{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland",
    "Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA",
    "Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA",
    "Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA",
    "Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA",
    "Massachusetts General Hospital, Broad Institute of MIT and Harvard",
    "Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia",
    "Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia",
    "Translational Genomics Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia",
    "Garvan Institute of Medical Research, Murdoch Childrens Research Institute",
    "Garvan Institute of Medical Research, University of New South Wales",
    "School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia",
    "Broad Institute of MIT and Harvard, Massachusetts General Hospital",
    "Broad Institute of MIT and Harvard",
    "Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA",
    "Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea",
    "Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, South Korea",
    "Garvan Institute of Medical Research and UNSW Sydney, Murdoch Childrens Research Institute",
    "Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA",
    "Garvan Institute of Medical Research, UNSW Cellular Genomics Futures Institute",
    "Massachusettts General Hospital, Broad Institute of MIT and Harvard, Institute for Molecular Medicine Finland",
    "Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA",
    "Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA",
    "Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA",
    "Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA",
    "UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 24,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Wei Zhou",
      "orcid": "0000-0001-7719-0859"
    },
    {
      "affs": [
        6,
        7,
        8,
        9
      ],
      "name": "Anna S. E. Cuomo",
      "orcid": "0000-0001-5168-6979"
    },
    {
      "affs": [
        2,
        4,
        10
      ],
      "name": "Linfeng Hu"
    },
    {
      "affs": [
        11,
        8,
        12
      ],
      "name": "Angli Xue",
      "orcid": "0000-0002-0285-0426"
    },
    {
      "affs": [
        13
      ],
      "name": "Masahiro Kanai",
      "orcid": "0000-0001-5165-4408"
    },
    {
      "affs": [
        14,
        2,
        4,
        5
      ],
      "name": "Yijia Christiana Liu",
      "orcid": "0009-0001-2078-651X"
    },
    {
      "affs": [
        15,
        5
      ],
      "name": "Taeyeop Kang",
      "orcid": "0009-0009-7038-5683"
    },
    {
      "affs": [
        15,
        16,
        17
      ],
      "name": "Youngdae Kim",
      "orcid": "0000-0002-0120-7965"
    },
    {
      "affs": [
        18,
        19
      ],
      "name": "Alex Rodriguez",
      "orcid": "0000-0003-0723-665X"
    },
    {
      "affs": [
        18,
        20
      ],
      "name": "Ravi Madduri",
      "orcid": "0000-0003-2130-2887"
    },
    {
      "affs": [
        14,
        1,
        4,
        5
      ],
      "name": "Bram L. Gorissen",
      "orcid": "0000-0001-5992-0432"
    },
    {
      "affs": [
        2,
        4
      ],
      "name": "Grant Chau"
    },
    {
      "affs": [
        8
      ],
      "name": "Anne Senabouth",
      "orcid": "0000-0002-8798-9821"
    },
    {
      "affs": [
        21
      ],
      "name": "M. Grace Gordon",
      "orcid": "0000-0003-4321-9187"
    },
    {
      "affs": [
        21
      ],
      "name": "Richard Perez"
    },
    {
      "affs": [
        21
      ],
      "name": "Pooja Kathail",
      "orcid": "0000-0001-8860-0753"
    },
    {
      "affs": [
        21
      ],
      "name": "Min Cheol Kim",
      "orcid": "0000-0001-9005-590X"
    },
    {
      "name": "Chun Jimmie Ye"
    },
    {
      "affs": [
        22,
        23,
        24
      ],
      "name": "Chirag Krishna",
      "orcid": "0000-0002-9166-4632"
    },
    {
      "affs": [
        22,
        23,
        24
      ],
      "name": "Ramnik J. Xavier",
      "orcid": "0000-0002-5630-5167"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Daniel G. MacArthur",
      "orcid": "0000-0002-5771-2290"
    },
    {
      "affs": [
        8,
        25
      ],
      "name": "Joseph E. Powell",
      "orcid": "0000-0002-5070-4124"
    },
    {
      "affs": [
        14,
        0,
        2,
        4
      ],
      "name": "Mark J. Daly",
      "orcid": "0000-0002-0949-8752"
    },
    {
      "affs": [
        14,
        1,
        2,
        3,
        4
      ],
      "name": "Benjamin M. Neale",
      "orcid": "0000-0003-1513-6077"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/396rja9qhx2py.bib",
  "cited_by": 24,
  "doi": "10.1038/s41588-026-02798-2",
  "fulltext_url": null,
  "id": "396rja9qhx2py",
  "issns": [
    "1061-4036",
    "1546-1718"
  ],
  "landing_url": "https://doi.org/10.1038/s41588-026-02798-2",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1311|Genetic Associations and Epidemiology"
    ]
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  "openalex": "W4397016827",
  "pdf_url": null,
  "pmcid": "PMC11118640",
  "pmid": "38798318",
  "published": "2026-11-06",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/396rja9qhx2py.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:41Z",
      "native_id": "W4397016827",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41588-026-02798-2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic Associations and Epidemiology",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Efficient and accurate mixed model association tool for single-cell eQTL analysis",
  "title_text": "Efficient and accurate mixed model association tool for single-cell eQTL analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:41Z",
  "url": "https://research.draconitic.com/paper/396rja9qhx2py",
  "venue": "Nature Genetics",
  "versions": []
}