Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography
Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography
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DOI：
10.1093/ve/vey043
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发表时间：
2019-01-01
期刊：
影响因子：
5.3
通讯作者：
Gonzalez-Hernandez, Graciela
中科院分区：
文献类型：
--
作者：
Scotch, Matthew;Tahsin, Tasnia;Gonzalez-Hernandez, Graciela
使用诸如BEAST之类的软件的离散系统地理学认为每个分类单元的采样位置是固定的；通常是一个地点，没有不确定性。在研究病毒时，这意味着该分类群的受感染宿主不可能在其他地方。在这里，我们放松了这种强烈的假设，并允许对离散病毒系统地理学的不确定性进行分析整合。我们使用自动语言处理方法来查找和分配不确定性到备选的潜在位置。我们考虑了两个流感病例研究：埃及的H5N1；H1N1 pdm09在北美。对于每个分类单元，我们实现了25%的分类单元具有不同数量的采样不确定性的场景，包括10%、30%和50%的不确定性，并改变了每个分类单元的分布方式。这包括以下场景：(i)在一个位置放置特定数量的不确定性，同时将剩余数量均匀分布在所有其他候选位置（相应标记为10、30和50）；（ii）将剩余的不确定性分配给另一个地点；因此，将不确定性“分割”到两个位置（即10/90、30/70和50/50）；（iii）通过两种预定义的启发式方法消除不确定性：分配到质心位置（CNTR）或该国最大人口（POP）。我们将所有情景与参考标准（RS）进行了比较，其中所有分类群都知道（绝对确定）的位置。在此基础上，我们对25%的分类群进行了5次随机选择，并用这些随机选择来确定不确定性。我们对每种情况进行了后验分析，包括：(a)病毒持久性，(b)迁移率，(c)主干奖励，以及(d)根状态的后验概率。与CNTR和POP相比，具有采样不确定性的情景更接近RS。对于H5N1，在具有上述(i)和（ii）采样不确定性的情况下，病毒持久性的绝对误差中值范围为0.005-0.047，而CNTR和POP的绝对误差中值范围为0.063-0.075。pdm09案例研究的持久性与我们对情景(i)和（ii）中的迁移率的分析遵循类似的趋势。在考虑根状态的后验概率时，我们发现除一种抽样不确定的H5N1情景外，其他情景都与RS关于暴发起源的观点一致，而CNTR和POP则不同意。我们的研究结果表明，与临时启发式方法相比，将地理空间不确定性分配给病毒系统地理学的分类群效益估计。我们还发现，一般来说，无论如何分配采样不确定度，结果的差异都是有限的；均匀分布或两个位置之间的分裂对后验结果没有很大影响。这个框架在BEAST v.1.10中可用。在未来的工作中，我们将探索流感以外的病毒。我们还将开发一个网络界面，供研究人员使用我们的语言处理方法来查找和分配不确定性，以确定病毒系统地理学的其他潜在位置。
Discrete phylogeography using software such as BEAST considers the sampling location of each taxon as fixed; often to a single location without uncertainty. When studying viruses, this implies that there is no possibility that the location of the infected host for that taxa is somewhere else. Here, we relaxed this strong assumption and allowed for analytic integration of uncertainty for discrete virus phylogeography. We used automatic language processing methods to find and assign uncertainty to alternative potential locations. We considered two influenza case studies: H5N1 in Egypt; H1N1 pdm09 in North America. For each, we implemented scenarios in which 25 per cent of the taxa had different amounts of sampling uncertainty including 10, 30, and 50 per cent uncertainty and varied how it was distributed for each taxon. This includes scenarios that: (i) placed a specific amount of uncertainty on one location while uniformly distributing the remaining amount across all other candidate locations (correspondingly labeled 10, 30, and 50); (ii) assigned the remaining uncertainty to just one other location; thus 'splitting' the uncertainty among two locations (i.e. 10/90, 30/70, and 50/50); and (iii) eliminated uncertainty via two predefined heuristic approaches: assignment to a centroid location (CNTR) or the largest population in the country (POP). We compared all scenarios to a reference standard (RS) in which all taxa had known (absolutely certain) locations. From this, we implemented five random selections of 25 per cent of the taxa and used these for specifying uncertainty. We performed posterior analyses for each scenario, including: (a) virus persistence, (b) migration rates, (c) trunk rewards, and (d) the posterior probability of the root state. The scenarios with sampling uncertainty were closer to the RS than CNTR and POP. For H5N1, the absolute error of virus persistence had a median range of 0.005-0.047 for scenarios with sampling uncertainty-(i) and (ii) above-versus a range of 0.063-0.075 for CNTR and POP. Persistence for the pdm09 case study followed a similar trend as did our analyses of migration rates across scenarios (i) and (ii). When considering the posterior probability of the root state, we found all but one of the H5N1 scenarios with sampling uncertainty had agreement with the RS on the origin of the outbreak whereas both CNTR and POP disagreed. Our results suggest that assigning geospatial uncertainty to taxa benefits estimation of virus phylogeography as compared to ad-hoc heuristics. We also found that, in general, there was limited difference in results regardless of how the sampling uncertainty was assigned; uniform distribution or split between two locations did not greatly impact posterior results. This framework is available in BEAST v.1.10. In future work, we will explore viruses beyond influenza. We will also develop a web interface for researchers to use our language processing methods to find and assign uncertainty to alternative potential locations for virus phylogeography.