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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)
Verlagsversion DOI PMC
Open Access Hybrid
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Association; Set
Sprache englisch
Veröffentlichungsjahr 2022
Prepublished im Jahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 0340-6717
e-ISSN 1432-1203
Zeitschrift Human Genetics
Quellenangaben Band: 141, Heft: 1, Seiten: 147-173 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort One New York Plaza, Suite 4600, New York, Ny, United States
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Virology (VIRO)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Immune Response and Infection
PSP-Element(e) G-502700-003
Förderungen CIHR
Center for Sepsis Control and Care
Italian Ministry of University and Research
MIUR
DOI 10.1007/s00439-021-02397-7
WOS ID WOS:000729021000001
Scopus ID 85123651761
PubMed ID 34889978
Erfassungsdatum 2022-02-01
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