Abstract
Mapping expression quantitative trait loci (eQTLs) is a powerful method to study how genetic variation influences gene expression. Traditional bulk eQTL methods rely on averaged gene expression across a possibly heterogeneous mixture of cells, which can obscure underlying regulatory heterogeneity. Single-cell eQTL methods circumvent the averaging artifacts, providing an immense opportunity to interrogate transcriptional regulation at a much finer resolution. Recent developments in metric space regression methods allow the use of full empirical distributions as response objects instead of simple summary statistics such as mean. Here, we leverage Fréchet regression to identify distribution QTLs (distQTLs) using population-scale single-cell RNA-sequencing (scRNA-seq) data. We apply distQTL to the OneK1K cohort, consisting of scRNA-seq data of peripheral blood mononuclear cells from 982 donors, and compare results to various eQTL approaches based on summary statistics and mixed effects modeling. We demonstrate the superior performance of distQTL across different gene expression contexts compared to other methods and benchmark our results against findings from the Genotype-Tissue Expression Project. Finally, we orthogonally validate calls from distQTL using cell-type-specific epigenomic profiles.