The within-host evolutionary dynamics of TB remain unclear, and underlying biological characteristics render standard population genetic approaches based upon the Wright-Fisher model largely inappropriate. In addition, the compact genome combined with an absence of recombination is expected to result in strong purifying selection effects. Thus, it is imperative to establish a biologically-relevant evolutionary framework incorporating these factors in order to enable an accurate study of this important human pathogen. Further, such a model is critical for inferring fundamental evolutionary parameters related to patient treatment, including mutation rates and the severity of infection bottlenecks. We here implement such a model and infer the underlying evolutionary parameters governing within-patient evolutionary dynamics. Results demonstrate that the progeny skew associated with the clonal nature of TB severely reduces genetic diversity and that the neglect of this parameter in previous studies has led to significant mis-inference of mutation rates. As such, our results suggest an underlying de novo mutation rate that is considerably faster than previously inferred, and a progeny distribution differing significantly from Wright-Fisher assumptions. This inference represents a more appropriate evolutionary null model, against which the periodic effects of positive selection, associated with drug-resistance for example, may be better assessed.

Table S1. Simulation results.

These data were generated using SLiM v.3. The script used for generating the data is also attached here.

Simulations were run through R and statistics were estimated, from ms files, using the R-package PopGenome.

Parameters simulated in the table are ordered by descending mutation rate (μ) and corresponding values of progeny skew (Ψ) and bottleneck severity (N2). Given are the mean and standard deviation of the summary statistics from 1000 replicates.

The last column from Table_S1 was not generated with PopGenome, but by counting the number of simulations that had zero segregating sites (given as percentage of invariables by parameter set).