Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse

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The rich fossil record of equids has made them a model for evolutionary processes1. Here we present a 1.12-times coverage draft genome from a horse bone recovered from permafrost dated to approximately 560–780 thousand years before present (kyr bp)2, 3. Our data represent the oldest full genome sequence determined so far by almost an order of magnitude. For comparison, we sequenced the genome of a Late Pleistocene horse (43kyr bp), and modern genomes of five domestic horse breeds (Equus ferus caballus), a Przewalski’s horse (E. f. przewalskii) and a donkey (E. asinus). Our analyses suggest that the Equus lineage giving rise to all contemporary horses, zebras and donkeys originated 4.0–4.5million years before present (Myr bp), twice the conventionally accepted time to the most recent common ancestor of the genus Equus4, 5. We also find that horse population size fluctuated multiple times over the past 2Myr, particularly during periods of severe climatic changes. We estimate that the Przewalski’s and domestic horse populations diverged 38–72kyr bp, and find no evidence of recent admixture between the domestic horse breeds and the Przewalski’s horse investigated. This supports the contention that Przewalski’s horses represent the last surviving wild horse population6. We find similar levels of genetic variation among Przewalski’s and domestic populations, indicating that the former are genetically viable and worthy of conservation efforts. We also find evidence for continuous selection on the immune system and olfaction throughout horse evolution. Finally, we identify 29 genomic regions among horse breeds that deviate from neutrality and show low levels of genetic variation compared to the Przewalski’s horse. Such regions could correspond to loci selected early during domestication.

At a glance


  1. The early Middle Pleistocene horse metapodial from Thistle Creek (TC).
    Figure 1: The early Middle Pleistocene horse metapodial from Thistle Creek (TC).

    a, Geographical localization. b, Stratigraphic setting. c, Morphological comparison to Middle and Late Pleistocene horses from Beringia. Simpson’s ratio diagrams contrasting log10 differences in 10 metapodial measurements between horse fossils and a reference (E. hemionus onager) are shown for a series of 9 and 30 horses from the Middle and the Late Pleistocene era, respectively (Supplementary Information, section 1.2). The full distribution range between minimal and maximal values is presented within shaded areas. Numbers reported on the x axis refer to the following measurements: 1, maximal length; 3, breadth at the middle of the diaphysis; 4, depth at the middle of the diaphysis; 5, proximal breadth; 6, proximal depth; 10, distal supra-articular breadth; 11, distal articular breadth; 12, depth of the keel; 13, least depth of medial condyle; 14, greatest depth of medial condyle.

  2. Amino acid, protein and DNA preservation of the Thistle Creek horse bone.
    Figure 2: Amino acid, protein and DNA preservation of the Thistle Creek horse bone.

    a, Amino acid signatures. Secondary ions, characteristic of five amino acids over- or under-represented in collagen, were detected by TOF-SIMS (Supplementary Information, section 7.1). The size of secondary ion maps is 500×500 μm2 with a resolution of 256×256 pixels. b, Glutamine deamidation. The observed distribution of glutamine deamidation levels (Supplementary Information, section 7.5) is blue for the Thistle Creek (TC) horse bone and green for a 43-kyr-old Siberian mammoth bone. c, Post-mortem DNA damage. Maximum likelihood estimates of cytosine deamination at 5′ overhangs were estimated for 29 permafrost-preserved horse bones, including the Thistle Creek bone (Supplementary Information, section 6.3). Mitochondrial and nuclear estimates are provided in red and blue, respectively. Calibrated radiocarbon dates (bc) are provided when available (Supplementary Tables 2.3–4). Error bars refer to 2.5% and 97.5% quantile values, estimated following convergence of the maximum likelihood procedure.

  3. Horse phylogenetic relationships and population divergence times.
    Figure 3: Horse phylogenetic relationships and population divergence times.

    a, Maximum likelihood phylogenetic inference. We performed a super-matrix analysis of 5,359 coding genes (Supplementary Information, section 8.3a, 100 bootstrap pseudo-replicates) and estimated the average age for the main nodes (r8s semi-parametric penalized likelihood (PL) method, Supplementary Information, section 8.3c; see Supplementary Table 8.3 for other analyses). Asterisk indicates previously published horse genomes. b, Population divergence times. We used ABC to recover a posterior distribution for the time when two horse populations split over a full range of possible mutation rate calibrations (Supplementary Information, section 10.1). The first population included the Thistle Creek horse; the second consisted of modern domestic horses. A conservative age range for the Thistle Creek horse is reported between the dashed lines (560–780kyr).

  4. Horse demographic history.
    Figure 4: Horse demographic history.

    a, Last 150kyrbp. PSMC based on nuclear data (100 bootstrap pseudo-replicates) and Bayesian skyline inference based on mitochondrial genomes (median, black; 2.5% and 97.5% quantiles, grey) are presented following the methodology described in Supplementary Information, section 9. The Last Glacial Maximum (19–26kyrbp) is shown in pink. b, Last 2Myrbp. PSMC profiles are scaled using the new calibration values proposed for the MRCA of all living members of the genus Equus (4.0Myr, blue; 4.5Myr, red), and assuming a generation time of 8years (for other generation times, see Supplementary Figs 9.4 and 9.5).

Accession codes

Referenced accessions

Sequence Read Archive


  1. Franzen, J. L. The Rise of Horses: 55 Million Years of Evolution (Johns Hopkins Univ. Press, 2010)
  2. Froese, D. G., Westgate, J. A., Reyes, A. V., Enkin, R. J. & Preece, S. J. Ancient permafrost and a future, warmer Arctic. Science 321, 1648 (2008)
  3. Westgate, J. A. et al. Gold Run tephra: A Middle Pleistocene stratigraphic and paleoenvironmental marker across west-central Yukon Territory, Canada. Can. J. Earth Sci. 46, 465478 (2009)
  4. Eisenmann, V. Origins, dispersals, and migrations of Equus (Mammalia, Perissofactyla). Courier Forschungsintitut Senckenberg 153, 161170 (1992)
  5. Forsten, A. Mitochondrial-DNA timetable and the evolution of Equus: Comparison of molecular and paleontological evidence. Ann. Zool. Fenn. 28, 301309 (1992)
  6. Goto, H. et al. A massively parallel sequencing approach uncovers ancient origins and high genetic variability of endangered Przewalski’s horses. Genome Biol. Evol. 3, 10961106 (2011)
  7. Reyes, A. V., Froese, D. G. & Jensen, B. J. Response of permafrost to last interglacial warming: field evidence from non-glaciated Yukon and Alaska. Quat. Sci. Rev. 29, 32563274 (2010)
  8. Orlando, L. et al. True single-molecule DNA sequencing of a Pleistocene horse bone. Genet. Res. 21, 17051719 (2011)
  9. Lindahl, T. Instability and decay of the primary structure of DNA. Nature 362, 709715 (1993)
  10. Willerslev, E. et al. Ancient biomolecules from deep ice cores reveal a forested southern Greenland. Science 317, 111114 (2007)
  11. Miller, W. et al. Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change. Proc. Natl Acad. Sci. USA 109, E2382E2390 (2012)
  12. Cappellini, E. et al. Proteomic analysis of a pleistocene mammoth femur reveals more than one hundred ancient bone proteins. J. Proteome Res. 11, 917926 (2012)
  13. Ginolhac, A. et al. Improving the performance of True Single Molecule Sequencing for ancient DNA. BMC Genomics 13, 177 (2012)
  14. Rasmussen, M. et al. Ancient human genome sequence of an extinct Palaeo-Eskimo. Nature 463, 757762 (2010)
  15. Meyer, M. et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222226 (2012)
  16. van Doorn, N. L., Wilson, J., Hollund, H., Soressi, M. & Collins, M. J. Site-specific deamidation of glutamine: a new marker of bone collagen deterioration. Rapid Commun. Mass Spectrom. 26, 23192327 (2012)
  17. Vilstrup, J. T. et al. Mitochondrial phylogenomics of modern and ancient equids. PLoS ONE 8, e55950 (2013)
  18. McFadden, B. J. & Carranza-Castaneda, O. Cranium of Dinohippus mexicanus (Mammalia Equidae) from the early Pliocene (latest Hemphillian) of central Mexico and the origin of Equus. Bull. Florida Museum Nat.. History 43, 163185 (2002)
  19. Weinstock, J. et al. Evolution, systematics, and phylogeography of Pleistocene horses in the new world: a molecular perspective. PLoS Biol. 3, e241 (2005)
  20. Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710722 (2010)
  21. Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493496 (2011)
  22. Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359364 (2011)
  23. International Union for Conservation of Nature. IUCN Red List of Threatened Species, Version 2010.1, (downloaded 11 March 2010)
  24. Reich, D. et al. Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468, 10531060 (2010)
  25. Bowling, A. T. et al. Genetic variation in Przewalski’s horses, with special focus on the last wild caught mare, 231 Orlitza III. Cytogenet. Genome Res. 102, 226234 (2003)
  26. Wade, C. M. et al. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science 326, 865867 (2009)
  27. Allentoft, M. E. et al. The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proc. R. Soc. Lond. B 279, 47244733 (2012)
  28. Kelstrup, C. D., Young, C., Lavallee, R., Nielsen, M. L. & Olsen, J. V. Optimized fast and sensitive acquisition methods for shotgun proteomics on a quadrupole orbitrap mass spectrometer. J. Proteome Res. 11, 34873497 (2012)
  29. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 17541760 (2009)
  30. Orlando, L. et al. Revising the recent evolutionary history of equids using ancient DNA. Proc. Natl Acad. Sci. USA 106, 2175421759 (2009)
  31. Rohland, N. & Hofreiter, M. Ancient DNA extraction from bones and teeth. Nature Protocols 2, 17561762 (2007)
  32. Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc.. 6, (2010)
  33. Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1, 18 (2012)
  34. Stanke, M., Steinkamp, R., Waack, S. & Morgenstern, B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 32, W309W312 (2004)
  35. Carlton, J. M. et al. Draft genome sequence of the sexually transmitted pathogen Trichonomas vaginalis. Science 315, 207212 (2007)
  36. Li, H. & Durbin, R. R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589595 (2010)
  37. Li, H. et al. The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 25, 20782079 (2009)
  38. McCue, M. E. et al. A high density SNP array for the domestic horse and extant Perissodactyla: utility for association mapping, genetic diversity, and phylogeny studies. PLoS Genet. 8, e1002451 (2012)
  39. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559575 (2007)
  40. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006)
  41. R Development Core Team. A language and environment for statistical computing, (R Foundation for Statistical Computing, 2011)
  42. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 20692070 (2010)
  43. Smith, C. I., Chamberlain, A. T., Riley, M. S., Stringer, C. & Collins, M. J. The thermal history of human fossils and the likelihood of successful DNA amplification. J. Hum. Evol. 45, 203217 (2003)
  44. Ginolhac, A., Rasmussen, M., Gilbert, T. M., Willerslev, E. & Orlando, L. mapDamage: testing for damage patterns in ancient DNA sequences. Bioinformatics 27, 21532155 (2011)
  45. Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl Acad. Sci. USA 104, 1461614621 (2007)
  46. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnol. 26, 13671372 (2008)
  47. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 17941805 (2011)
  48. Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 30593066 (2002)
  49. Katoh, K. & Toh, H. Recent developments in the MAFFT multiple sequence alignment program. Brief. Bioinform. 9, 286298 (2008)
  50. Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 26882690 (2006)
  51. Stamatakis, A. et al. RAxML-Light: a tool for computing Terabyte phylogenies. Bioinformatics 28, 20642066 (2012)
  52. Sanderson, M. J. r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19, 301302 (2003)
  53. Shimodaira, H. & Hasegawa, M. CONSEL: for assessing the confidence of phylogenetic tree selection. Bioinformatics 17, 12461247 (2001)
  54. Lippold, S., Matzke, N. J., Reissmann, M. & Hofreiter, M. Whole mitochondrial genome sequencing of domestic horses reveals incorporation of extensive wild horse diversity during domestication. BMC Evol. Biol. 11, 328 (2011)
  55. Achilli, A. et al. Mitochondrial genomes from modern horses reveal the major haplogroups that underwent domestication. Proc. Natl Acad. Sci. USA 109, 24492454 (2012)
  56. Warmuth, V. et al. Reconstructing the origin and spread of horse domestication in the Eurasian steppe. Proc. Natl Acad. Sci. USA 109, 82028206 (2012)
  57. Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007)
  58. Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 19691973 (2012)
  59. Rambaut, A. & Drummond, A. J. Tracer v1. 5, (2009)
  60. Hudson, R. R. Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18, 337338 (2002)
  61. Zhang, Z. Computational Molecular Evolution (Oxford Univ. Press, 2006)
  62. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protocols 4, 4457 (2009)
  63. Nielsen, R. Molecular signatures of natural selection. Annu. Rev. Genet. 39, 197218 (2005)
  64. Busing, F. M. T. A., Meijer, E. & Van Der Leeden, R. Delete-m Jackknife for Unequal m. Stat. Comput. 9, 38 (1999)
  65. Keane, T. M., Creevey, C. J., Pentony, M. M., Naughton, T. J. & McInerney, J. O. Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified. BMC Evol. Biol. 6, 29 (2006)
  66. Guindon, S. et al. New algorithms and methods to estimate Maximum-Likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307321 (2010)

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Author information

  1. These authors contributed equally to this work.

    • Ludovic Orlando,
    • Aurélien Ginolhac &
    • Guojie Zhang


  1. Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5–7, 1350 Copenhagen K, Denmark

    • Ludovic Orlando,
    • Aurélien Ginolhac,
    • Mikkel Schubert,
    • Enrico Cappellini,
    • Julia T. Vilstrup,
    • Maanasa Raghavan,
    • Thorfinn Korneliussen,
    • Anna-Sapfo Malaspinas,
    • Jesper Stenderup,
    • Amhed M. V. Velazquez,
    • Morten Rasmussen,
    • Andaine Seguin-Orlando,
    • Cecilie Mortensen,
    • Kim Magnussen,
    • Kristian Gregersen,
    • Anders Krogh,
    • M. Thomas P. Gilbert,
    • Kurt Kjær &
    • Eske Willerslev
  2. Shenzhen Key Laboratory of Transomics Biotechnologies, BGI-Shenzhen, Shenzhen 518083, China

    • Guojie Zhang,
    • Xiaoli Wang,
    • Jiumeng Min &
    • Jun Wang
  3. Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta T6G 2E3, Canada

    • Duane Froese
  4. The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark

    • Anders Albrechtsen,
    • Ida Moltke &
    • Anders Krogh
  5. Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California 95064, USA

    • Mathias Stiller,
    • James Cahill &
    • Beth Shapiro
  6. Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark

    • Bent Petersen,
    • Josef Vogt,
    • Søren Brunak &
    • Thomas Sicheritz-Ponten
  7. Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA

    • Ida Moltke
  8. Department of Biology, Emory University, Atlanta, Georgia 30322, USA

    • Philip L. F. Johnson
  9. Department of Integrative Biology, University of California, Berkeley, California 94720, USA

    • Matteo Fumagalli
  10. Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark

    • Damian Szklarczyk,
    • Christian D. Kelstrup,
    • Lars Juhl Jensen &
    • Jesper V. Olsen
  11. Jackson School of Geosciences, The University of Texas at Austin, 1 University Road, Austin, Texas 78712, USA

    • Jakob Vinther
  12. Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, USA

    • Andrei Dolocan
  13. Government of Yukon, Department of Tourism and Culture, Yukon Palaeontology Program, PO Box 2703 L2A, Whitehorse, Yukon Territory Y1A 2C6, Canada

    • Grant D. Zazula
  14. Danish National High-throughput DNA Sequencing Centre, University of Copenhagen, Øster Farimagsgade 2D, 1353 Copenhagen K, Denmark

    • Andaine Seguin-Orlando,
    • Cecilie Mortensen &
    • Kim Magnussen
  15. NABsys Inc, 60 Clifford Street, Providence, Rhode Island 02903, USA

    • John F. Thompson
  16. Archeology, University of Southampton, Avenue Campus, Highfield, Southampton SO17 1BF, UK

    • Jacobo Weinstock
  17. Zoological Museum, Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark

    • Kristian Gregersen
  18. Department of Basic Sciences and Aquatic Medicine, Norwegian School of Veterinary Science, Box 8146 Dep, N-0033 Oslo, Norway

    • Knut H. Røed
  19. Département histoire de la Terre, UMR 5143 du CNRS, paléobiodiversité et paléoenvironnements, MNHN, CP 38, 8, rue Buffon, 75005 Paris, France

    • Véra Eisenmann
  20. Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, SE-751 23 Uppsala, Sweden

    • Carl J. Rubin &
    • Leif Andersson
  21. Baker Institute for Animal Health, Cornell University, Ithaca, New York 14853, USA

    • Donald C. Miller &
    • Douglas F. Antczak
  22. Center for Zoo and Wild Animal Health, Copenhagen Zoo, 2000 Frederiksberg, Denmark

    • Mads F. Bertelsen
  23. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark

    • Søren Brunak &
    • Thomas Sicheritz-Ponten
  24. Zoology Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia

    • Khaled A. S. Al-Rasheid
  25. San Diego Zoo’s Institute for Conservation Research, Escondido, California 92027, USA

    • Oliver Ryder
  26. Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark

    • John Mundy &
    • Jun Wang
  27. Department of Biology, The University of York, Wentworth Way, Heslington, York YO10 5DD, UK

    • Michael Hofreiter
  28. Departments of Integrative Biology and Statistics, University of California, Berkeley, Berkeley, California 94720, USA

    • Rasmus Nielsen
  29. King Abdulaziz University, Jeddah 21589, Saudi Arabia

    • Jun Wang
  30. Macau University of Science and Technology, Avenida Wai long, Taipa, Macau 999078, China

    • Jun Wang
  31. Present addresses: Bioinformatics Group, Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland (D.S.); Departments of Earth Sciences and Biological Sciences, University of Bristol BS8 1UG, UK (Ja.V.).

    • Damian Szklarczyk &
    • Jakob Vinther


L.O. and E.W. initially conceived and headed the project; G.Z. and Ju.W. headed research at BGI; L.O. and E.W. designed the experimental research project set-up, with input from B.S. and R.N.; D.F. and G.D.Z. provided the Thistle Creek specimen, stratigraphic context and morphological information, with input from K.K.; K.H.R., B.S., K.G., D.C.M., D.F.A., K.A.S.A.-R. and M.F.B. provided samples; L.O., J.T.V., Ma.R., M.H., C.M. and J.S. did ancient and modern DNA extractions and constructed Illumina DNA libraries for shotgun sequencing; Ja.W. did the independent replication in Oxford; Ma.S. did ancient DNA extractions and generated target enrichment sequence data; Ji.M. and X.W. did Illumina libraries on donkey extracts; K.M., C.M. and A.S.-O. performed Illumina sequencing for the Middle Pleistocene and the 43-kyr-old horse genomes, the five domestic horse genomes and the Przewalski’s horse genome at Copenhagen, with input from Mo.R.; Ji.M. and X.W. performed Illumina sequencing for the Middle Pleistocene and the donkey genomes at BGI; J.F.T. headed true Single DNA Molecule Sequencing of the Middle Pleistocene genome; A.G., B.P. and Mi.S. did the mapping analyses and generated genome alignments, with input from L.O. and A.K.; Jo.V. and T.S.-P. did the metagenomic analyses, with input from A.G., B.P., S.B. and L.O.; Jo.V. and T.S.-P. did the ab initio prediction of the donkey genes and the identification of the Y chromosome scaffolds, with input from A.G. and Mi.S.; L.O., A.G. and P.L.F.J. did the damage analyses, with input from I.M.; A.G. did the functional SNP assignment; A.M.V.V. and L.O. did the PCA analyses, with input from O.R.; B.S. did the phylogenetic and Bayesian skyline reconstructions on mitochondrial data; Mi.S. did the phylogenetic and divergence dating based on nuclear data, with input from L.O.; A.G. did the PSMC analyses using data generated by C.J.R. and L.A.; L.O. and A.G. did the population divergence analyses, with input from J.C., R.N. and M.F.; L.O., A.G. and T.K. did the selection scans, with input from A.-S.M. and R.N.; A.A., I.M. and M.F. did the admixture analyses, with input from R.N.; L.O. and A.G. did the analysis of paralogues and structural variation; Ja.V. and A.D. did the amino-acid composition analyses; E.C., C.D.K., D.S., L.J.J. and J.V.O. did the proteomic analyses, with input from M.T.P.G. and A.M.V.V.; L.O. and V.E. performed the morphological analyses, with input from D.F. and G.D.Z.; L.O. and E.W. wrote the manuscript, with critical input from M.H., B.S., Jo.M. and all remaining authors.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

All sequence data have been submitted to Sequence Read Archive under accession number SRA082086 and are available for download, together with final BAM and VCF files, de novo donkey scaffolds, and proteomic data at

Author details

Supplementary information

PDF files

  1. Supplementary Information (22.1 MB)

    This file contains Supplementary Text and Data, Supplementary Figures, Supplementary Tables and additional references (see Contents for details). This file was updated on 3 July 2013 to correctly display figure S1.3

  2. Supplementary Figures (2.1 MB)

    This file contains Supplementary Figures S6.8-S6.38, which show DNA fragmentation and nucleotide misincorporation patterns for mitochondrial reads from other ancient samples analyzed in this study.

Zip files

  1. Supplementary Tables (9.9 MB)

    This zipped file contains Supplementary Tables 4.2, 4.3, 4.4, 5.9, 11.3, 11.4, 11.7 and 12.8.

Additional data