Population structure influences genealogical patterns, however data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealised genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past, and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with the appearance and expansion of mutations conferring antimicrobial resistance.

中文翻译：

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在时间尺度系统发育中识别隐藏的种群结构

人口结构会影响谱系模式，但是与人口结构有关的数据通常不可用或无法直接观察到。人口结构的推断在分子流行病学中非常重要，其中病原体系统发育学越来越多地用于推断传播模式和检测爆发。观察到的和理想化的谱系之间的差异（例如由合并过程产生的谱系）可以量化，如果出现显着差异，则可能揭示自然选择、宿主种群结构或其他人口统计学和流行病学异质性的作用。我们开发了一种快速的非参数统计检验，用于检测时间尺度系统发育树中的隐秘种群结构。该测试基于将估计的系统发育与理论预期的系统动力学排序进行对比，该排序是在合并框架内的两个进化枝中的共同祖先。这些统计测试还推动了算法的开发，这些算法可用于快速筛选系统发育树中可能共享不同人口统计或流行病学历史的进化枝。流行病学应用包括识别脆弱宿主人群的爆发或具有适应性优势的基因型的快速扩展。为了证明这些方法在爆发检测中的实用性，我们将新方法应用于从数千个 HIV-1 部分 pol 序列重建的大型系统发育。这揭示了最近快速增长的进化枝的存在，并且明显集中在年轻男性中，表明该群体最近和快速传播。此外，为了证明这些方法在抗菌素耐药性研究中的实用性，我们将新方法应用于从全基因组淋病奈瑟菌序列重建的大型系统发育。我们发现使用这些方法检测到的种群结构与赋予抗菌素耐药性的突变的出现和扩展密切重叠。