We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients.

Onoja, A., Picchiotti, N., Fallerini, C., Baldassarri, M., Fava, F., Mari, F., et al. (2022). An explainable model of host genetic interactions linked to COVID-19 severity. COMMUNICATIONS BIOLOGY, 5(1), 1133 [10.1038/s42003-022-04073-6].

An explainable model of host genetic interactions linked to COVID-19 severity

Chiara, Fallerini
Writing – Review & Editing
;
Margherita, Baldassarri;Francesca, Fava
Resources
;
Francesca Mari
Membro del Collaboration Group
;
Sergio Daga
Membro del Collaboration Group
;
Elisa Benetti
Membro del Collaboration Group
;
Mirella Bruttini
Membro del Collaboration Group
;
Maria Palmieri
Membro del Collaboration Group
;
Susanna Croci
Membro del Collaboration Group
;
Sara Amitrano
Membro del Collaboration Group
;
Ilaria Meloni
Membro del Collaboration Group
;
Elisa Frullanti
Membro del Collaboration Group
;
Gabriella Doddato
Membro del Collaboration Group
;
Mirjam Lista
Membro del Collaboration Group
;
Giada Beligni
Membro del Collaboration Group
;
Floriana Valentino
Membro del Collaboration Group
;
Kristina Zguro
Membro del Collaboration Group
;
Rossella Tita
Membro del Collaboration Group
;
Annarita Giliberti
Membro del Collaboration Group
;
Maria Antonietta Mencarelli
Membro del Collaboration Group
;
Caterina Lo Rizzo
Membro del Collaboration Group
;
Anna Maria Pinto
Membro del Collaboration Group
;
Francesca Ariani
Membro del Collaboration Group
;
Laura Di Sarno
Membro del Collaboration Group
;
Francesca Montagnani
Membro del Collaboration Group
;
Mario Tumbarello
Membro del Collaboration Group
;
Massimiliano Fabbiani
Membro del Collaboration Group
;
Barbara Rossetti
Membro del Collaboration Group
;
Laura Bergantini
Membro del Collaboration Group
;
Miriana D'Alessandro
Membro del Collaboration Group
;
Paolo Cameli
Membro del Collaboration Group
;
David Bennett
Membro del Collaboration Group
;
Federico Anedda
Membro del Collaboration Group
;
Simona Marcantonio
Membro del Collaboration Group
;
Sabino Scolletta
Membro del Collaboration Group
;
Federico Franchi
Membro del Collaboration Group
;
Maria Antonietta Mazzei
Membro del Collaboration Group
;
Edoardo Conticini
Membro del Collaboration Group
;
Luca Cantarini
Membro del Collaboration Group
;
Bruno Frediani
Membro del Collaboration Group
;
Danilo Tacconi
Membro del Collaboration Group
;
Chiara Spertilli Raffaelli
Membro del Collaboration Group
;
Marco Feri
Membro del Collaboration Group
;
Alice Donati
Membro del Collaboration Group
;
Raffaele Scala
Membro del Collaboration Group
;
Luca Guidelli
Membro del Collaboration Group
;
Genni Spargi
Membro del Collaboration Group
;
Leonardo Croci
Membro del Collaboration Group
;
Silvia Cappelli
Membro del Collaboration Group
;
Agnese Verzuri
Membro del Collaboration Group
;
Agostino Ognibene
Membro del Collaboration Group
;
Alessandra Vergori
Membro del Collaboration Group
;
Arianna Emiliozzi
Membro del Collaboration Group
;
Andrea Tommasi
Membro del Collaboration Group
;
Lucia Vietri
Membro del Collaboration Group
;
Francesca Gatti
Membro del Collaboration Group
;
Serafina Valente
Membro del Collaboration Group
;
Oreste De Vivo
Membro del Collaboration Group
;
Elena Bargagli
Membro del Collaboration Group
;
Alessia Giorli
Membro del Collaboration Group
;
Lorenzo Salerni
Membro del Collaboration Group
;
Enrico Martinelli
Membro del Collaboration Group
;
Katia Capitani
Membro del Collaboration Group
;
Simona Dei
Membro del Collaboration Group
;
Rosangela Artuso
Membro del Collaboration Group
;
Elena Andreucci
Membro del Collaboration Group
;
Angelica Pagliazzi
Membro del Collaboration Group
;
Riccardo Colombo
Membro del Collaboration Group
;
Sauro Luchi
Membro del Collaboration Group
;
Paola Petrocelli
Membro del Collaboration Group
;
Sara Modica
Membro del Collaboration Group
;
Silvia Baroni
Membro del Collaboration Group
;
Marco Falcone
Membro del Collaboration Group
;
Claudio Ferri
Membro del Collaboration Group
;
Francesco Brancati
Membro del Collaboration Group
;
Valentina Borgo
Membro del Collaboration Group
;
Gabriella Maria Squeo
Membro del Collaboration Group
;
Alessandra, Renieri
Writing – Review & Editing
;
Simone, Furini
Formal Analysis
;
2022-01-01

Abstract

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients.
2022
Onoja, A., Picchiotti, N., Fallerini, C., Baldassarri, M., Fava, F., Mari, F., et al. (2022). An explainable model of host genetic interactions linked to COVID-19 severity. COMMUNICATIONS BIOLOGY, 5(1), 1133 [10.1038/s42003-022-04073-6].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1223542
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo