Partitioned coalescence support reveals biases in species-tree methods and detects gene trees that determine phylogenomic conflicts
Partitioned coalescence support reveals biases in species-tree methods and detects gene trees that determine phylogenomic conflicts
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DOI：
10.1016/j.ympev.2019.106539
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发表时间：
2019-10-01
影响因子：
4.1
通讯作者：
Springer, Mark S.
中科院分区：
文献类型：
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作者：
Gatesy, John;Sloan, Daniel B.;Springer, Mark S.
当应用不同的树构建方法时，基因组数据集有时会支持相互冲突的系统发育关系。通过划分对系统发育数据集的组成基因之间有争议的关系的支持，可以对此类结果进行连贯的解释。对于超矩阵（=串联）方法，15 年前引入了几种测量基因座之间支持分布和冲突的方法。最近，为系统发育合并方法开发了分区合并支持（PCS），该方法解释了不完整的谱系排序，并使用基因树的总和拟合来估计物种树。在这里，我们自动计算 PCS，以允许将该索引应用于包含数百个位点的基因组规模矩阵。对羊膜动物、陆地植物、石龙子和被子植物的四个系统发育数据集的重新分析证明了如何使用 PCS 评分来：（1）比较替代合并方法所支持的相互冲突的结果，（2）识别对争议关系的解决具有不成比例影响的异常基因树，（3）评估物种树分析中缺失数据的影响，以及（4）澄清常用合并方法和支持指数中的偏差。我们表明，这些分析的关键系统发育学结论通常仅取决于少数基因树，并且结果可能由特定合并方法的特定偏差和/或高与低分类单元采样的基因树的不同权重驱动。某些基因树的权重异常高，而其他基因树的权重非常低，这违背了系统发育合并分析的基本逻辑；即使根据常用指数（似然比检验、引导程序、贝叶斯局部后验概率）具有高支持度的物种树中的进化枝，对于仅删除一两个具有高 PCS 的基因树也可能不稳定。计算机模拟无法充分描述经验遗传数据的所有偶然性和复杂性。 PCS 分数通过在所应用的系统发育合并方法的假设下提供对特定数据集的具体见解来补充模拟工作。结合节点支持的标准测量，PCS 可以更全面地了解物种树中有争议的进化关系的整体基因组证据。
Genomic datasets sometimes support conflicting phylogenetic relationships when different tree-building methods are applied. Coherent interpretations of such results are enabled by partitioning support for controversial relationships among the constituent genes of a phylogenomic dataset. For the supermatrix (= concatenation) approach, several methods that measure the distribution of support and conflict among loci were introduced over 15 years ago. More recently, partitioned coalescence support (PCS) was developed for phylogenetic coalescence methods that account for incomplete lineage sorting and use the summed fits of gene trees to estimate the species tree. Here, we automate computation of PCS to permit application of this index to genomescale matrices that include hundreds of loci. Reanalyses of four phylogenomic datasets for amniotes, land plants, skinks, and angiosperms demonstrate how PCS scores can be used to: (1) compare conflicting results favored by alternative coalescence methods, (2) identify outlier gene trees that have a disproportionate influence on the resolution of contentious relationships, (3) assess the effects of missing data in species-tree analysis, and (4) clarify biases in commonly-implemented coalescence methods and support indices. We show that key phylogenomic conclusions from these analyses often hinge on just a few gene trees and that results can be driven by specific biases of a particular coalescence method and/or the differential weight placed on gene trees with high versus low taxon sampling. The attribution of exceptionally high weight to some gene trees and very low weight to other gene trees counters the basic logic of phylogenomic coalescence analysis; even clades in species trees with high support according to commonly used indices (likelihood-ratio test, bootstrap, Bayesian local posterior probability) can be unstable to the removal of only one or two gene trees with high PCS. Computer simulations cannot adequately describe all of the contingencies and complexities of empirical genetic data. PCS scores complement simulation work by providing specific insights into a particular dataset given the assumptions of the phylogenetic coalescence method that is applied. In combination with standard measures of nodal support, PCS provides a more complete understanding of the overall genomic evidence for contested evolutionary relationships in species trees.