Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium.
Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA.
Mol Biol Evol. 2019 Aug 1;36(8):1793-1803. doi: 10.1093/molbev/msz094.
Many factors complicate the estimation of time scales for phylogenetic histories, requiring increasingly complex evolutionary models and inference procedures. The widespread application of molecular clock dating has led to the insight that evolutionary rate estimates may vary with the time frame of measurement. This is particularly well established for rapidly evolving viruses that can accumulate sequence divergence over years or even months. However, this rapid evolution stands at odds with a relatively high degree of conservation of viruses or endogenous virus elements over much longer time scales. Building on recent insights into time-dependent evolutionary rates, we develop a formal and flexible Bayesian statistical inference approach that accommodates rate variation through time. We evaluate the novel molecular clock model on a foamy virus cospeciation history and a lentivirus evolutionary history and compare the performance to other molecular clock models. For both virus examples, we estimate a similarly strong time-dependent effect that implies rates varying over four orders of magnitude. The application of an analogous codon substitution model does not implicate long-term purifying selection as the cause of this effect. However, selection does appear to affect divergence time estimates for the less deep evolutionary history of the Ebolavirus genus. Finally, we explore the application of our approach on woolly mammoth ancient DNA data, which shows a much weaker, but still important, time-dependent rate effect that has a noticeable impact on node age estimates. Future developments aimed at incorporating more complex evolutionary processes will further add to the broad applicability of our approach.
许多因素使系统发育历史时间尺度的估计变得复杂，这需要越来越复杂的进化模型和推断程序。分子钟测时的广泛应用使人们认识到，进化率的估计可能随测量的时间框架而变化。这在进化迅速的病毒中表现得尤为明显，病毒在数年甚至数月内就可以积累序列分歧。然而，这种快速进化与病毒或内源性病毒元件在更长时间尺度上相对较高的保守性相矛盾。在最近对时变进化率的深入了解的基础上，我们开发了一种正式而灵活的贝叶斯统计推断方法，通过时间来适应速率变化。我们在泡沫病毒共进化史和慢病毒进化史中评估新的分子钟模型，并将其性能与其他分子钟模型进行比较。对于这两个病毒示例，我们估计了一个类似的强时变效应，这意味着速率在四个数量级上变化。类似的密码子替代模型的应用并不意味着长期的纯化选择是造成这种效应的原因。然而，选择似乎确实影响了埃博拉病毒属进化历史较浅的分支时间估计。最后，我们探索了我们的方法在长毛猛犸象古 DNA 数据上的应用，结果显示出一个较弱但仍然重要的时变速率效应，对节点年龄估计有显著影响。未来旨在纳入更复杂进化过程的发展将进一步增加我们方法的广泛适用性。
