Genetic architecture of host proteins interacting with SARS-CoV-2
Maik PietzneriD, Eleanor WheeleriD, Julia Carrasco-ZaniniiD, Johannes RaffleriD, Nicola D. KerrisoniD, Erin OertoniD, Victoria P.W. AuyeungiD, Jian’an LuaniD, Chris FinaniD, Juan Pablo CasasiD, Rachel M. OstroffiD, Steve A. Williams, Gabi KastenmülleriD, Markus RalseriD, Eric R. GamazoniD, Nicholas J. WarehamiD, Aroon Dinesh HingoraniiD, Claudia LangenbergiD
Abstract
Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid in silico assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).
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