학술논문
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility.
Document Type
article
Author
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; 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
Source
PLoS ONE, Vol 16, Iss 8, p e0255402 (2021)
Subject
Language
English
ISSN
1932-6203
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.