Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Mol Biol Evol. 2024 Nov 1;41(11). doi: 10.1093/molbev/msae210.
Comparative genomics approaches seek to associate molecular evolution with the evolution of phenotypes across a phylogeny. Many of these methods lack the ability to analyze non-ordinal categorical traits with more than two categories. To address this limitation, we introduce an expansion to RERconverge that associates shifts in evolutionary rates with the convergent evolution of categorical traits. The categorical RERconverge expansion includes methods for performing categorical ancestral state reconstruction, statistical tests for associating relative evolutionary rates with categorical variables, and a new method for performing phylogeny-aware permutations, "permulations", on categorical traits. We demonstrate our new method on a three-category diet phenotype, and we compare its performance to binary RERconverge analyses and two existing methods for comparative genomic analyses of categorical traits: phylogenetic simulations and a phylogenetic signal based method. We present an analysis of how the categorical permulations scale with the number of species and the number of categories included in the analysis. Our results show that our new categorical method outperforms phylogenetic simulations at identifying genes and enriched pathways significantly associated with the diet phenotypes and that the categorical ancestral state reconstruction drives an improvement in our ability to capture diet-related enriched pathways compared to binary RERconverge when implemented without user input on phenotype evolution. The categorical expansion to RERconverge will provide a strong foundation for applying the comparative method to categorical traits on larger data sets with more species and more complex trait evolution than have previously been analyzed.
比较基因组学方法旨在将分子进化与系统发育上的表型进化联系起来。其中许多方法缺乏分析具有两个以上类别的非顺序分类特征的能力。为了解决这一限制，我们对 RERconverge 进行了扩展，将进化率的变化与分类特征的趋同进化联系起来。分类 RERconverge 扩展包括执行分类祖先状态重建、将相对进化率与分类变量相关联的统计检验以及对分类特征执行“排列”的新方法。我们在一个三分类饮食表型上展示了我们的新方法，并将其性能与二元 RERconverge 分析以及两种现有的分类特征比较基因组学分析方法进行了比较：系统发育模拟和基于系统发育信号的方法。我们介绍了一种分析方法，用于研究分类排列如何随物种数量和分析中包含的类别数量而变化。我们的结果表明，我们的新分类方法在识别与饮食表型显著相关的基因和富集途径方面优于系统发育模拟，并且在没有用户输入对表型进化进行干预的情况下，与二元 RERconverge 相比，分类祖先状态重建可以提高我们捕获与饮食相关的富集途径的能力。RERconverge 的分类扩展将为在更大的数据集上应用比较方法提供一个坚实的基础，这些数据集具有更多的物种和比以前分析更复杂的特征进化。
