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Model-Based Detection of Whole-Genome Duplications in a Phylogeny.

Authors
  • Zwaenepoel, Arthur1, 2, 3
  • Van de Peer, Yves1, 2, 3, 4
  • 1 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium. , (Belgium)
  • 2 Center for Plant Systems Biology, VIB, Ghent, Belgium. , (Belgium)
  • 3 Bioinformatics Institute Ghent, Ghent, Belgium. , (Belgium)
  • 4 Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa. , (South Africa)
Type
Published Article
Journal
Molecular Biology and Evolution
Publisher
Oxford University Press
Publication Date
Sep 01, 2020
Volume
37
Issue
9
Pages
2734–2746
Identifiers
DOI: 10.1093/molbev/msaa111
PMID: 32359154
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Ancient whole-genome duplications (WGDs) leave signatures in comparative genomic data sets that can be harnessed to detect these events of presumed evolutionary importance. Current statistical approaches for the detection of ancient WGDs in a phylogenetic context have two main drawbacks. The first is that unwarranted restrictive assumptions on the "background" gene duplication and loss rates make inferences unreliable in the face of model violations. The second is that most methods can only be used to examine a limited set of a priori selected WGD hypotheses and cannot be used to discover WGDs in a phylogeny. In this study, we develop an approach for WGD inference using gene count data that seeks to overcome both issues. We employ a phylogenetic birth-death model that includes WGD in a flexible hierarchical Bayesian approach and use reversible-jump Markov chain Monte Carlo to perform Bayesian inference of branch-specific duplication, loss, and WGD retention rates across the space of WGD configurations. We evaluate the proposed method using simulations, apply it to data sets from flowering plants, and discuss the statistical intricacies of model-based WGD inference. © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: [email protected]

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