PLoS ONE (Jan 2021)

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

  • 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,
  • Lude H. Franke

Journal volume & issue
Vol. 16, no. 8

Abstract

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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.