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

Authors :
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
Source :
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

There is a genetic component to the risk of severe COVID-19, but the genetic effects are difficult to separate from social constructs that covary with genetic ancestry. To address this, the authors identify determinants of COVID-19 severity using admixture mapping, viral phylodynamics, and host immune and metagenomic sequencing.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.10002eca5587461185e42393a7dc16a5
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-022-32397-8