Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.
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Authors
Fallerini, Chiara
Picchiotti, Nicola
Baldassarri, Margherita
Zguro, Kristina
Daga, Sergio
Fava, Francesca
Benetti, Elisa
Amitrano, Sara
Bruttini, Mirella
Palmieri, Maria
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
Furini, Simone
Publication Date
2022-01Journal Title
Hum Genet
ISSN
0340-6717
Publisher
Springer Science and Business Media LLC
Volume
141
Issue
1
Pages
147-173
Type
Article
This Version
VoR
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Fallerini, C., Picchiotti, N., Baldassarri, M., Zguro, K., Daga, S., Fava, F., Benetti, E., et al. (2022). Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.. Hum Genet, 141 (1), 147-173. https://doi.org/10.1007/s00439-021-02397-7
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.
Keywords
Adult, Aged, Aged, 80 and over, COVID-19, Cohort Studies, Female, Genetic Predisposition to Disease, Germany, Humans, Italy, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Quebec, SARS-CoV-2, Severity of Illness Index, Sweden, United Kingdom, Whole Exome Sequencing
Identifiers
External DOI: https://doi.org/10.1007/s00439-021-02397-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335997
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