Cargando…

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 underst...

Descripción completa

Detalles Bibliográficos
Autores principales: Garg, Kritika M, Lamba, Vinita, Chattopadhyay, Balaji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331448/
https://www.ncbi.nlm.nih.gov/pubmed/37456139
http://dx.doi.org/10.2196/40673
_version_ 1785070255423356928
author Garg, Kritika M
Lamba, Vinita
Chattopadhyay, Balaji
author_facet Garg, Kritika M
Lamba, Vinita
Chattopadhyay, Balaji
author_sort Garg, Kritika M
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10331448
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-103314482023-07-11 Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach Garg, Kritika M Lamba, Vinita Chattopadhyay, Balaji JMIR Bioinform Biotech Original Paper 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. JMIR Publications 2023-06-12 /pmc/articles/PMC10331448/ /pubmed/37456139 http://dx.doi.org/10.2196/40673 Text en ©Kritika M Garg, Vinita Lamba, Balaji Chattopadhyay. Originally published in JMIR Bioinformatics and Biotechnology (https://bioinform.jmir.org), 12.06.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Bioinformatics and Biotechnology, is properly cited. The complete bibliographic information, a link to the original publication on https://bioinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Garg, Kritika M
Lamba, Vinita
Chattopadhyay, Balaji
Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach
title Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach
title_full Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach
title_fullStr Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach
title_full_unstemmed Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach
title_short Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach
title_sort genomic insights into the evolution and demographic history of the sars-cov-2 omicron variant: population genomics approach
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331448/
https://www.ncbi.nlm.nih.gov/pubmed/37456139
http://dx.doi.org/10.2196/40673
work_keys_str_mv AT gargkritikam genomicinsightsintotheevolutionanddemographichistoryofthesarscov2omicronvariantpopulationgenomicsapproach
AT lambavinita genomicinsightsintotheevolutionanddemographichistoryofthesarscov2omicronvariantpopulationgenomicsapproach
AT chattopadhyaybalaji genomicinsightsintotheevolutionanddemographichistoryofthesarscov2omicronvariantpopulationgenomicsapproach