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. 2022 Feb 17;14:16. doi: 10.1186/s13073-022-01021-1

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© The Author(s) 2022

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — The workflow for this integrative genomic analysis. A Combination of single-cell RNA sequencing data and GWAS summary statistics on severe COVID-19 based on two independent methods. One method is the regression-based polygenic model based on whole scRNA-seq profiles, and another is the generalized linear regression model based on the top 10% most specific genes for each cell type. B An increase in genetics-risk genes and cytokines for severe COVID-19. C Cellular interaction analysis of genetics-influenced immune cell subsets with epithelial cells