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A polar bear paleogenome reveals extensive ancient gene flow from polar bears into brown bears

Nature Ecology & Evolution volume 6, pages 936–944 (2022)Cite this article

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Abstract

Polar bears (Ursus maritimus) and brown bears (Ursus arctos) are sister species possessing distinct physiological and behavioural adaptations that evolved over the last 500,000 years. However, comparative and population genomics analyses have revealed that several extant and extinct brown bear populations have relatively recent polar bear ancestry, probably as the result of geographically localized instances of gene flow from polar bears into brown bears. Here, we generate and analyse an approximate 20X paleogenome from an approximately 100,000-year-old polar bear that reveals a massive prehistoric admixture event, which is evident in the genomes of all living brown bears. This ancient admixture event was not visible from genomic data derived from living polar bears. Like more recent events, this massive admixture event mainly involved unidirectional gene flow from polar bears into brown bears and occurred as climate changes caused overlap in the ranges of the two species. These findings highlight the complex reticulate paths that evolution can take within a regime of radically shifting climate.

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Fig. 1: Sample location and genetic affinity of bears.
Fig. 2: PSMC plot of effective population size over time for bears.
Fig. 3: Admixture between polar and brown bears estimated using DFOIL.
Fig. 4: MSMC-IM analysis of gene flow between brown and polar bears over time.

Data availability

Raw reads generated from Bruno are available from the NCBI SRA BioProject accession no. PRJNA720153. Other data used are listed in Supplementary Tables 2 and 3.

Code availability

Scripts and codes for genome analysis can be accessed at https://github.com/PopGenomics-WMS/Bruno_aDNA_analysis.

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Acknowledgements

We thank K. Wang and S. Schiffels for help with the MSMC-IM and MSMC analyses and E. Palkopoulou for assistance with the PSMC analysis. We thank B. Nelson for assistance with some of the Bruno extractions and the Science for Life Laboratory, Knut and Alice Wallenberg Foundation, National Genomics Infrastructure funded by the Swedish Research Council and the Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. L.D. acknowledges support from Formas (grant no. 2018-01640). Field support and initial funding for analysis was provided by the Arctic Field Office, Bureau of Land Management, Department of Interior, Fairbanks, Alaska, USA. This work was supported in part by National Science Foundation Division of Environmental Biology grant no. 1754451 to B.S.

Author information

Authors and Affiliations

  1. Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, USA

    Ming-Shan Wang, Megan A. Supple & Beth Shapiro

  2. Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, USA

    Ming-Shan Wang, Alisa O. Vershinina, Megan A. Supple, Joshua D. Kapp, Sarah E. Crump & Beth Shapiro

  3. Department of Veterinary Medicine, University of Cambridge, Cambridge, UK

    Gemma G. R. Murray

  4. Department of Geosciences, University of Alaska, Fairbanks, AK, USA

    Daniel Mann

  5. Institute of Arctic Biology, University of Alaska, Fairbanks, AK, USA

    Daniel Mann & Pamela Groves

  6. Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA

    Russell Corbett-Detig & Richard E. Green

  7. Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

    Ian Stirling

  8. Wildlife Research Division, Environment and Climate Change Canada Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

    Ian Stirling

  9. Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USA

    Kristin L. Laidre

  10. University of Alaska Museum of the North, Fairbanks, AK, USA

    Michael Kunz

  11. Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden

    Love Dalén

  12. Centre for Palaeogenetics, Stockholm, Sweden

    Love Dalén

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  1. Ming-Shan Wang
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Contributions

D.M., P.G. and M.K. discovered and excavated Bruno from the field site. B.S. and R.E.G. designed the research project and supervised the analysis. M.-S.W. and R.E.G. performed the analysis, with contributions from R.C.-D. and G.G.R.M. J.D.K. performed the extraction of ancient DNA and prepared the sequencing library. L.D. coordinated the genome sequencing and edited the manuscript. B.S., M.-S.W., G.G.R.M., I.S. and R.E.G. drafted the manuscript. A.O.V., M.A.S., S.E.C. and P.G. improved the figures and supplementary material. K.L.L. contributed the genomes for modern polar bears. All authors contributed to the final manuscript.

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Correspondence to Beth Shapiro.

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Extended data

Extended Data Fig. 1 Images of Bruno’s skull.

Images of Bruno’s skull. (a) ventral, (b) lateral, (c) dorsal views, and (d) shown along with a brown bear skull (left) and a mature polar bear skull (right). Photos by Pam Groves.

Extended Data Fig. 2 MapDamage plot for each of the 14 sequencing libraries prepared from DNA extracts from Bruno.

MapDamage plot for each of the 14 sequencing libraries prepared from DNA extracts from Bruno. Plots show the frequency of C>T and G>A substitutions. SE5145_BAN036 and SE5145_BAN037 are single-stranded DNA libraries and appear to have less damage than the other libraries.

Extended Data Fig. 3 An example of read length distributions for each of the 14 DNA sequencing libraries prepared from DNA extracts from Bruno.

An example of read length distributions for each of the 14 DNA sequencing libraries prepared from DNA extracts from Bruno. The observed ~10 bp periodicity of recovered fragment lengths is commonly observed in nuclear DNA recovered from old tissues and is most likely explained by nucleosomal protection of DNA molecules.

Extended Data Fig. 4 The derived allele frequencies of polar bears, brown bears, and Bruno at polar bear polymorphic sites.

The derived allele frequencies of polar bears, brown bears, and Bruno at polar bear polymorphic sites. We measured the derived allele frequency for all sites observed to be segregating polymorphism in the polar bears in the panel. For each 5% frequency bin, we measured the observed derived allele frequency within each polar bear (left panels) and each brown bear (right panels). The Bruno bear is in each panel (thick blue line). As expected, each polar bear carries the derived allele as often as would be expected given the derived allele frequency within polar bears (red, x = y line). Brown bears carry far fewer polar bear derived alleles. Genetically, Bruno is clearly not a modern polar bear nor a brown bear. An American black bear genome was used to polarize the ancestral state of alleles. Seven brown bears (SAMN07422261, SAMN07422264, SAMN07422265, SAMN07422266, SAMEA4762870, SAMN07422267, and SAMN07422270) were excluded from this analysis due to low sequencing coverage.

Extended Data Fig. 5 MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between Bruno and an extant brown bear and between extant polar bear and the same brown bear.

MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between Bruno and an extant brown bear and between extant polar bear and the same brown bear. Two independent analyses using different polar bears (IDs: SAMN02261853 and SAMN02261826) and brown bears (IDs: SAMN07422272 and SAMN07422262) are shown as A and B. For each calculation, we performed 30 bootstrap replicates, which are shown as thin red lines. Based on a 50% RCCR, we estimate the divergence time for modern polar bears and brown bears (T2) to be 481,049 ± 4500 (mean ± SD) years ago, which is consistent with previous estimates (Liu et al. 2014), and the divergence time for Bruno and brown bears (T1) to be 376.9 ± 3.3 kya (mean ± SD). The resulting estimate for the time difference between when Bruno and the modern polar bear diverged from the brown bear (a rough estimate of how long ago Bruno died) is: ∆T = T2-T1 = 104.4 ± 3.4 kya.

Extended Data Fig. 6 D-statistics analysis of gene flow between polar bears and brown bears.

D-statistics analysis of gene flow between polar bears and brown bears. (a) D-statistics in the form of D(brown bear-Europe; brown bear-N America; Bruno/Polar bear, Black bear). All combinations give significant negative values (−21.8 < Z-score < −21.3), suggesting admixture occurred between polar bears and North American brown bears, consistent with previous work. (b) D-statistics in the forms of D(PB-Greenland,PB-Alaska;BB-x,BLK), D(Bruno,PB-Alaska;BB-x,BLK) and D(Bruno,PB-Greenland; BB-x,BLK) are mostly non-significant (−3 < Z-score < 3). (c) Results of D-statistics in the form of D(All polar bears,Bruno;Brown bear,BLK).

Extended Data Fig. 7 Allele sharing among Bruno, all brown bears, and a polar bear.

Allele sharing among Bruno, all brown bears, and a polar bear. (a) and (b) show the shared derived alleles (transversions+transitions) and transversions, respectively. Replacing data from Bruno with data from an extant polar bear, KB06, results in fewer alleles shared uniquely with all brown bears to the exclusion of other polar bears. Statistical significance was measured by the wilcox.test (two-sided) using R version 4.10.

Extended Data Fig. 8 MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between brown bears.

MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between brown bears. Three independent analyses were performed for each pair. Based on a 50% RCCR, divergence time for North American and European Brown bears is 23–65 kya, which is more recent than the death of Bruno. We note that polar bear ancestry in North American bear genomes may affect these MSMC estimates and therefore perform DFOIL in multiple configurations that assume both a more recent and more ancient divergence between North American and European brown bears than the age of Bruno.

Extended Data Fig. 9 IBDMix inferred the amount of the genome showing a signal of admixture between Bruno and brown bears.

IBDMix inferred the amount of the genome showing a signal of admixture between Bruno and brown bears. Blue, orange and gray lines show results when the minimum lengths of 30 kb, 40 kb and 50 kb were used as cut-off to recover admixed regions.

Extended Data Fig. 10 Analysis of simulated data shows that MSMC-IM is robust to the directionality of gene flow is robust.

Analysis of simulated data shows that MSMC-IM is robust to the directionality of gene flow is robust. We assumed eight scenarios in which two populations diverged 500 kya with asymmetrical and symmetrical migrations at 100 kya, as described in Methods. Labeling is as follows: ‘M01 = 5%’ indicates that pop0 contributed 5% ancestry to pop1, ‘M10 = 5%’ indicates that pop1 contributed 5% ancestry to pop0. Vertical red dashed lines show the time of migration at 100 kya. Models and magnitude of migration for each direction are shown within each plot.

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Wang, MS., Murray, G.G.R., Mann, D. et al. A polar bear paleogenome reveals extensive ancient gene flow from polar bears into brown bears. Nat Ecol Evol 6, 936–944 (2022). https://doi.org/10.1038/s41559-022-01753-8

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