Gavryushkina Alexandra, Welch David, Stadler Tanja, Drummond Alexei J
Department of Computer Science, University of Auckland, Auckland, New Zealand; Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand.
Department of Computer Science, University of Auckland, Auckland, New Zealand.
PLoS Comput Biol. 2014 Dec 4;10(12):e1003919. doi: 10.1371/journal.pcbi.1003919. eCollection 2014 Dec.
Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after sampling, in particular we extend the birth-death skyline model [Stadler et al., 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates. We also show that sampled ancestor birth-death models where every sample comes from a different time point are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply our phylogenetic inference accounting for sampled ancestors to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in the literature. The sampler is available as an open-source BEAST2 package (https://github.com/CompEvol/sampled-ancestors).
包括随时间采样的化石或分子序列的系统发育分析需要允许一个样本成为另一个样本直接祖先的模型。由于先前可用的系统发育推断工具假设所有样本都是叶节点，所以它们不考虑这种可能性。我们开发并实现了一种贝叶斯马尔可夫链蒙特卡罗（MCMC）算法，以推断我们所称的采样祖先树，即采样个体可以是其他采样个体直接祖先的树。我们使用一类出生 - 死亡模型，其中个体在采样后可能仍保留在树过程中，特别是我们将出生 - 死亡天际线模型[施塔德勒等人，2013]扩展到采样祖先树。该方法允许检测采样祖先，并估计个体在采样时从过程中被移除的概率。我们表明，即使在分析中采样祖先并非特别感兴趣，但不考虑它们会导致参数估计出现显著偏差。我们还表明，每个样本来自不同时间点的采样祖先出生 - 死亡模型是不可识别的，因此需要知道一个参数才能推断其他参数。我们将考虑采样祖先的系统发育推断应用于流行病学数据，其中采样祖先的可能性使我们能够识别采样后感染其他个体的个体，并推断基本的流行病学参数。我们还将该方法应用于在现存物种样本中包含化石时推断分歧时间和多样化速率，以便将化石形成事件建模为树分支过程的一部分。如文献中所论证的，这种建模有许多优点。该采样器可作为开源的BEAST2软件包获取（https://github.com/CompEvol/sampled - ancestors）。
