title: Estimating divergence times in the presence of an overdispersed molecular clock.
abstract: Molecular loci that fail relative-rate tests are said to be "overdispersed." Traditional molecular-clock approaches to estimating divergence times do not take this into account. In this study, a method was developed to estimate divergence times using loci that may be overdispersed. The approach was to replace the traditional Poisson process assumption with a more general stationary process assumption. A probability model was developed, and an accompanying computer program was written to find maximum-likelihood estimates of divergence times under both the Poisson process and the stationary process assumptions. In simulation, it was shown that confidence intervals under the traditional Poisson assumptions often vastly underestimate the true confidence limits for overdispersed loci. Both models were applied to two data sets: one from land plants, the other from the higher metazoans. In both cases, the traditional Poisson process model could be rejected with high confidence. Maximum-likelihood analysis of the metazoan data set under the more general stationary process suggested that their radiation occurred well over a billion years ago, but confidence intervals were extremely wide. It was also shown that a model consistent with a Cambrian (or nearly Cambrian) origination of the animal phyla, although significantly less likely than a much older divergence, fitted the data well. It is argued that without an a priori understanding of the variance in the time between substitutions, molecular data sets may be incapable of ever establishing the age of the metazoan radiation.
authors.affiliation: Center for Population Biology, University of California at Davis, CA, USA.
authors.forename: D J
authors.initials: DJ
authors.lastname: Cutler
mesh_terms: D000818:Animals; D019143:Evolution, Molecular; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D010802:Phylogeny; D010944:Plants; D013997:Time Factors