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Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.


ABSTRACT: 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.

SUBMITTER: Fallerini C 

PROVIDER: S-EPMC8661833 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

Fallerini Chiara C   Picchiotti Nicola N   Baldassarri Margherita M   Zguro Kristina K   Daga Sergio S   Fava Francesca F   Benetti Elisa E   Amitrano Sara S   Bruttini Mirella M   Palmieri Maria M   Croci Susanna S   Lista Mirjam M   Beligni Giada G   Valentino Floriana F   Meloni Ilaria I   Tanfoni Marco M   Minnai Francesca F   Colombo Francesca F   Cabri Enrico E   Fratelli Maddalena M   Gabbi Chiara C   Mantovani Stefania S   Frullanti Elisa E   Gori Marco M   Crawley Francis P FP   Butler-Laporte Guillaume G   Richards Brent B   Zeberg Hugo H   Lipcsey Miklos M   Hultström Michael M   Ludwig Kerstin U KU   Schulte Eva C EC   Pairo-Castineira Erola E   Baillie John Kenneth JK   Schmidt Axel A   Frithiof Robert R   Mari Francesca F   Renieri Alessandra A   Furini Simone S  

Human genetics 20211210 1


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 regress  ...[more]

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