Melding phylodynamic inference with molecular archeology to unravel the drivers of the pandemic emergence of HIV-1.

The analysis of viral genome data using evolutionary approaches has become a major asset in characterizing the epidemic dynamics of viruses. Fast-evolving viruses such as the Human Immunodeficiency Virus 1 (HIV-1) can accumulate significant diversity over short time scales, leading to a genomic imprint of the ecological context in which it spreads. By statistically analysing these genetic differences, it is possible to identify the factors underlying sustained viral transmission. Yet, these factors are still largely unresolved for the AIDS pandemic caused by HIV-1, which is in large part due to the inherent limitations of historical inferences based on contemporary virus genetic data. Moreover, the simplifying assumptions that underlie any statistical model can bias epidemiological inferences. Here, I propose to capitalise on the ongoing efforts in our group for generating 'fossil' HIV genetic sequences from the early epidemic history to test competing hypotheses about the causal factors underlying the success of the HIV-1 group M pandemic variant. To ensure that the best possible model is used for these inferences we will first study how substitution rate variation among sites and lineages impacts the dating of historical events, and how this can be adequately modeled. We also aim to further facilitate hypothesis testing in a Bayesian phylodynamic framework by advancing a broadly applicable model that can quantify and test for lineage effects on the evolutionary rate.