Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility
| Autor: | Caroline Hayward, Lude Franke, Francisco Tanudjaja, Meike Bartels, Chloe Fawns-Ritchie, Jimmy M. Ramirez, Kelly M. Schiabor Barrett, David J. Porteous, Patrick Deelen, Jouke-Jan Hottenga, Xueqing Wang, Simon D. M. White, Nicole L. Washington, Floranne Boulogne, Andrea Ganna, Esteban A. Lopera-Maya, Robert Warmerdam, Anil P.S. Ori, Alexandre Bolze, Elizabeth T. Cirulli, Stephen Riffle, Archie Campbell, Annique Claringbould, Eco J. C. de Geus, Johanna C. Herkert, Judith M. Vonk, H. Marike Boezen, Irene V. van Blokland, James T. Lu, Yan Wei Lim, Juha Karjalainen, Pauline Lanting |
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| Přispěvatelé: | Groningen Research Institute for Asthma and COPD (GRIAC), Life Course Epidemiology (LCE), Stem Cell Aging Leukemia and Lymphoma (SALL), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Institute for Molecular Medicine Finland, Data Science Genetic Epidemiology Lab, Biological Psychology, APH - Mental Health, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Viral Diseases
Single Nucleotide Polymorphisms Genome-wide association study Disease Medical Conditions Mathematical and Statistical Techniques 0302 clinical medicine Epidemiology Medicine and Health Sciences Proxy (statistics) Virus Testing 0303 health sciences Multidisciplinary Statistics Genomics SDG 10 - Reduced Inequalities 3. Good health Infectious Diseases Phenotype Area Under Curve Physical Sciences Medicine Research Article medicine.medical_specialty Science Single-nucleotide polymorphism Computational biology Biology Research and Analysis Methods Genetic Predisposition Microbiology Polymorphism Single Nucleotide 03 medical and health sciences SDG 3 - Good Health and Well-being Diagnostic Medicine Virology Genome-Wide Association Studies Genetics Genetic predisposition medicine Humans Genetic Predisposition to Disease Statistical Methods 030304 developmental biology SARS-CoV-2 Biology and Life Sciences Computational Biology COVID-19 Outbreak Covid 19 Human Genetics Genome Analysis Genetic architecture Cross-Sectional Studies ROC Curve Infectious disease (medical specialty) Sample size determination Genetics of Disease 3111 Biomedicine Mathematics Viral Transmission and Infection 030217 neurology & neurosurgery Forecasting Genome-Wide Association Study |
| Zdroj: | PLoS ONE, Vol 16, Iss 8, p e0255402 (2021) van Blokland, I V, Lanting, P, Ori, A P S, Vonk, J M, Warmerdam, R C A, Herkert, J C, Boulogne, F, Claringbould, A, Lopera-Maya, E A, Bartels, M, Hottenga, J J, Ganna, A, Karjalainen, J, Hayward, C, Fawns-Ritchie, C, Campbell, A, Porteous, D, Cirulli, E T, Barrett, K M S, Riffle, S, Bolze, A, White, S, Tanudjaja, F, Wang, X, Ramirez, J M, Lim, Y W, Lu, J T, Washington, N L, de Geus, E J C & Deelen, P & Boezen, H M & Franke, L H 2021, ' Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility ', PLoS ONE, vol. 16, no. 8, e0255402 . https://doi.org/10.1371/journal.pone.0255402 PLoS ONE, 16(8):e0255402. PUBLIC LIBRARY SCIENCE PLoS ONE, Vol 16, Iss 8 (2021) van Blokland, I V, Lanting, P, Ori, A P S, Vonk, J M, Warmerdam, R C A, Herkert, J C, Boulogne, F, Claringbould, A, Lopera-Maya, E A, Bartels, M, Hottenga, J J, Ganna, A, Karjalainen, J, Hayward, C, Fawns-Ritchie, C, Campbell, A, Porteous, D, Cirulli, E T, Barrett, K M S, Riffle, S, Bolze, A, White, S, Tanudjaja, F, Wang, X, Ramirez, J M, Lim, Y W, Lu, J T, Washington, N L, de Geus, E J C, Deelen, P, Boezen, H M & Franke, L H 2021, ' Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility ', PLoS ONE, vol. 16, no. 8, e0255402, pp. 1-18 . https://doi.org/10.1371/journal.pone.0255402 PLoS ONE, 16(8):e0255402, 1-18. Public Library of Science PLoS ONE |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0255402 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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