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;GEN-COVID Multicenter Study;Montagnani, FrancescaMembro del Collaboration Group
;Tumbarello, MarioMembro del Collaboration Group
;Rossetti, BarbaraMembro del Collaboration Group
;Bergantini, LauraMembro del Collaboration Group
;D'Alessandro, MirianaMembro del Collaboration Group
;Cameli, PaoloMembro del Collaboration Group
;Bennett, DavidMembro del Collaboration Group
;Anedda, FedericoMembro del Collaboration Group
;Marcantonio, SimonaMembro del Collaboration Group
;Scolletta, SabinoMembro del Collaboration Group
;Franchi, FedericoMembro del Collaboration Group
;Mazzei, Maria AntoniettaMembro del Collaboration Group
;Guerrini, SusannaMembro del Collaboration Group
;Conticini, EdoardoMembro del Collaboration Group
;Cantarini, LucaMembro del Collaboration Group
;Frediani, BrunoMembro del Collaboration Group
2022-01-01
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1174300
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