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An explainable model of host genetic interactions linked to COVID-19 severity.
Onoja, Anthony; Picchiotti, Nicola; Fallerini, Chiara; Baldassarri, Margherita; Fava, Francesca; Colombo, Francesca; Chiaromonte, Francesca; Renieri, Alessandra; Furini, Simone; Raimondi, Francesco.
  • Onoja A; Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Pisa, Italy.
  • Picchiotti N; University of Siena, DIISM-SAILAB, Siena, Italy.
  • Fallerini C; Department of Mathematics, University of Pavia, Pavia, Italy.
  • Baldassarri M; Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Fava F; Medical Genetics, University of Siena, Siena, Italy.
  • Colombo F; Medical Genetics, University of Siena, Siena, Italy.
  • Chiaromonte F; Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Renieri A; Medical Genetics, University of Siena, Siena, Italy.
  • Furini S; Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
Commun Biol ; 5(1): 1133, 2022 Oct 26.
Article in English | MEDLINE | ID: covidwho-2087324
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: Commun Biol Year: 2022 Document Type: Article Affiliation country: S42003-022-04073-6

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: Commun Biol Year: 2022 Document Type: Article Affiliation country: S42003-022-04073-6