利用Ornstein-Uhlenbeck模型检测系统发育比较分析中的适应性进化
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
摘要：
系统发育比较分析是一种结合系统发育和表型数据来推断进化过程的方法。在过去的几年里，已经看到越来越复杂的模型，用于评估越来越详细的进化假说，包括适应性假说与多个选择性最优和假说与率变化内和跨谱系。然而，这些复杂模型的统计性能却很少受到系统的关注。我们进行了广泛的模拟研究，以量化一类模型的统计特性，朝着更简单的一端的频谱模型表型进化使用Ornstein-Uhlenbeck过程。我们专注于确定这些方法在哪里，如何以及为什么会失败，以便用户可以在更好地了解其优点和缺点的情况下应用它们。我们的分析确定了性能的三个关键决定因素：可辨别性比，信噪比，和采样的类群数。有趣的是，我们发现，即使在以前被认为是困难的区域，例如当树的大小很小时，模型选择能力也可以很高。另一方面，我们发现模型参数在许多情况下难以准确估计，这表明数据中与这些参数相关的信息相对较少。然而，我们注意到，当参数仅被弱识别时，准确的模型选择通常是可能的。我们的研究结果有更复杂的方法的影响，因为后者是我们研究的情况下的概括。
英文摘要：
Phylogenetic comparative analysis is an approach to inferring evolutionary process from a combination of phylogenetic and phenotypic data. The last few years have seen increasingly sophisticated models employed in the evaluation of more and more detailed evolutionary hypotheses, including adaptive hypotheses with multiple selective optima and hypotheses with rate variation within and across lineages. The statistical performance of these sophisticated models has received relatively little systematic attention, however. We conducted an extensive simulation study to quantify the statistical properties of a class of models toward the simpler end of the spectrum that model phenotypic evolution using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and why these methods break down so that users can apply them with greater understanding of their strengths and weaknesses. Our analysis identifies three key determinants of performance: a discriminability ratio, a signal-to-noise ratio, and the number of taxa sampled. Interestingly, we find that model-selection power can be high even in regions that were previously thought to be difficult, such as when tree size is small. On the other hand, we find that model parameters are in many circumstances difficult to estimate accurately, indicating a relative paucity of information in the data relative to these parameters. Nevertheless, we note that accurate model selection is often possible when parameters are only weakly identified. Our results have implications for more sophisticated methods inasmuch as the latter are generalizations of the case we study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
