Nathan, AparnaAsgari, SamiraIshigaki, KazuyoshiValencia, CristianAmariuta, TiffanyLuo, YangBeynor, JessicaBaglaenko, YuriySuliman, SaraPrice, AlkesLecca, LeonidMurray, MeganModdy, D BranchRaychaudhuri, Soumya2023-08-222022-05-11Nathan, Aparna, Samira Asgari, Kazuyoshi Ishigaki, Cristian Valencia, Tiffany Amariuta, Yang Luo, Jessica Beynor et al. "Single-cell eQTL models reveal dynamic T cell state dependence of disease loci." Nature 606, no. 7912 (2022): 120-128. DOI: 10.1038/s41586-022-04713-10028-08361476-4687https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37376885Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by gene-regulation differences 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 modeled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using >500,000 unstimulated memory T cells from 259 Peruvians, we show that around one-third of 6,511 cis-eQTLs had effects 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 rheumatoid-arthritis risk variants near ORMDL3 and CTLA4, arguing that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more eQTL variation than conventional discrete categories, such as CD4+/CD8+, suggesting that modeling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.en-USMultidisciplinaryJournal Article2023-08-2210.1038/s41586-022-04713-1