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The genomic landscape of Neanderthal ancestry in present-day humans

Nature volume 507, pages 354357 (20 March 2014) | Download Citation


Genomic studies have shown that Neanderthals interbred with modern humans, and that non-Africans today are the products of this mixture1,2. The antiquity of Neanderthal gene flow into modern humans means that genomic regions that derive from Neanderthals in any one human today are usually less than a hundred kilobases in size. However, Neanderthal haplotypes are also distinctive enough that several studies have been able to detect Neanderthal ancestry at specific loci1,3,4,5,6,7,8. We systematically infer Neanderthal haplotypes in the genomes of 1,004 present-day humans9. Regions that harbour a high frequency of Neanderthal alleles are enriched for genes affecting keratin filaments, suggesting that Neanderthal alleles may have helped modern humans to adapt to non-African environments. We identify multiple Neanderthal-derived alleles that confer risk for disease, suggesting that Neanderthal alleles continue to shape human biology. An unexpected finding is that regions with reduced Neanderthal ancestry are enriched in genes, implying selection to remove genetic material derived from Neanderthals. Genes that are more highly expressed in testes than in any other tissue are especially reduced in Neanderthal ancestry, and there is an approximately fivefold reduction of Neanderthal ancestry on the X chromosome, which is known from studies of diverse species to be especially dense in male hybrid sterility genes10,11,12. These results suggest that part of the explanation for genomic regions of reduced Neanderthal ancestry is Neanderthal alleles that caused decreased fertility in males when moved to a modern human genetic background.

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  1. 1.

    et al. A draft sequence of the Neanderthal genome. Science 328, 710–722 (2010)

  2. 2.

    et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505, 43–49 (2014)

  3. 3.

    et al. The shaping of modern human immune systems by multiregional admixture with archaic humans. Science 334, 89–94 (2011)

  4. 4.

    , & A haplotype at STAT2 Introgressed from neanderthals and serves as a candidate of positive selection in Papua New Guinea. Am. J. Hum. Genet. 91, 265–274 (2012)

  5. 5.

    , & Neanderthal origin of genetic variation at the cluster of OAS immunity genes. Mol. Biol. Evol. 30, 798–801 (2013)

  6. 6.

    et al. An X-linked haplotype of Neanderthal origin is present among all non-African populations. Mol. Biol. Evol. 28, 1957–1962 (2011)

  7. 7.

    et al. Higher levels of neanderthal ancestry in East Asians than in Europeans. Genetics 194, 199–209 (2013)

  8. 8.

    et al. Evolutionary history and adaptation from high-coverage whole-genome sequences of diverse African hunter-gatherers. Cell 150, 457–469 (2012)

  9. 9.

    An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012)

  10. 10.

    , , & Abrupt cline for sex chromosomes in a hybrid zone between two species of mice. Evolution 46, 1146–1163 (1992)

  11. 11.

    , & A complex genetic basis to X-linked hybrid male sterility between two species of house mice. Genetics 179, 2213–2228 (2008)

  12. 12.

    Sex chromosomes and speciation in Drosophila. Trends Genet. 24, 336–343 (2008)

  13. 13.

    , & Conditional random fields: probabilistic models for segmenting and labeling sequence data. Proc. 18th Int. Conf. Machine Learn. 282–289. (2001)

  14. 14.

    , , , & The date of interbreeding between Neanderthals and modern humans. PLoS Genet. 8, e1002947 (2012)

  15. 15.

    & msHOT: modifying Hudson's ms simulator to incorporate crossover and gene conversion hotspots. Bioinformatics 23, 520–521 (2007)

  16. 16.

    , , , & Enredo and Pecan: genome-wide mammalian consistency-based multiple alignment with paralogs. Genome Res. 18, 1814–1828 (2008)

  17. 17.

    et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222–226 (2012)

  18. 18.

    Neanderthal Ancestry Estimator White paper 23-05 (23andMe, 2011)

  19. 19.

    et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009)

  20. 20.

    Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature (25 December 2014)

  21. 21.

    , , & Widespread genomic signatures of natural selection in hominid evolution. PLoS Genet. 5, e1000471 (2009)

  22. 22.

    et al. Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468, 1053–1060 (2010)

  23. 23.

    Speciation and Its Consequences (eds & , 180–207 Sinauer Associates, 1989)

  24. 24.

    & Evolution of postmating reproductive isolation: the composite nature of Haldane's rule and its genetic basis. Am. Nat. 142, 187–212 (1993)

  25. 25.

    et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789 (2012)

  26. 26.

    , , & Measurement of the human allele frequency spectrum demonstrates greater genetic drift in East Asians than in Europeans. Nature Genet. 39, 1251–1255 (2007)

  27. 27.

    et al. Genome-wide scan of 29,141 African Americans finds no evidence of selection since admixture. Preprint at (2013)

  28. 28.

    & The evolution of postzygotic isolation: accumulating Dobzhansky-Muller incompatibilities. Evolution 55, 1085–1094 (2001)

  29. 29.

    , , , & A fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321–324 (2005)

  30. 30.

    & in Introduction to Statistical Relational Learning (eds & ) Ch. 4, 93–128 (MIT Press, 2007)

  31. 31.

    , & Representations of quasi-Newton matrices and their use in limited memory methods. Mathematical Programming 63, 129–156 (1994)

  32. 32.

    Population genetics models of local ancestry. Genetics 191, 607–619 (2012)

  33. 33.

    et al. The consensus coding sequence (CCDS) project: identifying a common protein-coding gene set for the human and mouse genomes. Genome Res. 19, 1316–1323 (2009)

  34. 34.

    et al. Gene ontology: tool for the unification of biology. Nature Genet. 25, 25–29 (2000)

  35. 35.

    et al. FUNC: a package for detecting significant associations between gene sets and ontological annotations. BMC Bioinformatics 8, 41 (2007)

  36. 36.

    & Wavelet Methods for Time Series Analysis. (Cambridge Univ. Press, 2005)

  37. 37.

    The jackknife and the bootstrap for general stationary observations. Ann. Statist. 17, 1217–1241 (1989)

  38. 38.

    Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002)

  39. 39.

    & Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

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We thank A. Briggs, P. Moorjani, M. Przeworski, D. Presgraves and A. Williams for critical comments, and K. Kavanagh for help with Extended Data Fig. 2. We are grateful for support from the Presidential Innovation Fund of the Max Planck Society, NSF HOMINID grant 1032255 and NIH grant GM100233. S.S. was supported by a post-doctoral fellowship from the Initiative for the Science of the Human Past at Harvard University. D.R. is a Howard Hughes Medical Institute Investigator.

Author information


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

    • Sriram Sankararaman
    • , Swapan Mallick
    • , Nick Patterson
    •  & David Reich
  2. Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA

    • Sriram Sankararaman
    • , Swapan Mallick
    • , Nick Patterson
    •  & David Reich
  3. Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany

    • Michael Dannemann
    • , Kay Prüfer
    • , Janet Kelso
    •  & Svante Pääbo
  4. Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts 02115, USA

    • David Reich


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S.S., N.P., S.P. and D.R. conceived of the study. S.S., S.M. M.D., K.P., J.K. and D.R. performed analyses. J.K., S.P., N.P. and D.R. supervised the study. S.S. and D.R. wrote the manuscript with help from all co-authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Sriram Sankararaman or David Reich.

The tiling path of confidently inferred Neanderthal haplotypes, as well as the Neanderthal introgression map, can be found at http://genetics.med.harvard.edu/reichlab/Reich_Lab/Datasets.html.

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    Supplementary Information

    This file contains Supplementary Figures, Supplementary Tables and Supplementary Text and Data - see Contents for more information.

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