A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Cell type-specific gene expression quantitative trait loci (ct-eQTLs) analysis is crucial for understanding the genetic basis of complex traits.
• Current methods often transform RNA-seq count data, leading to distorted relationships and reduced statistical power or inflated error rates.

Purpose of the Study:

• To develop a novel statistical method, CSeQTL, for accurate ct-eQTL mapping using bulk RNA-seq data.
• To leverage allele-specific expression information within the statistical framework to improve ct-eQTL detection.

Main Methods:

• Developed CSeQTL, a statistical method for ct-eQTL mapping directly from bulk RNA-seq count data.
• Incorporated allele-specific expression analysis into the ct-eQTL mapping model.
• Validated CSeQTL performance through simulations and comparison with existing methods using purified and single-cell RNA-seq data.

Main Results:

• CSeQTL accurately maps ct-eQTLs from bulk RNA-seq data, overcoming limitations of previous transformation-based approaches.
• The method demonstrated robust performance in both simulated and real-world data analyses.
• Identified cell types associated with 21 categories of human traits based on ct-eQTL findings.

Conclusions:

• CSeQTL provides a powerful and accurate approach for cell type-specific genetic analysis using readily available bulk RNA-seq data.
• The method enhances the ability to uncover genetic influences on complex traits by considering cell type heterogeneity.
• Findings contribute to a deeper understanding of the genetic architecture of human traits through cell type-specific regulatory mechanisms.