Ancient whole-genome duplications (WGDs) leave signatures in comparative genomic data sets that can be harnessed to detect these events of presumed evolutionary importance. Current statistical approaches for the detection of ancient WGDs in a phylogenetic context have two main drawbacks. The first is that unwarranted restrictive assumptions on the 'background' gene duplication and loss rates make inferences unreliable in the face of model violations. The second is that most methods can only be used to examine a limited set of a priori selected WGD hypotheses; and cannot be used to discover WGDs in a phylogeny. In this study we develop an approach for WGD inference using gene count data that seeks to overcome both issues. We employ a phylogenetic birth-death model that includes WGD in a flexible hierarchical Bayesian approach, and use reversible-jump MCMC to perform Bayesian inference of branch-specific duplication, loss and WGD retention rates accross the space of WGD configurations. We evaluate the proposed method using simulations, apply it to data sets from flowering plants and discuss the statistical intricacies of model-based WGD inference.

中文翻译：

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基于模型的系统发育全基因组重复检测。

古代全基因组重复（WGD）在比较基因组数据集中留下了特征，可以利用这些特征来检测这些具有重要进化意义的事件。在系统发生背景下，用于检测古代WGD的当前统计方法有两个主要缺点。首先是对“背景”基因重复和丢失率的无用限制假设使得面对模型违规时推断不可靠。其次，大多数方法只能用于检查有限的一组先验选择的WGD假设；不能用于在系统发育中发现WGD。在这项研究中，我们开发了一种使用基因计数数据进行WGD推断的方法，旨在克服这两个问题。我们采用了系统发育的出生/死亡模型，该模型在灵活的分级贝叶斯方法中包括WGD，并使用可逆跳MCMC在WGD配置空间内执行特定于分支的重复，丢失和WGD保留率的贝叶斯推断。我们使用仿真评估提出的方法，将其应用于开花植物的数据集，并讨论基于模型的WGD推断的统计复杂性。