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

Abstract

Single cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to perform genetic analyses using scRNA-seq remain based on principles established for bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize heterogeneity in allelic effects across cellular contexts of different granularity, including cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to two recent studies of differentiating iPSCs, where we uncover a previously underappreciated heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.