Genomic analyses inform on migration events during the peopling of Eurasia (original) (raw)

Accession codes

Primary accessions

European Nucleotide Archive

Data deposits

The newly sequenced genomes are part of the Estonian Biocentre human Genome Diversity Panel (EGDP) and were deposited in the ENA archive under accession number PRJEB12437 and are also freely available through the Estonian Biocentre website (www.ebc.ee/free_data)

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Acknowledgements

Support was provided by: Estonian Research Infrastructure Roadmap grant no 3.2.0304.11-0312; Australian Research Council Discovery grants (DP110102635 and DP140101405) (D.M.L., M.W. and E.W.); Danish National Research Foundation; the Lundbeck Foundation and KU2016 (E.W.); ERC Starting Investigator grant (FP7 - 261213) (T.K.); Estonian Research Council grant PUT766 (G.C. and M.K.); EU European Regional Development Fund through the Centre of Excellence in Genomics to Estonian Biocentre (R.V.; M.Me. and A.Me.), and Centre of Excellence for Genomics and Translational Medicine Project No. 2014-2020.4.01.15-0012 to EGC of UT (A.Me.) and EBC (M.Me.); Estonian Institutional Research grant IUT24-1 (L.S., M.J., A.K., B.Y., K.T., C.B.M., Le.S., H.Sa., S.L., D.M.B., E.M., R.V., G.H., M.K., G.C., T.K. and M.Me.) and IUT20-60 (A.Me.); French Ministry of Foreign and European Affairs and French ANR grant number ANR-14-CE31-0013-01 (F.-X.R.); Gates Cambridge Trust Funding (E.J.); ICG SB RAS (No. VI.58.1.1) (D.V.L.); Leverhulme Programme grant no. RP2011-R-045 (A.B.M., P.G. and M.G.T.); Ministry of Education and Science of Russia; Project 6.656.2014/K (S.A.F.); NEFREX grant funded by the European Union (People Marie Curie Actions; International Research Staff Exchange Scheme; call FP7-PEOPLE-2012-IRSES-number 318979) (M.Me., G.H. and M.K.); NIH grants 5DP1ES022577 05, 1R01DK104339-01, and 1R01GM113657-01 (S.Tis.); Russian Foundation for Basic Research (grant N 14-06-00180a) (M.G.); Russian Foundation for Basic Research; grant 16-04-00890 (O.B. and E.B); Russian Science Foundation grant 14-14-00827 (O.B.); The Russian Foundation for Basic Research (14-04-00725-a), The Russian Humanitarian Scientific Foundation (13-11-02014) and the Program of the Basic Research of the RAS Presidium “Biological diversity” (E.K.K.); Wellcome Trust and Royal Society grant WT104125AIA & the Bristol Advanced Computing Research Centre (http://www.bris.ac.uk/acrc/) (D.J.L.); Wellcome Trust grant 098051 (Q.A.; C.T.-S. and Y.X.); Wellcome Trust Senior Research Fellowship grant 100719/Z/12/Z (M.G.T.); Young Explorers Grant from the National Geographic Society (8900-11) (C.A.E.); ERC Consolidator Grant 647787 ‘LocalAdaptatio’ (A.Ma.); Program of the RAS Presidium “Basic research for the development of the Russian Arctic” (B.M.); Russian Foundation for Basic Research grant 16-06-00303 (E.B.); a Rutherford Fellowship (RDF-10-MAU-001) from the Royal Society of New Zealand (M.P.C.).

Author information

Author notes

1. Luca Pagani, Daniel John Lawson, Evelyn Jagoda, Alexander Mörseburg, Anders Eriksson, Richard Villems, Eske Willerslev, Toomas Kivisild and Mait Metspalu: These authors contributed equally to this work.

Authors and Affiliations

1. Estonian Biocentre, Tartu, 51010, Estonia
   Luca Pagani, Georgi Hudjashov, Lauri Saag, Mari Järve, Monika Karmin, Alena Kushniarevich, Bayazit Yunusbayev, Kristiina Tambets, Chandana Basu Mallick, Hovhannes Sahakyan, Gyaneshwer Chaubey, Sergei Litvinov, Doron M. Behar, Ene Metspalu, Richard Villems, Toomas Kivisild & Mait Metspalu
2. Department of Archaeology and Anthropology, University of Cambridge, Cambridge, CB2 1QH, UK
   Luca Pagani, Evelyn Jagoda, Alexander Mörseburg, Florian Clemente, Alexia Cardona, Sarah Kaewert, Charlotte Inchley, Christiana L. Scheib, Florin Mircea Iliescu, Christina A. Eichstaedt & Toomas Kivisild
3. Department of Biological, Geological and Environmental Sciences, University of Bologna, Via Selmi 3, Bologna, 40126, Italy
   Luca Pagani
4. Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, BS8 2BN, UK
   Daniel John Lawson
5. Department of Human Evolutionary Biology, Harvard University, Cambridge, 02138, Massachusetts, USA
   Evelyn Jagoda
6. Division of Biological and Environmental Sciences & Engineering, Integrative Systems Biology Lab, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
   Anders Eriksson
7. Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, UK
   Anders Eriksson & Andrea Manica
8. Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia
   Mario Mitt, Reedik Mägi, Evelin Mihailov & Andres Metspalu
9. Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, 51010, Estonia
   Mario Mitt & Andres Metspalu
10. Institut de Biologie Computationnelle, Université Montpellier 2, Montpellier, 34095, France
    Florian Clemente
11. Department of Psychology, University of Auckland, Auckland, 1142, New Zealand
    Georgi Hudjashov & Monika Karmin
12. Statistics and Bioinformatics Group, Institute of Fundamental Sciences, Massey University, Palmerston North, 4442, New Zealand
    Georgi Hudjashov & Murray P. Cox
13. Department of Biology, Pennsylvania State University, University Park, Pennsylvania, 16802, USA
    Michael DeGiorgio
14. Institute for Human Genetics, University of California, San Francisco, 94143, California, USA
    Jeffrey D. Wall
15. MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Box 285, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
    Alexia Cardona
16. School of Life Sciences, Arizona State University, Tempe, 85287, Arizona, USA
    Melissa A. Wilson Sayres
17. Center for Evolution and Medicine, The Biodesign Institute, Tempe, 85287, Arizona, USA
    Melissa A. Wilson Sayres
18. Department of Evolutionary Biology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, 51010, Estonia
    Monika Karmin, Lehti Saag, Ene Metspalu & Richard Villems
19. Mathematical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
    Guy S. Jacobs
20. Institute for Complex Systems Simulation, University of Southampton, Southampton, SO17 1BJ, UK
    Guy S. Jacobs
21. Division of Biological Sciences, University of Montana, Missoula, 59812, Montana, USA
    Tiago Antao
22. Institute of Genetics and Cytology, National Academy of Sciences, BY-220072 Minsk, Belarus
    Alena Kushniarevich
23. The Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK
    Qasim Ayub, Chris Tyler-Smith & Yali Xue
24. Institute of Biochemistry and Genetics, Ufa Scientific Center of RAS, Ufa, 450054, Russia
    Bayazit Yunusbayev, Alexandra Karunas, Sergei Litvinov, Rita Khusainova, Vita Akhmetova, Irina Khidiyatova & Elza K. Khusnutdinova
25. Kuban State Medical University, Krasnodar, 350040, Russia
    Elvira Pocheshkhova
26. Scientific Research Center of the Caucasian Ethnic Groups, St. Andrews Georgian University, Tbilisi, 0162, Georgia
    George Andriadze
27. Center for GeoGenetics, University of Copenhagen, Copenhagen, 1350, Denmark
    Craig Muller, Rasmus Nielsen & Eske Willerslev
28. Research Centre for Human Evolution, Environmental Futures Research Institute, Griffith University, Nathan, 4111, Queensland, Australia
    Michael C. Westaway & David M. Lambert
29. Center of Molecular Diagnosis and Genetic Research, University Hospital of Obstetrics and Gynecology, Tirana, 1000, Albania
    Grigor Zoraqi
30. Center of High Technology, Academy of Sciences, Tashkent, 100047, Uzbekistan
    Shahlo Turdikulova
31. Institute of Bioorganic Chemistry Academy of Science, Tashkent, 100047, Uzbekistan
    Dilbar Dalimova
32. L.N. Gumilyov Eurasian National University, Astana, 010008, Kazakhstan
    Zhaxylyk Sabitov
33. Centre for Advanced Research in Sciences (CARS), DNA Sequencing Research Laboratory, University of Dhaka, Dhaka, 1000, Bangladesh
    Gazi Nurun Nahar Sultana
34. Department of Genetics, University of Pennsylvania, Philadelphia, 19104-6145, Pennsylvania, USA
    Joseph Lachance
35. School of Biological Sciences, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA
    Joseph Lachance
36. Departments of Genetics and Biology, University of Pennsylvania, Philadelphia, 19104-6313, Pennsylvania, USA
    Sarah Tishkoff
37. DNcode laboratories, Moscow, 117623, Russia
    Kuvat Momynaliev
38. Institute of Molecular Biology and Medicine, Bishkek, 720040, Kyrgyzstan
    Jainagul Isakova
39. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
    Larisa D. Damba, Marina Gubina, Daria V. Lichman, Mikhail Voevoda & Ludmila P. Osipova
40. Mongolian Academy of Medical Sciences, Ulaanbaatar, 210620, Mongolia
    Pagbajabyn Nymadawa
41. Northern State Medical University, Arkhangelsk, 163000, Russia
    Irina Evseeva
42. Anthony Nolan, The Royal Free Hospital, Pond Street, London, NW3 2QG, UK
    Irina Evseeva
43. V. N. Karazin Kharkiv National University, Kharkiv, 61022, Ukraine
    Lubov Atramentova & Olga Utevska
44. Evolutionary Medicine group, Laboratoire d’Anthropologie Moléculaire et Imagerie de Synthèse, UMR 5288, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse 31073, France
    François-Xavier Ricaut, Nicolas Brucato & Thierry Letellier
45. Genome Diversity and Diseases Laboratory, Eijkman Institute for Molecular Biology, Jakarta, 10430, Indonesia
    Herawati Sudoyo
46. Department of Molecular Genetics, Yakut Scientific Centre of Complex Medical Problems, Yakutsk, 677027, Russia
    Nikolay A. Barashkov & Sardana A. Fedorova
47. Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, 677027, Russia
    Nikolay A. Barashkov & Sardana A. Fedorova
48. Genos DNA laboratory, Zagreb, 10000, Croatia
    Vedrana Škaro
49. University of Osijek, Medical School, Osijek, 31000, Croatia
    Vedrana Škaro & Dragan Primorac
50. Center for Genomics and Transcriptomics, CeGaT, GmbH, Tübingen, D-72076, Germany
    Lejla Mulahasanovic´
51. St. Catherine Specialty Hospital, 49210 Zabok and 10000, Zagreb, Croatia
    Dragan Primorac
52. Eberly College of Science, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA
    Dragan Primorac
53. University of Split, Medical School, Split, 21000, Croatia
    Dragan Primorac
54. Laboratory of Ethnogenomics, Institute of Molecular Biology, National Academy of Sciences, Republic of Armenia, 7 Hasratyan Street, Yerevan, 0014, Armenia
    Hovhannes Sahakyan & Levon Yepiskoposyan
55. Department of Applied Social Sciences, University of Winchester, Winchester, SO22 4NR, Sparkford Road, UK
    Maru Mormina
56. Thoraxklinik Heidelberg, University Hospital Heidelberg, Heidelberg, 69120, Germany
    Christina A. Eichstaedt
57. Novosibirsk State University, Novosibirsk, 630090, Russia
    Daria V. Lichman, Mikhail Voevoda & Ludmila P. Osipova
58. RIPAS Hospital, Bandar Seri Begawan, BE1518, Brunei
    Syafiq Abdullah
59. National Cancer Centre Singapore, 169610, Singapore
    Joseph T. S. Wee
60. Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, 450000, Russia
    Alexandra Karunas, Sergei Litvinov, Rita Khusainova, Natalya Ekomasova, Irina Khidiyatova & Elza K. Khusnutdinova
61. Department of Genetics and Bioengineering. Faculty of Engineering and Information Technologies, International Burch University, Sarajevo, 71000, Bosnia and Herzegovina
    Damir Marjanović
62. Institute for Anthropological Researches, Zagreb, 10000, Croatia
    Damir Marjanović
63. Research Centre for Medical Genetics, Russian Academy of Sciences, Moscow, 115478, Russia
    Elena Balanovska & Oleg Balanovsky
64. Genetics Laboratory, Institute of Biological Problems of the North, Russian Academy of Sciences, Magadan, 685000, Russia
    Miroslava Derenko & Boris Malyarchuk
65. Institute of Internal Medicine, Siberian Branch of Russian Academy of Medical Sciences, Novosibirsk, 630009, Russia
    Mikhail Voevoda
66. Department of Archaeology and Anthropology, Leverhulme Centre for Human Evolutionary Studies, University of Cambridge, Cambridge, CB2 1QH, UK
    Marta Mirazón Lahr
67. Research Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
    Pascale Gerbault & Mark G. Thomas
68. Department of Archaeology, University of Papua New Guinea, University PO Box 320, Papua, 134 NCD, New Guinea
    Matthew Leavesley
69. College of Arts, Society and Education, James Cook University, PO Box 6811, Cairns, 4870, Queensland, Australia
    Matthew Leavesley
70. Department of Anthropology, University College London, London, WC1H 0BW, UK
    Andrea Bamberg Migliano
71. Max Planck Institute for the Science of Human History, Kahlaische Strasse 10, Jena, D-07743, Germany
    Michael Petraglia
72. Vavilov Institute for General Genetics, Russian Academy of Sciences, Moscow, 119333, Russia
    Oleg Balanovsky
73. Department of Integrative Biology, University of California Berkeley, Berkeley, 94720, California, USA
    Rasmus Nielsen
74. Estonian Academy of Sciences, 6 Kohtu Street, Tallinn, 10130, Estonia
    Richard Villems

Authors

1. Luca Pagani
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2. Daniel John Lawson
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3. Evelyn Jagoda
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4. Alexander Mörseburg
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5. Anders Eriksson
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6. Mario Mitt
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7. Florian Clemente
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8. Georgi Hudjashov
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9. Michael DeGiorgio
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10. Lauri Saag
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11. Jeffrey D. Wall
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12. Alexia Cardona
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13. Reedik Mägi
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14. Melissa A. Wilson Sayres
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15. Sarah Kaewert
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16. Charlotte Inchley
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17. Christiana L. Scheib
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18. Mari Järve
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19. Monika Karmin
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20. Guy S. Jacobs
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21. Tiago Antao
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22. Florin Mircea Iliescu
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23. Alena Kushniarevich
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24. Qasim Ayub
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25. Chris Tyler-Smith
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26. Yali Xue
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27. Bayazit Yunusbayev
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28. Kristiina Tambets
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29. Chandana Basu Mallick
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30. Lehti Saag
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31. Elvira Pocheshkhova
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32. George Andriadze
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33. Craig Muller
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34. Michael C. Westaway
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35. David M. Lambert
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36. Grigor Zoraqi
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37. Shahlo Turdikulova
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38. Dilbar Dalimova
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39. Zhaxylyk Sabitov
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40. Gazi Nurun Nahar Sultana
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41. Joseph Lachance
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42. Sarah Tishkoff
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43. Kuvat Momynaliev
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44. Jainagul Isakova
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45. Larisa D. Damba
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46. Marina Gubina
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47. Pagbajabyn Nymadawa
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48. Irina Evseeva
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49. Lubov Atramentova
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50. Olga Utevska
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51. François-Xavier Ricaut
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52. Nicolas Brucato
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53. Herawati Sudoyo
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54. Thierry Letellier
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55. Murray P. Cox
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56. Nikolay A. Barashkov
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57. Vedrana Škaro
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58. Lejla Mulahasanovic´
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59. Dragan Primorac
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60. Hovhannes Sahakyan
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61. Maru Mormina
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62. Christina A. Eichstaedt
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63. Daria V. Lichman
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64. Syafiq Abdullah
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65. Gyaneshwer Chaubey
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66. Joseph T. S. Wee
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67. Evelin Mihailov
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68. Alexandra Karunas
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69. Sergei Litvinov
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70. Rita Khusainova
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71. Natalya Ekomasova
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72. Vita Akhmetova
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73. Irina Khidiyatova
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74. Damir Marjanović
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75. Levon Yepiskoposyan
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76. Doron M. Behar
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77. Elena Balanovska
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78. Andres Metspalu
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79. Miroslava Derenko
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80. Boris Malyarchuk
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81. Mikhail Voevoda
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82. Sardana A. Fedorova
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83. Ludmila P. Osipova
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84. Marta Mirazón Lahr
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85. Pascale Gerbault
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86. Matthew Leavesley
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87. Andrea Bamberg Migliano
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88. Michael Petraglia
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89. Oleg Balanovsky
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90. Elza K. Khusnutdinova
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91. Ene Metspalu
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92. Mark G. Thomas
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93. Andrea Manica
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94. Rasmus Nielsen
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95. Richard Villems
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96. Eske Willerslev
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97. Toomas Kivisild
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98. Mait Metspalu
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Contributions

R.V., E.W., T.K. and M.Me. conceived the study. A.K., K.T., C.B.M., Le.S., E.P., G.A., C.M., M.W., D.L., G.Z., S.T., D.D., Z.S., G.N.N.S., K.M., J.I., L.D.D., M.G., P.N., I.E., L.At., O.U., F.-X.R., N.B., H.S., T.L., M.P.C., N.A.B., V.S., L.A., D.Pr., H.Sa., M.Mo., C.A.E., D.V.L., S.A., G.C., J.T.S.W., E.Mi., A.Ka., S.L., R.K., N.T., V.A., I.K., D.M., L.Y., D.M.B., E.B., A.Me., M.D., B.M., M.V., S.A.F., L.P.O., M.Mi., M.L., A.B.M., O.B., E.K.K, E.M., M.G.T. and E.W. conducted anthropological research and/or sample collection and management. J.L. and S.Ti. provided access to data. L.P., D.J.L, E.J., A.Mo., A.E., M.Mi., F.C., G.H., M.D., L.S., J.W., A.C., R.M., M.A.W.S., S.K., C.I., C.L.S., M.J., M.K., G.S.J., T.A., F.M.I., A.K., Q.A., C.T.-S., Y.X., B.Y., C.B.M., T.K. and M.Me. analysed data. L.P., D.J.L., E.J., A.Mo., L.S., M.K., K.T., C.B.M., Le.S., G.C., M.Mi., P.G., M.L., A.B.M., M.P., E.M., M.G.T., A.Ma., R.N., R.V., E.W., T.K. and M.Me. contributed to the interpretation of results. L.P., D.J.L., E.J., A.Mo., A.E., F.C., G.H., M.D., A.C., M.A.W.S., B.Y., J.L., S.Ti., M.Mi., P.G., M.L., A.B.M., M.P., M.G.T., A.Ma., R.N., R.V., E.W., T.K. and M.Me. wrote the manuscript.

Corresponding authors

Correspondence toLuca Pagani, Toomas Kivisild or Mait Metspalu.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks R. Dennell and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Sample Diversity and Archaic signals.

a, Map of location of samples highlighting the diversity/selection sets. b, Sample-level heterozygosity is plotted against distance from Addis Ababa. The trend line represents only non-African samples. The inset shows the waypoints used to arrive at the distance in kilometres for each sample. c, ADMIXTURE plot (K = 8 and 14) which relates general visual inspection of genetic structure to studied populations and their region of origin. d, Box plots were used to visualize the Denisova (red), Altai (green) and Croatian Neanderthal (blue) D distribution for each regional group of samples. Oceanian Altai D values show a remarkable similarity with the Denisova D values for the same region, in contrast with the other groups of samples where the Altai box plots tend to be more similar to the Croatian Neanderthal ones. Boxes show median, first and third quartiles, with 1.5× interquartile range whiskers and black dots as outliers.

Extended Data Figure 2 Data quality checks and heterozygosity patterns.

a, b, Concordance of DNA sequencing (Complete Genomics Inc.) and DNA genotyping (Illumina genotyping arrays) data (ref-ref; het-ref-alt and hom-alt-alt, see Supplementary Information 1.6) from chip (a) and sequence data (b). c, Coverage (depth) distribution of variable positions, divided by DNA source (blood or saliva) and complete genomic calling pipeline (release version). d, Genome-wide distribution of transition/transversion ratio subdivided by DNA source (saliva or blood) and by complete genomic calling pipeline. e, Genome-wide distribution of transition/transversion ratio subdivided by chromosomes. f, Inter-chromosome differences in observed heterozygosity in 447 samples from the diversity set. g, Inter-chromosome differences in observed heterozygosity in a set of 50 unpublished genomes from the Estonian Genome Center, sequenced on an Illumina platform at an average coverage exceeding 30×. h, Inter-chromosome differences in observed heterozygosity in the phase 3 of the 1000 Genomes Project. The total number of observed heterozygous sites was divided by the number of accessible base pairs reported by the 1000 Genomes Project.

Extended Data Figure 3 FineSTRUCTURE shared ancestry analysis.

ChromoPainter and FineSTRUCTURE results, showing both inferred populations and the underlying (averaged) number of haplotypes that an individual in a population receives (rows) from donor individuals in other populations (columns). 108 populations are inferred by FineSTRUCTURE. The dendrogram shows the inferred relationship between populations. The numbers on the dendrogram give the proportion of MCMC iterations for which each population split is observed (where this is less than 1). Each ‘geographical region’ has a unique colour from which individuals are labelled. The number of individuals in each population is given in the label; for example, ‘4Italians; 3Albanians’ is a population of size 7 containing 4 individuals from Italy and 3 from Albania.

Extended Data Figure 4 MSMC genetic split times and outgroup _f_3 results.

a, The MSMC split times estimated between each sample and a reference panel of nine genomes were linearly interpolated to infer the broader square matrix. b, c, Summary of outgroup f 3 statistics for each pair of non-African populations or an ancient sample using Yoruba as an outgroup. Populations are grouped by geographic region and are ordered with increasing distance from Africa (left to right for columns and bottom to top for rows). Colour bars at the left and top of the heat map indicate the colour coding used for the geographical region. Individual population labels are indicated at the right and bottom of the heat map. The _f_3 statistics are scaled to lie between 0 and 1, with a black colour indicating those close to 0 and a red colour indicating those close to 1. Let m and M be the minimum and maximum f_3 values within a given row (that is, focal population). That is, for focal population X (on rows), m = min_Y,Y_≠_X f_3(X, Y; Yoruba) and M = max_Y,_Y_≠_X f_3(X, Y; Yoruba). The scaled _f_3 statistic for a given cell in that row is given by _f_3scaled = (_f_3 − m)/(M − m), so that the smallest _f_3 in the row has value _f_3scaled = 0 (black) and the largest has value _f_3scaled = 1 (red). By default, the diagonal has value _f_3scaled = 1 (red). The heat map is therefore asymmetric, with the population closest to the focal population at a given row having value _f_3scaled = 1 (red colour) and the population farthest from the focal population at a given row having value _f_3scaled = 0 (black colour). Therefore, at a given row, scanning the columns of the heat map reveals the populations with the most shared ancestry with the focal population of that row in the heat map.

Extended Data Figure 5 Geographical patterns of genetic diversity.

Isolation by distance pattern across areas of high genetic gradient, using Europe as a baseline. The samples used in each analysis are indicated by coloured lines on the maps to the right of each plot. a–d, The panels show _F_ST as a function of distance across the Himalayas (a), the Ural mountains (b), and the Caucasus (c) as reported on the colour-coded map (d). e, Effect of creating gaps in the samples in Europe. f, g, We tested the effect of removing samples from stripes, either north to south (f) or west to east (g), to create gaps comparable in size to the gaps in samples in the dataset. h, Effective migration surfaces inferred by EEMS.

Extended Data Figure 6 Summary of positive selection results.

a, Bar plot comparing frequency distributions of functional variants in Africans and non-Africans. The distribution of exonic SNPs according to their functional impact (synonymous, missense and nonsense) as a function of allele frequency. Note that the data from both groups was normalized for a sample size of n = 21 and that the Africans show significantly (_χ_2 P < 1 × 10-15) more rare variants across all sites classes. b, Result of 1,000 bootstrap replica of the R X/Y test for a subset of pigmentation genes highlighted by Genome Wide Association Studies (GWAS, n = 32). The horizontal line provides the African reference (x = 1) against which all other groups are compared. The blue and red marks show the 95th and the 5th percentile of the bootstrap distributions respectively. If the 95th percentile is below 1, then the population shows a significant excess of missense variants in the pigmentation subset relative to the Africans. Note that this is the case for all non-Africans except the Oceanians. c, Pools of individuals for selection scans. fineSTRUCTURE-based co-ancestry matrix was used to define twelve groups of populations for the downstream selection scans. These groups are highlighted in the plot by boxes with broken line edges. The number of individuals in each group is reported in Supplementary Table 1:3.2-I.

Extended Data Figure 7 Length of haplotypes assigned as African by fineSTRUCTURE as a function of genome proportion.

a, 447 Diversity Panel results, showing label averages (large crosses) along with individuals (small dots). b, Relative excluded Diversity Panel results, to check for whether including related individuals affects African genome fraction. Individuals that shared more than 2% of genome fraction were forbidden from receiving haplotypes from each other, and the painting was re-run on a large subset of the genome (all run of homozygosity (ROH) regions from any individual). c, ROH-only African haplotypes. To guard against phasing errors, we analysed only regions for which an individual was in a long (>500 kb) run of homozygosity using the PLINK command ‘–homozyg-window-kb 500000–homozyg-window-het 0–homozyg-density 10’. Because there are so few such regions, we report only the population average for populations with two or more individuals, as well as the standard error in that estimate. Populations for which the 95% confidence interval passed 0 were also excluded. Note the logarithmic axis. d, Ancient DNA panel results. We used a different panel of 109 individuals which included three ancient genomes. We painted chromosomes 11, 21 and 22 and report as crosses the population averages for populations with two or more individuals. The solid thin lines represent the position of each population when modern samples only are analysed. The dashed lines lead off the figure to the position of the ancient hominins and the African samples.

Extended Data Figure 8 MSMC Linear behaviour of MSMC split estimates in presence of admixture.

a–c, The examined Central Asian (a), East African (b), and African–American (c) genomes yielded a signature of MSMC split time (truth, left-most column) that could be recapitulated (reconstruction, second left-most column) as a linear mixture of other MSMC split times. The admixture proportions inferred by our method (top of each admixture component column) were remarkably similar to the ones previously reported from the literature. d, MSMC split times calculated after re-phasing an Estonian and a Papuan (Koinanbe) genome together with all the available West African and Pygmy genomes from our dataset to minimize putative phasing artefacts. The cross coalescence rate curves reported here are quantitatively comparable with the ones of Fig. 2a, hence showing that phasing artefacts are unlikely to explain the observed past-ward shift of the Papuan–African split time. e, Box plot showing the distribution of differences between African–Papuan and African–Eurasian split times obtained from coalescent simulations assembled through random replacement to make 2,000 sets of 6 individuals (to match the 6 Papuans available from our empirical dataset), each made of 1.5 Gb of sequence. The simulation command line used to generate each chromosome made of 5 Mb was as follows, where x is the variable for the divergence time used. x = 0.064, 0.4 or 0.8 for the xOoA, Denisova (Den) and Divergent Denisova (DeepDen) cases, respectively. ms0ancient2 10 1. 065.05 -t 5000. -r 3000. 5000000 -I 7 1 1 1 1 2 2 2 -en 0. 1 .2 -en 0. 2 .2 -en 0. 3 .2 -en 0. 4 .2 -es .025 7.96 -en .025 8.2 -ej.03 7 6 -ej.04 6 5 -ej.060 8 3 -ej.061 4 3 -ej.062 2 1 -ej.063 3 1 -ej x 1 5.

Extended Data Figure 9 Modelling the xOoA components with FineSTRUCTURE.

a, Joint distribution of haplotype lengths and derived allele count, showing the median position of each cluster and all haplotypes assigned to it in the maximum a posteriori (MAP) estimate. Note that although a different proportion of points is assigned to each in the MAP, the total posterior is very close to 1/K for all. The dashed lines show a constant mutation rate. Haplotypes are ordered by mutation rate from low to high. b, Residual distribution comparison between the two-component mixture using EUR.AFR and EUR.PNG (left), and the three-component mixture including xOoA (using the same colour scale) (right). The root mean square error (RMSE) residuals without xOoA are larger (RMSE = 0.0055 compared to RMSE = 0.0018) but more importantly, they are also structured. c, Assuming a mutational clock and a correct assignment of haplotypes, we can estimate the relative age of the splits from the number of derived alleles observed on the haplotypes. This leads to an estimate of 1.5 times older for xOoA compared to the Eurasian–Africa split.

Extended Data Figure 10 Proposed xOoA model.

A schematic illustrating, as suggested by the results presented here, a model of an early, extinct Out-of-Africa (xOoA) signature in the genomes of Sahul populations at their arrival in the region. Given the overall small genomic contribution of this event to the genomes of modern Sahul individuals, we could not determine whether the documented Denisova admixture (question marks) and putative multiple Neanderthal admixtures took place along this extinct OoA. We also speculate (question mark) people who migrated along the xOoA route may have left a trace in the genomes of the Altai Neanderthal as reported by Kuhlwilm and colleagues12.

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Pagani, L., Lawson, D., Jagoda, E. et al. Genomic analyses inform on migration events during the peopling of Eurasia.Nature 538, 238–242 (2016). https://doi.org/10.1038/nature19792

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• Received: 20 September 2015
• Accepted: 24 August 2016
• Published: 21 September 2016
• Issue Date: 13 October 2016
• DOI: https://doi.org/10.1038/nature19792