Academic Journal
Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19
العنوان: | Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19 |
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المؤلفون: | Córdova-Palomera, Aldo, Siffel, Csaba, DeBoever, Chris, Wong, Emily, Diogo, Dorothée, Szalma, Sandor |
المساهمون: | Ulgen, Ayse, Takeda Development Center Americas, Inc. |
المصدر: | PLOS ONE ; volume 18, issue 5, page e0285991 ; ISSN 1932-6203 |
بيانات النشر: | Public Library of Science (PLoS) |
سنة النشر: | 2023 |
المجموعة: | PLOS Publications (via CrossRef) |
الوصف: | As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank ( p -values as low as 3.96e-9, all with R 2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of ... |
نوع الوثيقة: | article in journal/newspaper |
اللغة: | English |
DOI: | 10.1371/journal.pone.0285991 |
الاتاحة: | http://dx.doi.org/10.1371/journal.pone.0285991 https://dx.plos.org/10.1371/journal.pone.0285991 |
Rights: | http://creativecommons.org/licenses/by/4.0/ |
رقم الانضمام: | edsbas.A16ABD8C |
قاعدة البيانات: | BASE |
DOI: | 10.1371/journal.pone.0285991 |
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