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Single-cell eQTL models reveal dynamic T cell state dependence of disease loci

Author

Listed:
  • Aparna Nathan

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Samira Asgari

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Kazuyoshi Ishigaki

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Cristian Valencia

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Tiffany Amariuta

    (Broad Institute of MIT and Harvard
    Harvard T.H. Chan School of Public Health)

  • Yang Luo

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Jessica I. Beynor

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Yuriy Baglaenko

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Sara Suliman

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Alkes L. Price

    (Broad Institute of MIT and Harvard
    Harvard T.H. Chan School of Public Health
    Harvard T.H. Chan School of Public Health)

  • Leonid Lecca

    (Harvard Medical School
    Socios En Salud Sucursal Peru)

  • Megan B. Murray

    (Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School)

  • D. Branch Moody

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Soumya Raychaudhuri

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

Abstract

Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states—for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation—are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.

Suggested Citation

  • Aparna Nathan & Samira Asgari & Kazuyoshi Ishigaki & Cristian Valencia & Tiffany Amariuta & Yang Luo & Jessica I. Beynor & Yuriy Baglaenko & Sara Suliman & Alkes L. Price & Leonid Lecca & Megan B. Mur, 2022. "Single-cell eQTL models reveal dynamic T cell state dependence of disease loci," Nature, Nature, vol. 606(7912), pages 120-128, June.
    • Handle: RePEc:nat:nature:v:606:y:2022:i:7912:d:10.1038_s41586-022-04713-1
    DOI: 10.1038/s41586-022-04713-1
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    Cited by:

    1. Kaitlyn A. Lagattuta & Hannah L. Park & Laurie Rumker & Kazuyoshi Ishigaki & Aparna Nathan & Soumya Raychaudhuri, 2024. "The genetic basis of autoimmunity seen through the lens of T cell functional traits," Nature Communications, Nature, vol. 15(1), pages 1-6, December.
    2. Chang Su & Zichun Xu & Xinning Shan & Biao Cai & Hongyu Zhao & Jingfei Zhang, 2023. "Cell-type-specific co-expression inference from single cell RNA-sequencing data," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Matthew Tegtmeyer & Jatin Arora & Samira Asgari & Beth A. Cimini & Ajay Nadig & Emily Peirent & Dhara Liyanage & Gregory P. Way & Erin Weisbart & Aparna Nathan & Tiffany Amariuta & Kevin Eggan & Marzi, 2024. "High-dimensional phenotyping to define the genetic basis of cellular morphology," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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