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Selection against domestication alleles in introduced rabbit populations

Nature Ecology & Evolution volume 8, pages 1543–1555 (2024)Cite this article

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Abstract

Humans have moved domestic animals around the globe for thousands of years. These have occasionally established feral populations in nature, often with devastating ecological consequences. To understand how natural selection shapes re-adaptation into the wild, we investigated one of the most successful colonizers in history, the European rabbit. By sequencing the genomes of 297 rabbits across three continents, we show that introduced populations exhibit a mixed wild–domestic ancestry. We show that alleles that increased in frequency during domestication were preferentially selected against in novel natural environments. Interestingly, causative mutations for common domestication traits sometimes segregate at considerable frequencies if associated with less drastic phenotypes (for example, coat colour dilution), whereas mutations that are probably strongly maladaptive in nature are absent. Whereas natural selection largely targeted different genomic regions in each introduced population, some of the strongest signals of parallelism overlap genes associated with neuronal or brain function. This limited parallelism is probably explained by extensive standing genetic variation resulting from domestication together with the complex mixed ancestry of introduced populations. Our findings shed light on the selective and molecular mechanisms that enable domestic animals to re-adapt to the wild and provide important insights for the mitigation and management of invasive populations.

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Fig. 1: Population genomics of introduced rabbits across three continents.
Fig. 2: Introduced rabbit populations have a large proportion of domestic ancestry.
Fig. 3: Adaptation in an introduced population with a predominant domestic ancestry.
Fig. 4: Presence of mutations associated with multiple traits in domestic rabbits in the six introduced populations.
Fig. 5: Signatures of selection against domestic ancestry in introduced rabbit populations.

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Data availability

Whole-genome resequencing data are available in the Sequence Read Archive (www.ncbi.nlm.nih.gov/sra) under BioProject PRJNA936804.

Code availability

A script developed for the calculation of the hybrid index is available at https://github.com/PJADPereira/hybridindex.

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Acknowledgements

This work was supported by Fundação para a Ciência e Tecnologia (FCT) through projects PTDC/BIA-EVL/30628/2017 and UIDP/50027/2020; by FCT through research contracts CEECINST/00014/2018/CP1512/CT0002 (to M.C.), 2020.01405.CEECIND/CP1601/CT0011 (to P.A.) and 2020.00275.CEECIND/CP1601/CP1649/CT0001 (to R.F.); by the European Research Council (ERC; to M.C.) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 101000504); by research fellowships PD/BD/128492/2017 from FCT (to P.P.) under the Biodiversity, Genetics and Evolution (BIODIV) PhD programme; by project PID2020-114724RB-I00 supported by the Spanish Ministry of Science and Innovation (to R.V.); and by Vetenskapsrådet (2017-02907) and Knut and Alice Wallenberg Foundation (KAW 2016.0361) (to L.A.).

Author information

Author notes

  1. Erik Enbody

    Present address: Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA

  2. These authors contributed equally: Pedro Andrade, Joel M. Alves.

Authors and Affiliations

  1. CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, Vairão, Portugal

    Pedro Andrade, Joel M. Alves, Paulo Pereira, Eugénio Silva, Sandra Afonso, Rui Faria, Nuno Ferrand & Miguel Carneiro

  2. BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, Vairão, Portugal

    Pedro Andrade, Joel M. Alves, Paulo Pereira, Eugénio Silva, Sandra Afonso, Rui Faria, Nuno Ferrand & Miguel Carneiro

  3. Palaeogenomics and Bio-Archaeology Research Network Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK

    Joel M. Alves

  4. Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal

    Paulo Pereira, Eugénio Silva & Nuno Ferrand

  5. Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden

    Carl-Johan Rubin, C. Grace Sprehn, Erik Enbody & Leif Andersson

  6. Institute of Marine Research, Bergen, Norway

    Carl-Johan Rubin

  7. Department of Genetics, University of Cambridge, Cambridge, UK

    Yexin Zhang & Francis M. Jiggins

  8. Estación Experimental Bariloche, Instituto Nacional de Tecnología Agropecuaria, Casilla de Correo Bariloche, Argentina

    Never Bonino

  9. Wildlife Ecology and Management Group, Manaaki Whenua – Landcare Research, Lincoln, New Zealand

    Janine A. Duckworth

  10. Invasive Animals Cooperative Research Centre, University of Canberra, Bruce, Australian Capital Territory, Australia

    Janine A. Duckworth

  11. GenPhySE, Université de Toulouse, Castanet-Tolosan, France

    Hervé Garreau

  12. Centre for Ecosystem Science, School of BEES, University of New South Wales, Sydney, New South Wales, Australia

    Mike Letnic

  13. Evolution and Ecology Research Centre, School of BEES, University of New South Wales, Sydney, New South Wales, Australia

    Mike Letnic

  14. Centre for Invasive Species Solutions, University of Canberra, Bruce, Australian Capital Territory, Australia

    Tanja Strive

  15. Commonwealth Scientific and Industrial Research Organisation, Canberra, Australian Capital Territory, Australia

    Tanja Strive

  16. Department of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Carl-Gustaf Thulin

  17. ANTAGENE, Wildlife Genetics Laboratory, La Tour de Salvagny, France

    Guillaume Queney

  18. Instituto de Estudios Sociales Avanzados (IESA-CSIC), Córdoba, Spain

    Rafael Villafuerte

  19. Department of Zoology, Faculty of Sciences, University of Johannesburg, Auckland Park, South Africa

    Nuno Ferrand

  20. Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA

    Leif Andersson

Authors
  1. Pedro Andrade
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  2. Joel M. Alves
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  3. Paulo Pereira
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  4. Carl-Johan Rubin
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  5. Eugénio Silva
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  6. C. Grace Sprehn
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  7. Erik Enbody
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  8. Sandra Afonso
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  9. Rui Faria
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  10. Yexin Zhang
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  11. Never Bonino
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  12. Janine A. Duckworth
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  13. Hervé Garreau
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  14. Mike Letnic
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  15. Tanja Strive
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  16. Carl-Gustaf Thulin
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Contributions

M.C., L.A., N.F., P.A. and J.M.A. conceived the study; N.B., J.A.D., H.G., M.L., T.S., C.-G.T., G.Q., R.V. and N.F. coordinated and performed sample collection. C.G.S. and S.A. performed experiments. P.A., M.C., J.M.A., P.P., C.-J.R., E.S., E.E., R.F., Y.Z., F.M.J. and L.A. analysed data. P.A., J.M.A., L.A. and M.C. wrote the paper with input from all other authors.

Corresponding authors

Correspondence to Pedro Andrade, Leif Andersson or Miguel Carneiro.

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Nature Ecology & Evolution thanks Makenzie Mabry, Ang McGaughran and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Admixture in introduced rabbit populations.

Admixture proportions calculated for a sample of domestic rabbits, wild rabbits from the native range and six introduced populations, based on genotype likelihoods. Results for several values of K are shown and the population names are given at the bottom of the figure.

Extended Data Fig. 2 Median-joining haplotype network of the mtDNA cytochrome b locus.

The size of the circle is proportional to the number of individuals that share the same haplotype. The number of mutations for each branch is given by the number of smaller cross-dashes on the branch.

Extended Data Fig. 3 Bayesian tree of mtDNA sequences.

Each individual is coloured according to the population of origin. Support values (Bayesian posterior probabilities, BPP) for each node are coloured according to the scale.

Extended Data Fig. 4 Genome-wide differences in allele frequency (ΔAF) between domestic rabbits and each introduced population.

Dots correspond to 100 kb windows, with a step of 25 kb. The red line corresponds to the top 0.1% of the empirical distribution for each contrast, while the blue line corresponds to the 1%. Only autosomes were considered due to the lower effective population size of the X chromosome, which results in higher-than-average differentiation for variants within that chromosome.

Extended Data Fig. 5 Signatures of selection on genomic regions of high differentiation (differences in allele frequency, ΔAF) between domestic rabbits and each introduced population.

For each introduced population we calculated Tajima’s D in 100 kb windows, with a step of 25 kb. We then inspected D values for the top 0.1% ΔAF windows in each domestic-population contrast (light brown, left box in each plot, n = 82) and compared them to genome-wide D values (dark brown, right box in each plot, n = 1,000 randomly selected windows). For each plot, whisker ends represent minimum and maximum of the distribution; box edges represent first quartile and third quartile of the distribution; centre line represents the median. See Supplementary Table 3 for statistical testing on the full genome-wide dataset.

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Andrade, P., Alves, J.M., Pereira, P. et al. Selection against domestication alleles in introduced rabbit populations. Nat Ecol Evol 8, 1543–1555 (2024). https://doi.org/10.1038/s41559-024-02443-3

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