Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

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• dc.contributor.author Ioannidis, Alexander G.
• dc.contributor.author Cappello, Lorenzo
• dc.contributor.author Ashley, Euan A.
• dc.date.accessioned 2024-02-28T08:06:10Z
• dc.date.available 2024-02-28T08:06:10Z
• dc.date.issued 2022
• dc.description.abstract The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.
• dc.format.mimetype application/pdf
• dc.identifier.citation Parikh VN, Ioannidis AG, Jimenez-Morales D, Gorzynski JE, De Jong HN, Liu X, Roque J et al. Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy. Nat Commun. 2022;13:5107. DOI: 10.1038/s41467-022-32397-8
• dc.identifier.doi http://dx.doi.org/10.1038/s41467-022-32397-8
• dc.identifier.issn 2041-1723
• dc.identifier.uri http://hdl.handle.net/10230/59278
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nature Communications. 2022;13:5107.
• dc.relation.isreferencedby https://doi.org/10.1038/s41467-022-32397-8
• dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. © The Author(s) 2022
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Genetics
• dc.subject.keyword Sequencing
• dc.subject.keyword Viral infection
• dc.title Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion