Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models
Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models
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使用混合高斯系统发育模型自动生成进化假设
DOI：
10.1073/pnas.1813823116
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发表时间：
2019
影响因子：
11.1
通讯作者：
T. Stadler
中科院分区：
文献类型：
--
作者：
Venelin Mitov;K. Bartoszek;T. Stadler
意义 系统发育比较方法 (PCM) 用于研究从微生物到动物和植物的各种生物物种的进化。这些方法将性状测量（例如在一组物种中测量的体重）与物种的系统发育树结合起来，以量化性状沿树的进化。在这里，我们表明，当前的 PCM 无法重现哺乳动物大脑和体重的进化模式，因为它们使用的数学模型无法代表树的不同谱系中进化过程的异质性。作为一种解决方案，我们提出了混合高斯系统发育模型，允许人们推断树的特定分支上发生的进化力的类型和大小的变化。系统发育比较方法广泛用于基于系统发育树和现有物种的性状测量来理解和量化表型性状的进化。此类分析很大程度上取决于基础模型。像布朗运动和奥恩斯坦-乌伦贝克过程这样的高斯系统发育模型是连续性状进化建模的主力。然而，这些模型不适用于大树，因为它们忽略了树的不同谱系的进化过程的异质性。以前的工作通过引入进化模型中发生在树中推断点的变化来解决这个问题。然而，出于计算原因，在所有当前实现中，这些转变都是“模型内”的，这意味着它们允许在 1 或 2 个模型参数中跳跃，而使整个树的所有其他参数保持“全局”。没有生物学原因限制对单个模型参数的转变，甚至限制对单一类型模型的转变。混合高斯系统发育模型（MGPM）融合了联合推断与树的不同部分相关的不同类型高斯模型的想法。在这里，我们提出了一种近似最大似然方法，用于将 MGPM 拟合到比较数据，其中包括对现存和灭绝的系统发育相关物种的几个性状的可能不完整的测量。我们将该方法应用于已发表的最大的哺乳动物物种树上，并进行了身体质量和脑质量测量，显示出对具有 12 种不同进化机制的 MGPM 的强有力的统计支持。基于这一结果，我们提出了过去 1.6 亿年来大脑-身体-质量异速演化的假设。
Significance Phylogenetic comparative methods (PCMs) are used to study the evolution of various biological species, ranging from microorganisms to animals and plants. These methods combine trait measurements, such as body masses measured in a set of species, with the species’ phylogenetic tree, to quantify the trait’s evolution along the tree. Here, we show that current PCMs fail to reproduce the patterns of evolution of brain and body mass in mammals, because they use mathematical models that cannot represent the heterogeneity of the evolutionary processes acting in different lineages of the tree. As a solution, we propose mixed Gaussian phylogenetic models allowing one to infer changes in the type and magnitude of evolutionary forces occurring on specific branches of the tree. Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein–Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are “intramodel,” meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters “global” for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain–body-mass allometry over the past 160 million y.
DOI：
10.1093/emph/eot019
发表时间：
2013-01
期刊：
Evolution, medicine, and public health
影响因子：
--
作者：
Shirreff G;Alizon S;Cori A;Günthard HF;Laeyendecker O;van Sighem A;Bezemer D;Fraser C
通讯作者：
Fraser C