Abstract
The Kingman coalescent and its developments are often considered among the most important advances in population genetics of the last decades. Demographic inference based on coalescent theory has been used to reconstruct the population dynamics and evolutionary history of several species, including Mycobacterium tuberculosis (MTB), an important human pathogen causing tuberculosis. One key assumption of the Kingman coalescent is that the number of descendants of different individuals does not vary strongly, and violating this assumption could lead to severe biases caused by model misspecification. Individual lineages of MTB are expected to vary strongly in reproductive success because 1) MTB is potentially under constant selection due to the pressure of the host immune system and of antibiotic treatment, 2) MTB undergoes repeated population bottlenecks when it transmits from one host to the next, and 3) some hosts show much higher transmission rates compared with the average (superspreaders).Here, we used an approximate Bayesian computation approach to test whether multiple-merger coalescents (MMC), a class of models that allow for large variation in reproductive success among lineages, are more appropriate models to study MTB populations. We considered 11 publicly available whole-genome sequence data sets sampled from local MTB populations and outbreaks and found that MMC had a better fit compared with the Kingman coalescent for 10 of the 11 data sets. These results indicate that the null model for analyzing MTB outbreaks should be reassessed and that past findings based on the Kingman coalescent need to be revisited.
Highlights
The Kingman coalescent and its developments are often considered among the most important advances in population genetics of the last decades
The MTB genome can be considered as a single genetic locus, and one single genealogy describes the relationships among all MTB strains in 155 any data set
We modeled skewed offspring distributions with two multiple merger coalescents (MMC) models deriving from explicit population models: 1) the Beta coalescent, in which the probability of each individual to coalesce in a multiple merger event is regulated by a Beta distribution with parameters α and 2- α
Summary
The Kingman coalescent and its developments are often considered among the most important advances in population genetics of the last decades. Given the undergoing efforts in controlling and stopping the spread of tuberculosis, and the global impact of this pathogen that causes more than 1.4 million deaths each year (WHO 2019), it is important to evaluate the adequacy of the population genetic models used to study tuberculosis epidemics To this end, we considered eleven MTB whole genome sequence (WGS) data sets, and used an Approximate 140 Bayesian Computation (ABC) approach based on simulations to find the best fitting model among Kingman’s coalescent, and two MMC models, the Beta coalescent (Schweinsberg 2003) and the Dirac coalescent (Eldon and Wakeley 2006). Demographic inference based on models assuming non-skewed offspring distribution (i.e. Kingman’s coalescent) likely leads to inaccurate results when applied to MTB epidemics, and potentially to the epidemics of other pathogens with similar life histories
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