A General Comparison of Relaxed Molecular Clock Models - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Molecular Biology and Evolution Year : 2007

A General Comparison of Relaxed Molecular Clock Models


Several models have been proposed to relax the molecular clock in order to estimate divergence times. However, it is unclear which model has the best fit to real data, and should therefore be used to perform molecular dating. In particular, we do not know whether rate autocorrelation should be considered, or which prior on divergence times should be used. In this work, we propose a general benchmark of alternative relaxed clock models. We have reimplemented most of the already existing models, including the popular log-normal model, as well as various prior choices for divergence times (birth-death, Dirichlet, uniform), in a common Bayesian statistical framework. We also propose a new autocorrelated model, called the CIR process, with well defined stationary properties. We assess the relative fitness of these models and priors, when applied to three different protein data sets from eukaryotes, vertebrates and mammals, by computing Bayes factors using a numerical method called thermodynamic integration. We find that the two autocorrelated models, CIR and log-normal, have a similar fit, and clearly outperform uncorrelated models on all three datasets. In contrast, the optimal choice for the divergence time prior is more dependent on the data investigated. Altogether, our results provide useful guidelines for model choice in the field of molecular dating, while opening the way to more extensive model comparisons.
Fichier principal
Vignette du fichier
msm193.pdf (303.7 Ko) Télécharger le fichier
Origin Publisher files allowed on an open archive

Dates and versions

lirmm-00193697 , version 1 (15-06-2021)




Thomas Lepage, David Bryant, Herve Philippe, Nicolas Lartillot. A General Comparison of Relaxed Molecular Clock Models. Molecular Biology and Evolution, 2007, 24 (12), pp.2669-2680. ⟨10.1093/molbev/msm193⟩. ⟨lirmm-00193697⟩
121 View
43 Download



Gmail Mastodon Facebook X LinkedIn More