Model-based inference of recombination hotspots in a highly variable oncogene [corrected].
An emergent problem in the study of pathogen evolution is our ability to determine the extent to which their rapidly evolving genomes recombine. Such information is necessary and essential for locating pathogenicity loci using association studies, and it also directs future screening, therapeutic and vaccination strategies. Recombination also complicates the use of phylogenetic approaches to infer evolutionary parameters including selection pressures. Reliable methods that identify the presence of regions of recombination are therefore vital. We illustrate the use of an integrated model-based approach to inferring recombination structure using all available sequences of the highly variable, transforming Kaposi's sarcoma-associated herpesviral gene, ORF-K1. This technique learns the parameters of a statistical model that takes recombination hotspots, population genetic effects, and variable rates of mutation into account. As there are no known mechanisms to explain the high mutation rate in this DNA viral gene, recombination may account for some of the variability observed. We infer recombination hotspots in conserved sites such as the tyrosine kinase signaling motif, referred to here as recombination drift, as well as in nonconserved sites, a process described as recombination shift.