Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.
Crawley, Francis P
Ludwig, Kerstin U
Schulte, Eva C
Baillie, John Kenneth
WES/WGS Working Group Within the HGI
GEN-COVID Multicenter Study
Springer Science and Business Media LLC
MetadataShow full item record
Fallerini, C., Picchiotti, N., Baldassarri, M., Zguro, K., Daga, S., Fava, F., Benetti, E., et al. (2022). Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.. Hum Genet, 141 (1), 147-173. https://doi.org/10.1007/s00439-021-02397-7
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.
Adult, Aged, Aged, 80 and over, COVID-19, Cohort Studies, Female, Genetic Predisposition to Disease, Germany, Humans, Italy, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Quebec, SARS-CoV-2, Severity of Illness Index, Sweden, United Kingdom, Whole Exome Sequencing
External DOI: https://doi.org/10.1007/s00439-021-02397-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335997
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/