The large state space of gene genealogies is a major hurdle for inference methods based on Kingman's coalescent. Here, we present a new Bayesian approach for inferring past population sizes, which relies on a lower-resolution coalescent process that we refer to as "Tajima's coalescent." Tajima's coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima's Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman's coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.

中文翻译：

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通过对田岛树木采样进行人口规模变化的贝叶斯估计。


基因谱系的巨大状态空间是基于金曼合并的推理方法的主要障碍。在这里，我们提出了一种新的贝叶斯方法来推断过去的人口规模，该方法依赖于较低分辨率的合并过程，我们称之为“田岛合并”。 Tajima 的聚结模型的状态空间要小得多，因此与标准的 Kingman 聚结模型相比，它是一个计算效率更高的模型。我们提供了一种新算法，无需重组即可对数据进行高效且精确的似然计算，该算法利用有向无环图和相应定制的马尔可夫链蒙特卡罗方法。我们使用模拟数据和人类数据，将通过田岛树采样对种群规模变化进行贝叶斯估计 (BESTT) 的性能与 BEAST 中基于合并的推理的流行实现进行比较。我们凭经验证明 BESTT 可以准确推断有效种群规模，并且它进一步提供了 Kingman 合并的有效替代方案。这里描述的算法在 R 包 phylodyn 中实现，可以从 https://github.com/JuliaPalacios/phylodyn 下载。