Department of Biology, University of Washington, Seattle, WA, 98195, USA.
GeoBio-Center, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.
PLoS Comput Biol. 2020 Oct 28;16(10):e1007999. doi: 10.1371/journal.pcbi.1007999. eCollection 2020 Oct.
Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s.
birth-death 过程为生物学家提供了一个基于模型的框架，用于回答关于系统发育树中谱系的出生率和死亡率变化的问题。因此，birth-death 模型是宏观进化和系统发育分析的核心。早期使用 birth-death 模型研究出生率和死亡率随时间的变化面临困难，因为 birth-death 率曲线的选择受到限制。足够灵活的时变 birth-death 模型仍然缺乏。我们使用分段常数 birth-death 模型，结合高斯马尔可夫随机场 (GMRF) 和马蹄形马尔可夫随机场 (HSMRF) 先验分布，来近似出生率随时间的任意变化。我们在广泛使用的统计系统发育软件平台 RevBayes 中实现这些模型，使我们能够在贝叶斯框架中联合估计 birth-death 过程参数、系统发育和干扰参数。我们在各种模拟多样化场景中测试了基于 GMRF 和基于 HSMRF 的模型，然后将它们应用于宏观进化和流行病学数据集。我们发现这两种模型都能够推断出可变的出生率，并正确拒绝可变模型，转而支持有效恒定的模型。一般来说，基于 HSMRF 的模型比其 GMRF 对应模型具有更高的精度，几乎没有精度损失。将其应用于澳大利亚壁虎科 (Pygopodidae) 的宏观进化数据集 (其中出生率可解释为物种形成率)，基于 GMRF 的模型检测到出生率缓慢下降，而基于 HSMRF 的模型检测到最后 1200 万年的物种形成率快速下降。将其应用于俄罗斯和乌克兰 HIV 亚型 A 的传染病系统发育数据集 (其中出生率可解释为新感染的累积率)，我们的模型检测到 20 世纪 90 年代感染率大幅上升。
