The Power of Single-Cell RNA Sequencing in eQTL Discovery

Abstract

Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing (scRNA-Seq) technologies have created new exciting opportunities for spatiotemporal assessment of changes in gene expression at the single-cell level in complex and inherited conditions. A growing number of studies have demonstrated the power of scRNA-Seq in eQTL mapping across different cell types, developmental stages and stimuli that could be obscured when using bulk RNA-Seq methods. In this review, we outline the methodological principles, advantages, limitations and the future experimental and analytical considerations of single-cell eQTL studies. We look forward to the explosion of single-cell eQTL studies applied to large-scale population genetics to take us one step closer to understanding the molecular mechanisms of disease.

Keywords: cis-eQTL; eQTL; genetics; single cell; transcription.

Figure 1

Comparison of single-cell sequencing and bulk RNA-Seq for eQTL analysis. (A) The experimental workflow for single-cell and bulk RNA-Seq. (B) Single-cell RNA sequencing expression profile includes cellular heterogeneity and expression variability of each cell separately, whereas bulk RNA-Seq represents an average of all the cells in a tissue and cellular heterogeneity cannot be estimated. ScRNA-Seq also allows estimation of variability in gene expression across individual cells. (C) Violin plot of an example gene expression for a cis-eQTL. The variant is associated with significant allele specific gene expression in individual cell types (left panel) but are masked in bulk tissue analysis. The tissue and cell images were adapted from Servier Medical Art, licensed under a Creative Commons Attribution 3.0 Generic License.

Figure 2

Cell-type-specific locus zoom plot for rs2272245 using summary statistics from [4]. Arrows indicate the SNP rs2272245, a cis-eQTL significantly effecting TSPAN13 expression in CD4⁺ T cells only (p = 2.21 × 10⁻⁶). The number of cells per cell type are as follows: bulk-like PBMCs 25,291; CD4⁺ T cells 13,961; CD8⁺ T cells 4350; monocytes 2630, where classical monocytes 2175 and non-classical monocytes 455; B-cells 835; natural killer cell 2908; dendritic cells 379. Plots were drawn using LocusZoom suite on http://locuszoom.org/ (accessed on 3 February 2022).

Figure 3

Graphical illustration of the deconvolution of mixed samples. Bulk transcriptomics data for an allele of a given gene are a sum of expression of cell types 1, 2, 3 and 4. After computational deconvolution, cell types are separated, and gene expression of each cell type is estimated considering cell-type proportions from a reference dataset (e.g., scRNA-Seq). The tissue and cell images were adapted from Servier Medical Art, licensed under a Creative Commons Attribution 3.0 Generic License.