Population genomics of the Viking world


The maritime expansion of Scandinavian populations during the Viking Age (about ad 750–1050) was a far-flung transformation in world history1,2. Here we sequenced the genomes of 442 humans from archaeological sites across Europe and Greenland (to a median depth of about 1×) to understand the global influence of this expansion. We find the Viking period involved gene flow into Scandinavia from the south and east. We observe genetic structure within Scandinavia, with diversity hotspots in the south and restricted gene flow within Scandinavia. We find evidence for a major influx of Danish ancestry into England; a Swedish influx into the Baltic; and Norwegian influx into Ireland, Iceland and Greenland. Additionally, we see substantial ancestry from elsewhere in Europe entering Scandinavia during the Viking Age. Our ancient DNA analysis also revealed that a Viking expedition included close family members. By comparing with modern populations, we find that pigmentation-associated loci have undergone strong population differentiation during the past millennium, and trace positively selected loci—including the lactase-persistence allele of LCT and alleles of ANKA that are associated with the immune response—in detail. We conclude that the Viking diaspora was characterized by substantial transregional engagement: distinct populations influenced the genomic makeup of different regions of Europe, and Scandinavia experienced increased contact with the rest of the continent.

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Fig. 1: Overview of the Viking Age genomic dataset.
Fig. 2: Genetic structure of Viking Age samples.
Fig. 3: Genetic structure and diversity of ancient samples.
Fig. 4: Spatiotemporal patterns of Viking and non-Viking ancestry in Europe during the Iron Age, Early Viking Age and Viking Age.

Data availability

Sequence data are available at the European Nucleotide Archive under accession number PRJEB37976.

Code availability

Functions for calculating f-statistics are available as an R package at GitHub (https://github.com/martinsikora/admixr).


  1. 1.

    Brink, S. & Price, N. (eds) The Viking World (Routledge, 2008).

  2. 2.

    Jesch, J. The Viking Diaspora (Routledge, 2015).

  3. 3.

    Eriksen, M. H., Pedersen, U., Rundberget, B. & Axelsen, I. Viking Worlds: Things, Spaces and Movement (Oxbow Books, 2014).

  4. 4.

    Sindbæk, S. M. & Trakadas, A. The World in the Viking Age (Viking Ship Museum in Roskilde, 2014).

  5. 5.

    Sikora, M. et al. The population history of northeastern Siberia since the Pleistocene. Nature 570, 182–188 (2019).

    ADS  PubMed  CAS  Google Scholar 

  6. 6.

    Lamnidis, T. C. et al. Ancient Fennoscandian genomes reveal origin and spread of Siberian ancestry in Europe. Nat. Commun. 9, 5018 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Saag, L. et al. The arrival of Siberian ancestry connecting the eastern Baltic to Uralic speakers further east. Curr. Biol. 29, 1701–1711.e16 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  8. 8.

    Hedeager, L. in The Viking World (eds Brink, S. & Price, N.) 35–46 (Routledge, 2008).

  9. 9.

    Hedeager, L. Iron Age Myth and Materiality: An Archaeology of Scandinavia AD 400–1000 (Routledge, 2011).

  10. 10.

    Krzewińska, M. et al. Genomic and strontium isotope variation reveal immigration patterns in a Viking Age town. Curr. Biol. 28, 2730–2738.e10 (2018).

    PubMed  Google Scholar 

  11. 11.

    Lawson, D. J., Hellenthal, G., Myers, S. & Falush, D. Inference of population structure using dense haplotype data. PLoS Genet. 8, e1002453 (2012).

    PubMed  PubMed Central  CAS  Google Scholar 

  12. 12.

    Shepard, D. A Two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference (eds Blue, R. B. & Rosenberg, A. M.) 517–524 (ACM, 1968).

  13. 13.

    Hansen, U. L. Römischer Import im Norden: Warenaustausch zwischen dem Römischen Reich und dem freien Germanien während der Kaiserzeit unter besonderer Berücksichtigung Nordeuropas (Det Kongelige Nordiske Oldskriftselskab, 1987).

  14. 14.

    Andersson, K. I Skuggan av Rom: Romersk Kulturpåverkan i Norden (Atlantis, (2013).

  15. 15.

    Bill, J. in The Viking World (eds Brink, S. & Price, N.) 170–180 (Routledge, 2008).

  16. 16.

    Sindbæk, S. M. The small world of the Vikings: networks in early Medieval communication and exchange. Norw. Archaeol. Rev. 40, 59–74 (2007).

    Google Scholar 

  17. 17.

    Hilberg, V. & Kalmring, S. in Viking Archaeology in Iceland: Mosfell Archaeological Project (eds Zori, D. & Byock, J.) 221–245 (Brepols, 2014).

  18. 18.

    Ebenesersdóttir, S. S. et al. Ancient genomes from Iceland reveal the making of a human population. Science 360, 1028–1032 (2018).

    ADS  PubMed  Google Scholar 

  19. 19.

    Helgason, A. et al. mtDna and the islands of the North Atlantic: estimating the proportions of Norse and Gaelic ancestry. Am. J. Hum. Genet. 68, 723–737 (2001).

    PubMed  PubMed Central  CAS  Google Scholar 

  20. 20.

    Downham, C. Viking ethnicities: a historiographic overview. Hist. Compass 10, 1–12 (2012).

    Google Scholar 

  21. 21.

    Fellows-Jensen, G. in The Viking World (eds Brink, S. & Price, N.) 391–400 (Routledge, 2008).

  22. 22.

    Bowden, G. R. et al. Excavating past population structures by surname-based sampling: the genetic legacy of the Vikings in northwest England. Mol. Biol. Evol. 25, 301–309 (2008).

    PubMed  CAS  Google Scholar 

  23. 23.

    Leslie, S. et al. The fine-scale genetic structure of the British population. Nature 519, 309–314 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  24. 24.

    Athanasiadis, G. et al. Nationwide genomic study in Denmark reveals remarkable population homogeneity. Genetics 204, 711–722 (2016).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Loe, L., Boyle, A., Webb, H. & Score, D. ‘Given to the Ground’: A Viking Age Mass Grave on Ridgeway Hill, Weymouth (Dorset Natural History and Archaeological Society, 2014).

  26. 26.

    Wallis, S. The Oxford Henge and Late Saxon Massacre: With Medieval and Later Occupation at St John’s College, Oxford (Thames Valley Archaeological Services Limited, 2014).

  27. 27.

    Douglas Price, T., Frei, K. M., Dobat, A. S., Lynnerup, N. & Bennike, P. Who was in Harold Bluetooth’s army? Strontium isotope investigation of the cemetery at the Viking Age fortress at Trelleborg, Denmark. Antiquity 85, 476–489 (2011).

    Google Scholar 

  28. 28.

    Price, T. D. & Arneborg, J. The peopling of the North Atlantic: isotopic results from Greenland. Journal of the North Atlantic 7, 164–185 (2014).

    Google Scholar 

  29. 29.

    Arneborg, J. in The Viking World (eds Brink, S. & Price, N.) 588–603 (Routledge, 2008).

  30. 30.

    Dugmore, A. J. et al. Cultural adaptation, compounding vulnerabilities and conjunctures in Norse Greenland. Proc. Natl Acad. Sci. USA 109, 3658–3663 (2012).

    ADS  PubMed  CAS  Google Scholar 

  31. 31.

    Arneborg, J. in Medieval Archaeology in Scandinavia and Beyond: History, Trends and Tomorrow (eds Kristiansen, M. S. et al.) 247–271 (Aarhus Universitetsforlag, 2015).

  32. 32.

    Lynnerup, N. The Greenland Norse: A Biological–Anthropological Study (Museum Tusculanum, 1998).

  33. 33.

    Sindbæk, S. M. in Routledge Handbook of Archaeology and Globalization (ed. Hodos, T.) 553–565 (Routledge, 2016).

  34. 34.

    Peets, J. et al. Research results of the Salme ship burials in 2011–2012. Archaeological Fieldwork in Estonia 2012, 43–60 (2012).

    Google Scholar 

  35. 35.

    Douglas Price, T., Peets, J., Allmäe, R., Maldre, L. & Oras, E. Isotopic provenancing of the Salme ship burials in pre-Viking Age Estonia. Antiquity 90, 1022–1037 (2016).

    Google Scholar 

  36. 36.

    Cheng, J. Y., Racimo, F. & Nielsen, R. Ohana: detecting selection in multiple populations by modelling ancestral admixture components. Preprint at https://doi.org/10.1101/546408 (2019).

  37. 37.

    Alves, J. M. et al. Parallel adaptation of rabbit populations to myxoma virus. Science 363, 1319–1326 (2019).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  38. 38.

    Enattah, N. S. et al. Identification of a variant associated with adult-type hypolactasia. Nat. Genet. 30, 233–237 (2002).

    PubMed  CAS  Google Scholar 

  39. 39.

    Bersaglieri, T. et al. Genetic signatures of strong recent positive selection at the lactase gene. Am. J. Hum. Genet. 74, 1111–1120 (2004).

    PubMed  PubMed Central  CAS  Google Scholar 

  40. 40.

    Allentoft, M. E. et al. Population genomics of Bronze Age Eurasia. Nature 522, 167–172 (2015).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  41. 41.

    Mathieson, I. et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature 528, 499–503 (2015).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  42. 42.

    Fearon, E. R. et al. Identification of a chromosome 18q gene that is altered in colorectal cancers. Science 247, 49–56 (1990).

    ADS  PubMed  CAS  Google Scholar 

  43. 43.

    Siddiqa, A. et al. Regulation of CD40 and CD40 ligand by the AT-hook transcription factor AKNA. Nature 410, 383–387 (2001).

    ADS  PubMed  CAS  Google Scholar 

  44. 44.

    Pedersen, C. B. et al. The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).

    PubMed  CAS  Google Scholar 

  45. 45.

    Watanabe, K. et al. A global view of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    PubMed  CAS  Google Scholar 

  46. 46.

    Westerdahl, C. The maritime cultural landscape. Int. J. Naut. Archaeol. 21, 5–14 (1992).

    Google Scholar 

  47. 47.

    Hyenstrand, Å. Ancient Monuments and Prehistoric Society (Central Board of National Antiquities, 1979).

  48. 48.

    Callmer, J. in Regions and Reflections: In honour of Märta Strömberg (eds Jennbert, K. et al.) 257–273 (1991).

  49. 49.

    Jakobsen, J. G. G. & Dam, P. Atlas Over Danmark: Historisk-Geografisk Atlas (Det Kongelige Danske Geografiske Selskab, 2008).

  50. 50.

    Willerslev, E. & Cooper, A. Ancient DNA. Proc. R. Soc. Lond. B 272, 3–16 (2005).

    CAS  Google Scholar 

  51. 51.

    Gilbert, M. T. P., Bandelt, H.-J., Hofreiter, M. & Barnes, I. Assessing ancient DNA studies. Trends Ecol. Evol. 20, 541–544 (2005).

    PubMed  Google Scholar 

  52. 52.

    Schubert, M., Lindgreen, S. & Orlando, L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. Notes 9, 88 (2016).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    PubMed  PubMed Central  CAS  Google Scholar 

  54. 54.

    Schubert, M. et al. Improving ancient DNA read mapping against modern reference genomes. BMC Genomics 13, 178 (2012).

    PubMed  PubMed Central  CAS  Google Scholar 

  55. 55.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    PubMed  PubMed Central  CAS  Google Scholar 

  56. 56.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    PubMed  PubMed Central  CAS  Google Scholar 

  57. 57.

    Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Fu, Q. et al. A revised timescale for human evolution based on ancient mitochondrial genomes. Curr. Biol. 23, 553–559 (2013).

    PubMed  PubMed Central  CAS  Google Scholar 

  59. 59.

    Renaud, G., Slon, V., Duggan, A. T. & Kelso, J. Schmutzi: estimation of contamination and endogenous mitochondrial consensus calling for ancient DNA. Genome Biol. 16, 224 (2015).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 356 (2014).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Skoglund, P., Storå, J., Götherström, A. & Jakobsson, M. Accurate sex identification of ancient human remains using DNA shotgun sequencing. J. Archaeol. Sci. 40, 4477–4482 (2013).

    CAS  Google Scholar 

  62. 62.

    Weissensteiner, H. et al. HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing. Nucleic Acids Res. 44, W58–W63 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  63. 63.

    Ralf, A., Montiel González, D., Zhong, K. & Kayser, M. Yleaf: software for human Y-chromosomal haplogroup inference from next-generation sequencing data. Mol. Biol. Evol. 35, 1291–1294 (2018).

    PubMed  CAS  Google Scholar 

  64. 64.

    Korneliussen, T. S. & Moltke, I. NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data. Bioinformatics 31, 4009–4011 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  65. 65.

    Monroy Kuhn, J. M., Jakobsson, M. & Günther, T. Estimating genetic kin relationships in prehistoric populations. PLoS ONE 13, e0195491 (2018).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Staples, J., Nickerson, D. A. & Below, J. E. Utilizing graph theory to select the largest set of unrelated individuals for genetic analysis. Genet. Epidemiol. 37, 136–141 (2013).

    PubMed  Google Scholar 

  67. 67.

    Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    PubMed  PubMed Central  CAS  Google Scholar 

  68. 68.

    Martiniano, R. et al. The population genomics of archaeological transition in west Iberia: investigation of ancient substructure using imputation and haplotype-based methods. PLoS Genet. 13, e1006852 (2017).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    International Multiple Sclerosis Genetics Consortium & The Wellcome Trust Case Control Consortium 2. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).

    ADS  Google Scholar 

  70. 70.

    Haak, W. et al. Massive migration from the steppe was a source for Indo-European languages in Europe. Nature 522, 207–211 (2015).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  71. 71.

    Gamba, C. et al. Genome flux and stasis in a five millennium transect of European prehistory. Nat. Commun. 5, 5257 (2014).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  72. 72.

    Jones, E. R. et al. Upper Palaeolithic genomes reveal deep roots of modern Eurasians. Nat. Commun. 6, 8912 (2015).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  73. 73.

    Skoglund, P. et al. Genomic diversity and admixture differs for Stone-Age Scandinavian foragers and farmers. Science 344, 747–750 (2014).

    ADS  PubMed  CAS  Google Scholar 

  74. 74.

    Schiffels, S. et al. Iron Age and Anglo-Saxon genomes from east England reveal British migration history. Nat. Commun. 7, 10408 (2016).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  75. 75.

    Olalde, I. et al. Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European. Nature 507, 225–228 (2014).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  76. 76.

    Sikora, M. et al. Ancient genomes show social and reproductive behavior of early Upper Paleolithic foragers. Science 358, 659–662 (2017).

    ADS  PubMed  CAS  Google Scholar 

  77. 77.

    Fu, Q. et al. The genetic history of Ice Age Europe. Nature 534, 200–205 (2016).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  78. 78.

    Jones, E. R. et al. The Neolithic transition in the Baltic was not driven by admixture with early European farmers. Curr. Biol. 27, 576–582 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  79. 79.

    Seguin-Orlando, A. et al. Genomic structure in Europeans dating back at least 36,200 years. Science 346, 1113–1118 (2014).

    ADS  PubMed  CAS  Google Scholar 

  80. 80.

    Raghavan, M. et al. Upper Palaeolithic Siberian genome reveals dual ancestry of Native Americans. Nature 505, 87–91 (2014).

    ADS  PubMed  Google Scholar 

  81. 81.

    Hofmanová, Z. et al. Early farmers from across Europe directly descended from Neolithic Aegeans. Proc. Natl Acad. Sci. USA 113, 6886–6891 (2016).

    PubMed  Google Scholar 

  82. 82.

    Damgaard, P. B. et al. 137 ancient human genomes from across the Eurasian steppes. Nature 557, 369–374 (2018).

    ADS  PubMed  CAS  Google Scholar 

  83. 83.

    Günther, T. et al. Population genomics of Mesolithic Scandinavia: investigating early postglacial migration routes and high-latitude adaptation. PLoS Biol. 16, e2003703 (2018).

    PubMed  PubMed Central  Google Scholar 

  84. 84.

    Mittnik, A. et al. The genetic prehistory of the Baltic Sea region. Nat. Commun. 9, 442 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Kılınç, G. M. et al. The demographic development of the first farmers in Anatolia. Curr. Biol. 26, 2659–2666 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Lazaridis, I. et al. Genetic origins of the Minoans and Mycenaeans. Nature 548, 214–218 (2017).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  87. 87.

    de Barros Damgaard, P. et al. The first horse herders and the impact of early Bronze Age steppe expansions into Asia. Science 360, eaar7711 (2018).

    PubMed  PubMed Central  Google Scholar 

  88. 88.

    Valdiosera, C. et al. Four millennia of Iberian biomolecular prehistory illustrate the impact of prehistoric migrations at the far end of Eurasia. Proc. Natl Acad. Sci. USA 115, 3428–3433 (2018).

    PubMed  CAS  Google Scholar 

  89. 89.

    Martiniano, R. et al. Genomic signals of migration and continuity in Britain before the Anglo-Saxons. Nat. Commun. 7, 10326 (2016).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  90. 90.

    Mathieson, I. et al. The genomic history of southeastern Europe. Nature 555, 197–203 (2018).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  91. 91.

    Gallego Llorente, M. et al. Ancient Ethiopian genome reveals extensive Eurasian admixture throughout the African continent. Science 350, 820–822 (2015).

    ADS  PubMed  CAS  Google Scholar 

  92. 92.

    Broushaki, F. et al. Early Neolithic genomes from the eastern Fertile Crescent. Science 353, 499–503 (2016).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  93. 93.

    Veeramah, K. R. et al. Population genomic analysis of elongated skulls reveals extensive female-biased immigration in Early Medieval Bavaria. Proc. Natl Acad. Sci. USA 115, 3494–3499 (2018).

    PubMed  CAS  Google Scholar 

  94. 94.

    Amorim, C. E. G. et al. Understanding 6th-century barbarian social organization and migration through paleogenomics. Nat. Commun. 9, 3547 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Olalde, I. et al. A Common genetic origin for early farmers from Mediterranean Cardial and Central European LBK cultures. Mol. Biol. Evol. 32, 3132–3142 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  96. 96.

    Olalde, I. et al. The Beaker phenomenon and the genomic transformation of northwest Europe. Nature 555, 190–196 (2018).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  97. 97.

    Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    PubMed  PubMed Central  CAS  Google Scholar 

  98. 98.

    Behr, A. A., Liu, K. Z., Liu-Fang, G., Nakka, P. & Ramachandran, S. pong: fast analysis and visualization of latent clusters in population genetic data. Bioinformatics 32, 2817–2823 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  99. 99.

    Browning, B. L. & Browning, S. R. Detecting identity by descent and estimating genotype error rates in sequence data. Am. J. Hum. Genet. 93, 840–851 (2013).

    PubMed  PubMed Central  CAS  Google Scholar 

  100. 100.

    Hellenthal, G. et al. A genetic atlas of human admixture history. Science 343, 747–751 (2014).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  101. 101.

    Fortin, M.-J. & Dale, M. R. T. Spatial Analysis: A Guide for Ecologists (Cambridge Univ. Press, 2005).

  102. 102.

    Walsh, S. et al. The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA. Forensic Sci. Int. Genet. 7, 98–115 (2013).

    PubMed  PubMed Central  CAS  Google Scholar 

  103. 103.

    Cheng, J. Y., Mailund, T. & Nielsen, R. Fast admixture analysis and population tree estimation for SNP and NGS data. Bioinformatics 33, 2148–2155 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

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This work was supported by the Mærsk Foundation, the Lundbeck Foundation, the Novo Nordisk Foundation, the Danish National Research Foundation, University of Copenhagen (KU2016) and the Wellcome Trust (grant no. WT104125MA). E.W. thanks St John’s College, Cambridge for providing an excellent environment for scientific thoughts and collaborations. S.R. was supported by the Novo Nordisk Foundation (NNF14CC0001). F.R. was supported by a Villum Fonden Young Investigator Award (project no. 00025300). G.S. and E.C. were supported by a Marie Skłodowska-Curie Individual Fellowship ‘PALAEO-ENEO’, a project funded by the European Union EU Framework Programme for Research and Innovation Horizon 2020 (grant agreement number 751349). R.M. was supported by an EMBO Long-Term Fellowship (ALTF 133-2017). M.C. is supported by the Canada Research Chairs Program (231256), the Canada Foundation for Innovation (36801) and the British Columbia Knowledge Development Fund (962-805808). I. Moltke was supported by a YDUN grant from Independent Research Fund Denmark (DFF-4090-00244) and a Villum Fonden Young Investigator Award (project no. 19114). N.P. and C.H.J. are supported by the Swedish Research Council (2015-00466). N.G. was supported by the Program of Fundamental Scientific Research of the State Academies of Sciences, Russian Federation, state assignment no. 0184-2019-0006. D.G.B. and L.M.C. were supported by Science Foundation Ireland/Health Research Board/Wellcome Trust award no. 205072. We thank the iPSYCH Initiative, funded by the Lundbeck Foundation (grant numbers R102-A9118 and R155-2014-1724), for supplying SNP frequency estimates from the present-day Danish population for comparison with Viking Age samples; M. Jakobsson and A. Götherström for providing preliminary access to the sequencing data of 23 Viking Age samples from Sigtuna; M. Corrente for providing access to the skeletal remains from Cancarro; and N. M. Mangialardi and M. Maruotti for the useful suggestions; Greenland National Museum and Archives, as well as the Gotland Museum, for permission to sample their skeletons; J. Kavanagh for providing information on his excavation, and L. Buckley, D. Keating and B. Ó Donnabháin for analysing the remains; R. Breward and J. Murden from the Dorset County Museum for allowing access to their assemblage for DNA sampling; J. Hansen and M. B. Henriksen at Odense Bys Museer for allowing sampling of skeletal material from Hessum and Galjedil; Moesgaard Museum for allowing sampling of skeletal material from Hesselbjerg; C. Bertilsson, P. Lingström, B. Lundberg, K. Lidén and J. Andersson for their help in sampling the ancient human remains; L. Drenzel for permission to sample the human remains; C. Ödman for suggesting relevant material for this study; Ł. Stanaszek, M. Zaitz and the Regional Museum in Cedynia for providing samples; L. Vinner, A. Seguin-Orlando, K. Magnussen, L. Petersen, C. Mortensen and M. J. Jacobsen at the Danish National Sequencing Centre for producing the analysed sequences; P. S. Olsen and T. Brand for technical assistance in the laboratories; R. M. Durbin and J. H. Barrett for comments and suggestions; and J. Wilson, J. Jesch, E. Harlitz-Kern and F. Martín Racimo for their feedback.

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E.W. initiated and led the study. E.W., A.M., D.J.L., Martin Sikora, F.R., R.N., K.K., L.H., S.M.S., J.B., N.P., T.W., A.I., M.E.A., M.W.P., N.L., J.A., I. Moltke and A.A. designed the study. A.M., P.d.B.D., L.M.C., M.M.B., A.K.F., I.L. and J.S. produced the data. A.M., D.J.L., Martin Sikora, F.R., S.R., I. Moltke, R.N., T.W., L.M.C., E.J., A.I., M.W.P., T.K., R.M., G.R., C.B., J.V.M.-M., H.M., A.A., J.C., K.H.I. and M.E.A. analysed or assisted in analysis of data. E.W., A.M., D.J.L., Martin Sikora, F.R., S.M.S., K.K., L.H., R.N., M.C. and A.I. interpreted the results with considerable input from I. Moltke, M.E.A., M.W.P., T.K., H.W., R.M., G.R., T.W., C.H.J., J.A., N.L., N.P., J.B., A.A., M.T.P.G., L.O. and other authors. E.W., A.M., D.J.L., Martin Sikora, F.R., S.M.S., K.K. and L.H. wrote the manuscript with considerable input from M.C., J.B., N.P., I. Moltke, N.L., A.I., R.M., E.J., J.A., M.L.J., C.H.J., M.W.P., M.E.A., G.R. and M.M., with contributions from all authors. A.M., L.M.C., M.W.P., H.W., M.M.B., P.d.B.D., A.K.F., M.A., R.A., M.M., E.C., G.S., A.B., A.F., B. Schütz, B. Skar, C.A., C.F., D.B., D.P., G.T.-W., H.G., I.L., I.G., I. Mainland, I.P., I.M.M., J.M., J. Gibson, J.P., J. Gustafsson, L. Simpson, L. Strand, L.L., Maeve Sikora, M.F., M.V., M.R., M.B., T.P., M. Søvso, N.G., T.C., O.K., O.U., P.F., P.H., S.S., S.V.A., S.E., V.M., W.B., Y.M., P.P., M.D.J., A.P., D.G.B., M.L.J., J.A., N.L., N.P., M.T.P.G., M.E.A., J.B. and E.W. excavated, curated, sampled and/or described analysed skeletons.

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Correspondence to Rasmus Nielsen or Thomas Werge or Eske Willerslev.

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Extended data figures and tables

Extended Data Fig. 1 Viking Age archaeological sites.

Examples of a few archaeological Viking Age sites and samples used in this study. a, Salme II ship burial site of the Early Viking Age, excavated in present-day Estonia: schematic of skeletons (top left) and aerial images of skeletons (top right, and bottom). b, Ridgeway Hill mass grave dated to the tenth or eleventh century ad, located on the crest of Ridgeway Hill near Weymouth, on the south coast of England (reproduced with permission from Dorset County Council/Oxford Archaeology). Around 50 predominantly young adult male individuals were excavated. c, The site of Balladoole, around ad 900, a Viking was buried in an oak ship at Balladoole (Arbory) in the south east of the Isle of Man. d, Viking Age archaeological site in Varnhem, in Skara municipality (Sweden). Schematic map of the church foundation (left) and the excavated graves (red markings) at the early Christian cemetery in Varnhem; foundations of the Viking Age stone church in Varnhem (middle) and the remains of a 182-cm-long male individual (no. 17) buried in a lime stone coffin close to the church foundations (right).

Extended Data Fig. 2 Model-based clustering analysis.

Admixture plot (K = 2 to K = 5) for 567 ancient individuals, spanning 71 populations. This figure is a subset of the most relevant individuals and populations from Supplementary Fig. 7.2; see Supplementary Note 7 for further details. This plot consists of 378 ancient samples from this study; Viking Age samples from Sigtuna (Sweden)10 (n = 21), Iceland18 (n = 22) and other ancient comparative groups (n = 146).

Extended Data Fig. 3 Fine-scale population structure.

The point cloud at the top centre shows an alternative view of the UMAP result from Fig. 2b, with all ancient individuals coloured on the basis of analysis group. The framed panels surrounding the point cloud highlight particular ancestry clusters (as indicated), with labels and larger symbols corresponding to the median coordinates for the respective group. Similarly, the larger bottom panel shows median group coordinates for the large central point cloud, which includes the vast majority of European individuals from the Bronze Age onwards.

Extended Data Fig. 4 Ancestry modelling for distal sources.

a, Contrasting allele-sharing between Anatolian farmers (Barcin_EN) and Steppe pastoralists (Yamnaya_EBA) for European individuals from the Bronze Age and later. Violin plots showing distributions of statistics f4(YRI,test individual;Barcin_EN,Yamnaya_EBA) for n = 515 individuals with a minimum of 1,000,000 SNPs with genotypes and groups with at least 2 such individuals. b, Ancestry proportions of analysis groups from the Bronze Age and later inferred using qpAdm. Target groups were modelled using three distal sources representing European hunter-gatherer (Loschbour_M), Anatolian farmer (Barcin_EN) and Steppe pastoralist (Yamnaya_EBA) ancestry. Sample sizes for target groups can be found in Supplementary Table 10. Error bars indicate standard error obtained from qpAdm. c, Ancestry proportions of analysis groups for which the three-source model was rejected using qpAdm (P < 0.05). Target groups were modelled including one additional distal source representing either Steppe hunter-gatherer (Botai_EBA), Caucasus hunter-gatherer (CaucasusHG_M) or East-Asian-related (XiongNu_IA) ancestry.

Extended Data Fig. 5 Ancestry modelling for proximate sources.

a, Testing for continuity between European Iron Age and later Viking Age and Medieval groups. Coloured squares depict whether a particular target group (row) can be modelled using a single source group (column). P values for f4 rank of 0 (corresponding to a single source group) were obtained using qpAdm with a set of 15 outgroups, which included European Bronze Age groups that preceded the source groups. Sample sizes for target groups can be found in Supplementary Table 12. b, Two-way admixture ancestry proportions of target groups for which a single source was rejected (P ≤ 0.05). Target groups were modelled using additional proximate Bronze and Iron Age sources. Sample sizes for target groups can be found in Supplementary Table 13. For both a and b, only ancient groups containing at least 3 individuals with a minimum of 1,000,000 SNPs with genotypes are plotted. c, Contrasting allele-sharing between populations of present-day Denmark and other populations. Violin plots showing distributions of statistics f4(YRI,test individual;panel population,Denmark) for n = 489 individuals with a minimum of 50,000 SNPs with genotypes and groups with at least 2 such individuals. Median values for distributions are indicated with horizontal lines.

Extended Data Fig. 6 Ancestry diversity of different population groups.

Diversity of different labels (that is, sample locations combined with historical age) are shown as a function of their sample size. The diversity measure is the Kullback–Leibler divergence from the label means, capturing the diversity of a group with respect to the average of that group (see Supplementary Note 11 for details). Larger values are more diverse, although a dependence on sample size is expected. The simulation expectation for the best fit to the data (0 = 0.2) is shown.

Extended Data Fig. 7 Polygenic risk scores.

Polygenic risk scores (PRS) for 16 complex human traits in 148 Viking Age samples from Denmark, Sweden and Norway, compared against a reference sample of 20,551 Danish-ancestry individuals randomly drawn from all individuals born in Denmark in 1981–2005. The PRS is in each case based on allelic effects for >100 independent genome-wide significant SNPs from recent genome-wide association studies of the respective traits and standardised to a mean of 0 and standard deviation of 1 in the entire sample. Difference in PRS was estimated in a linear regression correcting for sex and 25 principal components of overall genetic structure. The plotted BETA indicates the coefficient for the test-group (Viking Age sample) PRS compared to that of the Danish comparison sample, with error bars indicating the 95% confidence interval of BETA, and P indicating the two-tailed P value of the corresponding t-test (not corrected for number of tests). Only PRS for black hair colour is significantly different between the groups after taking account of multiple testing.

Extended Data Fig. 8 Positive selection in Europe.

a, Manhattan plots of the likelihood ratio scores in favour of selection looking at the entire 10,000-year period (top, general scan), the period up to 4,000 years before present (middle, ancient scan) and the period from 4,000 years before present up to the present day (bottom, recent scan). The highlighted SNPs have a score larger than the 99.9% quantile of the empirical distribution of log-likelihood ratios, and have at least two neighbouring SNPs (± 500 kb) with a score larger than the same quantile. n = 1,185 genomes are used in the selection scan. b, Frequencies of the derived A allele rs4988235 SNP responsible for lactase persistence in humans for different Viking Age groups, present-day populations from the 1000 Genomes Project as well as relevant Bronze Age population panels. The numbers at the top of the bars denote the sample size on which the allele frequency estimates are based.

Supplementary information

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This file contains Supplementary Notes 1-15, including and Supplementary Figures, Supplementary Tables and Supplementary References.

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Margaryan, A., Lawson, D.J., Sikora, M. et al. Population genomics of the Viking world. Nature 585, 390–396 (2020). https://doi.org/10.1038/s41586-020-2688-8

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