Kalkauskas Antanas, Perron Umberto, Sun Yuxuan, Goldman Nick, Baele Guy, Guindon Stephane, De Maio Nicola
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom.
Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
PLoS Comput Biol. 2021 Jan 6;17(1):e1008561. doi: 10.1371/journal.pcbi.1008561. eCollection 2021 Jan.
Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography-with location data provided in the form of latitude and longitude coordinates-describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreak's spatial distribution. We therefore consider an alternative model, the spatial Λ-Fleming-Viot process (ΛFV), which has recently gained popularity in population genetics. Despite the ΛFV's robustness to sampling biases, we find that the different assumptions of the ΛFV and BMP models result in different applicabilities, with the ΛFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.
系统发育地理学推断通过整合遗传数据和地理数据，能够重建病原体或生物过去的地理传播情况。在连续系统发育地理学中，一种流行的模型——以经纬度坐标形式提供位置数据——将传播描述为连续空间和时间中的布朗运动（布朗运动系统发育地理学，BMP），类似于连续性状进化的类似模型。在这里，我们表明使用该模型的重建可能会受到抽样偏差的强烈影响，例如某些地区缺乏抽样。作为减少抽样偏差对BMP影响的一种尝试，我们考虑从抽样不足的地区添加无序列样本。虽然这种方法减轻了抽样偏差的影响，但在大多数情况下，由于需要事先了解疫情的空间分布，这不是一个可行的选择。因此，我们考虑另一种模型，即空间Λ-弗莱明-维奥特过程（ΛFV），该模型最近在群体遗传学中受到欢迎。尽管ΛFV对抽样偏差具有鲁棒性，但我们发现ΛFV和BMP模型的不同假设导致了不同的适用性，ΛFV更适合于地方病传播的情况，而BMP更适合于近期的疫情爆发或殖民化情况。
