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

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.