Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, is coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. We introduce a novel method for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables our method to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human–chimp–gorilla scenario, we show that our method has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies, and we report on their accuracy. Furthermore, we discuss a potential direction for scaling the method to larger data sets through a divide-and-conquer approach. This accuracy means our method is useful now, and by deriving transition rates by simulation, it is flexible enough to enable future implementations of various population models.

中文翻译：

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在合并重组下使用近似似然的变分推断

聚结方法已被证明是群体遗传学、系统发育学、流行病学和其他领域的强大工具。用于分析越来越普遍的大型基因组比对的一个有前途的途径是聚结隐马尔可夫模型（coalHMM）方法，但这些方法缺乏一般的可用性和灵活性。我们介绍了一种新的方法，用于自动学习煤 HMM 并使用黑盒变分推理推断进化参数的后验分布，并通过模拟经验性地推导出局部谱系之间的转换率。这种推导使我们的方法能够直接处理三个或四个分类单元，并通过分而治之的方法处理更多分类单元。使用类似于人类-黑猩猩-大猩猩场景的模拟数据集，我们证明了我们的方法与以前的coholHMM方法具有相当或更好的准确性。准确推断出物种分化时间和种群规模。该方法还推断了当地的家谱，我们报告了它们的准确性。此外，我们讨论了通过分而治之的方法将该方法扩展到更大数据集的潜在方向。这种准确性意味着我们的方法现在很有用，并且通过模拟得出转变率，它足够灵活，可以在未来实现各种人口模型。