New Routes to Phylogeography: A Bayesian Structured Coalescent Approximation.
New Routes to Phylogeography: A Bayesian Structured Coalescent Approximation.
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DOI：
10.1371/journal.pgen.1005421
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发表时间：
2015-08
期刊：
影响因子：
4.5
通讯作者：
Wilson D
中科院分区：
文献类型：
--
作者：
De Maio N;Wu CH;O'Reilly KM;Wilson D
系统地理学方法的目的是从遗传数据中推断迁移趋势和采样谱系的历史。生物地理学的应用是广泛的，在病原体的背景下，包括传播历史的重建和爆发的起源和出现。基于自下而上的群体遗传学模型的系统地理推断在计算上是昂贵的，因此基于离散性状进化的更快的替代方案已经变得流行。在本文中，我们证明了基于离散性状模型的迁移率和根位置的推断是非常不可靠的，并且对有偏抽样非常敏感。为了解决这个问题，我们引入了BASTA（BASTA结构化聚结近似），这是一种在BEAST 2中实现的新方法，它将基于结构化聚结的方法的准确性与处理不仅仅是少数群体所需的计算效率相结合。我们通过调查埃博拉病毒的人畜共患病传播，说明了模型选择不佳对地理分析的潜在严重影响。尽管结构化结合分析正确地推断出连续的人类埃博拉疫情是由大量未抽样的非人类宿主人群传播的，但离散性状分析令人难以置信地得出结论，未被发现的人际传播使病毒在过去四十年中持续存在。随着基因组学在传染病的控制和预防方面发挥越来越重要的作用，地理学推断为传播历史提供强有力的见解至关重要。在研究传染病时，了解细菌如何从一个地方传播到另一个地方，人与人之间，甚至身体的一个部位传播到另一个部位通常很重要。使用地理学方法，可以通过研究病原体（或其他生物体）的遗传物质来恢复它们的传播历史。在这里，我们揭示了一些流行的，快速的地理方法是不准确的，我们引入了一个新的更可靠的方法来解决这个问题。通过比较基于原则性种群模型和快速替代方案的不同地理学方法，我们发现不同的方法可以给出截然相反的结果，我们在西非正在发生的埃博拉疫情以及禽流感病毒和番茄黄叶卷曲病毒在全球范围内爆发的背景下提供了具体的例子。我们发现，最流行的地理学方法往往产生完全不准确的结论。其受欢迎的原因之一是其计算速度，这使得用户可以使用复杂的模型分析大型遗传数据集。到目前为止，更精确的方法要慢得多，因此我们提出了一种名为BASTA的新方法，它可以在合理的时间内实现良好的精度。我们越来越依赖基因测序来了解感染的起源和传播，随着这一作用的不断增强，在设计预防或遏制疾病传播的政策时，使用准确的地理学方法将至关重要。
Phylogeographic methods aim to infer migration trends and the history of sampled lineages from genetic data. Applications of phylogeography are broad, and in the context of pathogens include the reconstruction of transmission histories and the origin and emergence of outbreaks. Phylogeographic inference based on bottom-up population genetics models is computationally expensive, and as a result faster alternatives based on the evolution of discrete traits have become popular. In this paper, we show that inference of migration rates and root locations based on discrete trait models is extremely unreliable and sensitive to biased sampling. To address this problem, we introduce BASTA (BAyesian STructured coalescent Approximation), a new approach implemented in BEAST2 that combines the accuracy of methods based on the structured coalescent with the computational efficiency required to handle more than just few populations. We illustrate the potentially severe implications of poor model choice for phylogeographic analyses by investigating the zoonotic transmission of Ebola virus. Whereas the structured coalescent analysis correctly infers that successive human Ebola outbreaks have been seeded by a large unsampled non-human reservoir population, the discrete trait analysis implausibly concludes that undetected human-to-human transmission has allowed the virus to persist over the past four decades. As genomics takes on an increasingly prominent role informing the control and prevention of infectious diseases, it will be vital that phylogeographic inference provides robust insights into transmission history. When studying infectious diseases it is often important to understand how germs spread from location-to-location, person-to-person, or even one part of the body to another. Using phylogeographic methods, it is possible to recover the history of spread of pathogens (or other organisms) by studying their genetic material. Here we reveal that some popular, fast phylogeographic methods are inaccurate, and we introduce a new more reliable method to address the problem. By comparing different phylogeographic methods based on principled population models and fast alternatives, we found that different approaches can give diametrically opposed results, and we offer concrete examples in the context of the ongoing Ebola outbreak in West Africa and the world-wide outbreaks of Avian Influenza Virus and Tomato Yellow Leaf Curl Virus. We found that the most popular phylogeographic method often produces completely inaccurate conclusions. One of the reasons for its popularity has been its computational speed, which has allowed users to analyse large genetic datasets with complex models. More accurate approaches have until now been considerably slower, and therefore we propose a new method called BASTA that achieves good accuracy in a reasonable time. We are relying more and more on genetic sequencing to learn about the origin and spread of infections, and as this role continues to grow, it will be essential to use accurate phylogeographic methods when designing policies to prevent or curb the spread of disease.