Effects of branch length uncertainty on Bayesian posterior probabilities for phylogenetic hypotheses
Effects of branch length uncertainty on Bayesian posterior probabilities for phylogenetic hypotheses
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DOI：
10.1093/molbev/msm141
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发表时间：
2007-09-01
影响因子：
10.7
通讯作者：
Thornton, Joseph W.
中科院分区：
文献类型：
--
作者：
Kolaczkowski, Bryan;Thornton, Joseph W.
在贝叶斯遗传理论中，进化关系的置信度被表示为后验概率--在给定数据、进化模型和关于模型参数的先验假设的情况下，树或分支为真的概率。模型参数，如分支长度，从来不是事先知道的；贝叶斯方法通过在给定每个参数的假设先验概率分布的情况下在一系列似是而非的值上进行积分来合并这种不确定性。当假设不同的先验时，对分支长度不确定性的积分对后验概率的影响知之甚少。在这里，我们表明，使用广泛的典型先验假设对不确定性进行积分会强烈影响后验概率，导致它们与如果事先知道树枝长度而推断的后验概率相偏离；只有当不存在要积分的不确定性时，一组树的平均后验概率才能准确地预测组中正确树木的比例。真树上分枝长度的模式决定了在不确定性上进行积分是将后验概率向上还是向下推。影响的大小取决于所使用的特定先验分布和所分析的序列的长度。然而，在现实条件下，即使非常长的序列也不足以防止频繁地推断具有强大支持的错误分支。我们发现，在一系列条件下，扩散先验-无论是平坦分布还是具有中等到较大均值的指数分布-提供了比小平均指数先验更可靠的推断。将分支长度固定在其最大似然估计的经验贝叶斯方法产生的后验概率与如果提前知道真实分支长度而推断的后验概率更接近，并且与完全贝叶斯积分相比降低了强烈支持的错误推论的比率。
In Bayesian pbylogenetics, confidence in evolutionary relationships is expressed as posterior probability-the probability that a tree or clade is true given the data, evolutionary model, and prior assumptions about model parameters. Model parameters, such as branch lengths, are never known in advance; Bayesian methods incorporate this uncertainty by integrating over a range of plausible values given an assumed prior probability distribution for each parameter. Little is known about the effects of integrating over branch length uncertainty on posterior probabilities when different priors are assumed. Here, we show that integrating over uncertainty using a wide range of typical prior assumptions strongly affects posterior probabilities, causing them to deviate from those that would be inferred if branch lengths were known in advance; only when there is no uncertainty to integrate over does the average posterior probability of a group of trees accurately predict the proportion of correct trees in the group. The pattern of branch lengths on the true tree determines whether integrating over uncertainty pushes posterior probabilities upward or downward. The magnitude of the effect depends on the specific prior distributions used and the length of the sequences analyzed. Under realistic conditions, however, even extraordinarily long sequences are not enough to prevent frequent inference of incorrect clades with strong support. We found that across a range of conditions, diffuse priors-either flat or exponential distributions with moderate to large means-provide more reliable inferences than small-mean exponential priors. An empirical Bayes approach that fixes branch lengths at their maximum likelihood estimates yields posterior probabilities that more closely match those that would be inferred if the true branch lengths were known in advance and reduces the rate of strongly supported false inferences compared with fully Bayesian integration.