Efficient genome-wide mapping of reproducible, context-dependent eQTLs at single-cell resolution
- 1Brigham and Women's Hospital
Abstract
Single-cell technologies enable linking disease-risk variants to gene regulatory effects in specific cell-state contexts. However, most so called "single-cell eQTL" studies use a "pseudobulking" strategy to identify expression Quantitative Trait Loci (eQTLs), obscuring subtle dynamic regulatory effects of disease alleles. Here, we propose Dynema (Dynamic eQTL mapping in single cells) for fast and accurate genome-wide mapping of context-dependent and independent eQTL effects at true single-cell resolution. To identify eQTLs, Dynema uses a Poisson model with cluster robust variance estimators (CRVEs) to account for correlation of single-cell profiles from the same individual. In contrast to other common methods, Dynema achieves statistical calibration and scales to genome-wide analysis in large single-cell datasets in realistic timeframes. We applied Dynema to two independent T cell datasets and identified reproducible cell-state-dependent eQTL effects. Some cell-state-dependent eQTLs are missed by pseudobulking approaches, and many others are conditionally independent from lead eQTL effects. We show that TSPAN32 and other autoimmune loci colocalize with cell-state-dependent eQTLs. Mapping context-dependent eQTLs at single-cell resolution enables the definition of the molecular effects of complex disease alleles.
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BibTeX
@misc{alquicirahernandez2026efficient,
title = {{Efficient genome-wide mapping of reproducible, context-dependent eQTLs at single-cell resolution}},
author = {J. Alquicira-Hernandez and E. Dorans and Y. Tomofuji and A. Nathan and S. Raychaudhuri},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.25.747138},
url = {https://www.biorxiv.org/content/10.64898/2026.08.25.747138v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Efficient genome-wide mapping of reproducible, context-dependent eQTLs at single-cell resolution
AU - J. Alquicira-Hernandez
AU - E. Dorans
AU - Y. Tomofuji
AU - A. Nathan
AU - S. Raychaudhuri
PY - 2026
DA - 2026/08/25
PB - bioRxiv
DO - 10.64898/2026.08.25.747138
UR - https://www.biorxiv.org/content/10.64898/2026.08.25.747138v2
AB - Single-cell technologies enable linking disease-risk variants to gene regulatory effects in specific cell-state contexts. However, most so called "single-cell eQTL" studies use a "pseudobulking" strategy to identify expression Quantitative Trait Loci (eQTLs), obscuring subtle dynamic regulatory effects of disease alleles. Here, we propose Dynema (Dynamic eQTL mapping in single cells) for fast and accurate genome-wide mapping of context-dependent and independent eQTL effects at true single-cell resolution. To identify eQTLs, Dynema uses a Poisson model with cluster robust variance estimators (CRVEs) to account for correlation of single-cell profiles from the same individual. In contrast to other common methods, Dynema achieves statistical calibration and scales to genome-wide analysis in large single-cell datasets in realistic timeframes. We applied Dynema to two independent T cell datasets and identified reproducible cell-state-dependent eQTL effects. Some cell-state-dependent eQTLs are missed by pseudobulking approaches, and many others are conditionally independent from lead eQTL effects. We show that TSPAN32 and other autoimmune loci colocalize with cell-state-dependent eQTLs. Mapping context-dependent eQTLs at single-cell resolution enables the definition of the molecular effects of complex disease alleles.
ER -