对 SARS-CoV-2 Omicron 变异体的进化和人口历史的基因组学洞察:群体基因组学方法

JMIR bioinformatics and biotechnology Pub Date : 2023-06-12 eCollection Date: 2023-01-01 DOI:10.2196/40673
Kritika M Garg, Vinita Lamba, Balaji Chattopadhyay
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摘要

背景:透彻了解病原体的基因细分模式可以提供预防疾病传播所需的重要信息。对于 SARS-CoV-2 而言,数百万个基因组的存在使这项任务在分析上具有挑战性,而了解基因细分的传统方法往往会失败:我们研究的目的是利用群体基因组学方法,在 Pango 系的基础上确定 Omicron 变体的细分和人口历史:我们结合使用了进化网络方法和多元统计方案,以了解奥米克隆变体的细分和传播。我们确定了 BA.1 和 BA.2 系的细分,并进一步确定了与每个群相关的突变。我们进一步确定了 Omicron 变异体的整体基因组多样性,并评估了每个已确定基因簇的选择压力:结果:我们使用两种不同的方法来理解基因细分,观察到了一致的结果。奥米克隆变体的整体细分模式与潘戈系的定义基本一致。此外,BA.1系的1个聚类和BA.2系的3个聚类在统计学上显示出明显的选择或人口扩张特征(Tajima's DConclusions):我们为评估 SARS-CoV-2 的遗传结构和种群历史提供了一个简便的框架,这对了解病毒的本地历史特别有用。我们发现了一些重要的突变,这些突变对 Omicron 的某些品系有利,有助于病毒的传播。这对政策制定者来说是至关重要的信息,因为可以在全面了解病毒的变异性和帮助其传播的进化过程的基础上,设计预防措施,以减少病毒的进一步传播。
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Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach.

Background: A thorough understanding of the patterns of genetic subdivision in a pathogen can provide crucial information that is necessary to prevent disease spread. For SARS-CoV-2, the availability of millions of genomes makes this task analytically challenging, and traditional methods for understanding genetic subdivision often fail.

Objective: The aim of our study was to use population genomics methods to identify the subtle subdivisions and demographic history of the Omicron variant, in addition to those captured by the Pango lineage.

Methods: We used a combination of an evolutionary network approach and multivariate statistical protocols to understand the subdivision and spread of the Omicron variant. We identified subdivisions within the BA.1 and BA.2 lineages and further identified the mutations associated with each cluster. We further characterized the overall genomic diversity of the Omicron variant and assessed the selection pressure for each of the genetic clusters identified.

Results: We observed concordant results, using two different methods to understand genetic subdivision. The overall pattern of subdivision in the Omicron variant was in broad agreement with the Pango lineage definition. Further, 1 cluster of the BA.1 lineage and 3 clusters of the BA.2 lineage revealed statistically significant signatures of selection or demographic expansion (Tajima's D<-2), suggesting the role of microevolutionary processes in the spread of the virus.

Conclusions: We provide an easy framework for assessing the genetic structure and demographic history of SARS-CoV-2, which can be particularly useful for understanding the local history of the virus. We identified important mutations that are advantageous to some lineages of Omicron and aid in the transmission of the virus. This is crucial information for policy makers, as preventive measures can be designed to mitigate further spread based on a holistic understanding of the variability of the virus and the evolutionary processes aiding its spread.

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