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

V.N. Parikh; A.G. Ioannidis; J.E. Gorzynski; H.N. De Jong; X. Liu; J. Roque; K. Osoegawa; C. Hughes; S.C. Sutton; N. Youlton; R. Joshi; D. Amar; Y. Tanigawa; D. Russo; J. Wong; J.T. Lauzon; J. Edelson; D. Mas Montserrat; Y. Kwon; S. Rubinacci; O. Delaneau; L. Cappello; J. Kim; M.J. Shoura; A.N. Raja; N. Watson; N. Hammond; E. Spiteri; K.C. Mallempati; J. Christle; A. Kirillova; K. Seo; Y. Huang; C. Zhao; S.G. Hershman; K.P. Dalton; J. Zhen; J. Kamm; K.D. Bhatt; A. Isakova; M. Morri; T. Ranganath; C.A. Blish; A.J. Rogers; K. Nadeau; S. Yang; A. Blomkalns; R. O'Hara; N.F. Neff; C. DeBoever; S. Szalma; M.T. Wheeler; C.M. Gates; K. Farh; G.P. Schroth; P. Febbo; F. deSouza; O.E. Cornejo; A. Kistler; J.A. Palacios; B.A. Pinsky; C.D. Bustamante; M.A. Rivas; E.A. Ashley

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

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Date

30 août 2022

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Langue

Anglais

Identifiants

doi: 10.1038/s41467-022-32397-8
issn: 2041-1723

Source

Serveur académique Lausannois

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Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-022-32397-8

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/36042219

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/2041-1723

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_35D9A21D51D11

Collection

Serveur Académique Lausannois

Organisation

Université de Lausanne et CHUV

Licences

info:eu-repo/semantics/openAccess , CC BY 4.0 , https://creativecommons.org/licenses/by/4.0/

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COVID-19/epidemiology; Genome, Viral; Genome-Wide Association Study; Humans; Pandemics; SARS-CoV-2/genetics

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Military strategy Group dynamics Association Groups, Social

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V.N. Parikh et al., « Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy. », Serveur académique Lausannois, ID : 10.1038/s41467-022-32397-8

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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.

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

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