Nature CommunicationsGenetic & Genomic MedicinePeer-reviewed journal article
Deconvoluting complex correlates of COVID19 severity with local ancestry inference and viral phylodynamics: Results of a multiomic pandemic tracking strategy
Victoria Nicole ParikhiD, Alexander G. IoannidisiD, David Jimenez‐MoralesiD, John E. GorzynskiiD, Hannah N. De JongiD, Xiran LiuiD, Jonasel Roque, Victoria P. Cepeda-Espinoza, Kazutoyo OsoegawaiD, Chris HughesiD, Shirley C. SuttoniD, Nathan YoultoniD, Ruchi JoshiiD, David AmariD, Yosuke TanigawaiD, Douglas Russo, Justin WongiD, Jessie T. LauzoniD, Jacob L EdelsoniD, Daniel Mas MontserratiD, Yongchan KwoniD, Simone RubinacciiD, Olivier DelaneauiD, Lorenzo CappelloiD, Jaehee KimiD, , Euan Angus AshleyiD
- Citations
- 11
- FWCI
- 0.98
- Field percentile
- top 24%
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.
- organism: human
- assay: bulk RNA-seq
- assay: DNA methylation
- assay: metagenomics
- assay: proteomics
- research focus: deconvolution
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- approach: matrix factorization
- approach: benchmark study
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