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Peer-reviewedCC BYNature Genetics

Efficient and accurate mixed model association tool for single-cell eQTL analysis

  • Wei Zhou1,2,3,4,5,6
  • Anna S. E. Cuomo7,8,9,10
  • Linfeng Hu3,5,11
  • Angli Xue12,9,13
  • Masahiro Kanai14
  • Yijia Christiana Liu15,3,5,6
  • Taeyeop Kang16,6
  • Youngdae Kim16,17,18
  • Alex Rodriguez19,20
  • Ravi Madduri19,21
  • Bram L. Gorissen15,2,5,6
  • Grant Chau3,5
12 more authors
  • Anne Senabouth9
  • M. Grace Gordon22
  • Richard Perez22
  • Pooja Kathail22
  • Min Cheol Kim22
  • Chun Jimmie Ye
  • Chirag Krishna23,24,25
  • Ramnik J. Xavier23,24,25
  • Daniel G. MacArthur7,8
  • Joseph E. Powell9,26
  • Mark J. Daly15,1,3,5
  • Benjamin M. Neale15,2,3,4,5
26 affiliations
  1. 1Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
  2. 2Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  3. 3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  4. 4Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
  5. 5Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  6. 6Massachusetts General Hospital, Broad Institute of MIT and Harvard
  7. 7Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
  8. 8Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
  9. 9Translational Genomics Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  10. 10Garvan Institute of Medical Research, Murdoch Childrens Research Institute
  11. 11Garvan Institute of Medical Research, University of New South Wales
  12. 12School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
  13. 13Broad Institute of MIT and Harvard, Massachusetts General Hospital
  14. 14Broad Institute of MIT and Harvard
  15. 15Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
  16. 16Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
  17. 17Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, South Korea
  18. 18Garvan Institute of Medical Research and UNSW Sydney, Murdoch Childrens Research Institute
  19. 19Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA
  20. 20Garvan Institute of Medical Research, UNSW Cellular Genomics Futures Institute
  21. 21Massachusettts General Hospital, Broad Institute of MIT and Harvard, Institute for Molecular Medicine Finland
  22. 22Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
  23. 23Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
  24. 24Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
  25. 25Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  26. 26UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
Open at sourceDOICite

Abstract

Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.

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Cite

BibTeXRIS
BibTeX
@article{zhou2026efficient,
  title = {{Efficient and accurate mixed model association tool for single-cell eQTL analysis}},
  author = {Wei Zhou and Anna S. E. Cuomo and Linfeng Hu and Angli Xue and Masahiro Kanai and Yijia Christiana Liu and Taeyeop Kang and Youngdae Kim and Alex Rodriguez and Ravi Madduri and Bram L. Gorissen and Grant Chau and Anne Senabouth and M. Grace Gordon and Richard Perez and Pooja Kathail and Min Cheol Kim and Chun Jimmie Ye and Chirag Krishna and Ramnik J. Xavier and Daniel G. MacArthur and Joseph E. Powell and Mark J. Daly and Benjamin M. Neale},
  journal = {Nature Genetics},
  year = {2026},
  month = nov,
  doi = {10.1038/s41588-026-02798-2},
  url = {https://doi.org/10.1038/s41588-026-02798-2},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Efficient and accurate mixed model association tool for single-cell eQTL analysis
AU  - Wei Zhou
AU  - Anna S. E. Cuomo
AU  - Linfeng Hu
AU  - Angli Xue
AU  - Masahiro Kanai
AU  - Yijia Christiana Liu
AU  - Taeyeop Kang
AU  - Youngdae Kim
AU  - Alex Rodriguez
AU  - Ravi Madduri
AU  - Bram L. Gorissen
AU  - Grant Chau
AU  - Anne Senabouth
AU  - M. Grace Gordon
AU  - Richard Perez
AU  - Pooja Kathail
AU  - Min Cheol Kim
AU  - Chun Jimmie Ye
AU  - Chirag Krishna
AU  - Ramnik J. Xavier
AU  - Daniel G. MacArthur
AU  - Joseph E. Powell
AU  - Mark J. Daly
AU  - Benjamin M. Neale
PY  - 2026
DA  - 2026/11/06
JO  - Nature Genetics
DO  - 10.1038/s41588-026-02798-2
UR  - https://doi.org/10.1038/s41588-026-02798-2
AB  - Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.
SN  - 1061-4036
SN  - 1546-1718
ER  -