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      Genetic mechanisms of COVID-19 and its association with smoking and alcohol consumption

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          Abstract

          We aimed to investigate the genetic mechanisms associated with coronavirus disease of 2019 (COVID-19) outcomes in the host and to evaluate the possible associations between smoking and drinking behavior and three COVID-19 outcomes: severe COVID-19, hospitalized COVID-19 and COVID-19 infection. We described the genomic loci and risk genes associated with the COVID-19 outcomes, followed by functional analyses of the risk genes. Then, a summary data-based Mendelian randomization (SMR) analysis, and a transcriptome-wide association study (TWAS) were performed for the severe COVID-19 dataset. A two-sample Mendelian randomization (MR) analysis was used to evaluate the causal associations between various measures of smoking and alcohol consumption and the COVID-19 outcomes. A total of 26 protein-coding genes, enriched in chemokine binding, cytokine binding and senescence-related functions, were associated with either severe COVID-19 or hospitalized COVID-19. The SMR and the TWAS analyses highlighted functional implications of some GWAS hits and identified seven novel genes for severe COVID-19, including CCR5, CCR5AS, IL10RB, TAC4, RMI1 and TNFSF15, some of which are targets of approved or experimental drugs. According to our studies, increasing consumption of cigarettes per day by 1 standard deviation is related to a 2.3-fold increase in susceptibility to severe COVID-19 and a 1.6-fold increase in COVID-19-induced hospitalization. Contrarily, no significant links were found between alcohol consumption or binary smoking status and COVID-19 outcomes. Our study revealed some novel COVID-19 related genes and suggested that genetic liability to smoking may quantitatively contribute to an increased risk for a severe course of COVID-19.

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          Most cited references66

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          Is Open Access

          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

            There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).
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              Is Open Access

              OpenSAFELY: factors associated with COVID-19 death in 17 million patients

              COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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                Author and article information

                Journal
                Briefings in Bioinformatics
                Oxford University Press (OUP)
                1467-5463
                1477-4054
                November 2021
                November 05 2021
                November 2021
                November 05 2021
                July 26 2021
                : 22
                : 6
                Affiliations
                [1 ]State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, China
                [2 ]School of Systems Biology, George Mason University, China
                [3 ]Affiliated Brain Hospital of Nanjing Medical University, China
                Article
                10.1093/bib/bbab284
                34308962
                1ac5e552-ab1e-4591-946a-2b95144f1815
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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