1. The homoplasy excess test (HET) is a tree‐based screen for hybrid taxa in multilocus nuclear phylogenies. Homoplasy between a hybrid taxon and the clades containing the parental taxa reduces bootstrap support in the tree. The HET is based on the expectation that excluding the hybrid taxon from the data set increases the bootstrap support for the parental clades, whereas excluding non‐hybrid taxa has little effect on statistical node support. To carry out a HET, bootstrap trees are calculated with taxon‐jackknife data sets, that is excluding one taxon (species, population) at a time. Excess increase in bootstrap support for certain nodes upon exclusion of a particular taxon indicates the hybrid (the excluded taxon) and its parents (the clades with increased support).
2. We introduce a new software program, hext, which generates the taxon‐jackknife data sets, runs the bootstrap tree calculations, and identifies excess bootstrap increases as outlier values in boxplot graphs. hext is written in r language and accepts binary data (0/1; e.g. AFLP) as well as co‐dominant SNP and genotype data.
3. We demonstrate the usefulness of hext in large SNP data sets containing putative hybrids and their parents. For instance, using published data of the genus Vitis (˜6,000 SNP loci), hext output supports V. × champinii as a hybrid between V. rupestris and V. mustangensis.
4. With simulated SNP and AFLP data sets, excess increases in bootstrap support were not always connected with the hybrid taxon (false positives), whereas the expected bootstrap signal failed to appear on several occasions (false negatives). Potential causes for both types of spurious results are discussed.
5. With both empirical and simulated data sets, the taxon‐jackknife output generated by hext provided additional signatures of hybrid taxa, including changes in tree topology across trees, consistent effects of exclusions of the hybrid and the parent taxa, and moderate (rather than excessive) increases in bootstrap support. hext significantly facilitates the taxon‐jackknife approach to hybrid taxon detection, even though the simple test for excess bootstrap increase may not reliably identify hybrid taxa in all applications.

中文翻译：

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HEXT，一种软件，支持多位点数据集中混合类群的基于树的筛选，以及同质性过量测试的评估。

1. 
   同质性过剩测试（HET）是一种基于树的多位点核系统发育中混合类群的筛选。混合分类单元和包含亲本分类单元的进化枝之间的同质性减少了树中的自举支持。 HET 基于这样的预期：从数据集中排除混合分类单元会增加对亲本进化枝的引导支持，而排除非混合分类单元对统计节点支持影响不大。为了进行 HET，引导树使用分类单元折刀数据集进行计算，即一次排除一个分类单元（物种、种群）。排除特定分类单元后，某些节点的引导支持过度增加表明杂种（排除的分类单元）及其亲本（支持增加的进化枝）。
2. 
   我们引入了一个新的软件程序hext ，它生成分类单元折刀数据集，运行引导树计算，并将多余的引导增加识别为箱线图中的异常值。 hext用r语言编写，接受二进制数据（0/1；例如 AFLP）以及共显性 SNP 和基因型数据。
3. 
   我们证明了hext在包含假定杂种及其亲本的大型 SNP 数据集中的有用性。例如，使用葡萄属的已发表数据（约 6,000 个 SNP 位点），十六进制输出支持V. × champinii作为V. rupestris和V. Mustangensis之间的杂交种。
4. 
   通过模拟的 SNP 和 AFLP 数据集，引导程序支持的过度增加并不总是与混合分类单元相关（误报），而预期的引导程序信号在多次情况下未能出现（误报）。讨论了两种类型的虚假结果的潜在原因。
5. 
   通过经验数据集和模拟数据集， hext生成的分类单元-折刀输出提供了混合类群的额外特征，包括树间树拓扑的变化、混合类群和亲本类群排除的一致影响，以及中等（而不是过度）增加引导程序支持。 hext显着促进了混合分类单元检测的分类单元折刀方法，尽管对过度引导增加的简单测试可能无法可靠地识别所有应用中的混合分类单元。