The Structured Coalescent and Its Approximations.
The Structured Coalescent and Its Approximations.
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DOI：
10.1093/molbev/msx186
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发表时间：
2017-11-01
影响因子：
10.7
通讯作者：
Stadler T
中科院分区：
文献类型：
--
作者：
Müller NF;Rasmussen DA;Stadler T
系统地理学方法可以帮助揭示生物种群之间的基因移动。这已经被广泛地用来利用样本的位置或状态以及序列数据来量化病原体在不同宿主群体之间的移动、人类的迁徙历史以及语言或基因在物种之间的地理传播。因此，系统发生学提供了仅从经典的流行病学或发生数据中无法获得的对迁徙过程的洞察。然而，系统地理学方法有几个已知的缺点。特别是，最广泛使用的方法之一将移民等同于突变，因此没有纳入有关人口统计学的信息。这可能会导致数据集的估计迁移率出现严重偏差，其中抽样在不同人群之间存在偏差。另一方面，结构化合并允许我们一致地对迁移和合并过程进行建模，但由于需要推断祖先迁移历史，当前的实现难以处理复杂的数据集。因此，已经开发出整合了所有祖先迁徙历史的结构化聚合体的近似。然而，这些近似的有效性和稳健性仍然不清楚。我们给出了结构聚结体的精确数值解，它不需要对迁移历史的推断。虽然这种解决方案在计算上对于大型数据集是不可行的，但它澄清了以前开发的近似方法的假设，并允许我们提供对结构化合并的改进近似。我们已经在BEAST2中实现了这些方法，并展示了这些方法在不同场景下的比较。
Phylogeographic methods can help reveal the movement of genes between populations of organisms. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for data sets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex data sets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. Although this solution is computationally unfeasible for large data sets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.
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影响因子：
4.5
作者：
Mailund T;Halager AE;Westergaard M;Dutheil JY;Munch K;Andersen LN;Lunter G;Prüfer K;Scally A;Hobolth A;Schierup MH
通讯作者：
Schierup MH
DOI：
10.1093/bioinformatics/btu201
发表时间：
2014-08-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Vaughan TG;Kühnert D;Popinga A;Welch D;Drummond AJ
通讯作者：
Drummond AJ
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：
6.6
作者：
Revell, Liam J.
通讯作者：
Revell, Liam J.
影响因子：
10.7
作者：
Hey, Jody
通讯作者：
Hey, Jody