A dynastic elite in monumental Neolithic society


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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Fig. 1: Fine-scale haplotypic and dietary structure in the Neolithic.
Fig. 2: Genomic signals of dynasty among focal passage tomb interments.
Fig. 3: Origins and legacy of the Irish Mesolithic.

Data availability

Raw FASTQ and aligned BAM files are available through the European Nucleotide Archive under accession number PRJEB36854. Any other relevant data are available from the corresponding authors upon reasonable request.


  1. 1.

    Hinz, M., Müller, J. & Wunderlich, M. in Megaliths, Societies, Landscapes: Early Monumentality and Social Differentiation in Neolithic Europe Vol. 1 (eds Hinz, M. et al.) 21–23 (Habelt, 2019).

  2. 2.

    Cunliffe, B. Facing the Ocean: the Atlantic and its Peoples (Oxford Univ. Press, 2004).

  3. 3.

    Burenhult, G. Megalithic symbolism in Ireland and Scandinavia in light of new evidence from Carrowmore. Arkeos 6, 49–108 (1999).

    Google Scholar 

  4. 4.

    Wolf, A. P. Incest Avoidance and the Incest Taboos: Two Aspects of Human Nature (Stanford Univ. Press, 2014).

  5. 5.

    Goggin, J. M. & Sturtevant, W. P. in Explorations in Cultural Anthropology. Essays in Honour of George Peter Murdock (ed. Goodenough, W. H.) 179–219 (McGraw-Hill, 1964).

  6. 6.

    van den Berghe, P. L. & Mesher, G. M. Royal incest: a reply to Sturtevant. Am. Ethnol. 8, 187–188 (1981).

    Article  Google Scholar 

  7. 7.

    Cassidy, L. M. et al. Neolithic and Bronze Age migration to Ireland and establishment of the insular Atlantic genome. Proc. Natl Acad. Sci. USA 113, 368–373 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Sánchez-Quinto, F. et al. Megalithic tombs in western and northern Neolithic Europe were linked to a kindred society. Proc. Natl Acad. Sci. USA 116, 9469–9474 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. 10.

    Schulz Paulsson, B. Radiocarbon dates and Bayesian modeling support maritime diffusion model for megaliths in Europe. Proc. Natl Acad. Sci. USA 116, 3460–3465 (2019).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Brace, S. et al. Ancient genomes indicate population replacement in Early Neolithic Britain. Nat. Ecol. Evol. 3, 765–771 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    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  Article  CAS  Google Scholar 

  13. 13.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Lynch, A. Poulnabrone: An Early Neolithic Portal Tomb in Ireland (Stationery Office, 2014).

  15. 15.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    O’Kelly, M. J. Newgrange (Thames & Hudson, 1983).

  17. 17.

    Huebner, S. R. ‘Brother–sister’ marriage in Roman Egypt: a curiosity of humankind or a widespread family strategy? J. Roman Stud. 97, 21–49 (2007).

    Article  Google Scholar 

  18. 18.

    Kolb, M. J. et al. Monumentality and the rise of religious authority in precontact Hawaiʼi. Curr. Anthropol. 35, 521–547 (1994).

    Article  Google Scholar 

  19. 19.

    Gates, H. in Inbreeding, Incest, and the Incest Taboo: The State of Knowledge at the Turn of the Century (eds Wolf, A. P. & Durham, W. H.) 139–160 (Stanford Univ. Press, 2005).

  20. 20.

    Kirch, P. V. How Chiefs Became Kings: Divine Kingship and the Rise of Archaic States in Ancient Hawaiʼi (Univ. California Press, 2010).

  21. 21.

    Hensey, R. First Light: The Origins of Newgrange (Oxbow Books, 2015).

  22. 22.

    Hensey, R. in The Oxford Handbook of Light in Archaeology (eds Papadopoulos C. & Moyes H.) (Oxford Univ. Press, 2017).

  23. 23.

    Carey, J. Time, memory, and the Boyne necropolis. Proc. Harv. Celtic Colloq. 10, 24–36 (1990).

    Google Scholar 

  24. 24.

    Gwynn, E. The Metrical Dindshenchas. 4. Text, Translation, and Commentary (School of Celtic Studies, Dublin Institute for Advanced Studies, 1991).

  25. 25.

    Kador, T. et al. Rites of passage: mortuary practice, population dynamics, and chronology at the Carrowkeel passage tomb complex, Co. Sligo, Ireland. Proc. Prehist. Soc. 84, 225–255 (2018).

    Article  Google Scholar 

  26. 26.

    Lipatov, M., Sanjeev, K., Patro, R. & Veeramah, K. Maximum likelihood estimation of biological relatedness from low coverage sequencing data. Preprint at https://www.biorxiv.org/content/10.1101/023374v1 (2015).

  27. 27.

    Jones, C. in Megaliths, Societies, Landscapes: Early Monumentality and Social Differentiation in Neolithic Europe Vol. 18 (eds Müller, J. et al.) 979–1000 (Habelt, 2019).

  28. 28.

    Garrow, D. & Sturt, F. Grey waters bright with Neolithic argonauts? Maritime connections and the Mesolithic–Neolithic transition within the ‘western seaways’ of Britain, c. 5000–3500 BC. Antiquity 85, 59–72 (2011).

    Article  Google Scholar 

  29. 29.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Lazaridis, I. et al. Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513, 409–413 (2014).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Brooks, A. J., Bradley, S. L., Edwards, R. J. & Goodwyn, N. The palaeogeography of Northwest Europe during the last 20,000 years. J. Maps 7, 573–587 (2011).

    Article  Google Scholar 

  32. 32.

    Woodman, P. Ireland’s First Settlers: Time and the Mesolithic (Oxbow Books, 2015).

  33. 33.

    Villalba-Mouco, V. et al. Survival of Late Pleistocene hunter-gatherer ancestry in the Iberian peninsula. Curr. Biol. 29, 1169–1177.e7 (2019).

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Rivollat, M., Castex, D., Hauret, L. & Tillier, A.-M. Ancient Down syndrome: an osteological case from Saint-Jean-des-Vignes, northeastern France, from the 5–6th century AD. Int. J. Paleopathol. 7, 8–14 (2014).

    Article  PubMed  Google Scholar 

  35. 35.

    Yang, D. Y., Eng, B., Waye, J. S., Dudar, J. C. & Saunders, S. R. Technical note: improved DNA extraction from ancient bones using silica-based spin columns. Am. J. Phys. Anthropol.105, 539–543 (1998).

    CAS  Article  Google Scholar 

  36. 36.

    MacHugh, D. E., Edwards, C. J., Bailey, J. F., Bancroft, D. R. & Bradley, D. G. The extraction and analysis of ancient DNA from bone and teeth: a survey of current methodologies. Anc. Biomol. 3, 81–103 (2000).

    CAS  Google Scholar 

  37. 37.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, db.prot5448 (2010).

    Article  Google Scholar 

  39. 39.

    Andrews, S. FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  40. 40.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  41. 41.

    Renaud, G., Stenzel, U. & Kelso, J. leeHom: adaptor trimming and merging for Illumina sequencing reads. Nucleic Acids Res. 42, e141 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. 42.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Keller, A. et al. New insights into the Tyrolean Iceman’s origin and phenotype as inferred by whole-genome sequencing. Nat. Commun. 3, 698 (2012).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Günther, T. et al. Ancient genomes link early farmers from Atapuerca in Spain to modern-day Basques. Proc. Natl Acad. Sci. USA 112, 11917–11922 (2015).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. 50.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

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

    Article  CAS  PubMed  Google Scholar 

  56. 56.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  57. 57.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    González-Fortes, G. et al. Paleogenomic Evidence for multi-generational mixing between Neolithic farmers and Mesolithic hunter-gatherers in the lower Danube basin. Curr. Biol. 27, 1801–1810.e10 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. 61.

    Lipson, M. et al. Parallel palaeogenomic transects reveal complex genetic history of early European farmers. Nature 551, 368–372 (2017).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Sánchez-Quinto, F. et al. Genomic affinities of two 7,000-year-old Iberian hunter-gatherers. Curr. Biol. 22, 1494–1499 (2012).

    Article  CAS  PubMed  Google Scholar 

  64. 64.

    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).

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    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  Article  CAS  Google Scholar 

  66. 66.

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

    Article  CAS  PubMed  Google Scholar 

  67. 67.

    Krzewińska, M. et al. Ancient genomes suggest the eastern Pontic–Caspian steppe as the source of western Iron Age nomads. Sci. Adv. 4, eaat4457 (2018).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68.

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

    ADS  CAS  Article  PubMed  Google Scholar 

  69. 69.

    Scheib, C. L. et al. East Anglian early Neolithic monument burial linked to contemporary megaliths. Ann. Hum. Biol. 46, 145–149 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Saag, L. et al. Extensive farming in Estonia started through a sex-biased migration from the steppe. Curr. Biol. 27, 2185–2193.e6 (2017).

    CAS  Article  PubMed  Google Scholar 

  71. 71.

    Rodríguez-Varela, R. et al. Genomic analyses of pre-European conquest human remains from the Canary Islands reveal close affinity to modern North Africans. Curr. Biol. 28, 1677–1679 (2018).

    Article  CAS  PubMed  Google Scholar 

  72. 72.

    Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).

    CAS  Article  Google Scholar 

  73. 73.

    Eogan, G. Excavations at Knowth Volume 6: The Passage Tomb Archaeology of the Great Mound at Knowth (Royal Irish Academy, 2017).

  74. 74.

    Hamilton, J. & Hedges, R. E. M. in Gathering Time: Dating the Early Neolithic Enclosures of Southern Britain and Ireland (eds. Whittle, A. et al.) 670–681 (Oxbow Books, 2011).

  75. 75.

    Hutchison, M., Curtis, N. & Kidd, R. The Knowe of Rowiegar, Rousay, Orkney. Proc. Soc. Antiquar. Scotland 145, 41–89 (2015).

    Google Scholar 

  76. 76.

    Kador, T., Fibiger, L., Cooney, G. & Fullagar, P. Movement and diet in early Irish prehistory: first evidence from multi-isotope analysis. J. Irish Archaeol. 23, 83–96 (2015).

    Google Scholar 

  77. 77.

    Schulting, R. in The Origins and Spread of Domestic Animals in Southwest Asia and Europe (eds. Colledge, S. et al.) 313–338 (2013).

  78. 78.

    Schulting, R. J., Murphy, E., Jones, C. & Warren, G. New dates from the north and a proposed chronology for Irish court tombs. Proc. R. Ir. Acad. C Archaeol. Celt. Stud. Hist. Linguist. Lit. 112C, 1–60 (2012).

    Google Scholar 

  79. 79.

    Wysocki, M. et al. Dates, diet, and dismemberment: evidence from the Coldrum megalithic monument, Kent. Proc. Prehist. Soc. 79, 61–90 (2013).

    Article  Google Scholar 

  80. 80.

    Wysocki, M., Bayliss, A. & Whittle, A. serious mortality: the date of the Fussell’s Lodge long barrow. Camb. Archaeol. J. 17, 65–84 (2007).

    Article  Google Scholar 

  81. 81.

    Whittle, A. et al. Parc le Breos Cwm transepted long cairn, Gower, West Glamorgan: date, contents, and context. Proc. Prehist. Soc. 64, 139–182 (1998).

    Article  Google Scholar 

  82. 82.

    Whittle, A., Bayliss, A. & Wysocki, M. Once in a lifetime: the date of the Wayland’s Smithy long barrow. Camb. Archaeol. J. 17, 103–121 (2007).

    Article  Google Scholar 

  83. 83.

    Bayliss, A., Whittle, A. & Wysocki, M. Talking about my generation: the date of the West Kennet long barrow. Camb. Archaeol. J. 17, 85–101 (2007).

    Article  Google Scholar 

  84. 84.

    Brindley, A. L., Lanting, J. N. & van der Plicht, J. in Duma na nGial: The Mound of the Hostages, Tara (ed. O’Sullivan, M.) 281–296 (Wordwell, 2005).

  85. 85.

    Schulting, R. J., Chapman, M. & Chapman, E. J. AMS 14C dating and stable isotope (carbon, nitrogen) analysis of an earlier Neolithic human skeletal assemblage from hay wood cave, Mendip, Somerset. Proc. Univ. Bristol Spelaeol. Soc. 26, 9–26 (2013).

    Google Scholar 

  86. 86.

    Schulting, R. et al. Mesolithic and Neolithic human remains from Foxhole Cave, Gower, South Wales. Antiq. J. 93, 1–23 (2013).

    Article  Google Scholar 

  87. 87.

    Sheridan, J. A., Schulting, R., Quinnell, H. & Taylor, R. Revisiting a small passage tomb at Broadsands, Devon. Proc. Devon Archaeol. Soc. 66, 1–26 (2008).

    Google Scholar 

  88. 88.

    Schulting, R. J. & Richards, M. P. Finding the coastal Mesolithic in southwest Britain: AMS dates and stable isotope results on human remains from Caldey Island, South Wales. Antiquity 76, 1011–1025 (2002).

    Article  Google Scholar 

  89. 89.

    Stevens, R. E., Lightfoot, E., Allen, T. & Hedges, R. E. M. Palaeodiet at Eton College rowing course, Buckinghamshire: isotopic changes in human diet in the Neolithic, Bronze Age, Iron Age and Roman periods throughout the British Isles. Archaeol. Anthropol. Sci. 4, 167–184 (2012).

    Article  Google Scholar 

  90. 90.

    Chamberlain, A. T. & Witkin, A. V. A Neolithic cairn at Whitwell, Derbyshire. Derbyshire Archaeol. J. 131, 1–131 (2011).

    Google Scholar 

  91. 91.

    McLaughlin, T. R., Whitehouse, N. J. & Schulting, R. J. The changing face of Neolithic and Bronze Age Ireland: a big data approach to the settlement and burial records. J. World Prehist. 29, 117–153 (2016).

    Article  Google Scholar 

  92. 92.

    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  Article  Google Scholar 

  93. 93.

    Okonechnikov, K., Conesa, A. & García-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292–294 (2016).

    CAS  PubMed  Google Scholar 

  94. 94.

    Vianello, D. et al. HAPLOFIND: a new method for high-throughput mtDNA haplogroup assignment. Hum. Mutat. 34, 1189–1194 (2013).

    Article  PubMed  Google Scholar 

  95. 95.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    Gallego-Llorente, M. et al. The genetics of an early Neolithic pastoralist from the Zagros, Iran. Sci. Rep. 6, 31326 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    The 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  98. 98.

    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).

    CAS  Article  PubMed  Google Scholar 

  99. 99.

    Chaitanya, L. et al. The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: introduction and forensic developmental validation. Forensic Sci. Int. Genet. 35, 123–135 (2018).

    CAS  Article  PubMed  Google Scholar 

  100. 100.

    Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Reich, D., Thangaraj, K., Patterson, N., Price, A. L. & Singh, L. Reconstructing Indian population history. Nature 461, 489–494 (2009).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. 102.

    Patterson, N. et al. Ancient admixture in human history. Genetics 192, 1065–1093 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  104. 104.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  105. 105.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).

  106. 106.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  107. 107.

    Warnes, G. R. et al. gplots: various R programming tools for plotting data, R package version 3.0.1, http://CRAN.R-project.org/package=gplots (2016).

  108. 108.

    Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps: draw geographical maps, R package version 3.1.0. http://CRAN.R-project.org/package=maps (2016).

  109. 109.

    Becker, R. A., Brownrigg, R. & Wilks, A. R. mapdata: extra map databases, R package version 2.2-6, http://CRAN.R-project.org/package=mapdata (2016).

  110. 110.

    Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).

    Article  Google Scholar 

  111. 111.

    Wickham, H., François, R., Henry, L. & Müller, K. dplyr: a grammar of data manipulation, R package version 0.7.6, http://CRAN.R-project.org/package=dplyr (2018).

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We thank the National Museum of Ireland, particularly M. Cahill, N. O’Connor, E. Ashe, E. McLoughlin, M. Sikora and M. Seaver for help in the provision of archaeological samples under licence; National Museums NI; M. Mirazón Lahr and the Leverhulme Centre for Human Evolutionary Studies; R. Martiniano for assisting with an initial sample screening; Trinseq for sequencing support; the DJEI/DES/SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities; and M. Sinding, P. Maisano Delser, K. Daly, R. Hensey, P. Meehan and M. Teasdale for critical reading of the manuscript. This work was funded by the Science Foundation Ireland/Health Research Board/Wellcome Trust Biomedical Research Partnership Investigator Award no. 205072 to D.G.B., ‘Ancient Genomics and the Atlantic Burden’. In the early part of the study, L.M.C. was funded by Irish Research Council Government of Ireland Scholarship Scheme (GOIPG/2013/1219). E.R.J. was supported by the Herchel Smith Postdoctoral Fellowship Fund. Several radiocarbon determinations were funded by an NERC award to T.K. (NF/2016/2/18).

Author information




D.G.B. and L.M.C. designed this study. L.M.C., V.M., A.N. and C.C. performed laboratory work. L.M.C. processed and analysed data with contributions from E.R.J. R.Ó M., T.K., A.L., C.J., P.C.W., E.M., G.R. and M.D. provided access to samples and supplied archaeological information and interpretation. L.M.C. and D.G.B. co-wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Lara M. Cassidy or Daniel G. Bradley.

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The authors declare no competing interests.

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Peer review information Nature thanks Duncan Garrow, Michael Hofreiter and David Reich for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Genomic affinities of the Irish Neolithic.

a, ADMIXTURE plot (K = 14) for ancient Irish and British populations (first row), other ancient Eurasians (second and third rows) and global modern populations (fourth row). For components that reach their maximum in modern populations, the five individuals with highest values were selected for representation. If the majority of these individuals come from a single population the block is labelled as such; otherwise, it is labelled using the general geographic region from which these individuals originate. Three components reach their maximum in ancient populations, and we label these ‘European_HG’ (red), Early_Farmer (orange) and ‘Steppe’ (teal). b, Box plot (Tukey method) showing the distribution of the European_HG component among British and Irish Neolithic shotgun-sequenced individuals (n = 50). c, Normalized haplotypic length contributions, estimated with ChromoPainter, from Early Neolithic populations to later Neolithic and Chalcolithic individuals. The top two donors are outlined in black for each individual. Given the unsupervised nature of the analysis, regional differences in overall haplotypic donation levels should be ignored, as larger populations have more opportunity for within-group painting.

Extended Data Fig. 2 Haplotypic structure among ancient populations.

a, ChromoPainter principal component analysis of diverse ancient genomes (n = 149) generated using the output matrix of haplotypic lengths. The colour and shape key for the Irish samples follows Fig. 1. b, fineSTRUCTURE dendrogram derived from the same matrix as in a, with the passage tomb cluster highlighted. Dotted branches are shown at a quarter of their true length.

Extended Data Fig. 3 Inferring the relationship between the parents of NG10.

a, Whole-genome plot of heterozygosity in NG10, revealing extreme runs of homozygosity. b, Nine mating scenarios (coloured lines) that can lead to an inbreeding coefficient of 25%. c, Number and average lengths of homozygous-by-descent (HBD) segments for each of these simulated scenarios (500 iterations) and the same values observed for the NG10 genome. Box plots follow Tukey’s method. Scenarios in the subpanels i and ii best fit the homozygous-by-descent distribution of NG10; ii is less parsimonious than i when anthropological and biological factors are taken into consideration.

Extended Data Fig. 4 Levels of inbreeding through time in ancient populations.

Inbreeding coefficients for imputed ancient samples estimated by measuring the proportion of the genome that is homozygous by descent. Box plots follow Tukey’s method. Individuals are binned according to archaeological period. UP-MS, Upper Palaeolithic to Mesolithic (n = 24); EN, Early Neolithic (n = 13); MN-CA, Middle Neolithic to Chalcolithic (n = 69); BA, European Bronze Age (n = 12); IA-MA, Iron Age to Medieval (n = 21); Steppe CA-BA, steppe Chalcolithic to Bronze Age (n = 14). Outliers of note are labelled. The inferred degrees of relatedness between the parents of an individual are marked.

Extended Data Fig. 5 Detecting recent shared ancestry between pairs of British and Irish Neolithic samples.

a, lcMLkin31 kinship coefficients between pairs of Irish and British Neolithic samples, jittered by a height of 0.00018 and width of 0.00036 for visualization. Optimized duplicate tests are linked by dotted lines. Several standalone values are also shown (italics), in which one duplicate did not meet the threshold of overlapping sites (>20,000). The MB6 and car004 pairing (19,850 sites) is shown as a translucent point. An inset is shown for lower values of pi-HAT. Pairs over 5σ from the mean pi-HAT and K0 for subpanel ii (marked with a line) are highlighted using the same colour and shape key as in Fig. 1. Combined symbols are used for inter-site pairs. b, Total haplotypic lengths donated between all pairs (n = 2,162) of British and Irish samples from the ChromoPainter analysis of diverse ancient samples (Extended Data Fig. 2). Outlying pairs (4σ above the mean) are labelled. c, Outgroup f3-statistics measuring shared drift between pairs (n = 2,236) of Irish and British Neolithic samples (>25,000 informative sites). d, Total haplotypic lengths donated between all pairs of ‘passage tomb cluster’ (pink; n = 42) and ‘British–Irish cluster’ (grey; n = 1,190) samples from the ChromoPainter analysis of genomes from the Atlantic seaboard (Fig. 1d, e). Single members from the outlying pairs in b were removed for this analysis. Positions of passage tomb pairs are marked along the x axis, with two outlying pairs from Carrowkeel highlighted.

Extended Data Fig. 6 Regional-scale diversity in the Irish Neolithic.

a, Nitrogen stable-isotope values (an indicator of trophic level) plotted across time for samples from the neighbouring sites of Poulnabrone (blue) and Parknabinnia (yellow). For male samples, the Y chromosome haplogroup is given. Distant kinship connections are marked with a dotted line, and a closer (about fourth degree) relationship is highlighted with a solid line. b, Box plot (Tukey’s method) of normalized read coverage aligning to chromosome 21 for shotgun-sequenced ancient samples (n = 188), with a single outlier from Poulnabrone (representing an infant with trisomy).

Extended Data Fig. 7 Subclade distributions of Y chromosome haplogroup I2a1 in Ireland, Britain and Europe from the Mesolithic to the Bronze Age.

a, Y haplogroups observed for Neolithic individuals (jittered) in Britain and Ireland. Shape indicates the approximate time period within the Neolithic (based on ref. 91), and colour indicates haplogroup and follows the same keys as in bd. bd, Approximately 94% of the British and Irish Neolithic samples belong to haplogroups I2a1b1 (45%), I2a1a1 (14%) and I2a1a2 (35%). Incidences (jittered) of these haplogroups in European individuals from the Mesolithic to the Bronze Age are shown for I2a1b1 (b), I2a1a1 (c) and I2a1a2 (d). Haplogroup colour keys are shown with respect to phylogenetic placement; those haplogroups observed within Britain and Ireland are shown in bold. European individuals who share an identical set of haplotypic mutations (for sites covered) to an Irish Neolithic individual are highlighted with a black outline in c (for I2a1a1) and d (for I2a1a2).

Extended Data Fig. 8 Geographic and genomic distributions of northwestern European hunter-gatherer ancestry among British and Irish Neolithic individuals.

a, Geographic distribution of northwestern European hunter-gatherer introgression in Britain and Ireland across 103 Neolithic samples. Box plot (Tukey’s method) highlights four outliers, three from the Early-to-Middle Neolithic of Argyll and one from Ireland (designated Parknabinnia675 (PB675)). The next highest value is also from Parknabinnia (individual PB754). b, The same D-statistic run on separate chromosomes for individuals of sufficient coverage (n = 86). Outlying individuals are marked for each chromosome. Irish outliers follow the same shape and colour key as in Fig. 1. Outliers who are also outliers in the box plot in a are marked in bold. c, Box plot (Tukey’s method; n = 86) of sample standard deviations across the chromosomes for the same D-statistic. Four outliers with high variance across the chromosomes are marked, including three samples from Parknabinnia, two of whom are also top hits in a. df, Haplotypic affinities of imputed Irish and British Neolithic individuals (n = 47) to Irish hunter-gatherers, relative to other northwestern European hunter-gatherers (Bichon, Loschbour and Cheddar Man). Colour and shape key follows Fig. 1. The outlying individual PB675 shows a preference for Irish hunter-gatherer haplotypes in all measures. Regression lines are shown with 95% confidence interval shaded (sample size = 47). PB675 shows a higher-than-expected number of Irish hunter-gatherer haplotypes (d), has the highest overall hunter-gatherer haplotypic length contribution, with a ratio skewed towards Irish hunter-gatherers (e) and displays the longest average length of Irish-hunter-gatherer haplotype chunks (f).

Extended Data Fig. 9 SNP -sharing analyses of autosomal structure in Neolithic populations of the Atlantic seaboard.

a, Principal component analysis created using an identical sample (n = 57) and set of SNPs (about 488,000 sites; pseudo-haplodized) to that presented in Fig. 1d, e. b, Outgroup f3-statistics for all combinations of samples in a, using a reduced set of SNPs (about 270,000 sites; pseudo-haplodized). Results are presented in a heat map and corresponding dendrogram.

Extended Data Fig. 10 Imputation accuracies for chromosome 22 of the high-coverage NE1 genome, downsampled to 1×.

The levels of accuracy seen across all SNPs (solid line) (n = 204,316 for no minor allele frequency (MAF) filter and genotype probability of 80) is compared to that seen for transversions only (dashed line) (n = 62,374 for no minor allele frequency filter and genotype probability of 80). Accuracies at different genotype probability thresholds and minor allele frequency filters are shown for the three different genotype categories. Minor allele frequency filters are based on overall frequency in the 1000 Genomes phase 3 dataset.

Supplementary information

Supplementary Information

This file contains Supplementary Methods and Text, Supplementary Figures 1-6 and Supplementary References. (PDF file: 5,958 Kb).

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-12. Supplementary Table 1 contains archaeological information for all samples screened. Supplementary Table 2 contains sequencing statistics. Supplementary Table 3 contains published genomic data included in this study. Supplementary Table 4 contains published isotopic data included in this study. Supplementary Table 5 contains molecular sexing statistics. Supplementary Tables 6-8 contains sample mitochondrial and Y chromosome haplogroup information. Supplementary Tables 9-11 contain D- and f-statistic results. Supplementary Table 12 contains hIrisPlex-S results. (XLSX file: 1,071 Kb).

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Cassidy, L.M., Maoldúin, R.Ó., Kador, T. et al. A dynastic elite in monumental Neolithic society. Nature 582, 384–388 (2020). https://doi.org/10.1038/s41586-020-2378-6

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