Chromploid: An R package for chromosome number evolution across the plant tree of life. (11th April 2018)

• Record Type: Journal Article
• Authors: Zenil‐Ferguson, Rosana; Burleigh, J. Gordon; Ponciano, José Miguel
• Is Part Of: Applications in plant sciences. Volume 6:Number 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-04-11
• Subjects: BiChroM -- chromosome number evolution -- likelihood function -- polyploidy; Plants -- Periodicals; Plant physiology -- Periodicals; Plant Physiological Phenomena; Plant physiology; Plants; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 580
• DOI: 10.1002/aps3.1037
• Languages: English
• ISSNs: 2168-0450

Abstract

Abstract : Premise of the Study: Polyploidy has profound evolutionary consequences for land plants. Despite the availability of large phylogenetic and chromosomal data sets, estimating the rates of polyploidy and chromosomal evolution across the tree of life remains a challenging, computationally complex problem. We introduce the R package chromploid, which allows scientists to perform inference of chromosomal evolution rates across large phylogenetic trees. Methods and Results: chromploid is an open‐source package in the R environment that calculates the likelihood function of models of chromosome evolution. Models of discrete character evolution can be customized using chromploid. We demonstrate the performance of the BiChroM model, testing for associations between rates of chromosome doubling (as a proxy for polyploidy) and a binary phenotypic character, within chromploid using simulations and empirical data from Solanum . In simulations, estimated chromosome‐doubling rates were unbiased and the variance decreased with larger trees, but distinguishing small differences in rates of chromosome doubling, even from large data sets, remains challenging. In the Solanum data set, a custom model of chromosome number evolution demonstrated higher rates of chromosome doubling in herbaceous species compared to woody. Conclusions: chromploid enables researchers to perform robust likelihood‐based inferences using complex models of chromosome number evolution across large phylogenies.