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Modeling memory T cell states at single-cell resolution identifies in vivo state-dependence of eQTLs influencing disease

Nathan, A.; Asgari, S.; Ishigaki, K.; Amariuta, T.; Luo, Y.; Beynor, J. I.; Baglaenko, Y.; Suliman, S.; Price, A.; Lecca, L.; Murray, M. B.; Moody, D. B.; Raychaudhuri, S.

2021-07-30 genetics
10.1101/2021.07.29.454316 bioRxiv
Show abstract

Many non-coding genetic variants cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by gene-regulation differences between cell states. T cells, for example, have fluid, multifaceted functional states in vivo that cannot be modeled in eQTL studies that aggregate cells. Here, we modeled T cell states and eQTLs at single-cell resolution. Using >500,000 resting memory T cells from 259 Peruvians, we found over one-third of the 6,511 cis-eQTLs had state-dependent effects. By integrating single-cell RNA and surface protein measurements, we defined continuous cell states that explained more eQTL variation than discrete states like CD4+ or CD8+ T cells and could have opposing effects on independent eQTL variants in a locus. Autoimmune variants were enriched in cell-state-dependent eQTLs, such as a rheumatoid-arthritis variant near ORMDL3 strongest in cytotoxic CD8+ T cells. These results argue that fine-grained cell state context is crucial to understanding disease-associated eQTLs.

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