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A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity
Host genetic susceptibility is a key risk factor for severe illness associated with COVID-19. Despite numerous studies of COVID-19 host genetics, our knowledge of COVID-19-associated variants is still limited, and there is no resource comprising all the published variants and categorizing them based...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203938/ http://dx.doi.org/10.1016/j.clim.2023.109526 |
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author | Kars, Meltem Ece Stein, David Bayrak, Cigdem Sevim Stenson, Peter Cooper, David Itan, Yuval |
author_facet | Kars, Meltem Ece Stein, David Bayrak, Cigdem Sevim Stenson, Peter Cooper, David Itan, Yuval |
author_sort | Kars, Meltem Ece |
collection | PubMed |
description | Host genetic susceptibility is a key risk factor for severe illness associated with COVID-19. Despite numerous studies of COVID-19 host genetics, our knowledge of COVID-19-associated variants is still limited, and there is no resource comprising all the published variants and categorizing them based on their confidence level. Also, there are currently no computational tools available to predict novel COVID-19 severity variants. Therefore, we collated 820 host genetic variants reported to affect COVID-19 susceptibility by means of a systematic literature search and confidence evaluation, and obtained 196 high-confidence variants. We then developed the first machine learning classifier of severe COVID-19 variants to perform a genome-wide prediction of COVID-19 severity for 82,468,698 missense variants in the human genome. We further evaluated the classifier's predictions using feature importance analyses to investigate the biological properties of COVID-19 susceptibility variants, which identified conservation scores as the most impactful predictive features. The results of enrichment analyses revealed that genes carrying high-confidence COVID-19 susceptibility variants shared pathways, networks, diseases and biological functions, with the immune system and infectious disease being the most significant categories. Additionally, we investigated the pleiotropic effects of COVID-19-associated variants using phenome-wide association studies (PheWAS) in ~40,000 BioMe BioBank genotyped individuals, revealing pre-existing conditions that could serve to increase the risk of severe COVID-19 such as chronic liver disease and thromboembolism. Lastly, we generated a web-based interface for exploring, downloading and submitting genetic variants associated with COVID-19 susceptibility for use in both research and clinical settings (https://itanlab.shinyapps.io/COVID19webpage/). Taken together, our work provides the most comprehensive COVID-19 host genetics knowledgebase to date for the known and predicted genetic determinants of severe COVID-19, a resource that should further contribute to our understanding of the biology underlying COVID-19 susceptibility and facilitate the identification of individuals at high risk for severe COVID-19. |
format | Online Article Text |
id | pubmed-10203938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102039382023-05-23 A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity Kars, Meltem Ece Stein, David Bayrak, Cigdem Sevim Stenson, Peter Cooper, David Itan, Yuval Clin Immunol Poster Presentation Abstracts Host genetic susceptibility is a key risk factor for severe illness associated with COVID-19. Despite numerous studies of COVID-19 host genetics, our knowledge of COVID-19-associated variants is still limited, and there is no resource comprising all the published variants and categorizing them based on their confidence level. Also, there are currently no computational tools available to predict novel COVID-19 severity variants. Therefore, we collated 820 host genetic variants reported to affect COVID-19 susceptibility by means of a systematic literature search and confidence evaluation, and obtained 196 high-confidence variants. We then developed the first machine learning classifier of severe COVID-19 variants to perform a genome-wide prediction of COVID-19 severity for 82,468,698 missense variants in the human genome. We further evaluated the classifier's predictions using feature importance analyses to investigate the biological properties of COVID-19 susceptibility variants, which identified conservation scores as the most impactful predictive features. The results of enrichment analyses revealed that genes carrying high-confidence COVID-19 susceptibility variants shared pathways, networks, diseases and biological functions, with the immune system and infectious disease being the most significant categories. Additionally, we investigated the pleiotropic effects of COVID-19-associated variants using phenome-wide association studies (PheWAS) in ~40,000 BioMe BioBank genotyped individuals, revealing pre-existing conditions that could serve to increase the risk of severe COVID-19 such as chronic liver disease and thromboembolism. Lastly, we generated a web-based interface for exploring, downloading and submitting genetic variants associated with COVID-19 susceptibility for use in both research and clinical settings (https://itanlab.shinyapps.io/COVID19webpage/). Taken together, our work provides the most comprehensive COVID-19 host genetics knowledgebase to date for the known and predicted genetic determinants of severe COVID-19, a resource that should further contribute to our understanding of the biology underlying COVID-19 susceptibility and facilitate the identification of individuals at high risk for severe COVID-19. Elsevier Inc. 2023-05 2023-05-23 /pmc/articles/PMC10203938/ http://dx.doi.org/10.1016/j.clim.2023.109526 Text en Copyright © 2023 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Poster Presentation Abstracts Kars, Meltem Ece Stein, David Bayrak, Cigdem Sevim Stenson, Peter Cooper, David Itan, Yuval A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity |
title | A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity |
title_full | A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity |
title_fullStr | A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity |
title_full_unstemmed | A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity |
title_short | A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity |
title_sort | comprehensive knowledgebase of known and predicted human genetic variants associated with covid-19 susceptibility and severity |
topic | Poster Presentation Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203938/ http://dx.doi.org/10.1016/j.clim.2023.109526 |
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