Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal mechanisms of genome regulation
9 affiliations
- 1Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW Australia
- 2School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW Australia
- 3Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD Australia
- 4Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
- 5Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, NSW Australia
- 6University of California, San Francisco, San Francisco, CA USA
- 7Parker Institute for Cancer Immunotherapy, San Francisco, CA USA
- 8Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS Australia
- 9UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW Australia
Abstract
Gene expression varies substantially across cells, even within seemingly homogeneous populations. Understanding how genetic variation influences this variability is critical for elucidating mechanisms of genome regulation, yet the genetic control of cell-to-cell expression variability remains poorly characterised. Here, we introduce MEOTIVE (Mapping genetic Effects On inTra-Individual Variability of gene Expression), a statistical framework that identifies genetic effects on gene expression variability (sc-veQTLs) while modelling mean-variance dependence. Applying MEOTIVE to single-cell RNA-seq data from 1.2 million peripheral blood mononuclear cells across 980 donors in the OneK1K cohort, we identify 14–3,488 genes with significant sc-veQTLs across blood cell types, including 2,103 shared across multiple cell types. We further detect 55 SNP-gene pairs (34 genes) associated with gene expression dispersion (sc-deQTLs), enriched for immune and viral response pathways; 32 of these associations remain significant after additional adjustment for mean expression. Notably, rs1131017 in the 5′UTR of RPS26 is associated with widespread expression dispersion across cell types and reduced autoimmune disease risk. We also identify a monocyte-specific deQTL in LYZ (rs1384), which replicates in an independent cohort. MEOTIVE provides a robust framework for mapping the genetic regulation of gene-expression variability at single-cell resolution and reveals pathways linking cellular heterogeneity to autoimmune disease risk.
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BibTeX
@article{xue2026genetic,
title = {{Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal mechanisms of genome regulation}},
author = {Angli Xue and Seyhan Yazar and José Alquicira-Hernández and Anna S. E. Cuomo and Anne Senabouth and Gracie Gordon and Pooja Kathail and Chun Jimmie Ye and Alex W. Hewitt and Joseph E. Powell},
journal = {Nature Communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77453-9},
url = {https://doi.org/10.1038/s41467-026-77453-9},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal mechanisms of genome regulation
AU - Angli Xue
AU - Seyhan Yazar
AU - José Alquicira-Hernández
AU - Anna S. E. Cuomo
AU - Anne Senabouth
AU - Gracie Gordon
AU - Pooja Kathail
AU - Chun Jimmie Ye
AU - Alex W. Hewitt
AU - Joseph E. Powell
PY - 2026
DA - 2026/09/21
JO - Nature Communications
DO - 10.1038/s41467-026-77453-9
UR - https://doi.org/10.1038/s41467-026-77453-9
AB - Gene expression varies substantially across cells, even within seemingly homogeneous populations. Understanding how genetic variation influences this variability is critical for elucidating mechanisms of genome regulation, yet the genetic control of cell-to-cell expression variability remains poorly characterised. Here, we introduce MEOTIVE (Mapping genetic Effects On inTra-Individual Variability of gene Expression), a statistical framework that identifies genetic effects on gene expression variability (sc-veQTLs) while modelling mean-variance dependence. Applying MEOTIVE to single-cell RNA-seq data from 1.2 million peripheral blood mononuclear cells across 980 donors in the OneK1K cohort, we identify 14–3,488 genes with significant sc-veQTLs across blood cell types, including 2,103 shared across multiple cell types. We further detect 55 SNP-gene pairs (34 genes) associated with gene expression dispersion (sc-deQTLs), enriched for immune and viral response pathways; 32 of these associations remain significant after additional adjustment for mean expression. Notably, rs1131017 in the 5′UTR of RPS26 is associated with widespread expression dispersion across cell types and reduced autoimmune disease risk. We also identify a monocyte-specific deQTL in LYZ (rs1384), which replicates in an independent cohort. MEOTIVE provides a robust framework for mapping the genetic regulation of gene-expression variability at single-cell resolution and reveals pathways linking cellular heterogeneity to autoimmune disease risk.
SN - 2041-1723
ER -