Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility
By
Irene V van Blokland,
Pauline Lanting,
Anil PS 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 JC de Geus,
Patrick Deelen,
H. Marike Boezen,
Lude Franke
Posted 24 Aug 2020
medRxiv DOI: 10.1101/2020.08.21.20177246
Epidemiological and genetic studies on COVID-19 are 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 disease-related symptoms. Using a previously reported COVID-19 prediction model, we show that it is possible to conduct a GWAS on predicted COVID-19 which benefits from a larger sample size in order to gain 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, then we recommend repeatedly collecting data of disease-related symptoms.
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