Speaker
Description
Genetic recombination processes are a crucial driver of viral evolution and allow viruses to make large jumps in fitness space or to recombine parts of the genome with epistatic interactions. Different modes of recombination exist in RNA viruses, such as influenza or coronaviruses.
Reassortment reshuffles the spatially separated genome segments of influenza viruses and is at the heart of the emergence of novel pandemic influenza strains, while template switching in coronaviruses is suspected of having played an essential role in the emergence of SARS-CoV-1 and is an important driver of the evolutionary dynamics of SARS-like viruses. Beyond their implication in host switching, genetic recombination between different lineages of endemic human influenza viruses or human coronaviruses occurs at relatively high frequency and can lead to novel viral clades that sweep the population.
At its core, the recombination rates denote the recombination rate between viral lineages as a result of a co-infection event of an individual. As such, the recombination rate is intrinsically linked to co-infection rates.
Recombination can be detected from the genomes of pathogens, using, for example, Bayesian phylogenetic network approaches. These methods model viruses' ancestry using a joint coalescence and recombination process and allow the estimation of viral recombination rates.
In this talk, I will present how we can use recombination rates to quantify epidemiological dynamics that are otherwise complex to estimate directly. To do so, I will first show how we can quantify recombination rates from genomes using Bayesian inference. I will then show how genetic recombination rates relate to co-infection rates in the presence of superspreading or population structure. Next, I will show how the recombination rates relate to epidemiological parameters, such as disease prevalence or attack rates, and lastly, I will provide examples of how these parameters can be estimated for influenza viruses.
