Bayesian random local clocks, or one rate to rule them all
Open Access
- 31 August 2010
- journal article
- Published by Springer Science and Business Media LLC in BMC Biology
- Vol. 8 (1), 114
- https://doi.org/10.1186/1741-7007-8-114
Abstract
Relaxed molecular clock models allow divergence time dating and "relaxed phylogenetic" inference, in which a time tree is estimated in the face of unequal rates across lineages. We present a new method for relaxing the assumption of a strict molecular clock using Markov chain Monte Carlo to implement Bayesian modeling averaging over random local molecular clocks. The new method approaches the problem of rate variation among lineages by proposing a series of local molecular clocks, each extending over a subregion of the full phylogeny. Each branch in a phylogeny (subtending a clade) is a possible location for a change of rate from one local clock to a new one. Thus, including both the global molecular clock and the unconstrained model results, there are a total of 22n-2 possible rate models available for averaging with 1, 2, ..., 2n - 2 different rate categories.Keywords
This publication has 48 references indexed in Scilit:
- Choosing among Partition Models in Bayesian PhylogeneticsMolecular Biology and Evolution, 2010
- Unified Framework to Evaluate Panmixia and Migration Direction Among Multiple Sampling LocationsGenetics, 2010
- Fully Bayesian tests of neutrality using genealogical summary statisticsBMC Genomic Data, 2008
- BEAST: Bayesian evolutionary analysis by sampling treesBMC Evolutionary Biology, 2007
- PAML 4: Phylogenetic Analysis by Maximum LikelihoodMolecular Biology and Evolution, 2007
- Relaxed Phylogenetics and Dating with ConfidencePLoS Biology, 2006
- The modern molecular clockNature Reviews Genetics, 2003
- The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation ProblemJournal of the American Statistical Association, 1994
- Variable Selection via Gibbs SamplingJournal of the American Statistical Association, 1993
- Evolutionary trees from DNA sequences: A maximum likelihood approachJournal of Molecular Evolution, 1981