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

Crotti, Lia
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.
Articolo in rivista - Articolo scientifico
Adult; Aged; Aged, 80 and over; COVID-19; Cohort Studies; Female; Germany; Humans; Italy; Male; Middle Aged; Polymorphism, Single Nucleotide; Quebec; SARS-CoV-2; Sweden; United Kingdom; Genetic Predisposition to Disease; Phenotype; Severity of Illness Index; Whole Exome Sequencing
English
10-dic-2021
2022
141
1
147
173
open
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/10281/393716
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