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

Victoria N. Parikh; Alexander G. Ioannidis; David Jimenez-Morales; John E. Gorzynski; Hannah N. De Jong; Xiran Liu; Jonasel Roque; Victoria P. Cepeda-Espinoza; Kazutoyo Osoegawa; Chris Hughes; Shirley C. Sutton; Nathan Youlton; Ruchi Joshi; David Amar; Yosuke Tanigawa; Douglas Russo; Justin Wong; Jessie T. Lauzon; Jacob Edelson; Daniel Mas Montserrat; Yongchan Kwon; Simone Rubinacci; Olivier Delaneau; Lorenzo Cappello; Jaehee Kim; Massa J. Shoura; Archana N. Raja; Nathaniel Watson; Nathan Hammond; Elizabeth Spiteri; Kalyan C. Mallempati; Gonzalo Montero-Martín; Jeffrey Christle; Jennifer Kim; Anna Kirillova; Kinya Seo; Yong Huang; Chunli Zhao; Sonia Moreno-Grau; Steven G. Hershman; Karen P. Dalton; Jimmy Zhen; Jack Kamm; Karan D. Bhatt; Alina Isakova; Maurizio Morri; Thanmayi Ranganath; Catherine A. Blish; Angela J. Rogers; Kari Nadeau; Samuel Yang; Andra Blomkalns; Ruth O’Hara; Norma F. Neff; Christopher DeBoever; Sándor Szalma; Matthew T. Wheeler; Christian M. Gates; Kyle Farh; Gary P. Schroth; Phil Febbo; Francis deSouza; Omar E. Cornejo; Marcelo Fernandez-Vina; Amy Kistler; Julia A. Palacios; Benjamin A. Pinsky; Carlos D. Bustamante; Manuel A. Rivas; Euan A. Ashley

doi:10.1038/s41467-022-32397-8

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

Victoria N. Parikh, Alexander G. Ioannidis, David Jimenez-Morales, John E. Gorzynski, Hannah N. De Jong, Xiran Liu, Jonasel Roque, Victoria P. Cepeda-Espinoza, Kazutoyo Osoegawa, Chris Hughes, Shirley C. Sutton, Nathan Youlton, Ruchi Joshi, David Amar, Yosuke Tanigawa, Douglas Russo, Justin Wong, Jessie T. Lauzon, Jacob Edelson, Daniel Mas MontserratYongchan Kwon, Simone Rubinacci, Olivier Delaneau, Lorenzo Cappello, Jaehee Kim, Massa J. Shoura, Archana N. Raja, Nathaniel Watson, Nathan Hammond, Elizabeth Spiteri, Kalyan C. Mallempati, Gonzalo Montero-Martín, Jeffrey Christle, Jennifer Kim, Anna Kirillova, Kinya Seo, Yong Huang, Chunli Zhao, Sonia Moreno-Grau, Steven G. Hershman, Karen P. Dalton, Jimmy Zhen, Jack Kamm, Karan D. Bhatt, Alina Isakova, Maurizio Morri, Thanmayi Ranganath, Catherine A. Blish, Angela J. Rogers, Kari Nadeau, Samuel Yang, Andra Blomkalns, Ruth O’Hara, Norma F. Neff, Christopher DeBoever, Sándor Szalma, Matthew T. Wheeler, Christian M. Gates, Kyle Farh, Gary P. Schroth, Phil Febbo, Francis deSouza, Omar E. Cornejo, Marcelo Fernandez-Vina, Amy Kistler, Julia A. Palacios, Benjamin A. Pinsky, Carlos D. Bustamante, Manuel A. Rivas, Euan A. Ashley^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

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.

Original language	English
Article number	5107
Journal	Nature Communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-022-32397-8
State	Published - Dec 2022
Externally published	Yes

Funding

Funders	Funder number
John Taylor Babbitt Foundation
National Institutes of Health	U01HG009080, U24 OD026629 04S1, R01GM121404
National Heart, Lung, and Blood Institute	K08HL143185, R01HL144843
American Heart Association
Sarnoff Cardiovascular Research Foundation
Valuing Nature

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41467-022-32397-8

Cite this

Parikh, V. N., Ioannidis, A. G., Jimenez-Morales, D., Gorzynski, J. E., De Jong, H. N., Liu, X., Roque, J., Cepeda-Espinoza, V. P., Osoegawa, K., Hughes, C., Sutton, S. C., Youlton, N., Joshi, R., Amar, D., Tanigawa, Y., Russo, D., Wong, J., Lauzon, J. T., Edelson, J., ... Ashley, E. A. (2022). Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy. Nature Communications, 13(1), Article 5107. https://doi.org/10.1038/s41467-022-32397-8

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title = "Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy",

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.",

author = "Parikh, {Victoria N.} and Ioannidis, {Alexander G.} and David Jimenez-Morales and Gorzynski, {John E.} and {De Jong}, {Hannah N.} and Xiran Liu and Jonasel Roque and Cepeda-Espinoza, {Victoria P.} and Kazutoyo Osoegawa and Chris Hughes and Sutton, {Shirley C.} and Nathan Youlton and Ruchi Joshi and David Amar and Yosuke Tanigawa and Douglas Russo and Justin Wong and Lauzon, {Jessie T.} and Jacob Edelson and {Mas Montserrat}, Daniel and Yongchan Kwon and Simone Rubinacci and Olivier Delaneau and Lorenzo Cappello and Jaehee Kim and Shoura, {Massa J.} and Raja, {Archana N.} and Nathaniel Watson and Nathan Hammond and Elizabeth Spiteri and Mallempati, {Kalyan C.} and Gonzalo Montero-Mart{\'i}n and Jeffrey Christle and Jennifer Kim and Anna Kirillova and Kinya Seo and Yong Huang and Chunli Zhao and Sonia Moreno-Grau and Hershman, {Steven G.} and Dalton, {Karen P.} and Jimmy Zhen and Jack Kamm and Bhatt, {Karan D.} and Alina Isakova and Maurizio Morri and Thanmayi Ranganath and Blish, {Catherine A.} and Rogers, {Angela J.} and Kari Nadeau and Samuel Yang and Andra Blomkalns and Ruth O{\textquoteright}Hara and Neff, {Norma F.} and Christopher DeBoever and S{\'a}ndor Szalma and Wheeler, {Matthew T.} and Gates, {Christian M.} and Kyle Farh and Schroth, {Gary P.} and Phil Febbo and Francis deSouza and Cornejo, {Omar E.} and Marcelo Fernandez-Vina and Amy Kistler and Palacios, {Julia A.} and Pinsky, {Benjamin A.} and Bustamante, {Carlos D.} and Rivas, {Manuel A.} and Ashley, {Euan A.}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41467-022-32397-8",

language = "אנגלית",

volume = "13",

journal = "Nature Communications",

issn = "2041-1723",

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number = "1",

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Parikh, VN, Ioannidis, AG, Jimenez-Morales, D, Gorzynski, JE, De Jong, HN, Liu, X, Roque, J, Cepeda-Espinoza, VP, Osoegawa, K, Hughes, C, Sutton, SC, Youlton, N, Joshi, R, Amar, D, Tanigawa, Y, Russo, D, Wong, J, Lauzon, JT, Edelson, J, Mas Montserrat, D, Kwon, Y, Rubinacci, S, Delaneau, O, Cappello, L, Kim, J, Shoura, MJ, Raja, AN, Watson, N, Hammond, N, Spiteri, E, Mallempati, KC, Montero-Martín, G, Christle, J, Kim, J, Kirillova, A, Seo, K, Huang, Y, Zhao, C, Moreno-Grau, S, Hershman, SG, Dalton, KP, Zhen, J, Kamm, J, Bhatt, KD, Isakova, A, Morri, M, Ranganath, T, Blish, CA, Rogers, AJ, Nadeau, K, Yang, S, Blomkalns, A, O’Hara, R, Neff, NF, DeBoever, C, Szalma, S, Wheeler, MT, Gates, CM, Farh, K, Schroth, GP, Febbo, P, deSouza, F, Cornejo, OE, Fernandez-Vina, M, Kistler, A, Palacios, JA, Pinsky, BA, Bustamante, CD, Rivas, MA & Ashley, EA 2022, 'Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy', Nature Communications, vol. 13, no. 1, 5107. https://doi.org/10.1038/s41467-022-32397-8

TY - JOUR

T1 - Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

AU - Parikh, Victoria N.

AU - Ioannidis, Alexander G.

AU - Jimenez-Morales, David

AU - Gorzynski, John E.

AU - De Jong, Hannah N.

AU - Liu, Xiran

AU - Roque, Jonasel

AU - Cepeda-Espinoza, Victoria P.

AU - Osoegawa, Kazutoyo

AU - Hughes, Chris

AU - Sutton, Shirley C.

AU - Youlton, Nathan

AU - Joshi, Ruchi

AU - Amar, David

AU - Tanigawa, Yosuke

AU - Russo, Douglas

AU - Wong, Justin

AU - Lauzon, Jessie T.

AU - Edelson, Jacob

AU - Mas Montserrat, Daniel

AU - Kwon, Yongchan

AU - Rubinacci, Simone

AU - Delaneau, Olivier

AU - Cappello, Lorenzo

AU - Kim, Jaehee

AU - Shoura, Massa J.

AU - Raja, Archana N.

AU - Watson, Nathaniel

AU - Hammond, Nathan

AU - Spiteri, Elizabeth

AU - Mallempati, Kalyan C.

AU - Montero-Martín, Gonzalo

AU - Christle, Jeffrey

AU - Kim, Jennifer

AU - Kirillova, Anna

AU - Seo, Kinya

AU - Huang, Yong

AU - Zhao, Chunli

AU - Moreno-Grau, Sonia

AU - Hershman, Steven G.

AU - Dalton, Karen P.

AU - Zhen, Jimmy

AU - Kamm, Jack

AU - Bhatt, Karan D.

AU - Isakova, Alina

AU - Morri, Maurizio

AU - Ranganath, Thanmayi

AU - Blish, Catherine A.

AU - Rogers, Angela J.

AU - Nadeau, Kari

AU - Yang, Samuel

AU - Blomkalns, Andra

AU - O’Hara, Ruth

AU - Neff, Norma F.

AU - DeBoever, Christopher

AU - Szalma, Sándor

AU - Wheeler, Matthew T.

AU - Gates, Christian M.

AU - Farh, Kyle

AU - Schroth, Gary P.

AU - Febbo, Phil

AU - deSouza, Francis

AU - Cornejo, Omar E.

AU - Fernandez-Vina, Marcelo

AU - Kistler, Amy

AU - Palacios, Julia A.

AU - Pinsky, Benjamin A.

AU - Bustamante, Carlos D.

AU - Rivas, Manuel A.

AU - Ashley, Euan A.

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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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U2 - 10.1038/s41467-022-32397-8

DO - 10.1038/s41467-022-32397-8

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C2 - 36042219

AN - SCOPUS:85136931378

SN - 2041-1723

VL - 13

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 5107

ER -

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

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Funding

UN SDGs

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