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双盘姬蜂亚科(膜翅目,姬蜂科)系统发育信息量分布评估及分子系统发育研究。

An evaluation of phylogenetic informativeness profiles and the molecular phylogeny of diplazontinae (Hymenoptera, Ichneumonidae).

机构信息

Department of Invertebrates, Natural History Museum, Bernastrasse 15, CH-3005 Bern, Switzerland.

出版信息

Syst Biol. 2010 Mar;59(2):226-41. doi: 10.1093/sysbio/syp105. Epub 2010 Jan 13.

Abstract

How to quantify the phylogenetic information content of a data set is a longstanding question in phylogenetics, influencing both the assessment of data quality in completed studies and the planning of future phylogenetic projects. Recently, a method has been developed that profiles the phylogenetic informativeness (PI) of a data set through time by linking its site-specific rates of change to its power to resolve relationships at different timescales. Here, we evaluate the performance of this method in the case of 2 standard genetic markers for phylogenetic reconstruction, 28S ribosomal RNA and cytochrome oxidase subunit 1 (CO1) mitochondrial DNA, with maximum parsimony, maximum likelihood, and Bayesian analyses of relationships within a group of parasitoid wasps (Hymenoptera: Ichneumonidae, Diplazontinae). Retrieving PI profiles of the 2 genes from our own and from 3 additional data sets, we find that the method repeatedly overestimates the performance of the more quickly evolving CO1 compared with 28S. We explore possible reasons for this bias, including phylogenetic uncertainty, violation of the molecular clock assumption, model misspecification, and nonstationary nucleotide composition. As none of these provides a sufficient explanation of the observed discrepancy, we use simulated data sets, based on an idealized setting, to show that the optimum evolutionary rate decreases with increasing number of taxa. We suggest that this relationship could explain why the formula derived from the 4-taxon case overrates the performance of higher versus lower rates of evolution in our case and that caution should be taken when the method is applied to data sets including more than 4 taxa.

摘要

如何量化数据集的系统发育信息量是系统发育学中长期存在的问题,这既影响了对已完成研究中数据质量的评估,也影响了未来系统发育项目的规划。最近,已经开发出一种方法,通过将数据集的特定位置的变化率与其在不同时间尺度上解析关系的能力联系起来,来随时间分析数据集的系统发育信息量(PI)。在这里,我们评估了该方法在两种标准遗传标记物(28S 核糖体 RNA 和细胞色素氧化酶亚基 1(CO1)线粒体 DNA)用于解析膜翅目寄生蜂(膜翅目:Ichneumonidae,Diplazontinae)内关系的最大简约法、最大似然法和贝叶斯分析中的性能。从我们自己的和另外 3 个数据集检索这 2 个基因的 PI 曲线,我们发现该方法反复高估了快速进化的 CO1 与 28S 的性能。我们探讨了这种偏差的可能原因,包括系统发育不确定性、分子钟假设的违反、模型指定不当和非平稳核苷酸组成。由于这些都不能充分解释观察到的差异,我们使用模拟数据集,基于理想化的设置,表明最优进化率随分类单元数量的增加而降低。我们认为这种关系可以解释为什么从 4 个分类单元推导的公式在我们的情况下高估了较高进化率与较低进化率的性能,并且在将该方法应用于包含超过 4 个分类单元的数据集时应谨慎。

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