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van Blokland IV, Lanting P, Ori APS, Vonk JM, Warmerdam RCA, Herkert JC, Boulogne F, Claringbould A, Lopera-Maya EA, Bartels M, Hottenga JJ, Ganna A, Karjalainen J, Hayward C, Fawns-Ritchie C, Campbell A, Porteous D, Cirulli ET, Schiabor Barrett KM, Riffle S, Bolze A, White S, Tanudjaja F, Wang X, Ramirez JM, Lim YW, Lu JT, Washington NL, de Geus EJC, Deelen P, Boezen HM, Franke LH. Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. PLoS One 2021; 16:e0255402. [PMID: 34379666 PMCID: PMC8357137 DOI: 10.1371/journal.pone.0255402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 07/15/2021] [Indexed: 01/24/2023] Open
Abstract
Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.
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Affiliation(s)
- Irene V. van Blokland
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Pauline Lanting
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anil P. S. Ori
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Judith M. Vonk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert C. A. Warmerdam
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Johanna C. Herkert
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Floranne Boulogne
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Annique Claringbould
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Structural Computational Biology unit, EMBL, Heidelberg, Germany
| | - Esteban A. Lopera-Maya
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, FGB, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, FGB, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Juha Karjalainen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- Analytic and Translational Genetics Unit (ATGU), Massachusetts General Hospital, Boston, MA, United States of America
| | | | | | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Chloe Fawns-Ritchie
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Archie Campbell
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - David Porteous
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - Stephen Riffle
- Helix OpCo LLC, San Mateo, California, United States of America
| | - Alexandre Bolze
- Helix OpCo LLC, San Mateo, California, United States of America
| | - Simon White
- Helix OpCo LLC, San Mateo, California, United States of America
| | | | - Xueqing Wang
- Helix OpCo LLC, San Mateo, California, United States of America
| | | | - Yan Wei Lim
- Helix OpCo LLC, San Mateo, California, United States of America
| | - James T. Lu
- Helix OpCo LLC, San Mateo, California, United States of America
| | | | - Eco J. C. de Geus
- Department of Biological Psychology, FGB, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Amsterdam UMC, Amsterdam, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - H. Marike Boezen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Lude H. Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- * E-mail:
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D’Antonio M, Arthur TD, Nguyen JP, Matsui H, D’Antonio-Chronowska A, Frazer KA. Insights into genetic factors contributing to variability in SARS-CoV-2 susceptibility and COVID-19 disease severity. medRxiv 2021:2021.05.10.21256423. [PMID: 34013287 PMCID: PMC8132261 DOI: 10.1101/2021.05.10.21256423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we applied colocalization to compare summary statistics for 16 GWASs from the COVID-19 Host Genetics Initiative to investigate similarities and differences in their genetic signals. We identified 9 loci associated with susceptibility (one with two independent GWAS signals; one with an ethnicity-specific signal), 14 associated with severity (one with two independent GWAS signals; two with ethnicity-specific signals) and one harboring two discrepant GWAS signals (one for susceptibility; one for severity). Utilizing colocalization we also identified 45 GTEx tissues that had eQTL(s) for 18 genes strongly associated with GWAS signals in eleven loci (1-4 genes per locus). Some of these genes showed tissue-specific altered expression and others showed altered expression in up to 41 different tissue types. Our study provides insights into the complex molecular mechanisms underlying inherited predispositions to COVID-19-disease phenotypes.
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Affiliation(s)
- Matteo D’Antonio
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Timothy D. Arthur
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jennifer P. Nguyen
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A. Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
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