Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk.
Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk.
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DOI：
10.1371/journal.pcbi.1008561
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发表时间：
2021-01
影响因子：
4.3
通讯作者：
De Maio N
中科院分区：
文献类型：
--
作者：
Kalkauskas A;Perron U;Sun Y;Goldman N;Baele G;Guindon S;De Maio N
系统地理学推断允许通过整合遗传和地理数据来重建病原体或活生物体过去的地理传播。连续地理学中的一个流行模型-以经纬度坐标的形式提供位置数据-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，使用这种模型的重建可以强烈影响采样偏差，如缺乏从某些地区的采样。作为一种尝试，以减少BMP的采样偏差的影响，我们认为，从欠采样区域的无序列的样品。虽然这一方法消除了抽样偏差的影响，但在大多数情况下，由于需要事先了解疫情的空间分布，这将不是一个可行的选择。因此，我们考虑一个替代模型，空间Λ-Fleming-Viot过程（ΛFV），这是最近在群体遗传学中流行的。尽管ΛFV对采样偏差具有鲁棒性，但我们发现ΛFV和BMP模型的不同假设导致了不同的适用性，其中ΛFV更适合于地方病传播的情况，而BMP更适合于最近的爆发或殖民。系统地理学研究过去的位置和迁移使用的信息，从目前的地理位置的基因序列。例如，利用在不同时间和地点收集到的病原体的基因序列，可以利用地理学来重建疫情的地理传播历史。在这里，我们研究不同的模型假设对地理推理的影响。特别是，我们研究了用于收集样本的策略的影响。我们发现，样本收集偏差可以有很大的影响，地理重建的质量：地理上有偏见的抽样方案可以是非常有害的流行的连续地理模型。我们考虑不同的方法来对抗这些影响，从利用替代的地理模型，包括部分信息的样本（没有基因序列的已知病例）。虽然这些策略确实减轻了抽样偏差的影响，但它们也导致了相当大的额外计算负担。我们还研究了不同的地理模型的内在差异，以及它们在不同场景下对重建模式的影响。
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. Phylogeography studies past location and migration using information from current geographic locations of genetic sequences. For example, phylogeography can be used to reconstruct the history of geographical spread of an outbreak using the genetic sequences of the pathogen collected at different times and locations. Here, we investigate the effects of different model assumptions on phylogeographic inference. In particular, we examine the effects of the strategy used to collect samples. We show that sample collection biases can have a strong impact on the quality of phylogeographic reconstruction: geographically biased sampling scheme can be very detrimental for popular continuous phylogeography models. We consider different ways to counter these effects, from utilising alternative phylogeographic models, to the inclusion of partially informative samples (known cases without genetic sequences). While these strategies do alleviate the effects of sampling biases, they also lead to considerable additional computational burden. We also investigate the intrinsic differences of different phylogeographic models, and their effects on reconstructed patterns in different scenarios.
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DOI：
10.1016/j.cub.2011.05.058
发表时间：
2011-08-09
期刊：
Current biology : CB
影响因子：
--
作者：
Edwards CJ;Suchard MA;Lemey P;Welch JJ;Barnes I;Fulton TL;Barnett R;O'Connell TC;Coxon P;Monaghan N;Valdiosera CE;Lorenzen ED;Willerslev E;Baryshnikov GF;Rambaut A;Thomas MG;Bradley DG;Shapiro B
通讯作者：
Shapiro B
影响因子：
10.7
作者：
Dellicour S;Durkin K;Hong SL;Vanmechelen B;Martí-Carreras J;Gill MS;Meex C;Bontems S;André E;Gilbert M;Walker C;Maio N;Faria NR;Hadfield J;Hayette MP;Bours V;Wawina-Bokalanga T;Artesi M;Baele G;Maes P
通讯作者：
Maes P
影响因子：
4.9
作者：
Dellicour, Simon;Troupin, Cecile;Bourhy, Herve
通讯作者：
Bourhy, Herve
影响因子：
2.9
作者：
FELSENSTEIN, J
通讯作者：
FELSENSTEIN, J
影响因子：
5.8
作者：
Kelleher, Jerome;Barton, Nicholas H.;Etheridge, Alison M.
通讯作者：
Etheridge, Alison M.