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.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

“…Cell-type eQTLs have been generated with sc/snRNAseq data( 42, 43 ). However, the number of individuals profiled was typically limited( 11 ). To date, a total of 7,607 eQTLs have been identified in the largest single-cell eQTL study( 38 ) comprising 192 human brain samples.…”

“…Another limitation is the high cost of sc/snRNAseq. Even though multiplexing methods have been developed to simultaneously profile cells from numerous samples( 10 ), using sc/snRNAseq in large-scale studies typically requiring hundreds of subjects, such as disease association and expression quantitative trait loci (eQTL) mapping( 11 ), can be cost-prohibitive.…”

Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

“…Additionally, expressed quantitative loci (eQTL) approaches are commonly used to map variants to causal genes. However, a critical limitation in chromatin accessibility and eQTL analyses for mapping causal genes arises from cell type heterogeneity; bulk methods capture aggregated expression or accessibility across multiple cell types, with cell type diversity and proportionality complicating interpretation [reviewed in ( Maria et al, 2022 )]. In a pioneering study for kidney disease, Sheng, et al used single cell multi-omics (scRNA-seq and snATAC-seq), human genetic information, and advanced computational approaches to demonstrate how genetic variants render a functional effect on cell types and specific gene/pathway programs, resulting in the identification of more than 200 genes involved in kidney function and hypertension ( Sheng et al, 2021 ).…”

Single cell genomics as a transformative approach for aquaculture research and innovation

“…Single‐cell technologies also allow for more precise definition of connections between molecular phenotypes (e.g., gene expression) and genetic variation 118 . It is now known that most causative variants fall within regulatory regions of genomes (e.g., Ref.…”

“…Single-cell technologies also allow for more precise definition of connections between molecular phenotypes (e.g., gene expression) and genetic variation. 118 It is now known that most causative variants fall within regulatory regions of genomes (e.g., Ref. 119), making expression QTL (eQTL, i.e., genomic regions explaining individual variation in gene expression levels) increasingly to determine the genetic basis for trait variation in aquaculture populations.…”