Emmett Kevin J, Lee Albert, Khiabanian Hossein, Rabadan Raul
Department of Physics and Department of Systems Biology, Columbia University, New York, New York, USA.
Department of Systems Biology and Department of Biomedical Informatics, Columbia University, New York, New York, USA.
PLoS Curr. 2015 Feb 9;7:ecurrents.outbreaks.c7fd7946ba606c982668a96bcba43c90. doi: 10.1371/currents.outbreaks.c7fd7946ba606c982668a96bcba43c90.
Viral outbreaks, such as the 2014 ebolavirus, can spread rapidly and have complex evolutionary dynamics, including coinfection and bulk transmission of multiple viral populations. Genomic surveillance can be hindered when the spread of the outbreak exceeds the evolutionary rate, in which case consensus approaches will have limited resolution. Deep sequencing of infected patients can identify genomic variants present in intrahost populations at subclonal frequencies (i.e. <50%). Shared subclonal variants (SSVs) can provide additional phylogenetic resolution and inform about disease transmission patterns.
We use metrics from population genetics to analyze data from the 2014 ebolavirus outbreak in Sierra Leone and identify phylogenetic signal arising from SSVs. We use methods derived from information theory to measure a lower bound on transmission bottleneck size.
We identify several SSV that shed light on phylogenetic relationships not captured by consensus-based analyses. We find that transmission bottleneck size is larger than one founder population, yet significantly smaller than the intrahost effective population. Our results demonstrate the important role of shared subclonal variants in genomic surveillance.
病毒爆发，如2014年的埃博拉病毒，传播迅速且具有复杂的进化动态，包括多种病毒群体的共感染和大量传播。当疫情的传播速度超过进化速度时，基因组监测可能会受到阻碍，在这种情况下，共识方法的分辨率将有限。对感染患者进行深度测序可以识别宿主内群体中以亚克隆频率（即<50%）存在的基因组变异。共享亚克隆变异（SSV）可以提供额外的系统发育分辨率，并为疾病传播模式提供信息。
我们使用群体遗传学指标来分析2014年塞拉利昂埃博拉病毒爆发的数据，并识别由SSV产生的系统发育信号。我们使用从信息论衍生的方法来测量传播瓶颈大小的下限。
我们识别出几个SSV，它们揭示了基于共识的分析未捕捉到的系统发育关系。我们发现传播瓶颈大小大于一个创始群体，但明显小于宿主内有效群体。我们的结果证明了共享亚克隆变异在基因组监测中的重要作用。
