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Peer-reviewedCC BY-NC-NDCell Genomics

FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data

  • Lena Krockenberger1
  • Andrew Y. Lu2
  • Mike Thompson3
  • Lise A. Tucker4
  • Cuining Liu5
  • Mary Grace Gordon6
  • Amanda Ranta7
  • Ivan Carcamo‐Orive8
  • Joshua W. Knowles9
  • Andy Dahl10
  • Chun Ye11
  • Noah Zaitlen12
1 more authors
  • Brunilda Balliu13
13 affiliations
  1. 1Bioinformatics 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
  2. 2UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
  3. 3Systems and Synthetic Biology, Centre for Genomic Regulation, The Barcelona Institute for Science and Technology (BIST), 08003 Barcelona, Spain
  4. 4Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
  5. 5Bioinformatics 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
  6. 6Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
  7. 7Institute 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
  8. 8Department of Endocrinology, Metabolism, Nutrition, and Kidney Disease, Biobizkaia Health Research Institute, 48903 Barakaldo, Spain; IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain
  9. 9Division of Cardiovascular Medicine, Cardiovascular Institute, Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
  10. 10Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
  11. 11Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
  12. 12Department 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
  13. 13Department 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: [email protected]
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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.

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Cite

BibTeXRIS
BibTeX
@article{krockenberger2026fastgxc,
  title = {{FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data}},
  author = {Lena Krockenberger and Andrew Y. Lu and Mike Thompson and Lise A. Tucker and Cuining Liu and Mary Grace Gordon and Amanda Ranta and Ivan Carcamo‐Orive and Joshua W. Knowles and Andy Dahl and Chun Ye and Noah Zaitlen and Brunilda Balliu},
  journal = {Cell Genomics},
  year = {2026},
  month = may,
  doi = {10.1016/j.xgen.2026.101250},
  url = {https://doi.org/10.1016/j.xgen.2026.101250},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data
AU  - Lena Krockenberger
AU  - Andrew Y. Lu
AU  - Mike Thompson
AU  - Lise A. Tucker
AU  - Cuining Liu
AU  - Mary Grace Gordon
AU  - Amanda Ranta
AU  - Ivan Carcamo‐Orive
AU  - Joshua W. Knowles
AU  - Andy Dahl
AU  - Chun Ye
AU  - Noah Zaitlen
AU  - Brunilda Balliu
PY  - 2026
DA  - 2026/05/18
JO  - Cell Genomics
DO  - 10.1016/j.xgen.2026.101250
UR  - https://doi.org/10.1016/j.xgen.2026.101250
AB  - 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.
SN  - 2666-979X
ER  -