Stable population structure in Europe since the Iron Age, despite high mobility

  1. Margaret L Antonio
  2. Clemens L Weiß
  3. Ziyue Gao
  4. Susanna Sawyer
  5. Victoria Oberreiter
  6. Hannah M Moots
  7. Jeffrey P Spence
  8. Olivia Cheronet
  9. Brina Zagorc
  10. Elisa Praxmarer
  11. Kadir Toykan Özdoğan
  12. Lea Demetz
  13. Pere Gelabert
  14. Daniel Fernandes
  15. Michaela Lucci
  16. Timka Alihodžić
  17. Selma Amrani
  18. Pavel Avetisyan
  19. Christèle Baillif-Ducros
  20. Željka Bedić
  21. Audrey Bertrand
  22. Maja Bilić
  23. Luca Bondioli
  24. Paulina Borówka
  25. Emmanuel Botte
  26. Josip Burmaz
  27. Domagoj Bužanić
  28. Francesca Candilio
  29. Mirna Cvetko
  30. Daniela De Angelis
  31. Ivan Drnić
  32. Kristián Elschek
  33. Mounir Fantar
  34. Andrej Gaspari
  35. Gabriella Gasperetti
  36. Francesco Genchi
  37. Snežana Golubović
  38. Zuzana Hukeľová
  39. Rimantas Jankauskas
  40. Kristina Jelinčić Vučković
  41. Gordana Jeremić
  42. Iva Kaić
  43. Kevin Kazek
  44. Hamazasp Khachatryan
  45. Anahit Khudaverdyan
  46. Sylvia Kirchengast
  47. Miomir Korać
  48. Valérie Kozlowski
  49. Mária Krošláková
  50. Dora Kušan Špalj
  51. Francesco La Pastina
  52. Marie Laguardia
  53. Sandra Legrand
  54. Tino Leleković
  55. Tamara Leskovar
  56. Wiesław Lorkiewicz
  57. Dženi Los
  58. Ana Maria Silva
  59. Rene Masaryk
  60. Vinka Matijević
  61. Yahia Mehdi Seddik Cherifi
  62. Nicolas Meyer
  63. Ilija Mikić
  64. Nataša Miladinović-Radmilović
  65. Branka Milošević Zakić
  66. Lina Nacouzi
  67. Magdalena Natuniewicz-Sekuła
  68. Alessia Nava
  69. Christine Neugebauer-Maresch
  70. Jan Nováček
  71. Anna Osterholtz
  72. Julianne Paige
  73. Lujana Paraman
  74. Dominique Pieri
  75. Karol Pieta
  76. Stefan Pop-Lazić
  77. Matej Ruttkay
  78. Mirjana Sanader
  79. Arkadiusz Sołtysiak
  80. Alessandra Sperduti
  81. Tijana Stankovic Pesterac
  82. Maria Teschler-Nicola
  83. Iwona Teul
  84. Domagoj Tončinić
  85. Julien Trapp
  86. Dragana Vulović
  87. Tomasz Waliszewski
  88. Diethard Walter
  89. Miloš Živanović
  90. Mohamed el Mostefa Filah
  91. Morana Čaušević-Bully
  92. Mario Šlaus
  93. Dušan Borić
  94. Mario Novak
  95. Alfredo Coppa
  96. Ron Pinhasi  Is a corresponding author
  97. Jonathan K Pritchard  Is a corresponding author
  1. Biomedical Informatics Program, Stanford University, United States
  2. Department of Genetics, Stanford University, United States
  3. Department of Genetics, University of Pennsylvania, Perelman School of Medicine, United States
  4. Department of Evolutionary Anthropology, University of Vienna, Austria
  5. Human Evolution and Archaeological Sciences, University of Vienna, Austria
  6. Stanford Archaeology Center, Stanford University, United States
  7. University of Chicago, Department of Human Genetics, United States
  8. Department of History and Art History, Utrecht University, Netherlands
  9. CIAS, Department of Life Sciences, University of Coimbra, Portugal
  10. Dipartimento di Storia Antropologia Religioni Arte Spettacolo, Sapienza University, Italy
  11. Archaeological Museum Zadar, Croatia
  12. LBEIG, Population Genetics & Conservation Unit, Department of Cellular and Molecular Biology – Faculty of Biological Sciences, University of Sciences and Technology Houari Boumediene, Algeria
  13. National Academy of Sciences of Armenia, Institute of Archaeology and Ethnography, Armenia
  14. French National Institute for Preventive Archaeological Research (INRAP)/CAGT UMR 5288, France
  15. Centre for Applied Bioanthropology, Institute for Anthropological Research, Croatia
  16. Université Gustave Eiffel – Laboratoire ACP, France
  17. Palisada Ltd, Croatia
  18. Dipartimento dei Beni Culturali, Archeologia, Storia dell'arte, del Cinema e della Musica, Università di Padova, Italy
  19. Department of Anthropology, Faculty of Biology and Environmental Protection, University of Lodz, Poland
  20. Aix Marseille Université, CNRS, Centre Camille Jullian, France
  21. Kaducej Ltd, Croatia
  22. Faculty of Humanities and Social Sciences, University of Zagreb, Croatia
  23. Bioarchaeology Service, Museum of Civilizations, Italy
  24. Museo Archeologico Nazionale di Tarquinia, Direzione Regionale Musei Lazio, Italy
  25. Archaeological Museum in Zagreb, Croatia
  26. Institute of Archaeology, Slovak Academy of Sciences, Slovakia
  27. Département des Monuments et des Sites Antiques - Institut National du Patrimoine INP, Tunisia
  28. University of Ljubljana, Faculty of Arts, Department for Archaeology, Slovenia
  29. Soprintendenza Archeologia, belle arti e paesaggio per le province di Sassari e Nuoro, Italy
  30. Department of Oriental Studies, Sapienza University of Rome, Italy
  31. Institute of Archaeology Belgrade, Serbia
  32. Institute of Biomedical Sciences, Vilnius University, Lithuania
  33. Institute of Archaeology, Croatia
  34. Université de Lorraine, Centre de Recherche Universitaire Lorrain d' Histoire (CRULH), France
  35. Department of Archaeologi, Shirak Centere of Armenological Studies, National Academy of Sciences Republic of Armenia, Armenia
  36. Institute of Archaeology and Ethnography of the National Academy of Sciences of the Republic of Armenia, Armenia
  37. Musée Archéologique de l'Oise, France
  38. Department of Environmental Biology, Sapienza University of Rome, Italy
  39. UMR 7041 ArScAn / French Institute of the Near East, Lebanon
  40. Archaeology Division, Croatian Academy of Sciences and Arts, Croatia
  41. CEF - University of Coimbra, Portugal
  42. UNIARQ - University of Lisbon, Portugal
  43. Skupina STIK Zavod za preučevanje povezovalnih področij preteklosti in sedanjosti, Slovenia
  44. Cardiolo-Oncology Research Collaborative Group (CORCG), Faculty of Medicine, Benyoucef Benkhedda University, Algeria
  45. Molecular Pathology, University Paul Sabatier Toulouse III, France
  46. French National Institute for Preventive Archaeological Research (INRAP), France
  47. Museum of Croatian Archaeological Monuments, Croatia
  48. L’Institut français du Proche-Orient, Lebanon
  49. Institute of Archaeology and Ethnology Polish Academy of Sciences, Centre of Interdisciplinary Archaeological Research, Poland
  50. Department of Odontostomatological and Maxillofacial Sciences, Sapienza University of Rome, Italy
  51. Austrian Archaeological Institute, Austrian Academy of Sciences, Austria
  52. Institute of Prehistory and Early History, University of Vienna, Austria
  53. Thuringia State Service for Cultural Heritage and Archaeology Weimar, Germany
  54. Institute of Anatomy and Cell Biology, University Medical Centre, Georg-August University of Göttingen, Germany
  55. Mississippi State University, United States
  56. University of Nevada, United States
  57. Trogir Town Museum, Croatia
  58. Université Paris 1 Panthéon-Sorbonne, France
  59. Faculty of Archaeology, University of Warsaw, Poland
  60. Dipartimento Asia, Africa e Mediterraneo, Università degli Studi di Napoli “L’Orientale”, Italy
  61. Museum of Vojvodina, Serbia
  62. Department of Anthropology, Natural History Museum Vienna, Austria
  63. Chair and Department of Normal Anatomy, Faculty of Medicine and Dentistry, Pomeranian Medical University, Poland
  64. Musée de La Cour d'Or, Eurométropole de Metz, France
  65. Department of Archeology, Center for Conservation and Archeology of Montenegro, Montenegro
  66. Insitut d’Archeologie, University Algiers 2, Algeria
  67. Université de Franche Comté / UMR Chrono-Environnement, France
  68. Anthropological Centre, Croatian Academy of Sciences and Arts, Croatia
  69. Department of Anthropology, New York University, United States
  70. Department of Genetics, Harvard Medical School, United States
  71. Department of Biology, Stanford University, United States
24 figures and 5 additional files

Figures

Figure 1 with 1 supplement
Timeline of new and published genomes.

(A) 204 newly reported genomes (black circles) are shown alongside published genomes (gray circles), ordered by time and region (colored the same way as in B). (B) Sampling locations of newly reported (black) and published (gray) genomes are indicated by diamonds, sized according to the number of genomes at each location.

Figure 1—figure supplement 1
Detailed map of locations for newly reported samples.

Each circle represents a location, the size of the circle corresponds to the number of individuals sampled from that location. Circles are colored by their time period: Bronze Age is green (Pian Sultano), Iron Age is yellow (two recently reported sites Tarquinia and Kerkouane), Imperial Rome and Late Antiquity is dark blue, Medieval Ages and Early Modern are light blue (Palazzo della Cancelleria, Velić, Gardun, Mirine-Fulfinum). Note that the Bronze Age and Iron Age sites were recently reported in Moots et al., 2022.

Figure 2 with 3 supplements
Armenia: two homogeneous genetic clusters distinguished by a temporal shift.

(A) Sampling locations of ancient genomes (open circles) colored by their genetic cluster identified using qpAdm modeling. (B) Date ranges for the genomes: each line represents the 95% confidence interval for the radiocarbon date or the upper and lower limit of the inferred date, and the point represents the midpoint of that range. (C) Projections of the genomes onto a PCA of present-day genomes (gray points labeled by their population). Present-day genomes from Armenia are shown with dark gray open circles.

Figure 2—figure supplement 1
Principal component analysis of present-day genomes from Europe and the Mediterranean.

PCA was performed on 829 individuals (480,712 snps) using smartpca v1600. The following parameters were used: 5 outlier iterations (numoutlieriter), 10 principal components along which to remove outliers (numoutlierevec), altnormstyle set to NO, with least squares projection turned on (lsqproject set to YES).

Figure 2—figure supplement 2
Ancestry clusters identified within regions.

Each row displays data from a single study region. The first column shows a map with the sampling locations for the individuals, while columns two through four show the individuals projected onto a PCA space of present-day genomes (gray points) (populations are labeled in the far right panel in row 1 and in Figure 2—figure supplement 1). Individual ancient genomes in the map and PCA panels are colored by ancestry clusters identified using qpAdm. Colors are not matched across regions. Star points are putative outliers, that is individuals with ancestry that is underrepresented in the region. They are not colored by ancestry clusters so as to reduce visual clutter.

Figure 2—figure supplement 3
SNP coverage comparison across cluster sizes and downstream outlier status.

(left) No significant correlation was detected between the median number of SNPs covered across the individuals in a cluster and cluster size. (right) There also was no significant difference in the number of SNPs covered between outlier and non-outlier clusters.

Figure 3 with 1 supplement
Southeastern Europe: highly heterogeneous Imperial Roman and Late Antiquity period population.

(A) Sampling locations of genetic clusters are represented by a single point per location. Outlier ancestries are black stars, all others are open circles colored by genetic cluster. (B) Colored bars span the minimum and maximum of the date ranges of samples (95% confidence interval from radiocarbon dating or archaeological range). Points are the mean of an individual’s date range. (C) Projections of the ancient genomes onto a PCA of present-day genomes (gray points). Population labels for the PCA reference space are shown in Figure 2C. Present-day genomes from Southeastern Europe are shown with dark gray open circles.

Figure 3—figure supplement 1
Population structure of Italy during the Imperial Roman and Late Antiquity period.

Ancient Italian genomes (colored points) from the Imperial Roman and Late Antiquity period were projected onto principal components of present-day genomes (gray points, populations labeled in Figure 2—figure supplement 1). Present-day Italian genomes are highlighted by a gray filled ellipse. Star points are outliers and circle points are non-outliers. Outlier clusters that can be modeled using contemporaneous populations are labeled with the potential source region.

Western Europe: heterogeneous Imperial Roman and Late Antiquity period population.

(A) Sampling locations of genetic clusters are represented by a single point per location. Outlier ancestries are black stars, all others are open circles colored by genetic cluster. (B) Colored bars span the minimum and maximum of the date ranges of samples (95% confidence interval from radiocarbon dating or archaeological range). Points are the mean of an individual’s date range. (C) Projections of the ancient genomes onto a PCA of present-day genomes (gray points). Population labels for the PCA reference space are shown in Figure 2C. Present-day genomes from Southeastern Europe are shown with dark gray open circles.

Figure 5 with 3 supplements
Ancestry outliers and their potential sources.

(A) The proportions of outliers in each region were determined by individual pairwise qpAdm modeling followed by clustering. (B) Sources were inferred by one component qpAdm modeling of resulting clusters with all genetic clusters in the dataset. In the network visualizations, nodes are regions and directed edges are drawn from sources to outliers (i.e. potential migrants). The full network of source to outlier is shown. (C) Examples of individual regions are shown in greater detail.

Figure 5—figure supplement 1
Lack of sex-bias amongst outliers with valid qpAdm sources.

The proportions of males and females do not differ significantly between outlier and non-outlier groups (p=0.4117). When outliers (with and without source) are treated as one group, there is still no significant association with outlier status and sex (p=0.633).

Figure 5—figure supplement 2
Distances of outliers to their candidate sources.

Geographic distance between the sampling locations of ‘outlier with source’ and the location of their putative source was calculated for each outlier. The mean distance was calculated if there were multiple putative sources.

Figure 5—figure supplement 3
Example routes and travel times across the Roman Empire.

Routes and travel times were approximated using orbis.stanford.edu, a geospatial network model of the Roman Empire. Routes shown are the fastest routes during Summer for civilians, utilizing road, river, coastal sea, and open sea, and by foot if on road. Routes for military individuals (not shown) are marginally faster.

Relatively stable population structure from Bronze Age to present-day.

(A) Overall genetic differentiation between populations (measured by FST) and its relationship to geographical distance (spatial structure) is similar from Bronze Age onward. Confidence intervals were calculated through a bootstrap procedure, using 200 bootstrap replicates. (B) In PC space, each genome is represented by a point, colored based on their origin (for present-day individuals) or sampling location (for historical samples). The PC space is established by present-day samples (bottom), onto which either historical period (middle) or prehistoric genomes (top) were projected. For projections, the present-day samples are shown in gray, and their extent is visualized by a gray polygon.

Figure 7 with 2 supplements
Simulation of population structure with and without long-range dispersal.

(A) A base model of spatial structure is established by calibrating per-generation dispersal rate to generate a maximum FST of ~0.03 across the maximal spatial distance, and visualized using PCA. In addition to this base dispersal, either 4% (B) or 8% (C) of individuals disperse longer distances, and the effect is tracked by analyzing spatial FST through time, as well as PCA after 120 generations of long-range dispersal.

Figure 7—figure supplement 1
A sigmaDisp - N parameter pair was chosen to closely approximate the observed FSTmax of ~0.03 using grid search across a range of parameter pairs.

We used the pair N=50,000 & sigmaDisp = 0.02 for all other simulations we report.

Figure 7—figure supplement 2
A sigmaDispLR parameter was chosen to qualitatively resemble long-range dispersal distances observed in the data, by comparing the distribution of distances under long-range dispersal (outliers) to randomly chosen distances given the spatial distribution of samples.

We used a value of 0.20 for all other simulations we report.

Appendix 1—figure 1
Armenia.
Appendix 1—figure 2
Mainland Italy.
Appendix 1—figure 3
Sardinia.
Appendix 1—figure 4
Levant & Egypt.
Appendix 1—figure 5
Southeastern Europe.
Appendix 1—figure 6
Southwestern Europe.
Appendix 1—figure 7
Western Europe.
Appendix 1—figure 8
Northern Europe.
Appendix 1—figure 9
Great Britain & Ireland.
Appendix 1—figure 10
Eastern Central Europe.
Appendix 1—figure 11
Eastern Europe & Steppe.
Appendix 1—figure 12
North Africa.
Author response image 1
SNP coverage comparison between outliers and non-outliers in region-period pairings with “surprising” outliers (t-test p-value: 0.

242).

Author response image 2
PCA projection (left) and SNP coverage comparison (right) for “surprising” outliers and surrounding non-outliers in Italy_IRLA.
Author response image 3
Projection of qpAdm reference population individuals into present-day PCA.
Author response image 4
Comparison of pairwise PCA projection distance to outgroup-f3 similarity across all qpAdm reference population individuals.

PCA projection distance was calculated as the euclidean distance on the first two principal components. Outgroup-f3 statistics were calculated relative to Mbuti, which is itself also a qpAdm reference population. Both panels show the same data, but each point is colored by either of the two reference populations involved in the pairwise comparison.

Author response image 5
Comparing geographic distance to PCA distance between pairs of historical and pre-historical individuals matched by geographic space.

For each historical period individual we selected the closest pre-historical individual by geographic distance in an effort to match the distributions of pairwise geographic distance across the two time periods (left). For these distributions of individuals matched by geographic distance, we then queried the euclidean distance between their projection locations in the first two principal components (right).

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  1. Margaret L Antonio
  2. Clemens L Weiß
  3. Ziyue Gao
  4. Susanna Sawyer
  5. Victoria Oberreiter
  6. Hannah M Moots
  7. Jeffrey P Spence
  8. Olivia Cheronet
  9. Brina Zagorc
  10. Elisa Praxmarer
  11. Kadir Toykan Özdoğan
  12. Lea Demetz
  13. Pere Gelabert
  14. Daniel Fernandes
  15. Michaela Lucci
  16. Timka Alihodžić
  17. Selma Amrani
  18. Pavel Avetisyan
  19. Christèle Baillif-Ducros
  20. Željka Bedić
  21. Audrey Bertrand
  22. Maja Bilić
  23. Luca Bondioli
  24. Paulina Borówka
  25. Emmanuel Botte
  26. Josip Burmaz
  27. Domagoj Bužanić
  28. Francesca Candilio
  29. Mirna Cvetko
  30. Daniela De Angelis
  31. Ivan Drnić
  32. Kristián Elschek
  33. Mounir Fantar
  34. Andrej Gaspari
  35. Gabriella Gasperetti
  36. Francesco Genchi
  37. Snežana Golubović
  38. Zuzana Hukeľová
  39. Rimantas Jankauskas
  40. Kristina Jelinčić Vučković
  41. Gordana Jeremić
  42. Iva Kaić
  43. Kevin Kazek
  44. Hamazasp Khachatryan
  45. Anahit Khudaverdyan
  46. Sylvia Kirchengast
  47. Miomir Korać
  48. Valérie Kozlowski
  49. Mária Krošláková
  50. Dora Kušan Špalj
  51. Francesco La Pastina
  52. Marie Laguardia
  53. Sandra Legrand
  54. Tino Leleković
  55. Tamara Leskovar
  56. Wiesław Lorkiewicz
  57. Dženi Los
  58. Ana Maria Silva
  59. Rene Masaryk
  60. Vinka Matijević
  61. Yahia Mehdi Seddik Cherifi
  62. Nicolas Meyer
  63. Ilija Mikić
  64. Nataša Miladinović-Radmilović
  65. Branka Milošević Zakić
  66. Lina Nacouzi
  67. Magdalena Natuniewicz-Sekuła
  68. Alessia Nava
  69. Christine Neugebauer-Maresch
  70. Jan Nováček
  71. Anna Osterholtz
  72. Julianne Paige
  73. Lujana Paraman
  74. Dominique Pieri
  75. Karol Pieta
  76. Stefan Pop-Lazić
  77. Matej Ruttkay
  78. Mirjana Sanader
  79. Arkadiusz Sołtysiak
  80. Alessandra Sperduti
  81. Tijana Stankovic Pesterac
  82. Maria Teschler-Nicola
  83. Iwona Teul
  84. Domagoj Tončinić
  85. Julien Trapp
  86. Dragana Vulović
  87. Tomasz Waliszewski
  88. Diethard Walter
  89. Miloš Živanović
  90. Mohamed el Mostefa Filah
  91. Morana Čaušević-Bully
  92. Mario Šlaus
  93. Dušan Borić
  94. Mario Novak
  95. Alfredo Coppa
  96. Ron Pinhasi
  97. Jonathan K Pritchard
(2024)
Stable population structure in Europe since the Iron Age, despite high mobility
eLife 13:e79714.
https://doi.org/10.7554/eLife.79714