Zhang Julie, Palacios Julia A
Department of Statistics, Stanford University, CA 94305, USA.
Department of Biomedical Data Science, Stanford University, CA 94305, USA.
Philos Trans R Soc Lond B Biol Sci. 2025 Feb 13;380(1919):20230306. doi: 10.1098/rstb.2023.0306. Epub 2025 Feb 20.
Variation in a sample of molecular sequence data informs about the past evolutionary history of the sample's population. Traditionally, Bayesian modelling coupled with the standard coalescent is used to infer the sample's bifurcating genealogy and demographic and evolutionary parameters such as effective population size and mutation rates. However, there are many situations where binary coalescent models do not accurately reflect the true underlying ancestral processes. Here, we propose a Bayesian non-parametric method for inferring effective population size trajectories from a multifurcating genealogy under the [Formula: see text]-coalescent. In particular, we jointly estimate the effective population size and the model parameter for the Beta-coalescent model, a special type of [Formula: see text]-coalescent. Finally, we test our methods on simulations and apply them to study various viral dynamics as well as Japanese sardine population size changes over time. The code and vignettes can be found in the phylodyn package.This article is part of the theme issue '"A mathematical theory of evolution": phylogenetic models dating back 100 years'.
分子序列数据样本中的变异反映了该样本群体过去的进化历史。传统上,贝叶斯建模与标准的溯祖理论相结合,用于推断样本的分支谱系以及诸如有效种群大小和突变率等人口统计学和进化参数。然而,在许多情况下,二元溯祖模型并不能准确反映真实的潜在祖先过程。在此,我们提出一种贝叶斯非参数方法,用于在[公式:见原文]-溯祖理论下从多分支谱系推断有效种群大小轨迹。特别地,我们联合估计有效种群大小和Beta-溯祖模型(一种特殊类型的[公式:见原文]-溯祖模型)的模型参数。最后,我们在模拟中测试我们的方法,并将其应用于研究各种病毒动态以及日本沙丁鱼种群大小随时间的变化。代码和 vignettes 可在 phylodyn 包中找到。本文是主题为“进化的数学理论”的特刊的一部分:可追溯到 100 年前的系统发育模型。