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Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA

Nature 512, 194–197 (14 August 2014) | Download Citation

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Abstract

As modern humans migrated out of Africa, they encountered many new environmental conditions, including greater temperature extremes, different pathogens and higher altitudes. These diverse environments are likely to have acted as agents of natural selection and to have led to local adaptations. One of the most celebrated examples in humans is the adaptation of Tibetans to the hypoxic environment of the high-altitude Tibetan plateau1,2,3. A hypoxia pathway gene, EPAS1, was previously identified as having the most extreme signature of positive selection in Tibetans4,5,6,7,8,9,10, and was shown to be associated with differences in haemoglobin concentration at high altitude. Re-sequencing the region around EPAS1 in 40 Tibetan and 40 Han individuals, we find that this gene has a highly unusual haplotype structure that can only be convincingly explained by introgression of DNA from Denisovan or Denisovan-related individuals into humans. Scanning a larger set of worldwide populations, we find that the selected haplotype is only found in Denisovans and in Tibetans, and at very low frequency among Han Chinese. Furthermore, the length of the haplotype, and the fact that it is not found in any other populations, makes it unlikely that the haplotype sharing between Tibetans and Denisovans was caused by incomplete ancestral lineage sorting rather than introgression. Our findings illustrate that admixture with other hominin species has provided genetic variation that helped humans to adapt to new environments.

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Change history

  • 13 August 2014

    The affiliations list has been updated to correct the address of author Kui Li.

Accessions

Primary accessions

Sequence Read Archive

Data deposits

Sequence data have been deposited in the Sequence Read Archive under accession number SRP041218.

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Acknowledgements

This research was funded by the State Key Development Program for Basic Research of China, 973 Program (2011CB809203, 2012CB518201, 2011CB809201, 2011CB809202), China National GeneBank-Shenzhen and Shenzhen Key Laboratory of Transomics Biotechnologies (no. CXB201108250096A). This work was also supported by research grants from the US NIH; R01HG003229 to R.N. and R01HG003229-08S2 to E.H.S. We thank F. Jay, M. Liang and F. Casey for useful discussions.

Author information

    • Emilia Huerta-Sánchez
    • , Xin Jin
    • , Asan
    •  & Zhuoma Bianba

    These authors contributed equally to this work.

Affiliations

  1. BGI-Shenzhen, Shenzhen 518083, China

    • Emilia Huerta-Sánchez
    • , Xin Jin
    • , Asan
    • , Yu Liang
    • , Xin Yi
    • , Mingze He
    • , Peixiang Ni
    • , Bo Wang
    • , Xiaohua Ou
    • , Huasang
    • , Jiangbai Luosang
    • , Ye Yin
    • , Wei Wang
    • , Xiuqing Zhang
    • , Xun Xu
    • , Huanming Yang
    • , Yingrui Li
    • , Jian Wang
    • , Jun Wang
    •  & Rasmus Nielsen

  2. Department of Integrative Biology, University of California, Berkeley, California 94720 USA

    • Emilia Huerta-Sánchez
    • , Benjamin M. Peter
    • , Nicolas Vinckenbosch
    •  & Rasmus Nielsen

  3. School of Natural Sciences, University of California, Merced, California 95343 USA

    • Emilia Huerta-Sánchez

  4. School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China

    • Xin Jin

  5. Binhai Genomics Institute, BGI-Tianjin, Tianjin 300308, China

    • Asan
    • , Yu Liang
    •  & Xin Yi

  6. Tianjin Translational Genomics Center, BGI-Tianjin, Tianjin 300308, China

    • Asan
    • , Yu Liang
    •  & Xin Yi

  7. The People’s Hospital of Lhasa, Lhasa 850000, China

    • Zhuoma Bianba

  8. Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA

    • Mingze He

  9. Department of Biological Sciences, Middle East Technical University, 06800 Ankara, Turkey

    • Mehmet Somel

  10. The Second People’s Hospital of Tibet Autonomous Region, Lhasa 850000, China

    • Zha Xi Ping Cuo

  11. The People's Hospital of the Tibet Autonomous Region, Lhasa 850000, China

    • Kui Li

  12. The hospital of XiShuangBanNa Dai Nationalities, Autonomous Jinghong, 666100 Yunnan, China

    • Guoyi Gao

  13. The Guangdong Enterprise Key Laboratory of Human Disease Genomics, BGI-Shenzhen, 518083 Shenzhen, China

    • Xiuqing Zhang

  14. Shenzhen Key Laboratory of Transomics Biotechnologies, BGI-Shenzhen, 518083 Shenzhen, China

    • Xiuqing Zhang

  15. Princess Al Jawhara Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia

    • Huanming Yang
    •  & Jun Wang

  16. James D. Watson Institute of Genome Science, 310008 Hangzhou, China

    • Huanming Yang
    •  & Jian Wang

  17. Department of Biology, University of Copenhagen, Ole MaaløesVej 5, 2200 Copenhagen, Denmark

    • Jun Wang

  18. Macau University of Science and Technology, AvenidaWai long, Taipa, Macau 999078, China

    • Jun Wang

  19. Department of Medicine, University of Hong Kong 999077, Hong Kong

    • Jun Wang

  20. Department of Statistics, University of California, Berkeley, California 94720, USA

    • Rasmus Nielsen

  21. Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark

    • Rasmus Nielsen

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Contributions

R.N., Ji.W. and Ju.W. supervised the project. X.J., A., Z.B., Y.L., X.Y., M.H., P.N., B.W., X.O., H., J.L., Z.X.P.C., K.L., G.G., Y.Y., W.W., X.Z., X.X., H.Y., Y.L., Ji.W. and Ju.W. collected and generated the data, and performed the preliminary bioinformatic analyses to call SNPs and indels from the raw data. E.H.-S. and N.V. filtered the data and B.M.P. phased the data. E.H.-S. performed the majority of the population genetic analysis with some contributions from B.M.P. and M.S. E.H.-S. and R.N. wrote the manuscript with critical input from all the authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Jian Wang or Jun Wang or Rasmus Nielsen.

Extended data

Extended data figures

  1. 1.

    FST calculated for each SNP between Tibetan and Han populations.

  2. 2.

    Distribution of fixed differences.

  3. 3.

    Haplotype frequencies for Tibetans, our Han samples and the populations from the 1000 genomes project for the five-SNP motif in the EPAS1 region.

  4. 4.

    Derived allele frequency of the SNPs with the largest frequency difference between Tibetans and the 1000 Genomes Project populations.

  5. 5.

    Differences between haplotypes.

  6. 6.

    Other haplotype networks.

  7. 7.

    Number of pairwise differences.

  8. 8.

    Divergence distributions.

  9. 9.

    Null distributions of D for an assumed Tibet–Han divergence of 3,000 years.

  10. 10.

    S* statistics and PCA plot.

Supplementary information

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  1. 1.

    Supplementary Information

    This file contains Supplementary Text, Supplementary References and Supplementary Tables 1-11.

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DOI

https://doi.org/10.1038/nature13408

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