We propose to develop a model that unites viral population genetics and phylogenetics to investigate HIV transmission histories. This genealogy-based population genetic model will accommodate for variable population sizes within and between infected hosts, while taking into account phylogenetic relationships between the patients.
It is our objective to implement this model in a recently developed, highly flexible Bayesian framework and to validate this method on a comprehensive set of clonal sequences sampled over time from a well-documented HIV transmission chain. We aim to apply our novel approach to at least three well-characterized HIV transmission chains and to extend the application to other rapidly evolving virus populations.
This will allow us to estimate the strength and magnitude of the genetic bottleneck associated with viral transmission and to evaluate its impact on evolution at the population level. Thereby, we can address the switching of co-receptor usage within patients and the spread of immune-escape and drug resistance mutations in the HIV infected population. This research will also result in statistical methods that provide a more realistic description of viral evolution with important practical applications in viral epidemiology.
Fields of science
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