CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq

Area of Science:

• Genomics
• Computational Biology
• Systems Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) allows detailed analysis of cellular heterogeneity.
• Current genetic analysis methods for scRNA-seq data are limited by discrete cell type definitions.
• Assessing genetic effects across subtle cell states and continuous transitions remains a challenge.

Purpose of the Study:

• To develop a statistical framework, Cell Regulatory Map (CellRegMap), for analyzing genetic effects on gene expression at the single-cell level.
• To identify and quantify genotype-context interactions for known expression quantitative trait loci (eQTL) variants using scRNA-seq data.
• To resolve allelic effects across diverse cellular contexts, including subtypes and continuous cell transitions.

Main Methods:

• Developed Cell Regulatory Map (CellRegMap), a model-based statistical framework.
• Applied CellRegMap to analyze scRNA-seq data from differentiating induced pluripotent stem cells (iPSCs).
• Validated the framework using simulated data.

Main Results:

• Uncovered hundreds of eQTLs exhibiting heterogeneous genetic effects across cellular contexts in differentiating iPSCs.
• Demonstrated CellRegMap's ability to resolve allelic effects in cell subtypes and continuous cell transitions.
• Identified fine-grained genetic regulation in neuronal subtypes for eQTLs linked to human diseases.

Conclusions:

• CellRegMap provides a principled approach to analyze genetic regulation in individual cells using scRNA-seq.
• The framework enables the discovery of genotype-context interactions and genetic effects in subtle cellular states.
• This work advances our understanding of genetic influences on cellular heterogeneity and disease.