Journal article
Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity
- Abstract:
- The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 3.9MB, Terms of use)
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- Publisher copy:
- 10.1007/s00439-021-02397-7
Authors
Contributors
+ WES/WGS Working Group Within the HGI
- Role:
- Contributor
+ GenOMICC Consortium
- Role:
- Contributor
+ GEN-COVID Multicenter Study
- Role:
- Contributor
- Publisher:
- Springer Nature
- Journal:
- Human Genetics More from this journal
- Volume:
- 141
- Pages:
- 147-173
- Publication date:
- 2021-12-10
- Acceptance date:
- 2021-10-26
- DOI:
- EISSN:
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1432-1203
- ISSN:
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0340-6717
- Pmid:
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34889978
- Language:
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English
- Keywords:
- Pubs id:
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1238973
- Local pid:
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pubs:1238973
- Deposit date:
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2022-02-11
- ARK identifier:
Terms of use
- Copyright holder:
- Fallerini et al.
- Copyright date:
- 2021
- Rights statement:
- ©2021 The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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