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Federated single-cell QTL meta-analysis reveals novel disease mechanisms
Daniel Kaptijn1,2, Lieke Michielsen3,4, Drew Neavin5,6,7
1Department of Genetics, University of Groningen, University Medical Center Groningen.
Biorxiv : the Preprint Server for Biology
|February 6, 2026
Summary
Single-cell analysis reveals cell-type-specific genetic effects on gene expression (eQTLs) missed by bulk studies. This approach enhances understanding of complex traits and disease genetics by identifying novel regulatory relationships.
Area of Science:
- Genomics
- Immunology
- Systems Biology
Background:
- Genetic regulation of gene expression is often cell type-specific.
- Bulk analyses obscure context-dependent regulatory effects.
- Resolving cell-type-specific regulation is crucial for understanding complex traits.
Purpose of the Study:
- To perform a federated single-cell cis-eQTL meta-analysis across multiple peripheral blood mononuclear cell (PBMC) datasets.
- To identify cell type-specific genetic effects on gene expression.
- To integrate single-cell and bulk data for gene regulatory network reconstruction.
Main Methods:
- Federated cis-eQTL meta-analysis across 12 PBMC datasets (2,032 individuals, 2.5 million cells).
- Identification and fine-mapping of cis-eQTLs in six immune cell types.
- Analysis of loci affecting immune cell type abundance.
- Integration of single-cell cis-eQTLs with bulk trans-eQTLs.
Main Results:
- Identified 6,592 cis-eQTLs for genes and 14,985 independent loci across six immune cell types.
- Discovered 42% more eQTLs compared to bulk analysis, with stronger enrichment for disease GWAS loci.
- Found 3 genome-wide significant and 65 suggestive loci affecting immune cell abundance.
- Anchored 6,382 trans-eGenes to upstream regulators, revealing novel directed gene regulatory relationships.
Conclusions:
- Single-cell eQTL meta-analysis effectively resolves context-dependent genetic regulation.
- This approach significantly enhances the interpretation of complex trait genetics and disease associations.
- The study provides a powerful framework for dissecting gene regulatory networks in specific cellular contexts.
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