Dating phylogenies with sequentially sampled tips.

We develop a Bayesian Markov chain Monte Carlo (MCMC) algorithm for estimating divergence times using sequentially sampled molecular sequences. This type of data is commonly collected during viral epidemics and is sometimes available from different species in ancient DNA studies. We derive the distribution of ages of nodes in the tree under a birth-death-sequential-sampling (BDSS) model and use it as the prior for divergence times in the dating analysis. We implement the prior in the MCMCtree program in the PAML package for divergence dating. The BDSS prior is very flexible and, with different parameters, can generate trees of very different shapes, suitable for examining the sensitivity of posterior time estimates. We apply the method to a data set of SIV/HIV-2 genes in comparison with a likelihood-based dating method, and to a data set of influenza H1 genes from different hosts in comparison with the Bayesian program BEAST. We examined the impact of tree topology on time estimates and suggest that multifurcating consensus trees should be avoided in dating analysis. We found posterior time estimates for old nodes to be sensitive to the priors on times and rates and suggest that previous Bayesian dating studies may have produced overconfident estimates.