scReQTL

scReQTL quantifies correlations between gene expression and biallelic single nucleotide variants (SNVs) in transcribed genomic regions using single-cell RNA sequencing (scRNA-seq) data.

Key Features:

• Variant Allele Fraction Analysis: Uses Variant Allele Fraction (VAF_RNA) as the primary metric to assess SNV expression at the single-cell level.
• Cell-Specific Regulatory Insights: Correlates per-cell VAF_RNA with corresponding per-cell gene expression to reveal cell-specific regulatory mechanisms mediated by SNVs.
• Applicability to Identical Genotypes: Operates on datasets of identical genotypes to detect RNA-mediated genetic interactions within genetically homogeneous populations.
• Discovery of scReQTLs: Identified 1,272 unique single-cell regulatory QTLs (scReQTLs) in an analysis of 26,640 mesenchymal cells from adipose tissue across three healthy female donors.
• Comparative Analysis with Bulk eQTLs: Distinguishes SNV–gene correlations from bulk eQTLs and detects enrichments in known gene–gene interactions and genome-wide association study (GWAS) loci.

Scientific Applications:

• Single-cell SNV–expression mapping: Identifies SNV–gene correlations expressed at the single-cell level to map regulatory variation within tissues.
• Cell-type-specific regulatory discovery: Reveals transient and dynamic genetic interactions that are obscured in bulk RNA-seq analyses.
• Disease and treatment studies: Supports investigation of genetic mechanisms underlying disease progression and response to treatment at single-cell resolution.
• Integration with genetic association studies: Enables comparison and enrichment analysis with bulk eQTLs, gene–gene interaction datasets, and GWAS loci.

Methodology:

Compute VAF_RNA for biallelic SNVs per cell and correlate VAF_RNA with per-cell gene expression; apply analyses to identical-genotype datasets and perform enrichment comparisons to known gene–gene interactions and GWAS loci.