ArticleImmunogenetics2026
Deciphering the genetic control of immune cell function at single-cell resolution: Disease-Specific Cis-eQTLs analysis of COVID-19.
Article in Immunogenetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
22 authors.
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Abstract
Integrating whole-genome sequencing with single-cell RNA-seq data enhances the current understanding of how genomic differences across humans contribute to variations in gene activity. eQTL discovery offers a potent method for decoding genomic regulation and identifying population-specific genomic variants associated with gene expression differences. The current development of single-cell sequencing technologies provides a significant opportunity for precise and detailed profiling of both major and minor cell states, facilitating the identification of genomic variants and their effects within specific cellular contexts. Therefore, this study aims to investigate the combined analysis of paired whole-genome and single-cell RNA-seq data from 230,000 peripheral blood mononuclear cells (PBMCs) across 30 individuals. Overall, 1,233,644 cis-eQTLs were identified across 18 cell types in PBMCs. The results were thoroughly evaluated through conservation analysis, revealing that the most statistically significant eQTLs are associated with less conserved genomic regions and are concentrated in the regulatory areas of more divergent genes. Using deep learning-based cis-regulatory models, cis-eQTLs were further investigated to reveal the cell-type-specific context of variant activity. The analysis revealed how eQTLs influence the expression of key immune-related genes (NKG7, HLA, MIF, MS4A1), identifying transcription factors whose binding sites are disrupted by genomic variants. This study integrates genetics, single-cell transcriptomics, and deep learning models to reveal and understand the role of genomic variants in gene expression regulation.
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