{
  "abstract": "Context-specific eQTLs mediate genetic risk for complex diseases. However, limitations in current methods for identifying these eQTLs have hindered their comprehensive characterization and downstream interpretation of disease-associated variants. Here, we introduce FastGxC, a method to efficiently and powerfully map context-specific eQTLs by leveraging the correlation structure in genomic studies with repeated sampling, e.g., single-cell RNA-seq studies. Using simulations, we demonstrate that FastGxC is up to nine times more powerful and 10 6 times faster than existing approaches, reducing computation time from years to minutes. We applied FastGxC to bulk multi-tissue (N=698) and single-cell PBMC (N=1,218) RNA-seq datasets, generating comprehensive tissue- and cell-type-specific eQTL maps. These eQTLs exhibited up to four-fold enrichment in open chromatin regions from matched contexts and were twice as enriched as standard context-specific eQTLs, highlighting their biological relevance. Furthermore, we examined the relationship between context-specific eQTLs and complex human traits and diseases. FastGxC improved precision in identifying relevant contexts for each trait by three-fold and expanded candidate causal genes by 25% in cell types and 6% in tissues compared to standard eQTLs. In summary, FastGxC provides a powerful framework for mapping context-specific eQTLs, advancing our understanding of gene regulatory mechanisms underlying complex human traits and diseases.",
  "abstract_shown": true,
  "affiliations": [
    "Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA",
    "UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA",
    "Systems and Synthetic Biology, Centre for Genomic Regulation, The Barcelona Institute for Science and Technology (BIST), 08003 Barcelona, Spain",
    "Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA",
    "Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA",
    "Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA",
    "Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 00290 Helsinki, Finland; Division of Cardiovascular Medicine, Cardiovascular Institute, Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA",
    "Department of Endocrinology, Metabolism, Nutrition, and Kidney Disease, Biobizkaia Health Research Institute, 48903 Barakaldo, Spain; IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain",
    "Division of Cardiovascular Medicine, Cardiovascular Institute, Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA",
    "Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA",
    "Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA",
    "Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, University of California, Los Angeles, Los Angeles, CA 90095, USA",
    "Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: bballiu@ucla.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lena Krockenberger",
      "orcid": "0009-0002-5987-5496"
    },
    {
      "affs": [
        1
      ],
      "name": "Andrew Y. Lu",
      "orcid": "0009-0004-0578-629X"
    },
    {
      "affs": [
        2
      ],
      "name": "Mike Thompson"
    },
    {
      "affs": [
        3
      ],
      "name": "Lise A. Tucker"
    },
    {
      "affs": [
        4
      ],
      "name": "Cuining Liu",
      "orcid": "0000-0003-1079-0045"
    },
    {
      "affs": [
        5
      ],
      "name": "Mary Grace Gordon"
    },
    {
      "affs": [
        6
      ],
      "name": "Amanda Ranta",
      "orcid": "0000-0002-2612-5165"
    },
    {
      "affs": [
        7
      ],
      "name": "Ivan Carcamo‐Orive",
      "orcid": "0000-0001-8823-4925"
    },
    {
      "affs": [
        8
      ],
      "name": "Joshua W. Knowles",
      "orcid": "0000-0003-1922-7240"
    },
    {
      "affs": [
        9
      ],
      "name": "Andy Dahl"
    },
    {
      "affs": [
        10
      ],
      "name": "Chun Ye",
      "orcid": "0000-0001-6560-3783"
    },
    {
      "affs": [
        11
      ],
      "name": "Noah Zaitlen",
      "orcid": "0000-0002-3553-3670"
    },
    {
      "affs": [
        12
      ],
      "name": "Brunilda Balliu",
      "orcid": "0000-0001-8953-804X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f9vtjqawwz8xn.bib",
  "cited_by": 14,
  "doi": "10.1016/j.xgen.2026.101250",
  "fulltext_url": null,
  "id": "f9vtjqawwz8xn",
  "issns": [
    "2666-979X"
  ],
  "landing_url": "https://doi.org/10.1016/j.xgen.2026.101250",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
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      "1311|Genetic Associations and Epidemiology"
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  "pdf_url": null,
  "pmcid": "PMC13261669",
  "pmid": "42155441",
  "published": "2026-05-18",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/f9vtjqawwz8xn.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:06Z",
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      "url": "https://doi.org/10.1016/j.xgen.2026.101250"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic Associations and Epidemiology",
      "subfield": "genetics-genomics"
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  ],
  "title": "FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data",
  "title_text": "FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:05Z",
  "url": "https://research.draconitic.com/paper/f9vtjqawwz8xn",
  "venue": "Cell Genomics",
  "versions": []
}