Detection of Cell-type-specific eQTL for scRNA-seq Data with Unknown Cell Types

Music City Center 

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with complex traits, yet the majority of these variants reside in intergenic regions, making it challenging to link them to functional genes and regulatory mechanisms. Expression quantitative trait loci (eQTL) analysis connects genetic variants with gene expression and reveals cell-type-specific effects. Single-cell RNA sequencing (scRNA-seq) enables investigation of cell-type-specific eQTLs (ct-eQTLs) by capturing gene expression at single-cell resolution. However, existing methods rely on pre-annotated cell-type labels, which may not be accurate. Differential inference for regulatory effects across different cell types will be hampered by inaccurate cell-type annotation, leading to unexpected false positives. Thus, we propose a statistical model that simultaneously performs cell-type annotation and identifies ct-eQTLs. By leveraging allele-specific expression, our method improves the accuracy and interpretability of ct-eQTL detection.

Single-cell RNA Sequencing (scRNA-seq)

Expression quantitative trait loci (eQTL)

Integrative Analysis

Mixture Model 

Section on Statistics in Genomics and Genetics