Alignment Errors Strongly Impact Likelihood-Based Tests for Comparing Topologies
Alignment Errors Strongly Impact Likelihood-Based Tests for Comparing Topologies
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DOI：
10.1093/molbev/msu231
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发表时间：
2014-11-01
影响因子：
10.7
通讯作者：
Pupko, Tal
中科院分区：
文献类型：
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作者：
Karin, Eli Levy;Susko, Edward;Pupko, Tal
从序列数据估计系统发育树是一项极具挑战性和重要的统计任务。在最大似然范式中，最佳树是一个点估计。为了确定数据对这种进化情景的支持程度，需要一种假设检验方法。为此，开发了Kishino-Hasegawa （KH）测试，以确定一种拓扑结构是否明显比另一种拓扑结构更受序列数据的支持。该测试及其衍生物广泛应用于系统发育学和系统基因组学。在这里，我们表明，KH检验是有偏差的对准误差的存在，并可能导致错误的结论。通过模拟，我们证明了由于对齐误差，KH测试经常拒绝竞争的拓扑之一，即使数据支持这两种拓扑。具体来说，我们表明KH测试有利于用于对齐分析序列的引导树。此外，分支长度优化使测试过于保守。我们对这些偏差提出了两种可能的修正。首先，我们评估了去除不可靠的对准柱的影响，发现它以大幅降低测试功率为代价降低了偏差。其次，我们开发了一个参数测试，完全消除了没有数据过滤的偏差。本检验将对齐构建步骤纳入检验的假设中，从而消除了上述导树效应。我们将这种方法扩展到多拓扑比较的情况下，并展示了新方法在示例性数据集上的适用性。
Estimating phylogenetic trees from sequence data is an extremely challenging and important statistical task. Within the maximum-likelihood paradigm, the best tree is a point estimate. To determine how strongly the data support such an evolutionary scenario, a hypothesis testing methodology is required. To this end, the Kishino-Hasegawa (KH) test was developed to determine whether one topology is significantly more supported by the sequence data than another one. This test and its derivatives are widely used in phylogenetics and phylogenomics. Here, we show that the KH test is biased in the presence of alignment error and can lead to erroneous conclusions. Using simulations we demonstrated that due to alignment errors the KH test often rejects one of the competing topologies, even though both topologies are equally supported by the data. Specifically, we show that the KH test favors the guide tree used to align the analyzed sequences. Further, branch length optimization renders the test too conservative. We propose two possible corrections for these biases. First, we evaluated the impact of removing unreliable alignment columns and found out that it decreases the bias at the cost of substantially reducing the test's power. Second, we developed a parametric test that entirely abolishes the biases without data filtering. This test incorporates the alignment construction step into the test's hypothesis, thus removing the above guide tree effect. We extend this methodology for the case of multiple-topology comparisons and demonstrate the applicability of the new methodology on an exemplary data set.