Abstract
BackgroundSingle-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease.ResultsWhile previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches.ConclusionWe provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.
Highlights
Expression quantitative trait locus mapping is an established tool for identifying genetic variants that play a regulatory role in gene expression
Aggregation and normalization strategies Traditional bulk expression quantitative trait loci (eQTL) are germline genetic variants that are associated with differences in gene expression between donors, where the gene expression values represent the summary of a gene’s expression across all cells in the tissue sample
Given single-cell Linear mixed models (LMMs) and SCeQTL models do not perform better on a different type of eQTL, their overall higher false positive rate, and their increased computational burden, we focus on the aggregationbased approaches for the remainder of this paper
Summary
Expression quantitative trait locus (eQTL, see Table 1) mapping is an established tool for identifying genetic variants that play a regulatory role in gene expression. Matched datasets (Bulk and single-cell) expression data from the same set of individuals with closely matched expression quantification. Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
