TY - JOUR
AU - Fallerini, Chiara
AU - Picchiotti, Nicola
AU - Baldassarri, Margherita
AU - Zguro, Kristina
AU - Daga, Sergio
AU - Fava, Francesca
AU - Benetti, Elisa
AU - Amitrano, Sara
AU - Bruttini, Mirella
AU - Palmieri, Maria
AU - Croci, Susanna
AU - Lista, Mirjam
AU - Beligni, Giada
AU - Valentino, Floriana
AU - Meloni, Ilaria
AU - Tanfoni, Marco
AU - Minnai, Francesca
AU - Colombo, Francesca
AU - Cabri, Enrico
AU - Fratelli, Maddalena
AU - Gabbi, Chiara
AU - Mantovani, Stefania
AU - Frullanti, Elisa
AU - Gori, Marco
AU - Crawley, Francis P.
AU - Butler-Laporte, Guillaume
AU - Pairo-Castineira, Erola
AU - Baillie, J. Kenneth
AU - Richards, Brent
AU - Zeberg, Hugo
AU - Lipcsey, Miklos
AU - Hultström, Michael
AU - Ludwig, Kerstin U.
AU - Schulte, Eva C.
AU - Schmidt, Axel
AU - Frithiof, Robert
AU - WES/WGS Working Group Within the HGI
AU - GenOMICC Consortium
AU - GEN-COVID Multicenter Study
AU - Mari, Francesca
AU - Renieri, Alessandra
AU - Furini, Simone
PY - 2021
DA - December
TI - Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity
JO - Human Genetics
EP - 173
VL - 141
DO - https://doi.org/10.1007/s00439-021-02397-7
AB - 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.
PB - Springer Nature
UR - https://www.research.ed.ac.uk/en/publications/2eaa9d64-8a23-4269-8774-6d119b093d4e
KW - Coronavirus (COVID-19)
ER