Bayes Factors Unmask Highly Variable Information Content, Bias, and Extreme Influence in Phylogenomic Analyses
Bayes Factors Unmask Highly Variable Information Content, Bias, and Extreme Influence in Phylogenomic Analyses
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DOI：
10.1093/sysbio/syw101
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发表时间：
2017-07-01
影响因子：
6.5
通讯作者：
Thomson, Robert C.
中科院分区：
文献类型：
--
作者：
Brown, Jeremy M.;Thomson, Robert C.
随着基因组数据在系统发育学中的应用已成为例行公事，出现了许多替代数据集强烈支持相互矛盾的结论的案例。这种对分析决策的敏感性阻碍了对生命之树中一些最顽固的节点的坚定解决。为了更好地理解这种敏感性的原因和性质，我们使用另一种拓扑支持度量(贝叶斯因子)分析了几个系统发育数据集，该度量既证明并避免了更频繁使用的支持度量(如后验概率的马尔可夫链蒙特卡罗估计)的几个局限性。贝叶斯因子揭示了以前隐藏的重要差异，这些差异来自于为解决海龟在羊区系中的系统发育位置而收集的六个“系统基因组学”数据。这些数据集在支持已确立的羊膜关系方面存在很大差异，特别是在包含极端信息量的基因比例以及强烈拒绝这些无争议关系的比例方面。所有六个数据集包含的信息都很少，无法解决海龟相对于其他羊膜动物的系统发育位置。贝叶斯因子还显示，极少数极具影响力的基因(不到数据集中1%的基因)可以从根本上改变重大的系统发育结论。在一个例子中，这些基因被证明含有以前未被识别的并列基因。这项研究表明，困难的系统学问题的解决仍然对看似次要的分析细节很敏感，贝叶斯因子是识别和解决这些挑战的宝贵工具。
As the application of genomic data in phylogenetics has become routine, a number of cases have arisen where alternative data sets strongly support conflicting conclusions. This sensitivity to analytical decisions has prevented firm resolution of some of the most recalcitrant nodes in the tree of life. To better understand the causes and nature of this sensitivity, we analyzed several phylogenomic data sets using an alternative measure of topological support (the Bayes factor) that both demonstrates and averts several limitations of more frequently employed support measures (such as Markov chain Monte Carlo estimates of posterior probabilities). Bayes factors reveal important, previously hidden, differences across six "phylogenomic" data sets collected to resolve the phylogenetic placement of turtles within Amniota. These data sets vary substantially in their support for well-established amniote relationships, particularly in the proportion of genes that contain extreme amounts of information as well as the proportion that strongly reject these uncontroversial relationships. All six data sets contain little information to resolve the phylogenetic placement of turtles relative to other amniotes. Bayes factors also reveal that a very small number of extremely influential genes (less than 1% of genes in a data set) can fundamentally change significant phylogenetic conclusions. In one example, these genes are shown to contain previously unrecognized paralogs. This study demonstrates both that the resolution of difficult phylogenomic problems remains sensitive to seemingly minor analysis details and that Bayes factors are a valuable tool for identifying and solving these challenges.