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.

Fallerini, C., Picchiotti, N., Baldassarri, M., Zguro, K., Daga, S., Fava, F., et al. (2022). Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. HUMAN GENETICS, 141(1), 147-173 [10.1007/s00439-021-02397-7].

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

Fallerini, Chiara;Baldassarri, Margherita;Zguro, Kristina;Daga, Sergio;Fava, Francesca;Benetti, Elisa;Amitrano, Sara;Bruttini, Mirella;Palmieri, Maria;Croci, Susanna;Beligni, Giada;Meloni, Ilaria;Tanfoni, Marco;Frullanti, Elisa;Gori, Marco;Mari, Francesca;Renieri, Alessandra;Furini, Simone;Montagnani, Francesca
Membro del Collaboration Group
;
Tumbarello, Mario
Membro del Collaboration Group
;
Rossetti, Barbara
Membro del Collaboration Group
;
Bergantini, Laura
Membro del Collaboration Group
;
D'Alessandro, Miriana
Membro del Collaboration Group
;
Cameli, Paolo
Membro del Collaboration Group
;
Bennett, David
Membro del Collaboration Group
;
Anedda, Federico
Membro del Collaboration Group
;
Marcantonio, Simona
Membro del Collaboration Group
;
Scolletta, Sabino
Membro del Collaboration Group
;
Franchi, Federico
Membro del Collaboration Group
;
Mazzei, Maria Antonietta
Membro del Collaboration Group
;
Guerrini, Susanna
Membro del Collaboration Group
;
Conticini, Edoardo
Membro del Collaboration Group
;
Cantarini, Luca
Membro del Collaboration Group
;
Frediani, Bruno
Membro del Collaboration Group
2022

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.
Fallerini, C., Picchiotti, N., Baldassarri, M., Zguro, K., Daga, S., Fava, F., et al. (2022). Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. HUMAN GENETICS, 141(1), 147-173 [10.1007/s00439-021-02397-7].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1174300
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