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Coalescent Models of Demographic History: Application to Plant Domestication

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Population Genomics

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Abstract

A detailed understanding of the origins of domesticated species is important for many disciplines. Recent advances in this field have been made with the use of genome-wide polymorphisms and improved statistical methods. In this chapter, we review the most important developments of coalescent models for the inference of demographic history from genome-wide data. These methods include sequential Markovian models, range expansion models, and approximate Bayesian computation. We summarize the applications of the methods to some major cultivated cereals, including rice, maize, and millet. Then we discuss extensions of these methods that would better incorporate the interaction of demographic processes with selection and inclusion of gene flow with wild related species.

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Acknowledgments

This work has been supported by a grant from French National Research Agency ANR-13-BSV7-0017, AFRICROP. It was developed in the framework of the Grenoble Alpes Data Institute, supported by the French National Research Agency ANR-15-IDEX-02, “Investissements d’avenir.”

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Authors and Affiliations

  1. Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble, France

    Olivier François

  2. Université de Montpellier, Institut pour la Recherche et le Développement, Unité Diversité, Adaptation, Développement des plantes, Montpellier, France

    Philippe Cubry, Concetta Burgarella & Yves Vigouroux

  3. Human Evolution, Department of Organismal Biology, Uppsala University, Uppsala, Sweden

    Concetta Burgarella

Authors
  1. Olivier François
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  2. Philippe Cubry
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  3. Concetta Burgarella
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  4. Yves Vigouroux
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Correspondence to Olivier François .

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François, O., Cubry, P., Burgarella, C., Vigouroux, Y. (2020). Coalescent Models of Demographic History: Application to Plant Domestication. In: Population Genomics. Springer, Cham. https://doi.org/10.1007/13836_2020_74

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  • DOI: https://doi.org/10.1007/13836_2020_74

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