Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility Article Swipe
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· 2021
· Open Access
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· DOI: https://doi.org/10.1371/journal.pone.0255402
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.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0255402
- OA Status
- gold
- Cited By
- 10
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3188442326
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3188442326Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0255402Digital Object Identifier
- Title
-
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibilityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-11Full publication date if available
- Authors
-
Irene V. van Blokland, Pauline Lanting, Anil P. S. Ori, Judith M. Vonk, Robert Warmerdam, Johanna C. Herkert, Floranne Boulogne, Annique Claringbould, Esteban A. Lopera-Maya, Meike Bartels, Jouke‐Jan Hottenga, Andrea Ganna, Juha Karjalainen, Caroline Hayward, Chloe Fawns‐Ritchie, Archie Campbell, David J. 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 Washington, Eco J. C. de Geus, Patrick Deelen, H. Marike Boezen, Lude FrankeList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0255402Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0255402Direct OA link when available
- Concepts
-
Genome-wide association study, Coronavirus disease 2019 (COVID-19), Genetic predisposition, Biology, Single-nucleotide polymorphism, Genetic variation, Disease, Outbreak, Pandemic, Phenotype, Genetics, Medicine, Infectious disease (medical specialty), Genotype, Virology, Gene, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 2, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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