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

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...

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Detalles Bibliográficos
Autores: Alloza, Iraide, Aldekoa Etxabe, Ane, Tulloch Navarro, Raquel, Fiat-Arriola, Ainhoa, Mar, Carmen, Urrechaga, Eloisa, Ponga, Cristina, Artiga Folch, Isabel, García Bediaga, Naiara, Aspichueta, Patricia, Martín, César, Zarandona Garai, Aitor, Pérez Fernández, Silvia, Arana Arri, Eunate, Triviño, Juan Carlos, Uranga, Ane, España, Pedro Pablo, Vandenbroeck, Koen
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/388768
Acceso en línea:http://hdl.handle.net/10261/388768
https://api.elsevier.com/content/abstract/scopus_id/105001094375
Access Level:acceso abierto
Palabra clave:COVID-19
GWAS
HLA
KIF19
SARS-CoV-2
Severity
Descripción
Sumario: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.

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