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10.1007/s00439-021-02397-7 http://scihub22266oqcxt.onion/10.1007/s00439-021-02397-7 34889978!8661833!34889978     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34889978&cmd=llinks): Failed to open stream: HTTP request failed! 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Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity |pdf=|usr=}}{{34889978}} gab.com Text Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity JO: Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=34889978 #FOAvip The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. Twit Text FOAVip Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity ????Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=34889978 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34889978 #FOAvip English Wikipedia <ref>{{Cite pmid|34889978}}</ref> Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity #MMPMID34889978 Fallerini C; Picchiotti N; Baldassarri M; Zguro K; Daga S; Fava F; Benetti E; Amitrano S; Bruttini M; Palmieri M; Croci S; Lista M; Beligni G; Valentino F; Meloni I; Tanfoni M; Minnai F; Colombo F; Cabri E; Fratelli M; Gabbi C; Mantovani S; Frullanti E; Gori M; Crawley FP; Butler-Laporte G; Richards B; Zeberg H; Lipcsey M; Hultstrom M; Ludwig KU; Schulte EC; Pairo-Castineira E; Baillie JK; Schmidt A; Frithiof R; Mari F; Renieri A; Furini S Hum Genet 2022[Jan]; 141 (1): 147-173 PMID34889978show ga The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. |*Exome Sequencing[MESH] |*Genetic Predisposition to Disease[MESH] |*Phenotype[MESH] |*Severity of Illness Index[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/*genetics/*physiopathology[MESH] |Cohort Studies[MESH] |Female[MESH] |Germany[MESH] |Humans[MESH] |Italy[MESH] |Male[MESH] |Middle Aged[MESH] |Polymorphism, Single Nucleotide[MESH] |Quebec[MESH] |SARS-CoV-2[MESH] |Sweden[MESH]Common, low-frequency, rare, and ultra-rare coding variants contribute(epmc).pdf DeepDyve Pubget Overpricing