HENRY

The December issue of Molecular Biology and Evolution is out, and my pick is the new paper by Thomas Lepage, Dave Bryant, Hervé Philippe and Nicolas Lartillot which reviews and compares the recent work on Relaxed Clock models for phylogenetic dating. These relaxed clock models are rapidly becoming the weapon of choice for dating events in phylogenetics and I’ve only seen them implemented in BEAST, so far, but this paper looks to be a good update on the progress here.

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.

Posted on December 20th, 2007 by Simon Greenhill