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Peer-reviewedCC BY-NC-NDNature

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

  • Anna S. E. Cuomo1,2,3,4,5
  • Eleanor Spenceley3,4,5
  • Hope A. Tanudisastro1,2,3,6
  • Blake Bowen3,4,5
  • Albert Henry3,4,7,5
  • Hao Lawrence Huang3,4,5
  • Angli Xue3,4,5
  • Wei Zhou8,9
  • Matthew J. Welland1,2,3
  • Samantha J. Bryen1,2,3
  • Oscar A Dong8,4
  • Arthur S. Lee10,9
32 more authors
  • Jeffrey M. Pullin11
  • Kristof Wing12,13,14
  • Owen Tang15,6,16
  • Michael Patrick Gray15,6,16
  • Michael Franklin1,2,3
  • Michael Harper1,2,3
  • Michael A. Silk1,2,3
  • Katalina Bobowik1,2,3
  • Alexander Stuckey1,2,3
  • John Marshall1,2,3
  • Vivian Bakiris1,2,3
  • Caitlin Uren1,2,3
  • Rachael A. McCloy4
  • Bindu Swapna Madala1,2,3
  • Amy Miniter1,2,3
  • Caitlin Bartie3,4,5
  • Drew R. Neavin4
  • Zhen Qiao3,17
  • Eyal David18
  • Ling Chen18
  • Kyle Kai‐How Farh18
  • Stuart M. Grieve15
  • Trinh Xuan Nguyen15,6,16
  • Chris Wallace19,11
  • Jennifer Piscionere1,2,3
  • Owen M. Siggs3,17
  • Hannah Nicholas1,2,3
  • Katrina M. de Lange1,2,3
  • Alex William Hewitt12,13,14
  • Gemma A. Figtree15,6,16
  • Daniel G. MacArthur1,2,3
  • Joseph E. Powell20,21,22
22 affiliations
  1. 1Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  2. 2Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
  3. 3Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
  4. 4Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  5. 5UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
  6. 6Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
  7. 7Institute of Cardiovascular Science, University College London, London, UK
  8. 8Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
  9. 9Massachusetts General Hospital, Boston, MA, USA
  10. 10Broad Institute of MIT and Harvard, Cambridge, MA, USA
  11. 11MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
  12. 12Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
  13. 13Department of Ophthalmology, Royal Hobart Hospital, Hobart, Tasmania, Australia
  14. 14Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
  15. 15Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
  16. 16Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, New South Wales, Australia
  17. 17Genomics and Inherited Diseases Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  18. 18Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
  19. 19Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
  20. 20Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia. [email protected]
  21. 21Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia. [email protected]
  22. 22UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia. [email protected]
Open at sourceDOICite

Abstract

Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases. Single cell-resolved measures of RNA levels and single-cell expression quantitative trait loci (sc-eQTLs) have revealed genetic regulation that drives sub-tissue cell states and types across diverse human tissues. Here, we describe the first phase of TenK10K, the largest-to-date dataset of matched whole-genome sequencing (WGS) and single-cell RNA-sequencing (scRNA-seq). We leverage scRNA-seq data from over 5 million cells across 28 immune cell types, and matched WGS, from 1,925 individuals, which provides power to detect associations between rare and low-frequency genetic variants that have largely been uncharacterised 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 recently introduced method that increases power by modelling single cells directly rather than relying on 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 genes. We explore how genetic effects vary across cell type and state spectra, develop a framework to determine the degree to which sc-eQTLs are cell type-specific, 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 also further characterise 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 functions, and effects that vary cell state abundance directly.

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Cite

BibTeXRIS
BibTeX
@article{cuomo2026rare,
  title = {{Rare and common genetic variants shape single-cell immune gene expression}},
  author = {Anna S. E. Cuomo and Eleanor Spenceley and Hope A. Tanudisastro and Blake Bowen and Albert Henry and Hao Lawrence Huang and Angli Xue and Wei Zhou and Matthew J. Welland and Samantha J. Bryen and Oscar A Dong and Arthur S. Lee and Jeffrey M. Pullin and Kristof Wing and Owen Tang and Michael Patrick Gray and Michael Franklin and Michael Harper and Michael A. Silk and Katalina Bobowik and Alexander Stuckey and John Marshall and Vivian Bakiris and Caitlin Uren and Rachael A. McCloy and Bindu Swapna Madala and Amy Miniter and Caitlin Bartie and Drew R. Neavin and Zhen Qiao and Eyal David and Ling Chen and Kyle Kai‐How Farh and Stuart M. Grieve and Trinh Xuan Nguyen and Chris Wallace and Jennifer Piscionere and Owen M. Siggs and Hannah Nicholas and Katrina M. de Lange and Alex William Hewitt and Gemma A. Figtree and Daniel G. MacArthur and Joseph E. Powell},
  journal = {Nature},
  year = {2026},
  month = oct,
  doi = {10.1038/s41586-026-10721-2},
  url = {https://doi.org/10.1038/s41586-026-10721-2},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Rare and common genetic variants shape single-cell immune gene expression
AU  - Anna S. E. Cuomo
AU  - Eleanor Spenceley
AU  - Hope A. Tanudisastro
AU  - Blake Bowen
AU  - Albert Henry
AU  - Hao Lawrence Huang
AU  - Angli Xue
AU  - Wei Zhou
AU  - Matthew J. Welland
AU  - Samantha J. Bryen
AU  - Oscar A Dong
AU  - Arthur S. Lee
AU  - Jeffrey M. Pullin
AU  - Kristof Wing
AU  - Owen Tang
AU  - Michael Patrick Gray
AU  - Michael Franklin
AU  - Michael Harper
AU  - Michael A. Silk
AU  - Katalina Bobowik
AU  - Alexander Stuckey
AU  - John Marshall
AU  - Vivian Bakiris
AU  - Caitlin Uren
AU  - Rachael A. McCloy
AU  - Bindu Swapna Madala
AU  - Amy Miniter
AU  - Caitlin Bartie
AU  - Drew R. Neavin
AU  - Zhen Qiao
AU  - Eyal David
AU  - Ling Chen
AU  - Kyle Kai‐How Farh
AU  - Stuart M. Grieve
AU  - Trinh Xuan Nguyen
AU  - Chris Wallace
AU  - Jennifer Piscionere
AU  - Owen M. Siggs
AU  - Hannah Nicholas
AU  - Katrina M. de Lange
AU  - Alex William Hewitt
AU  - Gemma A. Figtree
AU  - Daniel G. MacArthur
AU  - Joseph E. Powell
PY  - 2026
DA  - 2026/10/28
JO  - Nature
DO  - 10.1038/s41586-026-10721-2
UR  - https://doi.org/10.1038/s41586-026-10721-2
AB  - Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases. Single cell-resolved measures of RNA levels and single-cell expression quantitative trait loci (sc-eQTLs) have revealed genetic regulation that drives sub-tissue cell states and types across diverse human tissues. Here, we describe the first phase of TenK10K, the largest-to-date dataset of matched whole-genome sequencing (WGS) and single-cell RNA-sequencing (scRNA-seq). We leverage scRNA-seq data from over 5 million cells across 28 immune cell types, and matched WGS, from 1,925 individuals, which provides power to detect associations between rare and low-frequency genetic variants that have largely been uncharacterised 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 recently introduced method that increases power by modelling single cells directly rather than relying on 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 genes. We explore how genetic effects vary across cell type and state spectra, develop a framework to determine the degree to which sc-eQTLs are cell type-specific, 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 also further characterise 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 functions, and effects that vary cell state abundance directly.
SN  - 0028-0836
SN  - 1476-4687
ER  -