CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq. Issue 8 (16th August 2022)

• Record Type: Journal Article
• Authors: Cuomo, Anna S E; Heinen, Tobias; Vagiaki, Danai; Horta, Danilo; Marioni, John C; Stegle, Oliver
• Is Part Of: Molecular systems biology. Volume 18:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-16
• Subjects: cell‐type specificity -- eQTL -- genetic interaction -- single‐cell sequencing; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202110663
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent 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 analyze these data remain based on principles established for the genetic analysis of 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 the 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 genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying 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. Synopsis: CellRegMap is a statistical framework to identify and characterise genetic effects on gene expression in single cells. The model has enabled the identification of hundreds of context‐specific eQTL, including variants that are colocalized with human disease variants. CellRegMap is a statistical framework to map eQTL using single‐cell RNA‐seq, mitigating the need to define discrete cell groups. CellRegMap can detect fine‐grained context‐specific genetic regulation and regulatory modules that comprise eQTL with shared patterns of activity in distinct cellular contexts. Cell‐context interactions identified using CellRegMap can help characterise colocalization events with human disease variants identified from genome‐wide association studies. Abstract : CellRegMap is a statistical framework to identify and characterise genetic effects on gene expression in single cells. The model has enabled the identification of hundreds of context‐specific eQTL, including variants that are colocalized with human disease variants.