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Inference of Ancient Polyploidy from Genomic Data

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Polyploidy

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2545))

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Abstract

Whole-genome sequence data have revealed that numerous eukaryotic organisms derive from distant polyploid ancestors, even when these same organisms are genetically and karyotypically diploid. Such ancient whole-genome duplications (WGDs) have been important for long-term genome evolution and are often speculatively associated with important evolutionary events such as key innovations, adaptive radiations, or survival after mass extinctions. Clearly, reliable methods for unveiling ancient WGDs are key toward furthering understanding of the long-term evolutionary significance of polyploidy. In this chapter, we describe a set of basic established comparative genomics approaches for the inference of ancient WGDs from genomic data based on empirical age distributions and collinearity analyses, explain the principles on which they are based, and illustrate a basic workflow using the software “wgd,” geared toward a typical exploratory analysis of a newly obtained genome sequence.

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Acknowledgments

We wish to thank Yves Van de Peer and Zhen Li for their support and helpful feedback. Hengchi Chen and Arthur Zwaenepoel acknowledge the PhD Fellowship of the Research Foundation—Flanders (FWO).

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Authors and Affiliations

  1. Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

    Hengchi Chen & Arthur Zwaenepoel

  2. VIB Center for Plant Systems Biology, VIB, Ghent, Belgium

    Hengchi Chen

Authors
  1. Hengchi Chen
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  2. Arthur Zwaenepoel
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Corresponding author

Correspondence to Arthur Zwaenepoel .

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Editors and Affiliations

  1. Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

    Yves Van de Peer

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Chen, H., Zwaenepoel, A. (2023). Inference of Ancient Polyploidy from Genomic Data. In: Van de Peer, Y. (eds) Polyploidy. Methods in Molecular Biology, vol 2545. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2561-3_1

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  • DOI: https://doi.org/10.1007/978-1-0716-2561-3_1

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2560-6

  • Online ISBN: 978-1-0716-2561-3

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