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10.1371/journal.pone.0255402 http://scihub22266oqcxt.onion/10.1371/journal.pone.0255402 34379666!8357137!34379666     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34379666&cmd=llinks): Failed to open stream: HTTP request failed! 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Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility |pdf=|usr=}}{{34379666}} gab.com Text Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34379666 #FOAvip 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. Twit Text FOAVip Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34379666 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34379666 #FOAvip English Wikipedia <ref>{{Cite pmid|34379666}}</ref> Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility #MMPMID34379666 van Blokland IV; Lanting P; Ori APS; Vonk JM; Warmerdam RCA; Herkert JC; Boulogne F; Claringbould A; Lopera-Maya EA; Bartels M; Hottenga JJ; Ganna A; Karjalainen J; Hayward C; Fawns-Ritchie C; Campbell A; Porteous D; Cirulli ET; Schiabor Barrett KM; Riffle S; Bolze A; White S; Tanudjaja F; Wang X; Ramirez JM 3rd; Lim YW; Lu JT; Washington NL; de Geus EJC; Deelen P; Boezen HM; Franke LH PLoS One 2021[]; 16 (8): e0255402 PMID34379666show ga 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. |*Genetic Predisposition to Disease[MESH] |Area Under Curve[MESH] |COVID-19/genetics/*pathology/virology[MESH] |Cross-Sectional Studies[MESH] |Genome-Wide Association Study[MESH] |Humans[MESH] |Phenotype[MESH] |Polymorphism, Single Nucleotide[MESH] |ROC Curve[MESH]Using symptom-based case predictions to identify host genetic factors (epmc).pdf DeepDyve Pubget Overpricing