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Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.

Chen Lin¹, Yingxin Lin¹, Wenxuan Li^1,2

¹Department of Biostatistics, Yale University, New Haven, CT, USA.

Nature Communications

|April 22, 2026

Summary

This summary is machine-generated.

We developed CASE, a Bayesian method for cell-type-specific expression quantitative trait loci (eQTL) fine-mapping. CASE improves accuracy by analyzing multiple cell types simultaneously, enhancing understanding of genetic regulation in complex traits.

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Area of Science:

Genomics
Statistical Genetics
Computational Biology

Background:

Identifying cell-type-specific expression quantitative trait loci (eQTL) is crucial for understanding gene regulation and complex traits.
Current eQTL fine-mapping methods lack power and accuracy when analyzing individual cell types separately.

Purpose of the Study:

To present CASE, a novel Bayesian framework for simultaneous cell-type-specific and shared eQTL fine-mapping across multiple cell types.
To improve the accuracy and power of eQTL mapping by leveraging multi-cell type data.

Main Methods:

Developed a Bayesian framework (CASE) to jointly analyze eQTLs across multiple cell types.
CASE simultaneously models cell-type-specific and shared eQTL effects.
Accounted for linkage disequilibrium to accurately disentangle genetic effects.

Main Results:

CASE significantly outperforms existing single-trait (SuSiE) and multi-trait (mvSuSiE) methods in simulations.
Applied to the OneK1K dataset, CASE identified more gene expression regulations.
CASE effectively captured cell type specificity and enriched functionally and disease-associated eQTLs.

Conclusions:

The CASE framework offers a powerful approach for cell-type-specific eQTL fine-mapping.
CASE enhances the discovery of genetic regulatory mechanisms underlying complex traits.
The framework is broadly applicable to multi-tissue and multi-trait genetic studies.