A combined method to identify gene activity differences in specific cell types using bulk and single-cell RNA data

A new statistical framework, IBSEP, integrates bulk and single-cell RNA sequencing data for enhanced identification of cell-type-specific expression-related genetic variants.

• Genome-wide association studies have identified numerous genetic variants linked to complex traits, particularly in non-coding regions.
• Expression quantitative trait locus studies connect these genetic variations to gene expression, which is crucial for understanding disease mechanisms.
• Traditional bulk RNA sequencing methods provide tissue-level insights but may lose important information due to cellular diversity.
• Single-cell RNA sequencing offers higher resolution for analyses but is often limited by smaller sample sizes and technical challenges.
• IBSEP combines data from both bulk and single-cell RNA sequencing using a hierarchical linear model to improve the prioritization of cell-type-specific genetic variants.
• Simulations and analyses of real datasets show that IBSEP outperforms existing methods in identifying cell-type-specific expression-related genetic variants.

Simplified