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. 2024 Jan;625(7994):301-311.
doi: 10.1038/s41586-023-06865-0. Epub 2024 Jan 10.

Population genomics of post-glacial western Eurasia

Morten E Allentoft #  1   2 , Martin Sikora #  3 , Alba Refoyo-Martínez #  4 , Evan K Irving-Pease #  4 , Anders Fischer #  4   5   6 , William Barrie #  7   8 , Andrés Ingason #  4   9 , Jesper Stenderup  4 , Karl-Göran Sjögren  5 , Alice Pearson  8 , Bárbara Sousa da Mota  10   11 , Bettina Schulz Paulsson  5 , Alma Halgren  12 , Ruairidh Macleod  4   7   13   14 , Marie Louise Schjellerup Jørkov  15 , Fabrice Demeter  4   16 , Lasse Sørensen  17 , Poul Otto Nielsen  17 , Rasmus A Henriksen  4 , Tharsika Vimala  4 , Hugh McColl  4 , Ashot Margaryan  18   19 , Melissa Ilardo  20 , Andrew Vaughn  21 , Morten Fischer Mortensen  17 , Anne Birgitte Nielsen  22 , Mikkel Ulfeldt Hede  23 , Niels Nørkjær Johannsen  24 , Peter Rasmussen  17 , Lasse Vinner  4 , Gabriel Renaud  25 , Aaron Stern  21 , Theis Zetner Trolle Jensen  18 , Gabriele Scorrano  4 , Hannes Schroeder  18 , Per Lysdahl  26 , Abigail Daisy Ramsøe  4 , Andrei Skorobogatov  27 , Andrew Joseph Schork  8   28 , Anders Rosengren  4   8 , Anthony Ruter  4 , Alan Outram  29 , Aleksey A Timoshenko  30 , Alexandra Buzhilova  31 , Alfredo Coppa  32 , Alisa Zubova  33 , Ana Maria Silva  34   35 , Anders J Hansen  4 , Andrey Gromov  33 , Andrey Logvin  36 , Anne Birgitte Gotfredsen  4 , Bjarne Henning Nielsen  37 , Borja González-Rabanal  38 , Carles Lalueza-Fox  39   40 , Catriona J McKenzie  29 , Charleen Gaunitz  4 , Concepción Blasco  41 , Corina Liesau  41 , Cristina Martinez-Labarga  42 , Dmitri V Pozdnyakov  30 , David Cuenca-Solana  43   44 , David O Lordkipanidze  45   46 , Dmitri En'shin  47 , Domingo C Salazar-García  48   49 , T Douglas Price  5   50 , Dušan Borić  32   51 , Elena Kostyleva  52 , Elizaveta V Veselovskaya  53 , Emma R Usmanova  54   55   56   57 , Enrico Cappellini  18 , Erik Brinch Petersen  58 , Esben Kannegaard  59 , Francesca Radina  60 , Fulya Eylem Yediay  4 , Henri Duday  61 , Igor Gutiérrez-Zugasti  43 , Ilya Merts  62 , Inna Potekhina  63   64 , Irina Shevnina  36 , Isin Altinkaya  4 , Jean Guilaine  65 , Jesper Hansen  66 , Joan Emili Aura Tortosa  48 , João Zilhão  35   67 , Jorge Vega  68 , Kristoffer Buck Pedersen  69 , Krzysztof Tunia  70 , Lei Zhao  4 , Liudmila N Mylnikova  30 , Lars Larsson  71 , Laure Metz  72 , Levon Yepiskoposyan  73   74 , Lisbeth Pedersen  75 , Lucia Sarti  76 , Ludovic Orlando  77 , Ludovic Slimak  77 , Lutz Klassen  59 , Malou Blank  5 , Manuel González-Morales  43 , Mara Silvestrini  78 , Maria Vretemark  79 , Marina S Nesterova  30 , Marina Rykun  80 , Mario Federico Rolfo  81 , Marzena Szmyt  82 , Marcin Przybyła  83 , Mauro Calattini  76 , Mikhail Sablin  84 , Miluše Dobisíková  85 , Morten Meldgaard  86 , Morten Johansen  87 , Natalia Berezina  31 , Nick Card  88 , Nikolai A Saveliev  89 , Olga Poshekhonova  47 , Olga Rickards  42 , Olga V Lozovskaya  90 , Olivér Gábor  91 , Otto Christian Uldum  87   92 , Paola Aurino  93 , Pavel Kosintsev  94   95 , Patrice Courtaud  61 , Patricia Ríos  41 , Peder Mortensen  96 , Per Lotz  97   98 , Per Persson  99 , Pernille Bangsgaard  100 , Peter de Barros Damgaard  4 , Peter Vang Petersen  17 , Pilar Prieto Martinez  101 , Piotr Włodarczak  70 , Roman V Smolyaninov  102 , Rikke Maring  25   59 , Roberto Menduiña  68 , Ruben Badalyan  103 , Rune Iversen  58 , Ruslan Turin  27 , Sergey Vasilyev  53   104 , Sidsel Wåhlin  26 , Svetlana Borutskaya  31 , Svetlana Skochina  47 , Søren Anker Sørensen  97 , Søren H Andersen  105 , Thomas Jørgensen  97 , Yuri B Serikov  106 , Vyacheslav I Molodin  30 , Vaclav Smrcka  107 , Victor Merts  108 , Vivek Appadurai  8 , Vyacheslav Moiseyev  33 , Yvonne Magnusson  109 , Kurt H Kjær  4 , Niels Lynnerup  15 , Daniel J Lawson  110 , Peter H Sudmant  12   21 , Simon Rasmussen  111 , Thorfinn Sand Korneliussen  4 , Richard Durbin  8   112 , Rasmus Nielsen  4   12 , Olivier Delaneau  10   11 , Thomas Werge  4   8   113 , Fernando Racimo  4 , Kristian Kristiansen  4   5 , Eske Willerslev  114   115   116
Affiliations

Population genomics of post-glacial western Eurasia

Morten E Allentoft et al. Nature. 2024 Jan.

Erratum in

  • Publisher Correction: Population genomics of post-glacial western Eurasia.
    Allentoft ME, Sikora M, Refoyo-Martínez A, Irving-Pease EK, Fischer A, Barrie W, Ingason A, Stenderup J, Sjögren KG, Pearson A, Sousa da Mota B, Schulz Paulsson B, Halgren A, Macleod R, Jørkov MLS, Demeter F, Sørensen L, Nielsen PO, Henriksen RA, Vimala T, McColl H, Margaryan A, Ilardo M, Vaughn A, Fischer Mortensen M, Nielsen AB, Ulfeldt Hede M, Johannsen NN, Rasmussen P, Vinner L, Renaud G, Stern A, Jensen TZT, Scorrano G, Schroeder H, Lysdahl P, Ramsøe AD, Skorobogatov A, Schork AJ, Rosengren A, Ruter A, Outram A, Timoshenko AA, Buzhilova A, Coppa A, Zubova A, Silva AM, Hansen AJ, Gromov A, Logvin A, Gotfredsen AB, Henning Nielsen B, González-Rabanal B, Lalueza-Fox C, McKenzie CJ, Gaunitz C, Blasco C, Liesau C, Martinez-Labarga C, Pozdnyakov DV, Cuenca-Solana D, Lordkipanidze DO, En'shin D, Salazar-García DC, Price TD, Borić D, Kostyleva E, Veselovskaya EV, Usmanova ER, Cappellini E, Brinch Petersen E, Kannegaard E, Radina F, Eylem Yediay F, Duday H, Gutiérrez-Zugasti I, Merts I, Potekhina I, Shevnina I, Altinkaya I, Guilaine J, Hansen J, Aura Tortosa JE, Zilhão J, Vega J, Buck Pedersen K, Tunia K, Zhao L, Mylnikova LN, Larsson L, Metz L, Yepiskoposyan L, Pedersen L, Sarti L, O… See abstract for full author list ➔ Allentoft ME, et al. Nature. 2024 Feb;626(7997):E3. doi: 10.1038/s41586-024-07044-5. Nature. 2024. PMID: 38238538 Free PMC article. No abstract available.

Abstract

Western Eurasia witnessed several large-scale human migrations during the Holocene1-5. Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes-mainly from the Mesolithic and Neolithic periods-from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a 'great divide' genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 BP, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a 'Neolithic steppe' cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Sample overview and broad-scale genetic structure.
a,b, Geographical (a) and temporal (b) distribution of the 317 ancient genomes sequenced and reported in this study. Insert shows dense sampling in Denmark. The age and the geographical region of ancient individuals are indicated by the colour and the shape of the symbols, respectively. Colour scale for age is capped at 15,000 years; older individuals are indicated with black. Random jitter was added to geographical coordinates to avoid overplotting. c,d, PCA of 3,316 modern and ancient individuals from Eurasia, Oceania and the Americas (c), and restricted to 2,126 individuals from western Eurasia (west of the Urals) (d). Principal components were defined using both modern and imputed ancient (n = 1,492) genomes passing all filters, with the remaining low-coverage ancient genomes projected. Ancient genomes sequenced in this study are indicated with black circles (imputed genomes passing all filters, n = 213) or grey diamonds (pseudo-haploid projected genomes; n = 104). Genomes of modern individuals are shown in grey, with population labels corresponding to their median coordinates. BA, Bronze Age.
Fig. 2
Fig. 2. Genetic ancestry transects of western Eurasia.
a, Regional timelines of genetic ancestry compositions within the past 12,000 years in western Eurasia. Ancestry proportions in 1,012 imputed ancient genomes (representing populations west of the Urals) inferred using supervised ancestry modelling with the ‘deep’ HG ancestry source groups. Coloured bars within the timelines represent ancestry proportions for temporally consecutive individuals, with the width corresponding to their age difference. Individuals with identical age were offset along the time axis by adding random jitter. b, Map highlighting geographical areas (coloured areas) for samples included in the individual regional timelines, and excavation locations (black crosses). Only shotgun-sequenced genomes were used in our study, so the exact timing of ancestry shifts might differ slightly from previous studies if they are based on different types of data from different individuals.
Fig. 3
Fig. 3. Spatiotemporal kriging analysis of major ancestries.
The temporal transects show how WHG ancestry (Italy_15000BP_9000BP) was replaced by Neolithic farmer ancestry (Boncuklu_10000BP) during the Neolithic transition in Europe. Later, the steppe migrations around 5,000 cal. bp introduced both EHG (MiddleDon_7500BP) and CHG (Caucasus_13000BP_10000BP) ancestry into Europe, thereby reducing Neolithic farmer ancestry.
Fig. 4
Fig. 4. Fine-scale structure and temporal dynamics of steppe-related ancestry during the second transition in Europe.
a, Correlation between the estimated proportions of steppe-related and GAC farmer-related ancestries (‘postNeol’ source set), across west Eurasian target individuals. b, Timeline of difference in estimated steppe-related ancestry proportions, using individuals from the genetic cluster ‘Steppe_5000BP_4300BP’ associated with either Yamnaya or Afanasievo cultural contexts as separate sources. Individuals from European post-Neolithic genetic clusters before 3,000 cal. bp are indicated with coloured symbols; other west Eurasian target individuals are indicated with grey symbols. Symbols with black outlines highlight early steppe-related individuals associated with either Corded Ware or related (for example, Battle Axe) cultural contexts.
Fig. 5
Fig. 5. Genetic transects east of the Urals.
Timelines of genetic ancestry compositions within the past 6,000 years east of the Urals. Shown are ancestry proportions in 148 imputed ancient genomes from this region, inferred using supervised ancestry modelling (‘postNeol’ source set). Panels separate ancestry proportions from local forest steppe HGs (HG) and sources representing ancestries originating further east or west.
Fig. 6
Fig. 6. Genetic relatedness across western Eurasia.
Maps showing networks of highest IBD sharing (top 10 highest sharing per individual) during different time periods for 579 imputed genomes predating 3,000 cal. bp and located in the geographical region shown. Shading and thickness of lines are scaled to represent the amount of IBD shared between two individuals. In the earliest periods, sharing networks exhibit strong links within relatively narrow geographical regions, representing predominantly close genetic ties between small HG communities, and rarely crossing the East–West divide extending from the Baltic to the Black Sea. From around 9,000 cal. bp onwards, a more extensive network with weaker individual ties appears in the south, linking Anatolia to the rest of Europe, as early Neolithic farmer communities spread across the continent. The period 7,000–5,000 cal. bp shows more connected subnetworks of western European and eastern/northern European Neolithic farmers, while locally connected networks of HG communities prevail on the eastern side of the divide. From c. 5,000 bp onwards the divide finally collapses, and continental-wide genetic relatedness unifies large parts of western Eurasia.
Extended Data Fig. 1
Extended Data Fig. 1. Genetic structure of the 317 herein-reported ancient genomes.
a–d, PCA of 3,316 modern and ancient individuals from Eurasia, Oceania and the Americas (a,b), as well as restricted to 2,126 individuals from western Eurasia (west of the Urals) (c,d). Shown are analyses with principal components inferred either using both modern and imputed ancient genomes passing all filters, and projecting low coverage ancient genomes (a,c); or only modern genomes and projecting all ancient genomes (b,d). Ancient genomes sequenced in this study are indicated either with black circles (imputed genomes) or grey diamonds (projected genomes). e, Model-based clustering results using ADMIXTURE for 284 newly reported genomes (excluding close relatives and individuals flagged for possible contamination). Results shown are based on ADMIXTURE runs from K = 2 to K = 15 on 1,593 ancient individuals, corresponding to the full set of 1,492 imputed genomes passing filters as well as 101 low coverage genomes represented by pseudo-haploid genotypes (flags “lowcov” or “lowGpAvg”, Supplementary Data 7; indicated with alpha transparency in plot).
Extended Data Fig. 2
Extended Data Fig. 2. Imputation accuracy of ancient DNA.
a, Imputation accuracy across 42 high-coverage ancient genomes when downsampled to lower depth of coverage values (see Supplementary Note 2 and Supplementary Table 2.1). b, Imputation accuracy for 1× depth of coverage across 9 prehistoric European genomes; c, across 5 Viking age genomes; and d, across 7 ancient genomes from Early Medieval Hungary. In all panels, imputation accuracy is shown as the squared Pearson correlation between imputed and true genotype dosages as a function of MAF of the target variant sites.
Extended Data Fig. 3
Extended Data Fig. 3. Genetic clustering of ancient individuals.
Characterization of genetic clusters for 1,401 imputed ancient individuals from Eurasia (that is, excluding 91 individuals from Africa and Americas), inferred from pairwise IBD sharing (indicated using coloured symbols throughout), a, Temporal distribution of clustered individuals, grouped by broad ancestry cluster. b,c, Geographical distribution of clustered individuals, shown for individuals predating 3,000 bp (b) and after 3,000 bp (c). d, Network graph of pairwise IBD sharing between 596 ancient Eurasians predating 3,000 bp, highlighting within- and between-cluster relationships. Each node represents an individual, and the width of edges connecting nodes indicates the fraction of the genome shared IBD between the respective pair of individuals. Network edges were restricted to the 10 highest sharing connections for each individual, and the layout was computed using the force-directed Fruchterman-Reingold algorithm. e, Neighbour-joining tree showing relationships between genetic clusters, inferred using total variation distance (TVD) of IBD painting palettes. f,g, PCA of 3,119 Eurasian (f) or 2,126 west Eurasian (g) ancient and modern individuals (“HO” dataset).
Extended Data Fig. 4
Extended Data Fig. 4. Genetic structure of European HGs after the LGM.
a, Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions for target individuals from genetic clusters representing European HGs, using different sets of increasingly proximal source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component. b, Residuals for model fit of target individuals from selected genetic clusters across different source sets. c, Moon charts showing spatial distribution of ancestry proportions in European HGs deriving from four European source groups (set “hgEur2”; source origins shown with coloured symbol). Estimated ancestry proportions are indicated by both size and amount of fill of moon symbols. Note that ‘Italy_15000BP_9000BP’ and ‘RussiaNW_11000BP_8000BP’ correspond to ‘WHG’ and ‘EHG’ labels used in previous studies. d, Maps showing networks of highest between-cluster IBD sharing (top 10 highest sharing per individual) for individuals from two genetic clusters representing Scandinavian HGs. See Supplementary Data 1 and 7 for details of individual sample IDs presented here.
Extended Data Fig. 5
Extended Data Fig. 5. Ancestry modelling for HG and Neolithic farmer-associated genetic clusters.
Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions of two global Eurasian clusters, corresponding to European HGs before 4,000 bp and individuals from Europe and western Asia from around 10,000 bp until historical times, including Anatolian-associated (Neolithic) farmers, Caucasus HGs and recent individuals with genetic affinity to the Levant. Columns show results of modelling target individuals using three panels of increasingly distal source groups: “postBA”: Bronze Age and Neolithic source groups; “postNeol”, Bronze Age and later targets using Late Neolithic/early Bronze Age and earlier source groups; “deep”, Mesolithic and later targets using deep ancestry source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component.
Extended Data Fig. 6
Extended Data Fig. 6. Ancestry modelling for post-Neolithic genetic clusters.
Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions of a global Eurasian cluster corresponding to European individuals after 5,000 bp, as well as pastoralist groups from the Eurasian steppe. Columns show results of modelling target individuals using three panels of increasingly distal source groups: “postBA”: Bronze Age and Neolithic source groups; “postNeol”, Bronze Age and later targets using Late Neolithic/early Bronze Age and earlier source groups; “deep”, Mesolithic and later targets using deep ancestry source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component.
Extended Data Fig. 7
Extended Data Fig. 7. Ancestry modelling for genetic clusters east of the Urals.
Supervised ancestry modelling using non-negative least squares on IBDaring profiles. Panels show estimated ancestry proportions of a global Eurasian cluster corresponding to central, east and north Asian individuals with east Eurasian genetic affinities. Columns show results of modelling target individuals using three panels of increasingly distal source groups: “postBA”: Bronze Age and Neolithic source groups; “postNeol”, Bronze Age and later targets using Late Neolithic/early Bronze Age and earlier source groups; “deep”, Mesolithic and later targets using deep ancestry source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component.
Extended Data Fig. 8
Extended Data Fig. 8. Dynamics of the Neolithic transition in Europe.
a, Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions for target individuals from genetic clusters representing European Neolithic farmer individuals (“fEur” source set). Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component. b, Composition of HG ancestry proportions from different source groups in individuals with Neolithic farmer ancestry, shown as bar plots. Grey bars represent contributions from a source with ancestry related to local HGs. c, Moon charts showing spatial distribution of estimated ancestry proportions related to local HGs across Europe. Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. Coloured areas indicate the geographical extent of individuals included as local sources in the respective regions. d, Estimated time of admixture between local HG groups and Neolithic farmers. Black diamonds and error bars represent point estimate and standard errors of admixture time, coloured bars show temporal range of included target individuals. The time to admixture was adjusted backwards by the average age of individuals for each region. e, Moon charts showing spatial distribution of estimated ancestry proportions derived from genetic clusters of early Neolithic European farmers (locations indicated with coloured symbols). Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. Red symbols indicate individuals where standard errors exceed the point estimates for the respective ancestry source.
Extended Data Fig. 9
Extended Data Fig. 9. Dynamics of the steppe transition in Europe.
a, Estimated time of admixture between local HG groups and Neolithic farmers. Black diamonds and error bars represent point estimate and standard errors of admixture time, coloured bars show temporal range of included target individuals. The time to admixture was adjusted backwards by the average age of individuals for each region. b, Moon charts showing spatial distribution of estimated ancestry proportions related to local Neolithic farmers across Europe. Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. Coloured areas indicate the geographical extent of individuals included as local sources in the respective regions. c, Map showing networks of highest between-cluster IBD sharing (top 10 highest sharing per individual) for individuals from genetic cluster “Steppe_5000BP_4300BP” representing the major steppe ancestry source for Europeans. d, Distributions of difference in estimated steppe-related ancestry proportions, using individuals from the genetic cluster “Steppe_5000BP_4300BP”, associated with either Yamnaya or Afanasievo cultural contexts as separate sources.
Extended Data Fig. 10
Extended Data Fig. 10. Genetic transformations across the Eurasian steppe.
a, Moon charts showing spatial distribution of estimated ancestry proportions of Siberian HGs from the “deep” Siberian ancestry sources (names and locations indicated with coloured symbols). Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. b, Timelines of ancestry proportions from “postNeol” sources in central and north Asian ancient individuals after 5,000 bp. Symbol shape and colour indicate the genetic cluster of each individual. Black lines indicate 1 standard error. c,d, Difference in estimated steppe-related ancestry proportions, using individuals from genetic cluster “Steppe_5000BP_4300BP” associated with either Yamnaya or Afanasievo cultural contexts as separate sources, as a function of time (c) or total estimated steppe-ancestry proportion (d). Individuals from genetic clusters of individuals associated with Okunevo (blue stars) or Sintashta/Andronovo (green diamonds) contexts are indicated with coloured symbols.
Extended Data Fig. 11
Extended Data Fig. 11. Patterns of co-ancestry.
a, Panels show within-cluster genetic relatedness over time, measured as the total length of genomic segments shared IBD between individuals. Results for both measures are shown separately for individuals from western versus eastern Eurasia. Small grey dots indicate estimates for individual pairs, with larger coloured symbols indicating median values within genetic clusters. Ranges of median values for major ancestry groups are indicated with labelled convex hulls. b, Distribution of ROH lengths for 29 individuals with evidence for recent parental relatedness (>50 cM total in ROHs > 20 cM). c, Karyogram showing genomic distribution of ROH in individual tem003, an ancient case of uniparental disomy for chromosome 2. Regions within ROH are indicated with blue colour.
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