Phylogeography Takes a Relaxed Random Walk in Continuous Space and Time
Phylogeography Takes a Relaxed Random Walk in Continuous Space and Time
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DOI：
10.1093/molbev/msq067
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发表时间：
2010-08-01
影响因子：
10.7
通讯作者：
Suchard, Marc A.
中科院分区：
文献类型：
--
作者：
Lemey, Philippe;Rambaut, Andrew;Suchard, Marc A.
旨在理解进化历史的地理背景的研究正在生物学科中蓬勃发展。最近的努力试图解释同时代的遗传变异的日益详细的地理和环境的观察。这种兴趣促进了明确旨在整合这种异构数据的地理信息推理技术的发展。一个有希望的发展是在连续的景观上重建非连续的地理历史。在这里，我们提出了一个贝叶斯统计方法来推断连续的地理扩散使用随机游走模型，同时重建的进化历史的时间从分子序列数据。此外，通过容纳分支特定的变化，在扩散率，我们放松了最严格的假设标准布朗扩散过程，并通过分析模拟和真实的病毒遗传数据证明了增加的统计效率在空间重建的过度分散的随机游动。我们进一步说明了如何从一个完全指定的随机过程在序列进化和空间运动的汇总统计推断揭示了狂犬病流行的重要特征。再加上最近的进展，在离散地理推断，连续模型的发展提供了一个灵活的统计框架，很容易扩展到适应各种景观遗传特征的地理重建。
Research aimed at understanding the geographic context of evolutionary histories is burgeoning across biological disciplines. Recent endeavors attempt to interpret contemporaneous genetic variation in the light of increasingly detailed geographical and environmental observations. Such interest has promoted the development of phylogeographic inference techniques that explicitly aim to integrate such heterogeneous data. One promising development involves reconstructing phylogeographic history on a continuous landscape. Here, we present a Bayesian statistical approach to infer continuous phylogeographic diffusion using random walk models while simultaneously reconstructing the evolutionary history in time from molecular sequence data. Moreover, by accommodating branch-specific variation in dispersal rates, we relax the most restrictive assumption of the standard Brownian diffusion process and demonstrate increased statistical efficiency in spatial reconstructions of overdispersed random walks by analyzing both simulated and real viral genetic data. We further illustrate how drawing inference about summary statistics from a fully specified stochastic process over both sequence evolution and spatial movement reveals important characteristics of a rabies epidemic. Together with recent advances in discrete phylogeographic inference, the continuous model developments furnish a flexible statistical framework for biogeographical reconstructions that is easily expanded upon to accommodate various landscape genetic features.