PDF(Pubmed)
Abstract：
The emergence of SARS-CoV-2 variants with increased fitness has had a strong impact on the epidemiology of COVID-19, with the higher effective reproduction number of the viral variants leading to new epidemic waves. Tracking such variants and their genetic signatures, using data collected through genomic surveillance, is therefore crucial for forecasting likely surges in incidence. Current methods of estimating fitness advantages of variants rely on tracking the changing proportion of a particular lineage over time, but describing successful lineages in a rapidly evolving viral population is a difficult task. We propose a method of estimating fitness gains directly from nucleotide information generated by genomic surveillance, without a priori assigning isolates to lineages from phylogenies, based solely on the abundance of single nucleotide polymorphisms (SNPs). The method is based on mapping changes in the genetic population structure over time. Changes in the abundance of SNPs associated with periods of increasing fitness allow for the unbiased discovery of new variants, thereby obviating a deliberate lineage assignment and phylogenetic inference. We conclude that the method provides a fast and reliable way to estimate fitness advantages of variants without the need for a priori assigning isolates to lineages.
摘要：
适应性增强的SARS-CoV-2变体的出现对COVID-19的流行病学产生了强烈影响，病毒变体的有效繁殖数量增加导致了新的流行浪潮。追踪这些变异及其遗传特征，使用通过基因组监测收集的数据，因此，对于预测发病率可能激增至关重要。当前估计变体适应度优势的方法依赖于跟踪特定谱系随时间变化的比例，但是在快速进化的病毒种群中描述成功的谱系是一项艰巨的任务。我们提出了一种直接从基因组监测产生的核苷酸信息中估计适应度增益的方法，没有先验地将分离物分配给系统发育的谱系，仅基于单核苷酸多态性（SNP）的丰度。该方法基于映射遗传群体结构随时间的变化。与适应度增加相关的SNP丰度的变化允许无偏见地发现新的变体，从而避免了故意的谱系分配和系统发育推断。我们得出的结论是，该方法提供了一种快速可靠的方法来估计变体的适应度优势，而无需先验地将分离株分配给谱系。
