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

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. Murray, D. Branch Moody and Soumya Raychaudhuri ()
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Aparna Nathan: Brigham and Women’s Hospital and Harvard Medical School
Samira Asgari: Brigham and Women’s Hospital and Harvard Medical School
Kazuyoshi Ishigaki: Brigham and Women’s Hospital and Harvard Medical School
Cristian Valencia: Brigham and Women’s Hospital and Harvard Medical School
Tiffany Amariuta: Broad Institute of MIT and Harvard
Yang Luo: Brigham and Women’s Hospital and Harvard Medical School
Jessica I. Beynor: Brigham and Women’s Hospital and Harvard Medical School
Yuriy Baglaenko: Brigham and Women’s Hospital and Harvard Medical School
Sara Suliman: Brigham and Women’s Hospital and Harvard Medical School
Alkes L. Price: Broad Institute of MIT and Harvard
Leonid Lecca: Harvard Medical School
Megan B. Murray: 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

Nature, 2022, vol. 606, issue 7912, 120-128

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41586-022-04713-1

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