Phylogenomic Analysis of the Parrots of the World Distinguishes Artifactual from Biological Sources of Gene Tree Discordance
Phylogenomic Analysis of the Parrots of the World Distinguishes Artifactual from Biological Sources of Gene Tree Discordance
复制标题
对世界鹦鹉的系统发育分析区分了基因树不一致的人工来源和生物来源
DOI：
10.1093/sysbio/syac055
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发表时间：
2022
影响因子：
6.5
通讯作者：
Joseph, Leo
中科院分区：
文献类型：
--
作者：
Smith, Brian Tilston;Merwin, Jon;Provost, Kaiya L.;Thom, Gregory;Brumfield, Robb T.;Ferreira, Mateus;Mauck, III, William M.;Moyle, Robert G.;Wright, Timothy F.;Joseph, Leo
基因树不一致性是基因组学中的普遍现象，通常用生物学过程来解释，但数据集中个体间系统发育信号的异质性可能导致拓扑不一致性的人为来源。我们研究了如何在提示和分支的信息内容影响拓扑不一致的鹦鹉（订单：鹦鹉形目），一个多样化和高度濒危的近400种的分支。使用ultraconserved元素的96%的分支的物种水平的多样性，我们估计连接和种树382内组类群。我们发现，树的拓扑结构之间的不一致是最常见的节点晚中新世和上新世之间的日期，并经常在属的分类水平。因此，我们使用了两个指标来表征提示中的信息内容，并评估树之间的冲突被低质量样本驱动的程度。利用这些指标可以客观地识别物种树中拓扑冲突和非单系属的大多数情况。对于基于提示的过滤后仍然不一致的亚分支，我们使用机器学习方法来确定系统发育信号或噪声是否是支持替代拓扑结构的指标的更重要的预测因子。我们发现，当信号倾向于其中一种拓扑结构时，噪声是表现不佳的模型中最重要的变量，这些模型倾向于替代拓扑结构。总之，我们表明，人工来源的基因树不一致，这可能是一种常见的现象，在许多数据集，可以区分从生物来源，通过量化的信息内容，在每个提示和建模的因素支持每个拓扑结构。[历史DNA;机器学习;博物馆组学;鹦鹉螺目;物种树。]
Gene tree discordance is expected in phylogenomic trees and biological processes are often invoked to explain it. However, heterogeneous levels of phylogenetic signal among individuals within data sets may cause artifactual sources of topological discordance. We examined how the information content in tips and subclades impacts topological discordance in the parrots (Order: Psittaciformes), a diverse and highly threatened clade of nearly 400 species. Using ultraconserved elements from 96% of the clade’s species-level diversity, we estimated concatenated and species trees for 382 ingroup taxa. We found that discordance among tree topologies was most common at nodes dating between the late Miocene and Pliocene, and often at the taxonomic level of the genus. Accordingly, we used two metrics to characterize information content in tips and assess the degree to which conflict between trees was being driven by lower-quality samples. Most instances of topological conflict and nonmonophyletic genera in the species tree could be objectively identified using these metrics. For subclades still discordant after tip-based filtering, we used a machine learning approach to determine whether phylogenetic signal or noise was the more important predictor of metrics supporting the alternative topologies. We found that when signal favored one of the topologies, the noise was the most important variable in poorly performing models that favored the alternative topology. In sum, we show that artifactual sources of gene tree discordance, which are likely a common phenomenon in many data sets, can be distinguished from biological sources by quantifying the information content in each tip and modeling which factors support each topology. [Historical DNA; machine learning; museomics; Psittaciformes; species tree.]