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1. Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

Author

Irene V. van Blokland, Pauline Lanting, Anil P. S. Ori, Judith M. Vonk, Robert C. A. Warmerdam, Johanna C. Herkert, Floranne Boulogne, Annique Claringbould, Esteban A. Lopera-Maya, Meike Bartels, Jouke-Jan Hottenga, Andrea Ganna, Juha Karjalainen, Lifelines COVID-19 cohort study, The COVID-19 Host Genetics Initiative, Caroline Hayward, Chloe Fawns-Ritchie, Archie Campbell, David Porteous, Elizabeth T. Cirulli, Kelly M. Schiabor Barrett, Stephen Riffle, Alexandre Bolze, Simon White, Francisco Tanudjaja, Xueqing Wang, Jimmy M. Ramirez, Yan Wei Lim, James T. Lu, Nicole L. Washington, Eco J. C. de Geus, Patrick Deelen, H. Marike Boezen, and Lude H. Franke

Subjects
Medicine, Science
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.

Published
2021

2. Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

Author

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, 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, and APH - Health Behaviors & Chronic Diseases

Subjects
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
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.

Published
2021

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