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, Caroline Hayward, Chloe Fawns-Ritchie, Archie Campbell, David Porteous, Elizabeth T. Cirulli, Kelly M. Schiabor Barrett, Stephen RiffleAlexandre Bolze, Simon White, Francisco Tanudjaja, Xueqing Wang, III 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*

*Corresponding author for this work

Research output: Other contributionAcademic

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 potential COVID-19 cases using cross-sectional self-reported diseaserelated symptoms. Using a previously reported COVID-19 prediction model, we show that it is
possible to conduct a GWAS on predicted COVID-19, and this GWAS benefits from the larger
sample size to provide new insights into the genetic susceptibility of the disease. Furthermore,
we find suggestive evidence that genetic variants for 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. Our findings demonstrate the added
value of using self-reported symptom assessments to quickly monitor novel endemic viral
outbreaks in a scenario of limited testing. Should there be another outbreak of a novel
infectious disease, we recommend repeatedly collecting data of disease-related symptoms.
Original languageUndefined/Unknown
PublishermedRxiv
Number of pages27
DOIs
Publication statusSubmitted - 24-Aug-2020

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