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用于适应性性状进化的系统发育曲线最优回归

Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution.

作者信息

Jhwueng Dwueng-Chwuan, Wang Chih-Ping

机构信息

Department of Statistics, Feng-Chia University, Taichung 40724, Taiwan.

出版信息

Entropy (Basel). 2021 Feb 10;23(2):218. doi: 10.3390/e23020218.

Abstract

Regression analysis using line equations has been broadly applied in studying the evolutionary relationship between the response trait and its covariates. However, the characteristics among closely related species in nature present abundant diversities where the nonlinear relationship between traits have been frequently observed. By treating the evolution of quantitative traits along a phylogenetic tree as a set of continuous stochastic variables, statistical models for describing the dynamics of the optimum of the response trait and its covariates are built herein. Analytical representations for the response trait variables, as well as their optima among a group of related species, are derived. Due to the models' lack of tractable likelihood, a procedure that implements the Approximate Bayesian Computation (ABC) technique is applied for statistical inference. Simulation results show that the new models perform well where the posterior means of the parameters are close to the true parameters. Empirical analysis supports the new models when analyzing the trait relationship among kangaroo species.

摘要

使用线性方程的回归分析已广泛应用于研究响应性状与其协变量之间的进化关系。然而,自然界中密切相关物种的特征呈现出丰富的多样性,其中性状之间的非线性关系经常被观察到。通过将沿着系统发育树的数量性状进化视为一组连续随机变量,本文构建了用于描述响应性状及其协变量最优值动态的统计模型。推导了响应性状变量及其在一组相关物种中的最优值的解析表达式。由于模型缺乏易于处理的似然性,应用了一种实施近似贝叶斯计算(ABC)技术的程序进行统计推断。模拟结果表明,新模型表现良好,参数的后验均值接近真实参数。在分析袋鼠物种之间的性状关系时,实证分析支持了新模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e826/7916804/11a88119d836/entropy-23-00218-g001.jpg

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