Final Activity Report Summary - HIV transmission (Population genetic modelling and analysis of HIV transmission histories)
We developed a model that allowed us to characterise the human immunodeficiency virus (HIV) transmission dynamics. Parameters of the transmission chain model could be inferred using Bayesian statistical inference from genetic data. The model accommodated population size changes between and within infected individuals, thus allowing for an accurate quantification of the genetic bottleneck during transmission. We implemented this model in Bayesian statistical inference software (BEAST), therefore allowing for flexible parameter estimation from serially sampled gene sequences.
We also generated an unprecedented amount of viral sequence data from a known HIV transmission chain. In particular, we obtained clonal sequences from multiple samples per patient taken at different time points. This data was analysed using the transmission chain model by the time of the project completion.
In addition to the transmission chain work, we developed novel methods to estimate absolute rates of synonymous and non-synonymous substitution rates and identify recombinant sequences.
We also generated an unprecedented amount of viral sequence data from a known HIV transmission chain. In particular, we obtained clonal sequences from multiple samples per patient taken at different time points. This data was analysed using the transmission chain model by the time of the project completion.
In addition to the transmission chain work, we developed novel methods to estimate absolute rates of synonymous and non-synonymous substitution rates and identify recombinant sequences.