eQTL Studies: from Bulk Tissues to Single Cells

Abstract

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Figure 1.

Illustration of eQTL analysis at different resolutions: single cells, purified cells, and bulk samples. Shown are data from three individuals with genotypes of AA, AG, and GG, respectively. There are two cell types making up the bulk samples, the oval shaped cells and the triangle shaped cells. For single cell data, we can observe expression level at the single cell level. For example, for the first individual with genotype AA, there are four oval shaped cells with expression level at 0.9, 1.1, 0.8, and 1.2, and two triangle shaped cells with expression level at 3.2 and 2.8, respectively. eQTL analysis can be performed for two cell types separately using single cells across these three individuals to correlate genotypes with observed single-cell level gene expression data. For data from purified cells, we observe aggregated gene expression levels for different cell types but without individual cell level measurements. The average expression level for the oval shaped cells is 1, 2, and 3, respectively, for the three individuals. For data from bulk samples, we can no longer distinguish contributions from two distinct cell types. The average expression level for the three individuals is 1.7, 2.0, and 1.7, respectively. For single cell data, not only we can study association between genotypes and cell-type-specific expressions, we can also correlate genotypes with cell type proportions. Through deconvolutions methods, the bulk samples may be deconvoluted to different cell types to allow cell-type-specific eQTL analysis with estimated cell type proportions from different individuals.