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Updated: Sep 15, 2025

Chronic Salmonella Infection Induced Intestinal Fibrosis
Published on: September 22, 2019
Cell-type-resolved genetic regulatory variation shapes inflammatory bowel disease risk
Tobi Alegbe1,2,3, Bradley T Harris1, Laura Fachal1
1Wellcome Sanger Institute, Hinxton, CB10 1SA, UK.
This study maps gene expression across millions of cells to pinpoint genetic variants influencing inflammatory bowel disease (IBD). It identifies specific genes and cell types involved in IBD pathogenesis, offering a new framework for complex disease research.
Area of Science:
- Genomics
- Immunology
- Cell Biology
Background:
- Genetic variants in non-coding regions complicate complex disease gene identification.
- Understanding cell-type-specific gene regulation is crucial for disease mechanisms.
Purpose of the Study:
- To map cis-eQTLs (expression quantitative trait loci) across single cells to identify genes and cell types implicated in inflammatory bowel disease (IBD).
- To provide a mechanistic link between genetic risk loci and specific genes/cell types in IBD.
Main Methods:
- Single-cell RNA sequencing of 2.2 million cells from blood and intestinal biopsies of 421 individuals, including 125 with IBD.
- Analysis of cis-eQTLs at both cell-type and tissue levels to compare their characteristics and association with IBD GWAS loci.
Main Results:
- Cell-type-level eQTLs were more distal, enriched in enhancers, and showed stronger colocalization with IBD GWAS loci compared to tissue-level eQTLs.
- Identified effector genes in myeloid cells (e.g., MAML2, PSEN2, ZMIZ1) suggesting impaired Notch signaling in IBD.
- Identified Wnt-regulated genes (e.g., MYC) in epithelial stem/progenitor cells, suggesting impaired renewal contributes to IBD.
Conclusions:
- Provides a high-resolution map linking genetic risk to specific genes and cell types in IBD.
- Nominates novel effector genes and implicates specific signaling pathways (Notch, Wnt) in IBD pathogenesis.
- Establishes a framework for effector gene discovery in complex diseases using single-cell eQTL analysis.
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