Cladogenetic and Anagenetic Models of Chromosome Number Evolution: A Bayesian Model Averaging Approach.

William A Freyman,Sebastian Höhna

doi:10.1093/sysbio/syx065

Abstract

Chromosome number is a key feature of the higher-order organization of the genome, and changes in chromosome number play a fundamental role in evolution. Dysploid gains and losses in chromosome number, as well as polyploidization events, may drive reproductive isolation and lineage diversification. The recent development of probabilistic models of chromosome number evolution in the groundbreaking work by Mayrose et al. (2010, ChromEvol) have enabled the inference of ancestral chromosome numbers over molecular phylogenies and generated new interest in studying the role of chromosome changes in evolution. However, the ChromEvol approach assumes all changes occur anagenetically (along branches), and does not model events that are specifically cladogenetic. Cladogenetic changes may be expected if chromosome changes result in reproductive isolation. Here we present a new class of models of chromosome number evolution (called ChromoSSE) that incorporate both anagenetic and cladogenetic change. The ChromoSSE models allow us to determine the mode of chromosome number evolution; is chromosome evolution occurring primarily within lineages, primarily at lineage splitting, or in clade-specific combinations of both? Furthermore, we can estimate the location and timing of possible chromosome speciation events over the phylogeny. We implemented ChromoSSE in a Bayesian statistical framework, specifically in the software RevBayes, to accommodate uncertainty in parameter estimates while leveraging the full power of likelihood based methods. We tested ChromoSSE's accuracy with simulations and re-examined chromosomal evolution in Aristolochia, Carex section Spirostachyae, Helianthus, Mimulus sensu lato (s.l.), and Primula section Aleuritia, finding evidence for clade-specific combinations of anagenetic and cladogenetic dysploid and polyploid modes of chromosome evolution. [Anagenetic; Bayes factors; chromosome evolution; chromosome speciation; chromoSSE; cladogenetic; dysploidy; phylogenetic models; polyploidy; reversible-jump Markov chain Monte Carlo; whole genome duplication.].

Highlights

A central organizing component of the higher-order architecture of the genome is chromosome number, and changes in chromosome number have long been understood to play a fundamental role in evolution
When the data was simulated under a process that included both cladogenetic and anagenetic evolution we found a decrease in accuracy in the root chromosome number estimates in all cases
When comparing estimates from ChromoSSE that account for unobserved speciation to estimates from the non-state-dependent speciation and extinction (SSE) model that does not account for unobserved speciation, we found that the accuracy in estimating model parameter values was mostly similar, though for some cladogenetic parameters there was higher accuracy with the model that did account for unobserved speciation

Summary

Introduction

A central organizing component of the higher-order architecture of the genome is chromosome number, and changes in chromosome number have long been understood to play a fundamental role in evolution. Evolutionary biologists have studied the macroevolutionary consequences of chromosome changes within a molecular phylogenetic framework, mostly due to the groundbreaking work of Mayrose et al (2010, ChromEvol) which introduced likelihood-based models of chromosome number evolution. Studying cladogenetic chromosome changes in a phylogenetic framework has been di cult since the approach used by ChromEvol models only anagenetic changes and ignores the changes that occur at speciation events and may be expected if chromosome changes result in reproductive isolation. Recent work by Zhan et al (2016) revealed phylogenetic evidence that polyploidization is frequently cladogenetic in land plants Their approach did not examine the role dysploid changes may play in speciation, and it required a two step analysis in which one first used ChromEvol to infer ploidy levels, and a second modeling step to infer the proportion of ploidy shifts that were cladogenetic.

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