Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Abstract

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

Keywords: cell state; eQTL; gene regulation; noncoding variants; sc-eQTL; single-cell sequencing.

Figure 1

Timeline of sc-eQTL studies. The graph shows the number of individuals (y axis) included in key publications for sc-eQTL studies (x axis); the dot size indicates the number of cell-type-specific genes with at least one eQTL (eGenes), and the color corresponds to the number of cells per individual. The table shows relevant selected features (rows) for each study (columns), where a gray dot indicates that the study included the feature. [Since eGenes were not reported in the study by Sarkar et al. (127), the number of eGenes indicated for that study corresponds to the number of eQTLs instead.] Abbreviations: eQTL, expression quantitative trait locus; iPSC, induced pluripotent stem cell; PBMC, peripheral blood mononuclear cell; sc-eQTL, single-cell expression quantitative trait locus.

Figure 2

Overview of sc-eQTL study design and context-dependent eQTLs. (a) For a given cohort, cell states of interest are assayed directly from their native tissue contexts or elicited ex vivo by experimental perturbations. (b) Single-cell whole-transcriptome and genomic data are generated and serve as input to sc-eQTL analysis. (c) Single-cell data can be used to define cell types and states. Shown here is an example UMAP plot that illustrates the relationship among cells, colored by major cell type. (d) In this example of a cell-type-specific eQTL in B cells, a higher dosage of the T allele increases eGene expression. (e) In this example of a dynamic, state-dependent eQTL in T cells, the effect of the eQTL becomes stronger along a continuous cell state (increasing slope with darker teal colors). Abbreviations: eQTL, expression quantitative trait locus; sc-eQTL, single-cell expression quantitative trait locus; scRNA-seq, single-cell RNA sequencing; SNP, single-nucleotide polymorphism; UMAP, uniform manifold approximation and projection. Figure adapted from images created with BioRender.com.

Figure 3

Modeling approaches for sc-eQTLs. (a) Pseudobulk models collapse cells (into potentially multiple discrete bins) and aggregate the cell profiles from each sample and bin. They can identify eQTLs by adapting methods for bulk analyses. (b) In sc-eQTL models, the expression of each cell is modeled individually; they are commonly implemented as mixed-effects models, with random effects for repeated sampling of cells from the same donor. They can test for eQTL interactions with both discrete and continuously defined cell states. (c) sc-eQTL modeling approaches must mitigate sparsity in single-cell measurements. In the presence of differential expression and sparsity, a linear mixed-effects model may identify a spurious interaction between eQTL and cell state, whereas a Poisson mixed-effects model remains robust. Abbreviations: CPM, counts per million; eQTL, expression quantitative trait locus; PC1, principal component 1; sc-eQTL, single-cell expression quantitative trait locus; scRNA-seq, single-cell RNA sequencing; UMI, unique molecular identifier. Figure adapted from images created with BioRender.com.