Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility.
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...
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Public Library of Science (PLoS)
2021
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oai:doaj.org-article:f56d0cd40e374f90802e5b2a9b90049e2021-12-02T20:18:24ZUsing symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility.1932-620310.1371/journal.pone.0255402https://doaj.org/article/f56d0cd40e374f90802e5b2a9b90049e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255402https://doaj.org/toc/1932-6203Epidemiological 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.Irene V van BloklandPauline LantingAnil P S OriJudith M VonkRobert C A WarmerdamJohanna C HerkertFloranne BoulogneAnnique ClaringbouldEsteban A Lopera-MayaMeike BartelsJouke-Jan HottengaAndrea GannaJuha KarjalainenLifelines COVID-19 cohort studyCOVID-19 Host Genetics InitiativeCaroline HaywardChloe Fawns-RitchieArchie CampbellDavid PorteousElizabeth T CirulliKelly M Schiabor BarrettStephen RiffleAlexandre BolzeSimon WhiteFrancisco TanudjajaXueqing WangJimmy M RamirezYan Wei LimJames T LuNicole L WashingtonEco J C de GeusPatrick DeelenH Marike BoezenLude H FrankePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255402 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q 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 Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. |
| description |
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. |
| format |
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 |
| author_facet |
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 |
| author_sort |
Irene V van Blokland |
| title |
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. |
| title_short |
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. |
| title_full |
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. |
| title_fullStr |
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. |
| title_full_unstemmed |
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. |
| title_sort |
using symptom-based case predictions to identify host genetic factors that contribute to covid-19 susceptibility. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/f56d0cd40e374f90802e5b2a9b90049e |
| work_keys_str_mv |
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