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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MDPI AG
2025-03-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/15/3/393 |
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| author | Iraide Alloza-Moral Ane Aldekoa-Etxabe Raquel Tulloch-Navarro Ainhoa Fiat-Arriola Carmen Mar Eloisa Urrechaga Cristina Ponga Isabel Artiga-Folch Naiara Garcia-Bediaga Patricia Aspichueta Cesar Martin Aitor Zarandona-Garai Silvia Pérez-Fernández Eunate Arana-Arri Juan-Carlos Triviño Ane Uranga Pedro-Pablo España Koen Vandenbroeck-van-Caeckenbergh |
| author_facet | Iraide Alloza-Moral Ane Aldekoa-Etxabe Raquel Tulloch-Navarro Ainhoa Fiat-Arriola Carmen Mar Eloisa Urrechaga Cristina Ponga Isabel Artiga-Folch Naiara Garcia-Bediaga Patricia Aspichueta Cesar Martin Aitor Zarandona-Garai Silvia Pérez-Fernández Eunate Arana-Arri Juan-Carlos Triviño Ane Uranga Pedro-Pablo España Koen Vandenbroeck-van-Caeckenbergh |
| author_sort | Iraide Alloza-Moral |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3f818ea942a54f2c841ff4f47f4bb6d3 |
| institution | DOAJ |
| issn | 2218-273X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-3f818ea942a54f2c841ff4f47f4bb6d32025-08-20T02:42:39ZengMDPI AGBiomolecules2218-273X2025-03-0115339310.3390/biom15030393Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient CohortIraide Alloza-Moral0Ane Aldekoa-Etxabe1Raquel Tulloch-Navarro2Ainhoa Fiat-Arriola3Carmen Mar4Eloisa Urrechaga5Cristina Ponga6Isabel Artiga-Folch7Naiara Garcia-Bediaga8Patricia Aspichueta9Cesar Martin10Aitor Zarandona-Garai11Silvia Pérez-Fernández12Eunate Arana-Arri13Juan-Carlos Triviño14Ane Uranga15Pedro-Pablo España16Koen Vandenbroeck-van-Caeckenbergh17Inflammation & Biomarkers Group, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainInflammation & Biomarkers Group, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainInflammation & Biomarkers Group, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainInflammation & Biomarkers Group, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainPneumology Department, Galdakao-Usansolo University Hospital, Biobizkaia Health Research Institute, 48960 Galdakao, SpainPneumology Department, Galdakao-Usansolo University Hospital, Biobizkaia Health Research Institute, 48960 Galdakao, SpainPneumology Department, Galdakao-Usansolo University Hospital, Biobizkaia Health Research Institute, 48960 Galdakao, SpainInflammation & Biomarkers Group, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainBioinformatic Unit, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainPhysiology Department, Faculty of Medicine and Nursery, Basque Country University (UPV/EHU), 48940 Leioa, SpainInflammation & Biomarkers Group, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainBioinformatic Unit, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainBioinformatic Unit, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainClinical Epidemiology Unit, Biobizkaia Health Research Institute, Cruces University Hospital, Plaza de Cruces s/n, 48903 Barakaldo, SpainBioinformatics Department, Sistemas Genómicos, 46980 Peterna, SpainPneumology Department, Galdakao-Usansolo University Hospital, Biobizkaia Health Research Institute, 48960 Galdakao, SpainPneumology Department, Galdakao-Usansolo University Hospital, Biobizkaia Health Research Institute, 48960 Galdakao, SpainInflammation & Biomarkers Group, Biobizkaia Health Research Institute, 48903 Barakaldo, SpainThe 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.https://www.mdpi.com/2218-273X/15/3/393SARS-CoV-2GWASCOVID-19severityHLAKIF19 |
| spellingShingle | Iraide Alloza-Moral Ane Aldekoa-Etxabe Raquel Tulloch-Navarro Ainhoa Fiat-Arriola Carmen Mar Eloisa Urrechaga Cristina Ponga Isabel Artiga-Folch Naiara Garcia-Bediaga Patricia Aspichueta Cesar Martin Aitor Zarandona-Garai Silvia Pérez-Fernández Eunate Arana-Arri Juan-Carlos Triviño Ane Uranga Pedro-Pablo España Koen Vandenbroeck-van-Caeckenbergh Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort Biomolecules SARS-CoV-2 GWAS COVID-19 severity HLA KIF19 |
| title | Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort |
| title_full | Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort |
| title_fullStr | Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort |
| title_full_unstemmed | Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort |
| title_short | Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort |
| title_sort | genetic analysis and predictive modeling of covid 19 severity in a hospital based patient cohort |
| topic | SARS-CoV-2 GWAS COVID-19 severity HLA KIF19 |
| url | https://www.mdpi.com/2218-273X/15/3/393 |
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