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. 2022 Jun 3;23:125. doi: 10.1186/s13059-022-02691-1

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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. 2 — Chromosome capture-based variant-to-gene mapping identifies candidate effector genes at COVID-19 GWAS loci. A Manhattan plot generated using the summary statistics from the COVID-19 severity GWAS. Genome-wide significant signals are shown together with the number of accessible gene-annotated proxies associated. B Depiction of the statistical sentinel-proxy SNP linkages and the PCC-derived physical gene-proxy connections identified in this study. Genes in yellow were implicated by an accessible proxy in the promoter regions, genes in blue were implicated through chromatin-based contact between the promoter region and a distal accessible proxy, and green indicates implication by both promoter and distal proxies. C Heatmap depicting genes implicated by variant-to-gene mapping in each cell type in red. Color of each gene corresponds to the signal shown in A