Department of Mathematics, Aarhus University, Denmark.
Bioinformatics Research Center, Aarhus University, Denmark.
Theor Popul Biol. 2024 Jun;157:14-32. doi: 10.1016/j.tpb.2024.03.001. Epub 2024 Mar 7.
A phase-type distribution is the time to absorption in a continuous- or discrete-time Markov chain. Phase-type distributions can be used as a general framework to calculate key properties of the standard coalescent model and many of its extensions. Here, the 'phases' in the phase-type distribution correspond to states in the ancestral process. For example, the time to the most recent common ancestor and the total branch length are phase-type distributed. Furthermore, the site frequency spectrum follows a multivariate discrete phase-type distribution and the joint distribution of total branch lengths in the two-locus coalescent-with-recombination model is multivariate phase-type distributed. In general, phase-type distributions provide a powerful mathematical framework for coalescent theory because they are analytically tractable using matrix manipulations. The purpose of this review is to explain the phase-type theory and demonstrate how the theory can be applied to derive basic properties of coalescent models. These properties can then be used to obtain insight into the ancestral process, or they can be applied for statistical inference. In particular, we show the relation between classical first-step analysis of coalescent models and phase-type calculations. We also show how reward transformations in phase-type theory lead to easy calculation of covariances and correlation coefficients between e.g. tree height, tree length, external branch length, and internal branch length. Furthermore, we discuss how these quantities can be used for statistical inference based on estimating equations. Providing an alternative to previous work based on the Laplace transform, we derive likelihoods for small-size coalescent trees based on phase-type theory. Overall, our main aim is to demonstrate that phase-type distributions provide a convenient general set of tools to understand aspects of coalescent models that are otherwise difficult to derive. Throughout the review, we emphasize the versatility of the phase-type framework, which is also illustrated by our accompanying R-code. All our analyses and figures can be reproduced from code available on GitHub.
相型分布是连续或离散时间马尔可夫链的吸收时间。相型分布可用作计算标准合并模型及其许多扩展的关键性质的通用框架。在这里,相型分布中的“相”对应于祖先过程中的状态。例如,最近共同祖先的时间和总分支长度是相型分布的。此外,位点频率谱遵循多元离散相型分布,并且两基因座合并-重组模型中的总分支长度的联合分布是多元相型分布的。一般来说,相型分布为合并理论提供了一个强大的数学框架,因为它们可以通过矩阵运算进行分析。本综述的目的是解释相型理论,并演示如何应用该理论推导出合并模型的基本性质。这些性质可以用于深入了解祖先过程,或者用于统计推断。特别是,我们展示了经典的合并模型第一阶段分析与相型计算之间的关系。我们还展示了相型理论中的奖励变换如何导致容易计算树高、树长、外部分支长和内部分支长等之间的协方差和相关系数。此外,我们讨论了如何根据估计方程利用这些数量进行统计推断。与基于拉普拉斯变换的先前工作提供替代方案,我们基于相型理论为小尺寸合并树推导出似然。总体而言,我们的主要目的是表明相型分布提供了一套方便的通用工具,可以理解合并模型的方面,否则这些方面很难推导出来。在整个综述中,我们强调了相型框架的多功能性,我们的 R 代码也说明了这一点。我们所有的分析和图形都可以从 GitHub 上提供的代码重现。