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Rapid maximum likelihood ancestral state reconstruction of continuous characters: A rerooting-free algorithm

机译:连续字符的快速最大似然祖先状态重构:一种无根算法

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Abstract Ancestral state reconstruction is a method used to study the evolutionary trajectories of quantitative characters on phylogenies. Although efficient methods for univariate ancestral state reconstruction under a Brownian motion model have been described for at least 25 years, to date no generalization has been described to allow more complex evolutionary models, such as multivariate trait evolution, non-Brownian models, missing data, and within-species variation. Furthermore, even for simple univariate Brownian motion models, most phylogenetic comparative R packages compute ancestral states via inefficient tree rerooting and full tree traversals at each tree node, making ancestral state reconstruction extremely time-consuming for large phylogenies. Here, a computationally efficient method for fast maximum likelihood ancestral state reconstruction of continuous characters is described. The algorithm has linear complexity relative to the number of species and outperforms the fastest existing R implementations by several orders of magnitude. The described algorithm is capable of performing ancestral state reconstruction on a 1,000,000-species phylogeny in fewer than 2 s using a standard laptop, whereas the next fastest R implementation would take several days to complete. The method is generalizable to more complex evolutionary models, such as phylogenetic regression, within-species variation, non-Brownian evolutionary models, and multivariate trait evolution. Because this method enables fast repeated computations on phylogenies of virtually any size, implementation of the described algorithm can drastically alleviate the computational burden of many otherwise prohibitively time-consuming tasks requiring reconstruction of ancestral states, such as phylogenetic imputation of missing data, bootstrapping procedures, Expectation-Maximization algorithms, and Bayesian estimation. The described ancestral state reconstruction algorithm is implemented in the Rphylopars functions anc.recon and phylopars .
机译:摘要祖先状态重建是研究系统发育数量特征进化轨迹的一种方法。尽管在至少25年的时间里已经描述了在布朗运动模型下进行单变量祖先状态重建的有效方法,但迄今为止,还没有任何概括可以描述更复杂的进化模型,例如多元特征进化,非布朗模型,数据丢失,和种内变异。此外,即使对于简单的单变量Brownian运动模型,大多数系统发育比较R包都通过效率低下的树重新生根和每个树节点上的完整树遍历来计算祖先状态,对于大系统发育而言,祖先状态重建非常耗时。这里,描述了一种用于连续字符的快速最大似然祖先状态重建的计算有效方法。该算法相对于物种数量具有线性复杂度,并且比最快的现有R实现​​要好几个数量级。所描述的算法能够使用标准笔记本电脑在不到2 s的时间内对1,000,000物种的系统发育进行祖先状态重建,而下一个最快的R实现将需要几天的时间才能完成。该方法可推广到更复杂的进化模型,例如系统进化回归,物种内变异,非布朗进化模型和多元性状进化。由于该方法可以对几乎任何大小的系统发育进行快速重复计算,因此,所描述算法的实现可以大大减轻许多原本需要重建祖先状态的耗时耗力的任务的计算负担,例如丢失数据的系统发育估算,自举程序,期望最大化算法和贝叶斯估计。所描述的祖先状态重建算法在Rphylopars函数anc.recon和phylopars中实现。

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