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

Nat Commun. 2022 Aug 30;13(1):5107. doi: 10.1038/s41467-022-32397-8.

Authors

Victoria N Parikh^#¹, Alexander G Ioannidis^#^{2

3}, David Jimenez-Morales¹, John E Gorzynski^{1

4}, Hannah N De Jong^{1

4}, Xiran Liu³, Jonasel Roque¹, Victoria P Cepeda-Espinoza², Kazutoyo Osoegawa⁵, Chris Hughes^{1

4}, Shirley C Sutton^{1

4}, Nathan Youlton^{1

4}, Ruchi Joshi¹, David Amar¹, Yosuke Tanigawa², Douglas Russo⁶, Justin Wong⁶, Jessie T Lauzon⁷, Jacob Edelson², Daniel Mas Montserrat², Yongchan Kwon², Simone Rubinacci⁸, Olivier Delaneau⁸, Lorenzo Cappello⁶, Jaehee Kim⁹, Massa J Shoura^{4

10}, Archana N Raja¹, Nathaniel Watson¹⁰, Nathan Hammond¹⁰, Elizabeth Spiteri¹⁰, Kalyan C Mallempati⁵, Gonzalo Montero-Martín⁵, Jeffrey Christle¹, Jennifer Kim¹, Anna Kirillova¹¹, Kinya Seo¹, Yong Huang¹, Chunli Zhao¹, Sonia Moreno-Grau², Steven G Hershman¹, Karen P Dalton¹, Jimmy Zhen¹, Jack Kamm¹², Karan D Bhatt¹², Alina Isakova¹³, Maurizio Morri¹², Thanmayi Ranganath¹, Catherine A Blish¹, Angela J Rogers¹, Kari Nadeau^{1

14}, Samuel Yang¹⁵, Andra Blomkalns¹⁵, Ruth O'Hara¹⁶, Norma F Neff¹², Christopher DeBoever¹⁷, Sándor Szalma¹⁷, Matthew T Wheeler¹, Christian M Gates¹⁸, Kyle Farh¹⁸, Gary P Schroth¹⁸, Phil Febbo¹⁸, Francis deSouza¹⁸, Omar E Cornejo¹⁹, Marcelo Fernandez-Vina^{5

10}, Amy Kistler¹², Julia A Palacios^{2

6}, Benjamin A Pinsky^{1

10}, Carlos D Bustamante^#², Manuel A Rivas^#², Euan A Ashley^#^{20

21}

Affiliations

¹ Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
² Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
³ Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
⁴ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
⁵ Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care, Stanford, USA.
⁶ Department of Statistics, Stanford University, Stanford, CA, USA.
⁷ Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA.
⁸ Department of Computational Biology and Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland.
⁹ Department of Computational Biology, Cornell University, Ithaca, NY, USA.
¹⁰ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
¹¹ Medical Scientist Training Program, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA.
¹² Chan Zuckerburg Biohub, San Francisco, CA, USA.
¹³ Department of Bioengineering, Stanford University, Stanford, CA, USA.
¹⁴ Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA, USA.
¹⁵ Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹⁶ Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
¹⁷ Takeda Development Center, Americas, Inc, San Diego, CA, USA.
¹⁸ Illumina, Inc, San Diego, CA, USA.
¹⁹ School of Biological Sciences, Washington State University, Pullman, WA, USA.
²⁰ Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. euan@stanford.edu.
²¹ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. euan@stanford.edu.

^# Contributed equally.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

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

Abstract

Publication types

MeSH terms

Grants and funding