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

van Blokland, Irene V; Lanting, Pauline; Ori, Anil P S; Vonk, Judith M; Warmerdam, Robert C A; Herkert, Johanna C; Boulogne, Floranne; Claringbould, Annique; Lopera-Maya, Esteban A; Bartels, Meike; Hottenga, Jouke-Jan; Ganna, Andrea; Karjalainen, Juha; Study, Lifelines COVID-19 cohort; Initiative, The COVID-19 Host Genetics; Hayward, Caroline; Fawns-Ritchie, Chloe; Campbell, Archie; Porteous, David; Cirulli, Elizabeth T; Barrett, Kelly M Schiabor; Riffle, Stephen; Bolze, Alexandre; White, Simon; Tanudjaja, Francisco; Wang, Xueqing; Ramirez, Jimmy M; Lim, Yan Wei; Lu, James T; Washington, Nicole L; de Geus, Eco J C; Deelen, Patrick; Boezen, H Marike; Franke, Lude H

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 and Lude H Franke

PLOS ONE, 2021, vol. 16, issue 8, 1-18

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0255402

DOI: 10.1371/journal.pone.0255402

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