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Ancient DNA reveals genetic admixture in China during tiger evolution

Nature Ecology & Evolution volume 7, pages 1914–1929 (2023)Cite this article

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Abstract

The tiger (Panthera tigris) is a charismatic megafauna species that originated and diversified in Asia and probably experienced population contraction and expansion during the Pleistocene, resulting in low genetic diversity of modern tigers. However, little is known about patterns of genomic diversity in ancient populations. Here we generated whole-genome sequences from ancient or historical (100–10,000 yr old) specimens collected across mainland Asia, including a 10,600-yr-old Russian Far East specimen (RUSA21, 8× coverage) plus six ancient mitogenomes, 14 South China tigers (0.1–12×) and three Caspian tigers (4–8×). Admixture analysis showed that RUSA21 clustered within modern Northeast Asian phylogroups and partially derived from an extinct Late Pleistocene lineage. While some of the 8,000–10,000-yr-old Russian Far East mitogenomes are basal to all tigers, one 2,000-yr-old specimen resembles present Amur tigers. Phylogenomic analyses suggested that the Caspian tiger probably dispersed from an ancestral Northeast Asian population and experienced gene flow from southern Bengal tigers. Lastly, genome-wide monophyly supported the South China tiger as a distinct subspecies, albeit with mitochondrial paraphyly, hence resolving its longstanding taxonomic controversy. The distribution of mitochondrial haplogroups corroborated by biogeographical modelling suggested that Southwest China was a Late Pleistocene refugium for a relic basal lineage. As suitable habitat returned, admixture between divergent lineages of South China tigers took place in Eastern China, promoting the evolution of other northern subspecies. Altogether, our analysis of ancient genomes sheds light on the evolutionary history of tigers and supports the existence of nine modern subspecies.

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Fig. 1: Ancient tiger specimens collected in this study and the inferred dispersal routes of the species based on joint interpretation from modern and ancient tiger genomes.
Fig. 2: Phylogenetic relationships among ancient and modern tigers.
Fig. 3: Population genomic structure of 13 ancient and 33 modern tigers based on 1,242,142 transversions from autosomal neutral regions.
Fig. 4: The D-statistic inferences for possible scenarios of gene flow and f-statistic-based admixture graph modelling based on ancient and modern specimens.
Fig. 5: Population history modelling of tigers based on modern and ancient specimens.
Fig. 6: The projected tiger habitat suitability at present and during the LGM based on ENM in Maxent overlaid with the preferred vegetation layer.

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Data availability

The next-generation-sequencing raw data of the tiger samples have been deposited in the Sequence Read Archive (BioProject ID: PRJNA822019). The data processing pipeline is available at https://github.com/xinsun1/Ancient_tiger_pop_gen. The processed data file is available at https://doi.org/10.5061/dryad.73n5tb324.

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Acknowledgements

All samples were recruited in compliance with the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) through permissions issued to the School of Life Sciences (PI: S.-J.L.), Peking University, by the State Forestry Administration of China. We thank all the collaborators, institutes and zoos that provided the specimens listed in Supplementary Table 2 upon which this study is based. Special thanks are given to the following people who provided important help during various stages of the project: X. Zhou, L. Liao, J. Wu, C. Feng, S. Xiang, Y. Shen, C. Xie, L. Zhang, Y. Chen, F. Tang, E. Cappellini, M. Mackie, L. Miao, X. Hu, J. Huang, H. Yu, H. Meng, Q. Fu, E. Hoeger, M. Surovy, N. Duncan, S. Ketelsen, M.-D. Wandhammer, V. Rakotondrahaja, A. Abramov, I. Y. Pavlinov, E. I. Zholnerovskaya, N. V. Lopatina, X. Gu, H. Gu, D. Miquelle and D. Smith. We also pay tribute to the late U. Seal and P. Jackson for their dedication to tiger conservation and pioneer effort in assembling voucher specimens for genetic study. This work was supported by the National Key Research and Development Program of China (SQ2022YFF0802300), the National Natural Science Foundation of China (NSFC32070598) and the Peking-Tsinghua Center for Life Sciences. M.P.T. conducted the research within the state assignment of the Ministry of Science and Higher Education of the Russian Federation (theme No. 121031000153-7).

Author information

Author notes

  1. Xin Sun

    Present address: Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

  2. Yue-Chen Liu

    Present address: Department of Genetics, Harvard Medical School, Boston, MA, USA

  3. Yue-Chen Liu

    Present address: Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA

Authors and Affiliations

  1. The State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Xin Sun, Yue-Chen Liu, Yan Zhuang, Yu Han, Rui-Zheng Yang, Xiao Xu & Shu-Jin Luo

  2. Federal Scientific Center of the East Asia Terrestrial Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Mikhail P. Tiunov & Olga Uphyrkina

  3. Institute of Plant and Animal Ecology, Ural Branch of the Russian Academy of Sciences, Yekaterinburg, Russia

    Dmitry O. Gimranov

  4. Ural Federal University, Yekaterinburg, Russia

    Dmitry O. Gimranov

  5. Section of Comparative Behavioral Genomics, National Institute on Alcohol Abuse and Alcoholism, NIH, Rockville, MD, USA

    Carlos A. Driscoll

  6. Beijing Advanced Innovation Center for Genomics (ICG), Biodynamic Optical Imaging Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China

    Yuhong Pang, Chunmei Li & Yanyi Huang

  7. School of Archaeology and Museology, Peking University, Beijing, China

    Yan Pan & Xiao-Hong Wu

  8. Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Marcela Sandoval Velasco, Shyam Gopalakrishnan & M. Thomas P. Gilbert

  9. Shaanxi Key Laboratory for Animal Conservation, College of Life Sciences, Northwest University, Xi’an, China

    Bao-Guo Li

  10. Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, China

    Kun Jin

  11. College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China

    Yanyi Huang

  12. Institute for Cell Analysis, Shenzhen Bay Laboratory, Guangdong, China

    Yanyi Huang

  13. University Museum, Norwegian University of Science and Technology, Trondheim, Norway

    M. Thomas P. Gilbert

  14. Guy Harvey Oceanographic Center, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, FL, USA

    Stephen J. O’Brien

  15. Institute of Tropical Biodiversity and Sustainable Development, University of Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia

    Nobuyuki Yamaguchi

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  1. Xin Sun
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  2. Yue-Chen Liu
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  15. Kun Jin
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  22. Nobuyuki Yamaguchi
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  23. Shu-Jin Luo
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The project was conceived and designed by S.-J.L. Laboratory work was done by X.S., Y.-C.L., Y.Z., Y.H., Y.-H.P, C.L., Y.P., M.S.V. and Y.-Y.H. Data analysis was performed by X.S., S.G. and X.-H.W. Samples were provided by M.P.T., D.O.G., C.A.D., R.-Z.Y, B.-G.L., K.J., X.X., O.U. and N.Y. The initial manuscript draft was written by X.S. and S.-J.L., with helpful input from M.T.P.G., S.J.O. and N.Y. All authors contributed to interpreting the data and editing the manuscript.

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Correspondence to Stephen J. O’Brien, Nobuyuki Yamaguchi or Shu-Jin Luo.

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Extended data

Extended Data Fig. 1 Authentication of ancient DNA sequencing data from RUSA0021.

Panels A and B show the different DNA substitution patterns at the 5′ (left) and 3′ (right) ends of reads from RUSA0021 before and after USER mix treatment, respectively. Red lines refer to C-to-T substitutions and blue lines refer to G-to-A substitutions.

Extended Data Fig. 2 Variable sites profiling with raw and filtered dataset.

(a) Error rate estimation by comparing an ancient genome sequencing data to a high-quality genome data using ANGSD. (b) Mutation rate distribution of the filtered SNP dataset.

Extended Data Fig. 3 Heterozygosity estimation of tigers using ANGSD.

Only transitions were included for the analysis. Samples with sequencing depth lower than 4× were excluded. In the box plot, center line is the median, box bounds represent the interquartile range (IQR), whiskers extend to 1.5 × IQR from both end, and outliers are data beyond the range of whiskers.

Extended Data Fig. 4 PCA and admixture results based on genotype likelihood dataset.

(a) The first three principal components using PCAngsd. (b) The supervised population structure admixture analyses using NGSadmix assuming 2 to 8 ancestral populations.

Extended Data Fig. 5 D-statistic inferences for possible scenarios of gene flow between tigers.

The D-statistic values are shown in x-axis and sample IDs of tigers representing different PopX in each testing are shown in y-axis with symbol color-coded by subspecies. Symbol shapes indicate different representatives of Pop3 in the tree topology above the D-statistic plot. A D-statistic test is considered significant with the absolute value of transformed z-score |z| > 3. Error bars show 1 × s.e.m. from a block jackknife resampling method with a block size of 5 cM. The corresponding scenarios of excessive allele sharing inferred from D-statistics are indicated in the tree above each plot. (a) Support for excessive allele sharing between different South China tiger individuals and other tiger populations (D > 0). (b and c) Support for excessive allele sharing between Bengal tigers and the outgroup species (D < 0 in B and D > 0 in C). (d) Support for excessive allele sharing between Bengal tigers and Caspian tigers relative to that between Bengal tigers and other tiger populations (d < 0). (e) Support for excessive allele sharing between the ancient RFE tiger (RUSA) and outgroup relative to that between RUSA and other modern tigers (D < 0). Abbreviations for the specimens are as follows based on the geographic origin of the individual: AMO, the South China tiger (P. t. amoyensis); ALT, the Amur tiger (P. t. altaica); COR, the Indochinese tiger (P. t. corbetti); JAX, the Malayan tiger (P. t. jacksoni); SUM, the Sumatran tiger (P. t. sumatrae); TIG, the Bengal tiger (P. t. tigris); VIR, the Caspian tiger (P. t. virgata); RUSA, the ancient Russian Far East tiger population dated to approximately 10,000 years ago; PLE, the lion P. leo; PUN, the snow leopard P. uncia.

Extended Data Fig. 6 Ecological niche modeling results of the tiger distribution during four different periods.

(a) The Last Interglacial period (LIG, approximately 120,000–140,000 years ago), (b) the Last Glacial Maximum (LGM, approximately 22,000 years ago), (c) the mid-Holocene (approximately 6,000 years ago), and (d) present day (data from approximately 1960–1990).

Extended Data Fig. 7 Three postulated scenarios concerning the ancient colonization of Central Asia and the establishment of the Caspian tiger.

The three dispersal routes included a southern route via the Indian subcontinent, a northern route via the Siberian plain, and a historical ‘Silk Road’ route through the Gansu corridor in Northwest China. Phylogenomic and demographic analyses and biogeographic modeling supported a possible initial expansion from East Asia to the modern range in Central Asia via the northern Siberian route, followed by subsequent gene flow from the ancient Bengal tiger counterpart through the Himalayan corridor.

Extended Data Fig. 8 TreeMix phylogeny of ancient and modern tigers.

Samples were grouped by modern subspecies or ancient RFE population. South China tigers with admixed ancestry were assigned as a separate group (AMO_MIX). Migration edges (m) were inferred from 0 to 7. The migration band inference results were similar to the results for gene flow and population admixture obtained with D-statistics and admixture graph modeling. We used the topology with 7 inferred migration bands for further demographic history modeling.

Extended Data Fig. 9 Demographic history analysis for different tiger subspecies estimated in PSMC.

PSMC was applied for each tiger, and one for each subspecies is shown here. The generation time g was set to 5 years, and the mutation rate μ was calculated to be 0.64 × 10−9 substitutions per site per year. Support values from 100 bootstrap replicates for each run are shown in gray.

Extended Data Table 1 Ancient and modern tiger samples of the study including all nine modern subspecies and ancient Russian Far East population
Full size table

Supplementary information

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Supplementary Figs.1–6 and Tables 1–5.

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Sun, X., Liu, YC., Tiunov, M.P. et al. Ancient DNA reveals genetic admixture in China during tiger evolution. Nat Ecol Evol 7, 1914–1929 (2023). https://doi.org/10.1038/s41559-023-02185-8

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