When Trees Grow Too Long: Investigating the Causes of Highly Inaccurate Bayesian Branch-Length Estimates
When Trees Grow Too Long: Investigating the Causes of Highly Inaccurate Bayesian Branch-Length Estimates
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DOI：
10.1093/sysbio/syp081
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发表时间：
2010-03-01
影响因子：
6.5
通讯作者：
Lemmon, Emily Moriarty
中科院分区：
文献类型：
--
作者：
Brown, Jeremy M.;Hedtke, Shannon M.;Lemmon, Emily Moriarty
最近的贝叶斯系统发育分析中包含的分支长度估计比相应的最大似然估计长几个数量级，数量惊人。这种分支长度所隐含的发散程度对于使用生物数据的研究是不合理的，并且对于使用模拟数据的研究被认为是错误的。我们进行了更多的贝叶斯分析，并研究了近似后表面，以调查这些大误差背后的原因。我们操纵了马尔可夫链蒙特卡罗(MCMC)分析的起始参数值、MCMC分析使用的移动以及分支长度的先验概率分布。我们证明，不准确的分支长度估计是由于1)MCMC链的不良混合或2)后验分布在长树长度处权重过大造成的。这两种影响都是由于随着树枝变长，树枝长度空间的体积迅速增加所致。在前一种情况下，MCMC移动同时缩放树中的所有分支长度，以及使用过度分散的起始分支长度，都允许链对后验分布进行准确采样，并应用于系统发育的贝叶斯分析。在后一种情况下，分支长度的先验可能对结果推论有很大的影响，应该仔细选择以反映生物学期望。我们给出了一个计算分支长度先验的指数速率参数的公式，该公式在许多情况下应该消除有偏分支长度的推断。在任何系统发育分析中，必须仔细考虑分枝长度产量的生物学合理性。
A surprising number of recent Bayesian phylogenetic analyses contain branch-length estimates that are several orders of magnitude longer than corresponding maximum-likelihood estimates. The levels of divergence implied by such branch lengths are unreasonable for studies using biological data and are known to be false for studies using simulated data. We conducted additional Bayesian analyses and studied approximate-posterior surfaces to investigate the causes underlying these large errors. We manipulated the starting parameter values of the Markov chain Monte Carlo (MCMC) analyses, the moves used by the MCMC analyses, and the prior-probability distribution on branch lengths. We demonstrate that inaccurate branch-length estimates result from either 1) poor mixing of MCMC chains or 2) posterior distributions with excessive weight at long tree lengths. Both effects are caused by a rapid increase in the volume of branch-length space as branches become longer. In the former case, both an MCMC move that scales all branch lengths in the tree simultaneously and the use of overdispersed starting branch lengths allow the chain to accurately sample the posterior distribution and should be used in Bayesian analyses of phylogeny. In the latter case, branch-length priors can have strong effects on resulting inferences and should be carefully chosen to reflect biological expectations. We provide a formula to calculate an exponential rate parameter for the branch-length prior that should eliminate inference of biased branch lengths in many cases. In any phylogenetic analysis, the biological plausibility of branch-length output must be carefully considered.