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Fallerini, C.* ; Picchiotti, N.* ; Baldassarri, M.* ; Zguro, K.* ; Daga, S.* ; Fava, F.* ; Benetti, E.* ; Amitrano, S.* ; Bruttini, M.* ; Palmieri, M.C.* ; 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, F.P.* ; Butler-Laporte, G.* ; Richards, B.* ; Zeberg, H.* ; Lipcsey, M.* ; Hultström, M.* ; Ludwig, K.U.* ; Schulte, E.C. ; Pairo-Castineira, E.* ; Baillie, J.K.* ; Schmidt, A.* ; Frithiof, R.* ; GEN-COVID Multicenter Study (Protzer, U.) ; Mari, F.* ; Renieri, A.* ; Furini, S.*

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

Hum. Genet. 141, 147-173 (2022)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Association; Set
ISSN (print) / ISBN 0340-6717
e-ISSN 1432-1203
Journal Human Genetics
Quellenangaben Volume: 141, Issue: 1, Pages: 147-173 Article Number: , Supplement: ,
Publisher Springer
Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Virology (VIRO)
Grants CIHR
Center for Sepsis Control and Care
Italian Ministry of University and Research
MIUR