Bayesian phylogenetic analysis of combined data
Bayesian phylogenetic analysis of combined data
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DOI：
10.1080/10635150490264699
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发表时间：
2004-02-01
影响因子：
6.5
通讯作者：
Nieves-Aldrey, JL
中科院分区：
文献类型：
--
作者：
Nylander, JAA;Ronquist, F;Nieves-Aldrey, JL
最近发展的贝叶斯系统发育推断使用马尔可夫链蒙特卡罗（MCMC）技术，促进了丰富的参数的进化模型的探索。与此同时，随机模型变得更加真实（和复杂），并已扩展到新类型的数据，如形态学。在此基础上，我们开发了一个贝叶斯MCMC方法来分析组合数据集，并探讨了其效用的基础上，从形态和四个基因（核和线粒体，核糖体和蛋白质编码）的数据来推断瘿蜂之间的关系。检查模型的复杂性范围从那些只识别形态和分子分区的那些具有复杂的替代模型与独立的参数为每个基因。贝叶斯MCMC分析有效地处理复杂的模型：收敛速度更快，更可预测的复杂模型，混合是足够的所有参数，即使在非常复杂的模型，参数更新周期几乎不受跨站点的模型分区。形态学贡献的数据集中的字符只有5%，但仍然影响组合数据树，支持多基因分析的形态学数据的效用。我们使用贝叶斯准则（贝叶斯因子）表明，数据分区的过程异质性是一个重要的模型组成部分，虽然不像重复站点速率变化那么重要。更复杂的进化模型与更多的拓扑不确定性和更少的形态和分子之间的冲突。贝叶斯因子有时会倾向于更简单的模型，而不是参数更丰富的模型，但总体上最好的模型也是最复杂的，贝叶斯因子不支持从该模型中排除明显弱的参数。因此，贝叶斯因素似乎是有用的选择复杂的模型，但它仍然不清楚是否使用罢工模型的复杂性和参数估计误差之间的合理平衡。
The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo( MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic ( and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes ( nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria ( Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.