestuous union from remains that were discovered within the most elaborate recess of the Newgrange passage tomb. Socially sanctioned matings of this nature are very rare, and are documented almost exclusively among politico-religious elites⁴—specifically within polygynous and patrilineal royal families that are headed by god-kings5,6. We identify relatives of this individual within two other major complexes of passage tombs 150 km to the west of Newgrange, as well as dietary differences and fine-scale haplotypic structure (which is unprecedented in resolution for a prehistoric population) between passage tomb samples and the larger dataset, which together imply hierarchy. This elite emerged against a backdrop of rapid maritime colonization that displaced a unique Mesolithic isolate population, although we also detected rare Irish hunter-gatherer introgression within the Neolithic population.
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The Neolithic Pitted Ware culture foragers were culturally but not genetically influenced by the Battle Axe culture herders
Article
Full-text available
  • Jun 2020
Objectives In order to understand contacts between cultural spheres in the third millennium BC, we investigated the impact of a new herder culture, the Battle Axe culture, arriving to Scandinavia on the people of the sub‐Neolithic hunter‐gatherer Pitted Ware culture. By investigating the genetic make‐up of Pitted Ware culture people from two types of burials (typical Pitted Ware culture burials and Battle Axe culture‐influenced burials), we could determine the impact of migration and the impact of cultural influences. Methods We sequenced and analyzed the genomes of 25 individuals from typical Pitted Ware culture burials and from Pitted Ware culture burials with Battle Axe culture influences in order to determine if the different burial types were associated with different gene‐pools. Results The genomic data show that all individuals belonged to one genetic population—a population associated with the Pitted Ware culture—irrespective of the burial style. Conclusion We conclude that the Pitted Ware culture communities were not impacted by gene‐flow, that is, via migration or exchange of mates. These different cultural expressions in the Pitted Ware culture burials are instead a consequence of cultural exchange.
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Ancient genomes reveal social and genetic structure of Late Neolithic Switzerland
Article
Full-text available
  • Apr 2020
Genetic studies of Neolithic and Bronze Age skeletons from Europe have provided evidence for strong population genetic changes at the beginning and the end of the Neolithic period. To further understand the implications of these in Southern Central Europe, we analyze 96 ancient genomes from Switzerland, Southern Germany, and the Alsace region in France, covering the Middle/Late Neolithic to Early Bronze Age. Similar to previously described genetic changes in other parts of Europe from the early 3rd millennium BCE, we detect an arrival of ancestry related to Late Neolithic pastoralists from the Pontic-Caspian steppe in Switzerland as early as 2860–2460 calBCE. Our analyses suggest that this genetic turnover was a complex process lasting almost 1000 years and involved highly genetically structured populations in this region.
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Improved DNA extraction from ancient bones using silica‐based spin columns
Article
  • Apr 1998
  • AM J PHYS ANTHROPOL
We describe a simple method for extracting polymerase chain reaction‐amplifiable DNA from ancient bones without the use of organic solvents. Bone powders are digested with proteinase K, and the DNA is purified directly using silica‐based spin columns (QIAquick™, QIAGEN). The efficiency of this protocol is demonstrated using human bone samples ranging in age from 15 to 5,000 years old. Am J Phys Anthropol 105:539–543, 1998. © 1998 Wiley‐Liss, Inc.
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