Quantifying and Comparing Phylogenetic Evolutionary Rates for Shape and Other High-Dimensional Phenotypic Data
Quantifying and Comparing Phylogenetic Evolutionary Rates for Shape and Other High-Dimensional Phenotypic Data
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DOI：
10.1093/sysbio/syt105
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发表时间：
2014-03-01
影响因子：
6.5
通讯作者：
Adams, Dean C.
中科院分区：
文献类型：
--
作者：
Adams, Dean C.
进化生物学中的许多问题需要对表型进化速率进行量化和比较。最近，已经开发出系统发育比较方法，用于比较单个单变量性状（（2））的系统发育进化速率，以及同时处理的一组性状的进化速率矩阵（R）。然而，该框架对形状等高维特征的检查仍然不足，因为适合此类数据的方法尚未完全开发出来。在本文中，我描述了一种量化高维多元数据的系统发育进化率的方法，该方法是根据基于协方差矩阵的统计方法和基于距离矩阵的统计方法（R 模式和 Q 模式方法）之间的等效性发现的。然后，我使用模拟来评估假设检验程序的统计性能，该程序比较系统发育中的两组或更多组物种。在各向同性和非各向同性条件下，对于不同数量的性状维度，所提出的方法显示出适当的 I 型错误和高统计功效，用于检测组间已知差异。相比之下，基于进化率矩阵（R）的似然检验的I类错误率随着性状维度（p）数量的增加而增加，并且当仅考虑少数性状维度时变得难以接受。此外，当性状维度的数量等于或超过系统发育中分类单元的数量时（即，当 pN 时），无法计算基于 R 的似然检验。这些结果表明，基于进化率矩阵的测试提供了一种比较高维数据进化率的有用方法，否则基于进化率矩阵的方法无法分析访问这些数据。因此，这一进展扩展了用于高维表型性状（如形状）的系统发育比较工具包。最后，我通过评估 Plethodon 蝾螈谱系的头部形状进化速率来说明新方法的实用性。
Many questions in evolutionary biology require the quantification and comparison of rates of phenotypic evolution. Recently, phylogenetic comparative methods have been developed for comparing evolutionary rates on a phylogeny for single, univariate traits ((2)), and evolutionary rate matrices (R) for sets of traits treated simultaneously. However, high-dimensional traits like shape remain under-examined with this framework, because methods suited for such data have not been fully developed. In this article, I describe a method to quantify phylogenetic evolutionary rates for high-dimensional multivariate data, found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices (R-mode and Q-mode methods). I then use simulations to evaluate the statistical performance of hypothesis-testing procedures that compare for two or more groups of species on a phylogeny. Under both isotropic and non-isotropic conditions, and for differing numbers of trait dimensions, the proposed method displays appropriate Type I error and high statistical power for detecting known differences in among groups. In contrast, the Type I error rate of likelihood tests based on the evolutionary rate matrix (R) increases as the number of trait dimensions (p) increases, and becomes unacceptably large when only a few trait dimensions are considered. Further, likelihood tests based on R cannot be computed when the number of trait dimensions equals or exceeds the number of taxa in the phylogeny (i.e., when pN). These results demonstrate that tests based on provide a useful means of comparing evolutionary rates for high-dimensional data that are otherwise not analytically accessible to methods based on the evolutionary rate matrix. This advance thus expands the phylogenetic comparative toolkit for high-dimensional phenotypic traits like shape. Finally, I illustrate the utility of the new approach by evaluating rates of head shape evolution in a lineage of Plethodon salamanders.