Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity
Author(s)
Type
Journal Article
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.
Date Issued
2022-01-01
Date Acceptance
2021-10-26
Citation
Human Genetics, 2022, 141 (1), pp.147-173
ISSN
0340-6717
Publisher
Springer
Start Page
147
End Page
173
Journal / Book Title
Human Genetics
Volume
141
Issue
1
Copyright Statement
© The Author(s) 2021. 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/.
License URL
Sponsor
NIHR
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/34889978
PII: 10.1007/s00439-021-02397-7
Subjects
Adult
Aged
Aged, 80 and over
COVID-19
Cohort Studies
Female
Genetic Predisposition to Disease
Germany
Humans
Italy
Male
Middle Aged
Phenotype
Polymorphism, Single Nucleotide
Quebec
SARS-CoV-2
Severity of Illness Index
Sweden
United Kingdom
Whole Exome Sequencing
Publication Status
Published
Coverage Spatial
Germany
Date Publish Online
2021-12-10