Inferring Spatial Phylogenetic Variation Along Nucleotide Sequences: A Multiple Changepoint Model.

We develop a Bayesian multiple changepoint model to infer spatial phylogenetic variation (SPV) along aligned molecular sequence data. SPV occurs in sequences from organisms that have undergone biological recombination or when evolutionary rates and selective pressures vary along the sequences. This Bayesian approach permits estimation of uncertainty regarding recombination, the crossing-over locations, and all other model parameters. The model assumes that the sites along the data separate into an unknown number of contiguous segments, each with possibly different evolutionary relationships between organisms, evolutionary rates, and transition: transversion ratios. We develop a transition kernel, use reversible-jump Markov chain Monte Carlo to fit our model, and draw inference from both simulated and real data. Through simulation, we examine the minimal length recombinant segment that our model can detect for several levels of evolutionary divergence. We examine the entire genome of a reported human immunodeficiency virus (HIV)-1 isolate, related to a purported recombinant virus thought to be the causative agent of an epidemic outbreak of HIV-1 infection among intravenous drug users in Russia. We find that regions of the genome differ in their evolutionary history and selective pressures. There is strong evidence for multiple crossovers along the genome and frequent shifts in selective pressure changes throughout the vif through env genes. [ABSTRACT FROM AUTHOR]