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 to the average (super-spreaders). 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 eleven publicly available whole genome sequence data sets sampled from local MTB populations and outbreaks, and found that MMC had a better fit compared to the Kingman coalescent for ten of the eleven 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.

中文翻译：

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结核分枝杆菌暴发中的多种合并家谱

金曼联盟及其发展通常被认为是过去几十年来人口遗传学最重要的进步之一。基于聚结理论的人口统计学推论已被用于重建几种物种的种群动态和进化历史，包括结核分枝杆菌（MTB），一种引起结核病的重要人类病原体。金曼联盟的一个关键假设是，不同个体的后代数量变化不大，违反这一假设可能会导致模型错误指定导致严重偏差。预计MTB的各个谱系在繁殖成功方面会有很大差异，因为1）由于宿主免疫系统和抗生素治疗的压力，MTB可能处于不断选择中，2）MTB在从一台主机传输到另一台主机时经历反复的人口瓶颈，并且3）一些主机的传输速率比平均水平（超级扩展器）高得多。在这里，我们使用近似贝叶斯计算方法来测试多重合并合并（MMC）是否是更合适的模型来研究MTB种群，MMC是一类允许世系间生殖成功发生较大差异的模型。我们考虑了从本地MTB种群和暴发中采样的11个可公开获得的全基因组序列数据集，发现11个数据集中的10个数据集与Kingman联盟相比，MMC的拟合度更高。这些结果表明，应该重新评估用于分析MTB爆发的无效模型，并且需要重新审视基于Kingman联盟的以往发现。