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Genome-wide patterns of selection in 230 ancient Eurasians

Naturevolume 528, pages499–503 (2015)Cite this article

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Abstract

Ancient DNA makes it possible to observe natural selection directly by analysing samples from populations before, during and after adaptation events. Here we report a genome-wide scan for selection using ancient DNA, capitalizing on the largest ancient DNA data set yet assembled: 230 West Eurasians who lived between 6500 and 300bc, including 163 with newly reported data. The new samples include, to our knowledge, the first genome-wide ancient DNA from Anatolian Neolithic farmers, whose genetic material we obtained by extracting from petrous bones, and who we show were members of the population that was the source of Europe’s first farmers. We also report a transect of the steppe region in Samara between 5600 and 300bc, which allows us to identify admixture into the steppe from at least two external sources. We detect selection at loci associated with diet, pigmentation and immunity, and two independent episodes of selection on height.

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Figure 1: Population relationships of samples.
Figure 2: Genome-wide scan for selection.
Figure 3: Allele frequencies for five genome-wide significant signals of selection.
Figure 4: Polygenic selection on height.

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Accession codes

Primary accessions

European Nucleotide Archive

Data deposits

The aligned sequences are available through the European Nucleotide Archive under accession numberPRJEB11450. The Human Origins genotype datasets including ancient individuals can be found at (http://genetics.med.harvard.edu/reich/Reich_Lab/Datasets.html).

References

  1. Grossman, S. R. et al. Identifying recent adaptations in large-scale genomic data.Cell152, 703–713 (2013)

    Article CAS  Google Scholar 

  2. Wilde, S. et al. Direct evidence for positive selection of skin, hair, and eye pigmentation in Europeans during the last 5,000 y.Proc. Natl Acad. Sci. USA111, 4832–4837 (2014)

    Article ADS CAS  Google Scholar 

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

    Article ADS CAS  Google Scholar 

  4. Lazaridis, I. et al. Ancient human genomes suggest three ancestral populations for present-day Europeans.Nature513, 409–413 (2014)

    Article ADS CAS  Google Scholar 

  5. Allentoft, M. E. et al. Population genomics of Bronze Age Eurasia.Nature522, 167–172 (2015)

    Article ADS CAS  Google Scholar 

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

    Article ADS  Google Scholar 

  7. Haak, W. et al. Massive migration from the steppe was a source for Indo-European languages in Europe.Nature522, 207–211 (2015)

    Article ADS CAS  Google Scholar 

  8. Olalde, I. et al. Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European.Nature507, 225–228 (2014)

    Article ADS CAS  Google Scholar 

  9. Pinhasi, R. et al. Optimal ancient DNA yields from the inner ear part of the human petrous bone.PLoS ONE10, e0129102 (2015)

    Article  Google Scholar 

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

    Article CAS  Google Scholar 

  11. Patterson, N. et al. Ancient admixture in human history.Genetics192, 1065–1093 (2012)

    Article  Google Scholar 

  12. Underhill, P. A. et al. The phylogenetic and geographic structure of Y-chromosome haplogroup R1a.Eur. J. Hum. Genet.23, 124–131 (2015)

    Article  Google Scholar 

  13. The 1000 Genomes Project Consortium. A global reference for human genetic variation.Nature526, 68–74 (2015)

  14. Devlin, B. & Roeder, K. Genomic control for association studies.Biometrics55, 997–1004 (1999)

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

  17. Burger, J., Kirchner, M., Bramanti, B., Haak, W. & Thomas, M. G. Absence of the lactase-persistence-associated allele in early Neolithic Europeans.Proc. Natl Acad. Sci. USA104, 3736–3741 (2007)

    Article ADS CAS  Google Scholar 

  18. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids.Nature466, 707–713 (2010)

    Article ADS CAS  Google Scholar 

  19. Fumagalli, M. et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation.Science349, 1343–1347 (2015)

    Article ADS CAS  Google Scholar 

  20. Mathias, R. A. et al. Adaptive evolution of the FADS gene cluster withinAfrica. PLoS ONE7, e44926 (2012)

    Article ADS CAS  Google Scholar 

  21. Wang, T. J. et al. Common genetic determinants of vitamin D insufficiency: a genome-wide association study.Lancet376, 180–188 (2010)

    Article CAS  Google Scholar 

  22. Price, A. L. et al. The impact of divergence time on the nature of population structure: an example from Iceland.PLoS Genet.5, e1000505 (2009)

    Article  Google Scholar 

  23. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.Nature447, 661–678 (2007)

  24. Huff, C. D. et al. Crohn’s disease and genetic hitchhiking at IBD5.Mol. Biol. Evol.29, 101–111 (2012)

    Article CAS  Google Scholar 

  25. Hunt, K. A. et al. Newly identified genetic risk variants for celiac disease related to the immune response.Nature Genet.40, 395–402 (2008)

    Article CAS  Google Scholar 

  26. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease.Nature491, 119–124 (2012)

    Article CAS  Google Scholar 

  27. Beleza, S. et al. Genetic architecture of skin and eye color in an African–European admixed population.PLoS Genet.9, e1003372 (2013)

    Article CAS  Google Scholar 

  28. Sturm, R. A. et al. A single SNP in an evolutionary conserved region within intron 86 of theHERC2 gene determines human blue-brown eye color.Am. J. Hum. Genet.82, 424–431 (2008)

    Article CAS  Google Scholar 

  29. Eiberg, H. et al. Blue eye color in humans may be caused by a perfectly associated founder mutation in a regulatory element located within theHERC2 gene inhibitingOCA2 expression.Hum. Genet.123, 177–187 (2008)

    Article CAS  Google Scholar 

  30. Barreiro, L. B. et al. Evolutionary dynamics of human Toll-like receptors and their different contributions to host defense.PLoS Genet.5, e1000562 (2009)

    Article  Google Scholar 

  31. Uciechowski, P. et al. Susceptibility to tuberculosis is associated with TLR1 polymorphisms resulting in a lack of TLR1 cell surface expression.J. Leukoc. Biol.90, 377–388 (2011)

    Article CAS  Google Scholar 

  32. Wong, S. H. et al. Leprosy and the adaptation of human toll-like receptor 1.PLoS Pathog.6, e1000979 (2010)

    Article  Google Scholar 

  33. Fujimoto, A. et al. A scan for genetic determinants of human hair morphology: EDAR is associated with Asian hair thickness.Hum. Mol. Genet.17, 835–843 (2008)

    Article CAS  Google Scholar 

  34. Kimura, R. et al. A common variation in EDAR is a genetic determinant of shovel-shaped incisors.Am. J. Hum. Genet.85, 528–535 (2009)

    Article CAS  Google Scholar 

  35. Kamberov, Y. G. et al. Modeling recent human evolution in mice by expression of a selected EDAR variant.Cell152, 691–702 (2013)

    Article CAS  Google Scholar 

  36. Turchin, M. C. et al. Evidence of widespread selection on standing variation in Europe at height-associated SNPs.Nature Genet.44, 1015–1019 (2012)

    Article CAS  Google Scholar 

  37. Berg, J. J. & Coop, G. et al. A population genetic signal of polygenic adaptation.PLoS Genet.10, e1004412 (2014)

    Article  Google Scholar 

  38. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height.Nature467, 832–838 (2010)

    Article ADS CAS  Google Scholar 

  39. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.Nature Genet.42, 937–948 (2010)

    Article CAS  Google Scholar 

  40. Heid, I. M. et al. Meta-analysis identifies 13 new loci associated with waist–hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.Nature Genet.42, 949–960 (2010)

    Article CAS  Google Scholar 

  41. Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes.Nature Genet.44, 981–990 (2012)

    Article CAS  Google Scholar 

  42. Briggs, A. W. et al. Removal of deaminated cytosines and detection ofin vivo methylation in ancient DNA.Nucleic Acids Res.38, e87 (2010)

    Article  Google Scholar 

  43. Fu, Q. et al. DNA analysis of an early modern human from Tianyuan Cave, China.Proc. Natl Acad. Sci. USA110, 2223–2227 (2013)

    Article ADS CAS  Google Scholar 

  44. Fu, Q. et al. An early modern human from Romania with a recent Neanderthal ancestor.Nature.524, 216–219 (2015)

    Article ADS CAS  Google Scholar 

  45. Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data.BMC Bioinformatics15, 356 (2014)

    Article  Google Scholar 

  46. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs.Nature449, 851–861 (2007)

  47. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans.Nature501, 506–511 (2013)

    Article ADS CAS  Google Scholar 

  48. Li, J. Z. et al. Worldwide human relationships inferred from genome-wide patterns of variation.Science319, 1100–1104 (2008)

    Article ADS CAS  Google Scholar 

  49. Loh, P. R. et al. Inferring admixture histories of human populations using linkage disequilibrium.Genetics193, 1233–1254 (2013)

    Article  Google Scholar 

  50. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets.GigaScience4 (2015)

  51. 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)

    Article CAS  Google Scholar 

  52. Li, H. et al. The sequence alignment/map format and SAMtools.Bioinformatics25, 2078–2079 (2009)

    Article  Google Scholar 

  53. Norton, H. L. et al. Genetic evidence for the convergent evolution of light skin in Europeans and East Asians.Mol. Biol. Evol.24, 710–722 (2007)

    Article CAS  Google Scholar 

  54. Bokor, S. et al. Single nucleotide polymorphisms in theFADS gene cluster are associated with delta-5 and delta-6 desaturase activities estimated by serum fatty acid ratios.J. Lipid Res.51, 2325–2333 (2010)

    Article CAS  Google Scholar 

  55. Tanaka, T. et al. Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study.PLoS Genet.5, e1000338 (2009)

    Article  Google Scholar 

  56. Ahn, J. et al. Genome-wide association study of circulating vitamin D levels.Hum. Mol. Genet.19, 2739–2745 (2010)

    Article CAS  Google Scholar 

  57. Gründemann, D. et al. Discovery of the ergothioneine transporter.Proc. Natl Acad. Sci. USA102, 5256–5261 (2005)

    Article ADS  Google Scholar 

  58. Chauhan, S. et al. ZKSCAN3 is a master transcriptional repressor of autophagy.Mol. Cell50, 16–28 (2013)

    Article CAS  Google Scholar 

  59. Soler Artigas, M. et al. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function.Nature Genet.43, 1082–1090 (2011)

    Article  Google Scholar 

  60. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results.Bioinformatics26, 2336–2337 (2010)

    Article CAS  Google Scholar 

Download references

Acknowledgements

We thank P. de Bakker, J. Burger, C. Economou, E. Fornander, Q. Fu, F. Hallgren, K. Kirsanow, A. Mittnik, I. Olalde, A. Powell, P. Skoglund, S. Tabrizi and A. Tandon for discussions, suggestions about SNPs to include, or contribution to sample preparation or data curation. We thank S. Pääbo, M. Meyer, Q. Fu and B. Nickel for collaboration in developing the 1240k capture reagent. We thank J. M. V. Encinas and M. E. Prada for allowing us to resample La Braña 1. I.M. was supported by the Human Frontier Science Program LT001095/2014-L. C.G. was supported by the Irish Research Council for Humanities and Social Sciences (IRCHSS). F.G. was supported by a grant of the Netherlands Organization for Scientific Research, no. 380-62-005. A.K., P.K. and O.M. were supported by RFBR no. 15-06-01916 and RFH no. 15-11-63008 and O.M. by a state grant of the Ministry of Education and Science of the Russia Federation no. 33.1195.2014/k. J.K. was supported by ERC starting grant APGREID and DFG grant KR 4015/1-1. K.W.A. was supported by DFG grant AL 287 / 14-1. C.L.-F. was supported by a BFU2015-64699-P grant from the Spanish government. W.H. and B.L. were supported by Australian Research Council DP130102158. R.P. was supported by ERC starting grant ADNABIOARC (263441), and an Irish Research Council ERC support grant. D.R. was supported by US National Science Foundation HOMINID grant BCS-1032255, US National Institutes of Health grant GM100233, and the Howard Hughes Medical Institute.

Author information

Author notes

  1. Cristina Gamba & Joseph Pickrell

    Present address: † Present addresses: Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5–7, 1350 Copenhagen, Denmark (C.G.); New York Genome Center, New York, New York 10013, USA (J.P.).,

  2. Wolfgang Haak, Ron Pinhasi and David Reich: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Iain Mathieson, Iosif Lazaridis, Nadin Rohland, Swapan Mallick, Eadaoin Harney, Kristin Stewardson, Joseph Pickrell & David Reich

  2. Broad Institute of MIT and Harvard, Cambridge, 02142, Massachusetts, USA

    Iosif Lazaridis, Nadin Rohland, Swapan Mallick, Nick Patterson & David Reich

  3. Howard Hughes Medical Institute, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Swapan Mallick, Eadaoin Harney, Kristin Stewardson & David Reich

  4. Independent researcher, Santpoort-Noord, The Netherlands

    Songül Alpaslan Roodenberg

  5. School of Archaeology and Earth Institute, Belfield, University College Dublin, Dublin 4, Ireland

    Daniel Fernandes, Mario Novak, Kendra Sirak, Cristina Gamba & Ron Pinhasi

  6. Institute for Anthropological Research, Zagreb, 10000, Croatia

    Mario Novak

  7. Department of Anthropology, Emory University, Atlanta, 30322, Georgia, USA

    Kendra Sirak

  8. Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland

    Cristina Gamba & Eppie R. Jones

  9. Australian Centre for Ancient DNA, School of Biological Sciences & Environment Institute, University of Adelaide, Adelaide, 5005, South Australia, Australia

    Bastien Llamas, Alan Cooper & Wolfgang Haak

  10. Laboratory of Human Molecular Genetics, Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia

    Stanislav Dryomov

  11. Department of Paleolithic Archaeology, Institute of Archaeology and Ethnography, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia

    Stanislav Dryomov

  12. Centro Mixto UCM-ISCIII de Evolución y Comportamiento Humanos, Madrid, 280, 40, Spain

    Juan Luís Arsuaga

  13. Departamento de Paleontología, Facultad Ciencias Geológicas, Universidad Complutense de Madrid, Madrid, 28040, Spain

    Juan Luís Arsuaga

  14. Centro Nacional de Investigacíon sobre Evolución Humana (CENIEH), Burgos, 09002, Spain

    José María Bermúdez de Castro

  15. IPHES. Institut Català de Paleoecologia Humana i Evolució Social, Campus Sescelades-URV, Tarragona, 43007, Spain

    Eudald Carbonell, Marina Lozano & Josep Maria Vergès

  16. Area de Prehistoria, Universitat Rovira i Virgili (URV), Tarragona, 43002, Spain

    Eudald Carbonell, Marina Lozano & Josep Maria Vergès

  17. Netherlands Institute in Turkey, Istiklal Caddesi, Nur-i Ziya Sokak 5, Beyog˘ lu, 34433, Istanbul, Turkey

    Fokke Gerritsen

  18. Volga State Academy of Social Sciences and Humanities, Samara, 443099, Russia

    Aleksandr Khokhlov, Pavel Kuznetsov & Oleg Mochalov

  19. State Office for Heritage Management and Archaeology Saxony-Anhalt and State Museum of Prehistory, Halle, D-06114, Germany

    Harald Meller & Kurt W. Alt

  20. Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) RAS, St Petersburg, 199034, Russia

    Vyacheslav Moiseyev

  21. Department of Prehistory and Archaeology, University of Valladolid, Valladolid, 47002, Spain

    Manuel A. Rojo Guerra

  22. The Netherlands Institute for the Near East, Leiden, RA-2300, the Netherlands

    Jacob Roodenberg

  23. Max Planck Institute for the Science of Human History, Jena, D-07745, Germany

    Johannes Krause & Wolfgang Haak

  24. Institute for Archaeological Sciences, University of Tübingen, Tübingen, D-72070, Germany

    Johannes Krause

  25. Danube Private University, Krems, A-3500, Austria

    Kurt W. Alt

  26. Institute for Prehistory and Archaeological Science, University of Basel, Basel, CH-4003, Switzerland

    Kurt W. Alt

  27. Anthropology Department, Hartwick College, Oneonta, 13820, New York, USA

    Dorcas Brown & David Anthony

  28. Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), Barcelona, 08003, Spain

    Carles Lalueza-Fox

Authors
  1. Iain Mathieson
  2. Iosif Lazaridis
  3. Nadin Rohland
  4. Swapan Mallick
  5. Nick Patterson
  6. Songül Alpaslan Roodenberg
  7. Eadaoin Harney
  8. Kristin Stewardson
  9. Daniel Fernandes
  10. Mario Novak
  11. Kendra Sirak
  12. Cristina Gamba
  13. Eppie R. Jones
  14. Bastien Llamas
  15. Stanislav Dryomov
  16. Joseph Pickrell
  17. Juan Luís Arsuaga
  18. José María Bermúdez de Castro
  19. Eudald Carbonell
  20. Fokke Gerritsen
  21. Aleksandr Khokhlov
  22. Pavel Kuznetsov
  23. Marina Lozano
  24. Harald Meller
  25. Oleg Mochalov
  26. Vyacheslav Moiseyev
  27. Manuel A. Rojo Guerra
  28. Jacob Roodenberg
  29. Josep Maria Vergès
  30. Johannes Krause
  31. Alan Cooper
  32. Kurt W. Alt
  33. Dorcas Brown
  34. David Anthony
  35. Carles Lalueza-Fox
  36. Wolfgang Haak
  37. Ron Pinhasi
  38. David Reich

Contributions

W.H., R.P. and D.R. supervised the study. S.A.R., J.L.A., J.M.B., E.C., F.G., A.K., P.K., M.L., H.M., O.M., V.M., M.A.R., J.R., J.M.V., J.K., A.C., K.W.A., D.B., D.A., C.L., W.H., R.P. and D.R. assembled archaeological material. I.M., I.L., N.R., S.M., N.P., S.D., J.P., W.H. and D.R. analysed genetic data. N.R., E.H., K.St., D.F., M.N., K.Si., C.G., E.R.J., B.L., C.L. and W.H. performed wet laboratory ancient DNA work. I.M., I.L. and D.R. wrote the manuscript with input from all co-authors.

Corresponding authors

Correspondence toIain Mathieson,Wolfgang Haak,Ron Pinhasi orDavid Reich.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Efficiency and cost-effectiveness of 1240k capture.

We plot the number of raw sequences against the mean coverage of analysed SNPs after removal of duplicates, comparing the 163 samples for which capture data are newly reported in this study, against the 102 samples analysed by shotgun sequencing in ref.5. We caution that the true cost is more than that of sequencing alone.

Extended Data Figure 2 Early isolation and later admixture between farmers and steppe populations.

a, Mainland European populations later than 3000bc are better modelled with steppe ancestry as a third ancestral population, (closer correspondence between empirical and estimatedf4-statistics as estimated by resnorm; Methods). b, Later (post-Poltavka) steppe populations are better modelled with Anatolian Neolithic as a third ancestral population.c, Estimated mixture proportions of mainland European populations without steppe ancestry.d, Estimated mixture proportions of Eurasian steppe populations without Anatolian Neolithic ancestry.e, Estimated mixture proportions of later populations with both steppe and Anatolian Neolithic ancestry.f, Admixture plot atk = 17 showing population differences over time and space. EN, Early Neolithic; MN, Middle Neolithic; LN, Late Neolithic; BA, Bronze Age; LNBA, Late Neolithic and Bronze Age.

Extended Data Figure 3 Regional association plots.

Locuszoom60 plots for genome-wide significant signals. Points show the –log10P value for each SNP, coloured according to their linkage disequilibrium (LD; units ofr2) with the most associated SNP. The blue line shows the recombination rate, with scale on right hand axis in centimorgans per megabase (cM/Mb). Genes are shown in the lower panel of each subplot.

Extended Data Figure 4 PCA of selection populations and derived allele frequencies for genome-wide significant signals.

a, Ancient samples projected onto principal components of modern samples, as inFig. 1, but labelled according to selection populations defined inExtended Data Table 1.b, Allele frequency plots as inFig. 3. Six signals not included inFig. 3—forSLC22A4 we show both rs272872, which is our strongest signal, and rs1050152, which was previously hypothesized to be under selection, and we also showSLC24A5, which is not genome-wide significant but is discussed in the main text.

Extended Data Figure 5 Motala haplotypes carrying the derived, selectedEDAR allele.

This figure compares the genotypes at all sites within 150 kb of rs3827760 (in blue) for the 6 Motala samples and 20 randomly chosen CHB (Chinese from Beijing) and CEU (Utah residents with northern and western European ancestry) samples. Each row is a sample and each column is a SNP. Grey means homozygous for the major (in CEU) allele. Pink denotes heterozygous and red indicates homozygous for the other allele. For the Motala samples, an open circle means that there is only a single sequence, otherwise the circle is coloured according to the number of sequences observed. Three of the Motala samples are heterozygous for rs3827760 and the derived allele lies on the same haplotype background as in present-day East Asians. The only other ancient samples with evidence of the derivedEDAR allele in this data set are two Afanasievo samples dating to 3300–3000bc, and one Scythian dating to 400–200bc (not shown).

Extended Data Figure 6 Estimated power of the selection scan.

a, Estimated power for different selection coefficients (s) for a SNP that is selected in all populations for either 50, 100 or 200 generations.b, Effect of increasing sample size, showing estimated power for a SNP selected for 100 generations, with different amounts of data, relative to the main text.c, Effect of admixture from Yoruba (YRI) into one of the modern populations, showing the effect on the genomic inflation factor (blue, left axis) and the power to detect selection on a SNP selected for 100 generations with a selection coefficient of 0.02.d, Effect of mis-specification of the mixture proportions. Here 0 on thex axis corresponds to the proportions we used, and 1 corresponds to a random mixture matrix.

Extended Data Table 1 230 ancient individuals analysed in this study
Extended Data Table 2 Key f-statistics used to support claims about population history
Extended Data Table 3 Twelve genome-wide significant signals of selection

Supplementary information

Supplementary Information

This file contains Supplementary Text comprising: Archaeological context for 83 newly reported ancient samples (Section 1) and Population interactions between Anatolia, mainland Europe, and the Eurasian steppe (Section 2) with additional references. (PDF 1045 kb)

Supplementary Data 1

This file contains information about 230 ancient samples used in this study. (XLSX 101 kb)

Supplementary Data 2

This file shows FST between ancient and modern populations. (XLSX 26 kb)

Supplementary Data 3

This file contains Genome-wide selection scan results and allele frequencies. (TXT 71292 kb)

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Mathieson, I., Lazaridis, I., Rohland, N.et al. Genome-wide patterns of selection in 230 ancient Eurasians.Nature528, 499–503 (2015). https://doi.org/10.1038/nature16152

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  1. RAOULT Didier

    It is time to change, there is only one Homo sapiens

    Ancient DNA studies have clarified our understanding of human history^1^. The definition of races and species in humans was the subject of much debate in the 19th century and this had consequences in the definition of our ancestors. Darwin thought that different human groups, called races, were in fact species or subspecies^2^. He even proposed that interbreeding between different human groups was either sterile, or generated sterile beings such as mules^2^. The Darwinian theory of continuous separation of species influenced paleontologists throughout the 20th century, and led to consider that several hominid species had coexisted among which our ancestor Homo sapiens. CroMagnon was considered an already modern Homo sapiens, while Neanderthal was another species, becoming extinct because of his lack of fitness. Ancient DNA studies have shown that modern humans arose from the mating of different archaic humans^3^. CroMagnon is thus only one of our ancestors. By definition, a species consists of members likely to interbreed and produce fertile progeny. There is therefore no reason to consider that Neanderthal and CroMagnon were not the same species. We can call it Homo sapiens, if one wishes to name the same as ours or another name as we don?t know if we may be able to interbreed with them. In any case, we must stop talking about different species of archaic humans when we have evidence that they mated.

    1. Mathieson I. et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature 528, 499-503 (2015).
    2. C. Darwin , the descent of man. (John Murray, London, 1871).
    3. Abi-Rached L. et al. The shaping of modern human immune systems by multiregional admixture with archaic humans. Science 334, 89-94 (2011).

    didier.raoult@gmail.com

    Aix Marseille Université, URMITE, UMR 63, CNRS 7278, IRD 198, Inserm 1095, Faculté de Médecine, 27 Bd Jean MOULIN, 13385 Marseille Cedex 5, France

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Editorial Summary

Selection pressures deduced from ancient DNA

This study uses ancient DNA as a window on a crucial period of human evolution — the arrival of farming in Europe around 8,500 years ago. Genome-wide scanning data was obtained from 230 West Eurasians from between 6500 BC and 300 BC, including samples from 26 Anatolian Neolithic individuals, representing the first genome-wide ancient DNA from the eastern Mediterranean. The authors find evidence of selection on loci associated with diet, pigmentation and immunity. The strongest signal of selection is at the allele responsible for lactase persistence, supporting the view that an appreciable frequency of lactase persistence in Europe only dates to the past four thousand years.

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Isolating the human cochlea to generate bone powder for ancient DNA analysis

  • Ron Pinhasi
  • Daniel M. Fernandes
  • Olivia Cheronet
Nature ProtocolsProtocol

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