Evidence map›Paper›PMID 41064594›Full record

ArticleNAR genomics and bioinformatics2025

Cell type-specific eQTL analysis of COVID-19 based on single-cell transcriptomic data.

Chao Wang, Xinyu Chen, Sainan Zhang, Zijun Zhu, Meiyu Du, Guanzhi He, Senwei Tan, Hailong Li, Duoyi Zhang, Jingqiao Bai and 1 more

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Article in NAR genomics and bioinformatics, 2025. 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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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Chao WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Xinyu ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Sainan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Zijun ZhuCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Meiyu DuCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Guanzhi HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Senwei TanCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Hailong LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Duoyi ZhangThe 2nd Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Jingqiao BaiCollege of Basic Medicine, Harbin Medical University, Heilongjiang, Harbin 150081, China.
Liang ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang, Harbin 150081, China.ORCID https://orcid.org/0000-0002-6665-6710

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-wide association study analysis has revealed the significant influence of genetic factors in the progression of COVID-19. However, the impact of these genetic variants on gene expression in various cell types remains largely unexplored. Here, we profiled immune cells from 42 COVID-19 cases (involving five stages) and 11 healthy individuals, and performed a single-cell RNA-seq assay with >200 000 cells to investigate cell type-specific and stage-specific genetic effects of genetic variants. Single-cell expression quantitative trait loci analysis of eight distinct cell types showed that the expression of 2593 genes is significantly associated with common genetic polymorphisms, and that the majority of these genes show their most prominent effects in specific cell types. Furthermore, we also discovered new gene associations for COVID-19-risk variants identified from genome-wide association studies and highlighted the monocytes in which their effects are most prominent. We found that 57 genes were regulated by variants associated with COVID-19, with significant enrichment for dynamic effects. In summary, our results highlight the importance of studying context-specific genetic regulation of gene expression and provide insights into the mechanisms underlying genetic susceptibility to COVID-19.

Indexed as

COVID-19Quantitative Trait LociTranscriptomeFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMalePolymorphism, Single NucleotideSARS-CoV-2Single-Cell Analysis

Identifiers

PMID41064594
PMCPMC12501775

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