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

Author

Listed:
  • 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 III
  • Yan Wei Lim
  • James T Lu
  • Nicole L Washington
  • Eco J C de Geus
  • Patrick Deelen
  • H Marike Boezen
  • Lude H Franke

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.

Suggested Citation

  • 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 & Jo, 2021. "Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-18, August.
    • Handle: RePEc:plo:pone00:0255402
    DOI: 10.1371/journal.pone.0255402
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    References listed on IDEAS

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    1. Chao Tian & Bethann S. Hromatka & Amy K. Kiefer & Nicholas Eriksson & Suzanne M. Noble & Joyce Y. Tung & David A. Hinds, 2017. "Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
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