A General Comparison of Relaxed Molecular Clock Models

TLDR

Several relaxed molecular clock models exist, but it remains unclear which best fits real data, especially regarding rate autocorrelation and divergence‑time priors. This study introduces a general benchmark to evaluate alternative relaxed clock models. The authors reimplemented existing models—including a lognormal clock and various priors (birth–death, Dirichlet, uniform)—and added a new autocorrelated CIR process, then compared their fit on three protein datasets using Bayes factors from thermodynamic integration within a unified Bayesian framework. The autocorrelated CIR and lognormal models performed similarly and outperformed uncorrelated models across all datasets, while the optimal divergence‑time prior varied with the data, providing practical guidelines for model selection in molecular dating.

Abstract

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 bench mark of alternative relaxed clock models. We have reimplemented most of the already existing models, including the popular lognormal 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 3 different protein data sets from eukaryotes, vertebrates, and mammals, by computing Bayes factors using a numerical method called thermodynamic integration. We find that the 2 autocorrelated models, CIR and lognormal, have a similar fit and clearly outperform uncorrelated models on all 3 data sets. 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.