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Peer-reviewedCC BY-NC-NDNature

Genetic variants shape single-cell chromatin accessibility and disease risk

  • Angli Xue1,2
  • Jianan Fan3,4
  • Oscar A Dong5,4
  • Hao Lawrence Huang3,4,6
  • Ling Chen7
  • Peter C. Allen3,4
  • Eleanor Spenceley3,4
  • Eszter Sagi-Zsigmond4
  • Blake Bowen3,4
  • Albert Henry3,4
  • Anna S. E. Cuomo8,9,3,4,6
  • Hope A. Tanudisastro8,9,3,10
19 more authors
  • Zhen Qiao3,11,4
  • Elizabeth Dorans12,13,14
  • Eyal David7
  • Kyle Kai‐How Farh7
  • Linfeng Hu15,14,16
  • Yijia Christiana Liu15,14,16
  • Drew R. Neavin4
  • Arthur S. Lee17,18
  • Anne Senabouth3,4
  • Caitlin Bartie4
  • Rachael A. McCloy4
  • Venessa T. Chin3,19,4
  • Wei Zhou15,20,14,21,16
  • Alkes L. Price22,12,14
  • Katrina M. de Lange8,9
  • Gemma A. Figtree23,24
  • Alex William Hewitt25,26
  • Daniel G. MacArthur8,9,3
  • Joseph E. Powell27,28,29
29 affiliations
  1. 1Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia. [email protected]
  2. 2Translational Genomics Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia. [email protected]
  3. 3Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
  4. 4Translational Genomics Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  5. 5Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
  6. 6UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
  7. 7Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
  8. 8Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  9. 9Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
  10. 10Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
  11. 11Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  12. 12Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
  13. 13Harvard Medical School, Boston, MA, USA
  14. 14Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  15. 15Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
  16. 16Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  17. 17Massachusetts General Hospital, Boston, MA, USA
  18. 18The Broad Institute of MIT and Harvard, Cambridge, MA, USA
  19. 19The Kinghorn Cancer Centre, St Vincent's Hospital, Sydney, New South Wales, Australia
  20. 20Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  21. 21Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
  22. 22Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
  23. 23Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
  24. 24Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, New South Wales, Australia
  25. 25Department of Ophthalmology, Royal Hobart Hospital, Hobart, Tasmania, Australia
  26. 26Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
  27. 27Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia. [email protected]
  28. 28Translational Genomics Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia. [email protected]
  29. 29UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia. [email protected]
Open at sourceDOICite

Abstract

Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways linking variants to cell type-specific gene expression. Here we present chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1,042 donors, generated using single-cell ATAC-seq, including ∼100,000 PBMCs from 90 donors using multiome (RNA+ATAC) sequencing, with matched whole-genome sequencing. We identified 440,996 chromatin peaks across 28 immune cell types and mapped 243,225 chromatin accessibility quantitative trait loci (caQTLs), of which 60% were cell type-specific. Rare variant analysis revealed associations with 27,927 peaks, recapitulating the same cell type-specific regulatory architecture. Colocalization with the eQTLs from scRNA-seq data (5.4 million PBMCs) identified 31,688 candidate cis -regulatory elements; around half (44.40%) show evidence of putative mediating effects via chromatin accessibility. Integrating caQTLs with GWAS summary statistics for 16 diseases and 44 blood traits uncovered 4.5% - 22.6% more colocalized signals compared with using eQTLs alone, many of which have not been reported in prior studies. We show that the limited concordance between GWAS and eQTL signals can be substantially improved by incorporating cell type– and tissue-specific context, distal regulatory effects, models allowing for multiple causal variants, and promoter/enhancer priming, together increasing overlap from ∼20% to ∼60%. In addition, using a graph neural network, we inferred peak-to-gene relationships from unpaired multiome data by integrating caQTL and eQTL signals, achieving up to 80% higher accuracy than with paired multiome data lacking QTL information. This gain translated into improved gene regulatory network inference, enabling the identification of 128 additional transcription factors (TF)–target gene pairs (a 22% increase). Together, these results provide a comprehensive single-cell map of chromatin accessibility and genetic variation in human circulating immune cells, establishing a powerful resource for dissecting cell type-specific regulation and advancing our understanding of genetic risk for complex diseases.

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Cite

BibTeXRIS
BibTeX
@article{xue2026genetic,
  title = {{Genetic variants shape single-cell chromatin accessibility and disease risk}},
  author = {Angli Xue and Jianan Fan and Oscar A Dong and Hao Lawrence Huang and Ling Chen and Peter C. Allen and Eleanor Spenceley and Eszter Sagi-Zsigmond and Blake Bowen and Albert Henry and Anna S. E. Cuomo and Hope A. Tanudisastro and Zhen Qiao and Elizabeth Dorans and Eyal David and Kyle Kai‐How Farh and Linfeng Hu and Yijia Christiana Liu and Drew R. Neavin and Arthur S. Lee and Anne Senabouth and Caitlin Bartie and Rachael A. McCloy and Venessa T. Chin and Wei Zhou and Alkes L. Price and Katrina M. de Lange and Gemma A. Figtree and Alex William Hewitt and Daniel G. MacArthur and Joseph E. Powell},
  journal = {Nature},
  year = {2026},
  month = oct,
  doi = {10.1038/s41586-026-11173-4},
  url = {https://doi.org/10.1038/s41586-026-11173-4},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Genetic variants shape single-cell chromatin accessibility and disease risk
AU  - Angli Xue
AU  - Jianan Fan
AU  - Oscar A Dong
AU  - Hao Lawrence Huang
AU  - Ling Chen
AU  - Peter C. Allen
AU  - Eleanor Spenceley
AU  - Eszter Sagi-Zsigmond
AU  - Blake Bowen
AU  - Albert Henry
AU  - Anna S. E. Cuomo
AU  - Hope A. Tanudisastro
AU  - Zhen Qiao
AU  - Elizabeth Dorans
AU  - Eyal David
AU  - Kyle Kai‐How Farh
AU  - Linfeng Hu
AU  - Yijia Christiana Liu
AU  - Drew R. Neavin
AU  - Arthur S. Lee
AU  - Anne Senabouth
AU  - Caitlin Bartie
AU  - Rachael A. McCloy
AU  - Venessa T. Chin
AU  - Wei Zhou
AU  - Alkes L. Price
AU  - Katrina M. de Lange
AU  - Gemma A. Figtree
AU  - Alex William Hewitt
AU  - Daniel G. MacArthur
AU  - Joseph E. Powell
PY  - 2026
DA  - 2026/10/28
JO  - Nature
DO  - 10.1038/s41586-026-11173-4
UR  - https://doi.org/10.1038/s41586-026-11173-4
AB  - Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways linking variants to cell type-specific gene expression. Here we present chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1,042 donors, generated using single-cell ATAC-seq, including ∼100,000 PBMCs from 90 donors using multiome (RNA+ATAC) sequencing, with matched whole-genome sequencing. We identified 440,996 chromatin peaks across 28 immune cell types and mapped 243,225 chromatin accessibility quantitative trait loci (caQTLs), of which 60% were cell type-specific. Rare variant analysis revealed associations with 27,927 peaks, recapitulating the same cell type-specific regulatory architecture. Colocalization with the eQTLs from scRNA-seq data (5.4 million PBMCs) identified 31,688 candidate cis -regulatory elements; around half (44.40%) show evidence of putative mediating effects via chromatin accessibility. Integrating caQTLs with GWAS summary statistics for 16 diseases and 44 blood traits uncovered 4.5% - 22.6% more colocalized signals compared with using eQTLs alone, many of which have not been reported in prior studies. We show that the limited concordance between GWAS and eQTL signals can be substantially improved by incorporating cell type– and tissue-specific context, distal regulatory effects, models allowing for multiple causal variants, and promoter/enhancer priming, together increasing overlap from ∼20% to ∼60%. In addition, using a graph neural network, we inferred peak-to-gene relationships from unpaired multiome data by integrating caQTL and eQTL signals, achieving up to 80% higher accuracy than with paired multiome data lacking QTL information. This gain translated into improved gene regulatory network inference, enabling the identification of 128 additional transcription factors (TF)–target gene pairs (a 22% increase). Together, these results provide a comprehensive single-cell map of chromatin accessibility and genetic variation in human circulating immune cells, establishing a powerful resource for dissecting cell type-specific regulation and advancing our understanding of genetic risk for complex diseases.
SN  - 0028-0836
SN  - 1476-4687
ER  -