Epidemiological associations with genomic variation in SARS-CoV-2

Abstract SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features a...

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Autores principales:	Ali Rahnavard, Tyson Dawson, Rebecca Clement, Nathaniel Stearrett, Marcos Pérez-Losada, Keith A. Crandall
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f38b7a7ac21d46d6bbf4eef2b9aa3065
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spelling	oai:doaj.org-article:f38b7a7ac21d46d6bbf4eef2b9aa30652021-11-28T12:15:32ZEpidemiological associations with genomic variation in SARS-CoV-210.1038/s41598-021-02548-w2045-2322https://doaj.org/article/f38b7a7ac21d46d6bbf4eef2b9aa30652021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02548-whttps://doaj.org/toc/2045-2322Abstract SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3′-to-5′ exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic—coherence—and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.Ali RahnavardTyson DawsonRebecca ClementNathaniel StearrettMarcos Pérez-LosadaKeith A. CrandallNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ali Rahnavard Tyson Dawson Rebecca Clement Nathaniel Stearrett Marcos Pérez-Losada Keith A. Crandall Epidemiological associations with genomic variation in SARS-CoV-2
description	Abstract SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3′-to-5′ exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic—coherence—and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.
format	article
author	Ali Rahnavard Tyson Dawson Rebecca Clement Nathaniel Stearrett Marcos Pérez-Losada Keith A. Crandall
author_facet	Ali Rahnavard Tyson Dawson Rebecca Clement Nathaniel Stearrett Marcos Pérez-Losada Keith A. Crandall
author_sort	Ali Rahnavard
title	Epidemiological associations with genomic variation in SARS-CoV-2
title_short	Epidemiological associations with genomic variation in SARS-CoV-2
title_full	Epidemiological associations with genomic variation in SARS-CoV-2
title_fullStr	Epidemiological associations with genomic variation in SARS-CoV-2
title_full_unstemmed	Epidemiological associations with genomic variation in SARS-CoV-2
title_sort	epidemiological associations with genomic variation in sars-cov-2
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f38b7a7ac21d46d6bbf4eef2b9aa3065
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Epidemiological associations with genomic variation in SARS-CoV-2

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