The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to anti-retroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate them [1, 2, 3, 4]. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action [5, 6, 7]. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches.\n\nSpecifically, we focus on our recent paper [2], in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. We interpret this as evidence that resistance to early HIV drugs that failed quickly and predictably was driven by soft sweeps while evolution of resistance to better drugs is both less frequent and when it takes place it is associated with harder sweeps due to an effectively lower HIV population mutation rate ({theta}). Recently, Harris et al. have proposed an alternative interpretation [8]: the signal could be due to an increase in the selective benefit of mutations conferring resistance to better drugs. Therefore, better drugs lead to faster sweeps with less opportunity for recombination to rescue diversity. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.