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Deconvoluting complex correlates of COVID19 severity with local ancestry inference and viral phylodynamics: Results of a multiomic pandemic tracking strategy
V N Parikh; A G Ioannidis; D Jimenez-Morales; J E Gorzynski; H N De Jong; X Liu; J Roque; V P Cepeda-Espinoza; 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 M 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; G Montero-Martin; J Christle; J Kim; A Kirillova; K Seo; Y Huang; C Zhao; S Moreno-Grau; S Hershman; K P Dalton; J Zhen; J Kamm; K Bhatt; A Isakova; M Morri; T Ranganath; C A Blish; A J Rogers; K Nadeau; S Yang; A Blomkalns; R OHara; N F Neff; C DeBoever; S Szalma; M T Wheeler; K Farh; G P Schroth; P Febbo; F deSouza; M Fernandez-Vina; A Kistler; J Palacios; B A Pinsky; C D Bustamante; M A Rivas; E A Ashley.
Afiliación
  • V N Parikh; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine
  • A G Ioannidis; Department of Biomedical Data Science and Institute for Computational and Mathematical Engineering, Stanford University
  • D Jimenez-Morales; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • J E Gorzynski; Department of Genetics and Department of Medicine, Stanford University School of Medicine
  • H N De Jong; Department of Genetics and Department of Medicine, Stanford University School of Medicine
  • X Liu; Institute for Computational and Mathematical Engineering, Stanford University
  • J Roque; Department of Medicine, Stanford University School of Medicine
  • V P Cepeda-Espinoza; Department of Biomedical Data Science, Stanford University
  • K Osoegawa; Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care
  • C Hughes; Department of Genetics, Stanford University School of Medicine
  • S C Sutton; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine
  • N Youlton; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine
  • R Joshi; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine
  • D Amar; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine
  • Y Tanigawa; Department of Biomedical Data Science, Stanford University
  • D Russo; Department of Statistics, Stanford University
  • J Wong; Department of Statistics, Stanford University
  • J T Lauzon; Department of Aeronautics and Astronautics, Stanford University
  • J Edelson; Department of Biomedical Data Science, Stanford University
  • D M Montserrat; Department of Biomedical Data Science, Stanford University
  • Y Kwon; Department of Biomedical Data Science, Stanford University
  • S Rubinacci; Swiss Institute of Bioinformatics and Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
  • O Delaneau; Swiss Institute of Bioinformatics and Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
  • L Cappello; Department of Statistics, Stanford University
  • J Kim; Department of Biology, Stanford University
  • M J Shoura; Departments of Pathology & Genetics, Stanford University School of Medicine
  • A N Raja; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • N Watson; Department of Pathology, Stanford University School of Medicine
  • N Hammond; Department of Pathology, Stanford University School of Medicine
  • E Spiteri; Department of Pathology, Stanford University School of Medicine
  • K C Mallempati; Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care
  • G Montero-Martin; Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Stanford Health Care
  • J Christle; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • J Kim; Departments of Statistics and Biomedical Data Science, Stanford University
  • A Kirillova; Medical Scientist Training Program, University of Pittsburgh and Carnegie Mellon University
  • K Seo; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • Y Huang; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • C Zhao; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • S Moreno-Grau; Department of Biomedical Data Science, Stanford University
  • S Hershman; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • K P Dalton; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • J Zhen; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • J Kamm; Chan Zuckerberg Biohub
  • K Bhatt; Chan Zuckerberg Biohub
  • A Isakova; Department of Bioengineering, Stanford University
  • M Morri; Chan Zuckerberg Biohub
  • T Ranganath; Department of Medicine, Stanford University School of Medicine
  • C A Blish; Department of Medicine, Stanford University School of Medicine
  • A J Rogers; Department of Medicine, Stanford University School of Medicine
  • K Nadeau; Department of Medicine and Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine
  • S Yang; Department of Emergency Medicine, Stanford University School of Medicine
  • A Blomkalns; Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA, USA
  • R OHara; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
  • N F Neff; Chan Zuckerberg Biohub
  • C DeBoever; Takeda Development Center Americas, Inc
  • S Szalma; Takeda Development Center Americas, Inc
  • M T Wheeler; Department of Medicine, Division of Cardiovascular Medicine, Stanford University
  • K Farh; Illumina, Inc.
  • G P Schroth; Illumina, Inc.
  • P Febbo; Illumina, Inc.
  • F deSouza; Illumina, Inc.
  • M Fernandez-Vina; Department of Pathology, Stanford University School of Medicine
  • A Kistler; Chan Zuckerberg Biohub
  • J Palacios; Departments of Statistics and Biomedical Data Science, Stanford University
  • B A Pinsky; Departments of Pathology and Medicine, Stanford University School of Medicine
  • C D Bustamante; Department of Biomedical Data Science, Stanford University
  • M A Rivas; Department of Biomedical Data Science, Stanford University
  • E A Ashley; Department of Genetics and Dept of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-21261547
ABSTRACT
The SARS-CoV-2 pandemic has differentially impacted populations of varied race, ethnicity and socioeconomic status. Admixture mapping and local ancestry inference represent powerful tools to examine genetic risk within multi-ancestry genomes independent of these confounding social constructs. Here, we leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from 1,327 nasopharyngeal swab residuals and integrate them with digital phenotypes from electronic health records. We demonstrate over-representation of individuals possessing Oceanian and Indigenous American ancestry in SARS-CoV-2 positive populations. Genome-wide-association disaggregated by admixture mapping reveals regions of chromosomes 5 and 14 associated with COVID19 severity within African and Oceanic local ancestries, respectively, independent of overall ancestry fraction. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. We further present summary data from a multi-omic investigation of human-leukocyte-antigen (HLA) typing, nasopharyngeal microbiome and human transcriptomics that reveal metagenomic and HLA associations with severe COVID19 infection. This work demonstrates the power of multi-omic pandemic tracking and genomic analyses to reveal distinct epidemiologic, genetic and biological associations for those at the highest risk.
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Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint
Texto completo
Añadir a Mi BVS
Imprimir
XML
Buscar en Google
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint