Efficient and accurate mixed model association tool for single-cell eQTL analysis
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.
Authors
- Daniel G. MacArthur (ORCID: https://orcid.org/0000-0002-5771-2290)
- Masahiro Kanai (ORCID: https://orcid.org/0000-0001-5165-4408)
- Benjamin M. Neale (ORCID: https://orcid.org/0000-0003-1513-6077)
- Ravi Madduri (ORCID: https://orcid.org/0000-0003-2130-2887)
- Bram L. Gorissen (ORCID: https://orcid.org/0000-0001-5992-0432)
- Angli Xue (ORCID: https://orcid.org/0000-0002-0285-0426)
- Chirag Krishna (ORCID: https://orcid.org/0000-0002-9166-4632)
- Youngdae Kim (ORCID: https://orcid.org/0000-0002-0120-7965)
- Ramnik J. Xavier (ORCID: https://orcid.org/0000-0002-5630-5167)
- M. Grace Gordon (ORCID: https://orcid.org/0000-0003-4321-9187)
- Joseph E. Powell (ORCID: https://orcid.org/0000-0002-5070-4124)
- Anne Senabouth (ORCID: https://orcid.org/0000-0002-8798-9821)
- Anna S. E. Cuomo (ORCID: https://orcid.org/0000-0001-5168-6979)
- Pooja Kathail (ORCID: https://orcid.org/0000-0001-8860-0753)
- Min Cheol Kim (ORCID: https://orcid.org/0000-0001-9005-590X)
- Grant Chau
- Wei Zhou (ORCID: https://orcid.org/0000-0001-7719-0859)
- Mark J. Daly (ORCID: https://orcid.org/0000-0002-0949-8752)
- Alex Rodriguez (ORCID: https://orcid.org/0000-0003-0723-665X)
- Taeyeop Kang (ORCID: https://orcid.org/0009-0009-7038-5683)
- Yijia Christiana Liu (ORCID: https://orcid.org/0009-0001-2078-651X)
- Richard Perez
- Chun Jimmie Ye
- Linfeng Hu
Institutions
- Broad Institute (US)
- Argonne National Laboratory (US)
- University of Helsinki (FI)
- Harvard University (US)
- Garvan Institute of Medical Research (AU)
- University of California, San Francisco (US)
- UNSW Sydney (AU)
- Massachusetts General Hospital (US)
- Institute for Molecular Medicine Finland (FI)
- Murdoch Children's Research Institute (AU)
- Stanley Center for Psychiatric Research (US)
- Center for Computational and Integrative Biology, Massachusetts General Hospital (US)
- Ulsan National Institute of Science and Technology (KR)
Publication Details
- Journal
- Nature Genetics
- Published
- 2026-11-06
- DOI
- https://doi.org/10.1038/s41588-026-02798-2
- Citations
- 24
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 39.63
Funders
- U.S. Department of Defense
- U.S. Department of Energy
- Parker Institute for Cancer Immunotherapy
- Ulsan National Institute of Science and Technology
- National Research Foundation of Korea
- Novo Nordisk
- Ministry of Science and ICT, South Korea
- National Institutes of Health
- Office of Science
- National Health and Medical Research Council
- Institute for Information and Communications Technology Promotion
- National Human Genome Research Institute
- National Institute of Allergy and Infectious Diseases
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Arthritis and Musculoskeletal and Skin Diseases
- National Institute of Dental and Craniofacial Research
- Argonne National Laboratory