Arnold Arboretum, Harvard University, Boston, MA 02131, USA and Interdisciplinary Toxicology Program, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA.
Syst Biol. 2016 Sep;65(5):852-70. doi: 10.1093/sysbio/syw051. Epub 2016 Jun 16.
Recently, a suite of distance-based multivariate phylogenetic comparative methods has been proposed for studying the evolution of high-dimensional traits, such as morphometric coordinates, gene expression data, and function-valued traits. These methods allow for the statistical comparison of evolutionary rates, assessment of phylogenetic signal, and tests of correlated high-dimensional trait evolution. Simulations reveal that distance-based comparative methods exhibit low statistical power and high Type I error under various evolutionary scenarios. Distance-based methods are also limited to relatively simple model specification (e.g., Brownian motion evolution) due to the lack of a likelihood function for parameter estimation. Here I propose an alternative method for studying high-dimensional trait evolution which overcomes some of the statistical limitations associated with distance-based methods. This framework, based on parametric bootstrapping and maximum pseudolikelihood parameter estimation, opens up the ability to estimate alternative evolutionary models, combine multiple evolutionary hypotheses, and potentially allow missing data and within-species variation. Simulations reveal that pairwise composite likelihood methods demonstrate appropriate Type I error and high statistical power, thus providing a robust framework for studying high-dimensional trait evolution. These methods are implemented in the R package phylocurve [Covariance; distance; evolutionary rate; function-valued trait; high-dimensional; morphometric; multivariate; pairwise composite likelihood; phylogenetic comparative method; phylogenetic generalized least squares; phylogenetic signal.].
最近，提出了一系列基于距离的多变量系统发育比较方法，用于研究高维性状（如形态坐标、基因表达数据和函数值性状）的进化。这些方法允许对进化率进行统计比较、评估系统发育信号和检验相关的高维性状进化。模拟表明，基于距离的比较方法在各种进化情景下表现出低统计能力和高 I 型错误率。由于缺乏参数估计的似然函数，基于距离的方法也限于相对简单的模型规范（例如，布朗运动进化）。在这里，我提出了一种用于研究高维性状进化的替代方法，该方法克服了与基于距离的方法相关的一些统计限制。该框架基于参数 bootstrap 和最大拟似然参数估计，开辟了估计替代进化模型、组合多个进化假设以及潜在允许缺失数据和种内变异的能力。模拟表明，成对复合似然方法表现出适当的 I 型错误和高统计能力，因此为研究高维性状进化提供了一个稳健的框架。这些方法在 R 包 phylocurve 中实现[协方差；距离；进化率；函数值性状；高维；形态；多变量；成对复合似然；系统发育比较方法；系统发育广义最小二乘法；系统发育信号。]。
