Federated single-cell QTL meta-analysis reveals novel disease mechanisms

2026 · doi:10.64898/2026.01.20.700519
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This federated single-cell QTL meta-analysis across 12 PBMC datasets identified cell-type-specific eQTLs that better explained disease GWAS loci and reconstructed gene regulatory relationships.

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This study performed a federated cis-eQTL meta-analysis using 12 PBMC single-cell RNA-seq datasets (2,032 individuals; 2.5 million cells) across six immune cell types to characterize cell type-specific genetic effects on gene expression. The authors identified cis-eQTLs for 6,592 genes and fine-mapped 14,985 independent loci, including many eQTLs undetected in a prior whole-blood bulk eQTL study that were enriched for disease GWAS loci. They also reported genome-wide significant and suggestive loci associated with the abundance of rare immune cell types, and integrated single-cell cis-eQTLs with bulk trans-eQTLs to anchor thousands of trans-eGenes to upstream regulators and reconstruct directed gene regulatory relationships, though the work is limited to PBMC immune cell contexts and relies on bulk trans-eQTL datasets for parts of the integration. Relevance to endometriosis: the paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Genetic effects on gene expression are often cell type-specific and obscured in bulk analyses. To resolve this context-dependent regulation, we performed a federated cis- eQTL meta-analysis across 12 PBMC datasets (2,032 individuals, 2.5 million cells). Across six immune cell types, we identified cis- eQTLs for 6,592 genes and fine-mapped 14,985 independent loci. Notably, the 42% of eQTLs that were undetected in a bulk eQTL study on 43,301 whole blood samples also showed stronger enrichment for disease GWAS loci. We further identified three genome-wide significant and 65 suggestive loci affecting the abundance of (rare) immune cell types and validated these using previously reported hematological GWAS and bulk-derived trans- eQTLs. Integrating single-cell cis -eQTLs with bulk trans- eQTLs enabled us to anchor 6,382 trans- eGenes (37.2% novel) to upstream regulators and reconstruct directed gene regulatory relationships. For example, a hemorrhoidal disease-associated variant showed a CD4+ T cell-specific cis- eQTL on BACH1 that colocalized with 45 immune and metabolic trans -eGenes. These results demonstrate the power of single-cell QTL meta-analysis in interpreting complex trait genetics.
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Federated single-cell QTL meta-analysis reveals novel disease mechanisms Abstract Genetic effects on gene expression are often cell type-specific and obscured in bulk analyses. To resolve this context-dependent regulation, we performed a federated cis-eQTL meta-analysis across 12 PBMC datasets (2,032 individuals, 2.5 million cells). Across six immune cell types, we identified cis-eQTLs for 6,592 genes and fine-mapped 14,985 independent loci. Notably, the 42% of eQTLs that were undetected in a bulk eQTL study on 43,301 whole blood samples also showed stronger enrichment for disease GWAS loci. We further identified three genome-wide significant and 65 suggestive loci affecting the abundance of (rare) immune cell types and validated these using previously reported hematological GWAS and bulk-derived trans-eQTLs. Integrating single-cell cis-eQTLs with bulk trans-eQTLs enabled us to anchor 6,382 trans-eGenes (37.2% novel) to upstream regulators and reconstruct directed gene regulatory relationships. For example, a hemorrhoidal disease-associated variant showed a CD4+ T cell-specific cis-eQTL on BACH1 that colocalized with 45 immune and metabolic trans-eGenes. These results demonstrate the power of single-cell QTL meta-analysis in interpreting complex trait genetics. Competing Interest Statement M.V. is currently a full-time employee of Illumina and owns stock in the company. These interests began after M.V. contribution to this work was completed. L.F. has ongoing contract-based research with Biogen and Roche, not related to this work. Footnotes ↵50 Banner author, see Note S1 The wrong supplementary files were uploaded. Subject Area - Biochemistry (17690) - Bioengineering (13892) - Bioinformatics (41936) - Biophysics (21451) - Cancer Biology (18588) - Cell Biology (25499) - Clinical Trials (138) - Developmental Biology (13378) - Ecology (19899) - Epidemiology (2067) - Evolutionary Biology (24320) - Genetics (15609) - Genomics (22506) - Immunology (17736) - Microbiology (40394) - Molecular Biology (17181) - Neuroscience (88603) - Paleontology (666) - Pathology (2832) - Pharmacology and Toxicology (4824) - Physiology (7641) - Plant Biology (15152) - Synthetic Biology (4294) - Systems Biology (9825) - Zoology (2271)

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