Rare and common genetic variants shape single-cell immune gene expression

. Here we describe phase 1 of TenK10K, which leverages single-cell RNA sequencing data from more than 5 million cells across 28 immune cell types, along with matched whole-genome sequencing data from 1,925 individuals. This provides power to detect associations for rare and low-frequency genetic variants that have largely been uncharacterized in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell-type-specific manner using a recent method that increases power by modelling single cells directly rather than aggregated 'pseudobulk' counts. We identify putative common regulatory variants for 83% of all 21,404 genes tested and cumulative rare-variant signals for 47% of those genes. We explore how genetic effects vary across cell types and states, develop a framework to determine the degree of single-cell expression quantitative trait loci cell-type specificity and show that about half of the effects are observed only in one or a few cell types. By integrating our results with functional annotations and disease information, we further characterize the likely molecular modes of action for many disease-variant associations. Finally, we explore the effects that genetic variants have on gene expression across continuous cell states, and effects that vary cell-state abundance directly.

Authors

Institutions

Publication Details

Journal
Nature
Published
2026-10-28
DOI
https://doi.org/10.1038/s41586-026-10721-2
Citations
3
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
12.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rare and common genetic variants shape single-cell immune gene expression

Katalina Bobowik, Albert Henry, Daniel G. MacArthur, Chris Wallace et al.
3 citations
Nature
Genetic Associations and Epidemiology
12.01
article

Rare and common genetic variants shape single-cell immune gene expression

Katalina Bobowik, Albert Henry, Daniel G. MacArthur, Chris Wallace, Hannah Nicholas, Michael Patrick Gray, Kyle Kai‐How Farh, Michael A. Silk, Katrina M. de Lange, Angli Xue, Jeffrey M. Pullin, Rachael A. McCloy, Eyal David, Alex William Hewitt, Caitlin Uren, Hope A. Tanudisastro, Alexander Stuckey, Owen M. Siggs, Kristof Wing, Joseph E. Powell, Bindu Swapna Madala, Hao Lawrence Huang, Jennifer Piscionere, Anna S. E. Cuomo, Arthur S. Lee, Owen Tang, John Marshall, Drew R. Neavin, Matthew J. Welland, Samantha J. Bryen, Stuart M. Grieve, Ling Chen, Wei Zhou, Trinh Xuan Nguyen, Vivian Bakiris, Caitlin Bartie, Eleanor Spenceley, Amy Miniter, Oscar A Dong, Michael Franklin, Gemma A. Figtree, Michael Harper, Blake Bowen, Zhen Qiao
article en
3 citations

Abstract

. Here we describe phase 1 of TenK10K, which leverages single-cell RNA sequencing data from more than 5 million cells across 28 immune cell types, along with matched whole-genome sequencing data from 1,925 individuals. This provides power to detect associations for rare and low-frequency genetic variants that have largely been uncharacterized in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell-type-specific manner using a recent method that increases power by modelling single cells directly rather than aggregated 'pseudobulk' counts. We identify putative common regulatory variants for 83% of all 21,404 genes tested and cumulative rare-variant signals for 47% of those genes. We explore how genetic effects vary across cell types and states, develop a framework to determine the degree of single-cell expression quantitative trait loci cell-type specificity and show that about half of the effects are observed only in one or a few cell types. By integrating our results with functional annotations and disease information, we further characterize the likely molecular modes of action for many disease-variant associations. Finally, we explore the effects that genetic variants have on gene expression across continuous cell states, and effects that vary cell-state abundance directly.

Nature
Broad Institute (US), The University of Sydney (AU), University of Tasmania (AU), Garvan Institute of Medical Research (AU), The University of Melbourne (AU), Illumina (United States) (US), University of Cambridge (GB), Royal North Shore Hospital (AU), Royal Hobart Hospital (AU), UNSW Sydney (AU), Massachusetts General Hospital (US), Centre for Eye Research Australia (AU), MRC Biostatistics Unit (GB), Murdoch Children's Research Institute (AU), Menzies Institute for Medical Research (AU), University College London (GB)
Good health and well-being
Openalex Percentile: Top 1%
Genetic Associations and Epidemiology
12.01
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon