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

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Fallerini, Chiara, Picchiotti, Nicola, Baldassarri, Margherita, Zguro, Kristina, Daga, Sergio, Fava, Francesca, Benetti, Elisa, Amitrano, Sara, Bruttini, Mirella, Palmieri, Maria et al (show 32 more authors) , Croci, Susanna, Lista, Mirjam, Beligni, Giada, Valentino, Floriana, Meloni, Ilaria, Tanfoni, Marco, Minnai, Francesca, Colombo, Francesca, Cabri, Enrico, Fratelli, Maddalena, Gabbi, Chiara, Mantovani, Stefania, Frullanti, Elisa, Gori, Marco, Crawley, Francis P, Butler-Laporte, Guillaume, Richards, Brent, Zeberg, Hugo, Lipcsey, Miklos, Hultström, Michael, Ludwig, Kerstin U, Schulte, Eva C, Pairo-Castineira, Erola, Baillie, John Kenneth, Schmidt, Axel, Frithiof, Robert, WES/WGS Working Group Within the HGI, , GenOMICC Consortium, , GEN-COVID Multicenter Study, , Mari, Francesca, Renieri, Alessandra and Furini, Simone (2021) Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. Human genetics, 141 (1). pp. 147-173.

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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.

Item Type:	Article
Uncontrolled Keywords:	WES/WGS Working Group Within the HGI, GenOMICC Consortium, GEN-COVID Multicenter Study
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	28 Feb 2022 08:46
Last Modified:	18 Jan 2023 21:12
DOI:	10.1007/s00439-021-02397-7
Open Access URL:	https://doi.org/10.1007/s00439-021-02397-7
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3148698

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• Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. (deposited 13 Jan 2022 15:01)
  • Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. (deposited 28 Feb 2022 08:46) [Currently Displayed]