A non-homogeneous model of chromosome-number evolution to reveal shifts in the transition patterns across the phylogeny

Anat Shafir, Keren Halabi, Marcial Escudero, Itay Mayrose*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Changes in chromosome numbers, including polyploidy and dysploidy events, play a key role in eukaryote evolution as they could expediate reproductive isolation and have the potential to foster phenotypic diversification. Deciphering the pattern of chromosome-number change within a phylogeny currently relies on probabilistic evolutionary models. All currently available models assume time homogeneity, such that the transition rates are identical throughout the phylogeny. Here, we develop heterogeneous models of chromosome-number evolution that allow multiple transition regimes to operate in distinct parts of the phylogeny. The partition of the phylogeny to distinct transition regimes may be specified by the researcher or, alternatively, identified using a sequential testing approach. Once the number and locations of shifts in the transition pattern are determined, a second search phase identifies regimes with similar transition dynamics, which could indicate on convergent evolution. Using simulations, we study the performance of the developed model to detect shifts in patterns of chromosome-number evolution and demonstrate its applicability by analyzing the evolution of chromosome numbers within the Cyperaceae plant family. The developed model extends the capabilities of probabilistic models of chromosome-number evolution and should be particularly helpful for the analyses of large phylogenies that include multiple distinct subclades.

Original languageEnglish
Pages (from-to)1733-1744
Number of pages12
JournalNew Phytologist
Volume238
Issue number4
DOIs
StatePublished - May 2023

Keywords

  • chromosome-number evolution
  • convergent evolution
  • dysploidy
  • evolutionary models
  • polyploidy
  • rate heterogeneity

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