1. Phylogenetic signal quantifies the degree to which resemblance in continuously valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias.
2. To incorporate the observation process and phylogenetic uncertainty in a model‐based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that models continuous trait evolution as a Brownian motion process and incorporates Pagel's λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package.
3. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators and demonstrate the use of our coherent framework to address several virus‐host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution.

中文翻译：

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同时估计进化历史和重复性状的系统发生信号：在病毒和宿主表型进化中的应用。

1. 系统发育信号量化了连续值特性中相似性反映系统发育相关性的程度。系统发生信号的测量已广泛用于生态和进化研究中，并且最近在病毒进化研究中越来越受到关注。系统发生信号的标准估计器经常以重复性状观察和固定的系统发生树的数据摘要统计为条件，从而导致信息丢失和潜在的偏见。
2. 为了将观察过程和系统发育不确定性纳入基于模型的方法中，我们开发了一种新颖的贝叶斯推理方法，可同时根据分子序列数据和重复的多性状特征估算进化历史和系统发育信号。我们的方法建立在系统发育扩散框架的基础上，该框架将连续性状演变建模为布朗运动过程，并结合了Pagel的λ变换参数来估计性状之间的依赖性。我们在BEAST软件包中提供了计算有效的推理实现。
3. 我们评估了系统发育信号的贝叶斯估计量相对于标准估计量的综合性能，并证明了我们使用一致的框架来解决几个病毒宿主进化问题，包括对HIV的毒力遗传性，流感和HIV中的抗原进化以及果蝇对σ的敏感性病毒感染。最后，我们讨论模型扩展，这将为同时研究序列和性状进化的灵活框架做出有益的贡献。