Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort

• Published in: Biomolecules (Basel, Switzerland) Vol. 15; no. 3; p. 393
• Main Authors: Alloza-Moral, Iraide, Aldekoa-Etxabe, Ane, Tulloch-Navarro, Raquel, Fiat-Arriola, Ainhoa, Mar, Carmen, Urrechaga, Eloisa, Ponga, Cristina, Artiga-Folch, Isabel, Garcia-Bediaga, Naiara, Aspichueta, Patricia, Martin, Cesar, Zarandona-Garai, Aitor, Pérez-Fernández, Silvia, Arana-Arri, Eunate, Triviño, Juan-Carlos, Uranga, Ane, España, Pedro-Pablo, Vandenbroeck-van-Caeckenbergh, Koen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.03.2025; MDPI
• Subjects: Aged; Biomarkers; Cohort Studies; Comorbidity; Comparative analysis; COVID-19; COVID-19 - epidemiology; COVID-19 - genetics; COVID-19 - pathology; Datasets; Diabetes; Disease; DMBT1 protein; Epidemics; Female; Genetic analysis; Genetic aspects; Genetic factors; Genetic Predisposition to Disease; Genetic testing; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Genotype; Genotype & phenotype; Genotypes; GWAS; Health aspects; Health risk assessment; Histocompatibility antigen HLA; HLA; Hospital patients; Hospitalization; Humans; Intensive Care Units; KIF19; Male; Microbiota; Middle Aged; Pandemics; Patients; Phenotypes; Polymorphism, Single Nucleotide; Prediction models; Quality control; Risk assessment; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; severity; Severity of Illness Index; Software; Spain; United States; United States; Spain; United Kingdom > UK; United States > US; COVID-19; severity; SARS-CoV-2; GWAS; KIF19; HLA
• ISSN: 2218-273X; 2218-273X
• DOI: 10.3390/biom15030393

The COVID-19 pandemic has had a devastating impact, with more than 7 million deaths worldwide. Advanced age and comorbidities partially explain severe cases of the disease, but genetic factors also play a significant role. Genome-wide association studies (GWASs) have been instrumental in identifying loci associated with SARS-CoV-2 infection. Here, we report the results from a >820 K variant GWAS in a COVID-19 patient cohort from the hospitals associated with IIS Biobizkaia. We compared intensive care unit (ICU)-hospitalized patients with non-ICU-hospitalized patients. The GWAS was complemented with an integrated phenotype and genetic modeling analysis using HLA genotypes, a previously identified COVID-19 polygenic risk score (PRS) and clinical data. We identified four variants associated with COVID-19 severity with genome-wide significance (rs58027632 in KIF19; rs736962 in HTRA1; rs77927946 in DMBT1; and rs115020813 in LINC01283). In addition, we designed a multivariate predictive model including HLA, PRS and clinical data which displayed an area under the curve (AUC) value of 0.79. Our results combining human genetic information with clinical data may help to improve risk assessment for the development of a severe outcome of COVID-19.