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.
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Data availability
The single-cell mRNA and surface protein data that support the findings of this study were published previously and are available in the Gene Expression Omnibus (GSE158769) and in dbGaP (phs002467). Genotype data were also previously published and are available in the Database of Genotypes and Phenotypes (dbGaP) (phs002025, for authorized general research use). We also used published datasets for validation and additional analyses: DICE (https://dice-database.org/downloads), BLUEPRINT (http://dcc.blueprint-epigenome.eu/), ref. 48 (GSE118189), ref. 24 (https://doi.org/10.5281/zenodo.4273999) and ref. 49 (Single Cell Portal accession SCP259).
Code availability
Scripts to reproduce analyses are available on GitHub (https://github.com/immunogenomics/sceQTL) and Zenodo (https://doi.org/10.5281/zenodo.6216850).
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Acknowledgements
We thank H. E. Randolph and L. B. Barreiro for sharing insights and access to and the influenza PBMC dataset. This work is supported in part by funding from the National Institutes of Health (U19AI111224, UH2AR067677, T32HG002295, T32AR007530, U01HG009379, R01AI049313, R01AR063759 and U01HG012009)
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A.N. and S.R. conceptualized the study. A.N. and S.R. designed the statistical and computational strategy and analysed the data, with input from A.L.P. S.A., K.I., C.V., T.A. and Y.L. conducted additional statistical analyses. J.I.B., Y.B., S.S. and D.B.M. designed and conducted immunoprofiling experiments. L.L. and M.B.M. recruited, phenotyped and collected samples from individuals. A.N. and S.R. wrote the initial manuscript. All authors contributed to writing and editing the final manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Memory T cell eQTLs.
a, (left) Box plot and (right) locus plot of rs3087243 eQTL for CTLA4 and b, rs716848 eQTL for ERAP2. Except where indicated, each point in box plots (panels a, b, d, f) represents the average log2(UMI counts + 1) across all cells in a donor (n = 259), grouped by genotype. Box plots show median (horizontal bar), 25th and 75th percentiles (lower and upper bounds of the box, respectively) and 1.5 times the IQR (or minimum/maximum values if they fall within that range; end of whiskers). Each locus plot shows the variants in a +/250kb window around the TSS plotted based on their nominal pseudobulk eQTL p value and genomic coordinate. The purple diamond is the lead variant and other variants are coloured based on their r2 with the lead variant in 1000 Genomes AMR (American ancestry, including Puerto Rican in Puerto Rico, Colombian in Medellín, Peruvian in Lima, and Mexican ancestry in Los Angeles). c, Pie charts of the allele frequencies at rs9927852 in 1000 Genomes EUR (European) and PEL populations. d, Box plot and locus plot of rs9927852 eQTL for MAF (n = 259). e, Number of eGenes with 1, 2, or 3+ independent eQTLs. f, Box plots for lead (rs9349050, left) secondary (rs6901281, middle), and secondary conditioned on lead (right) eQTL variants for MDGA1. In the box plot for rs6901281 conditioned on rs9349050, each point represents the average residual of log2(UMI counts + 1) after regressing out genotype at rs9349050 across all cells in a donor (n = 259). In the locus plot, the pink diamond is the secondary variant.
Extended Data Fig. 2 Concordance of published bulk T cell eQTLs and Peruvian (pseudo)bulk memory T cell eQTLs.
Z scores of β values from pseudobulk analysis of Peruvian dataset compared to z scores from a, inverse-variance-weighted meta-analysis of memory T cell subsets in DICE, b, BLUEPRINT bulk eQTL analysis of naïve CD4+ T cells, c, BLUEPRINT bulk eQTL analysis of monocytes, and d, BLUEPRINT bulk eQTL analysis of neutrophils. Each point represents an eGene/Peruvian lead variant pair significant in both datasets (q < 0.05).
Extended Data Fig. 3 Assessing the robustness of the single-cell Poisson model.
a, b, Dot plot of each memory-T-cell eGene based on Wald z score for total β (βG+βG×CD4) in PME interaction model of all cells and LRT-based statistic from a, pseudobulk model or b, Wald z score for βG from a PME model of only CD4+ cells. c, Proportion of eGenes with significant βG×CD4 under genotype permutation. Each dot represents the proportion significant at the given alpha threshold. d, e, Dot plot of memory-T-cell eGenes (n = 6,511) based on z score for cell state β (βCD4 or βCD8) and z score for cell state interaction β (βG×CD4 or βG×CD8) in d, LME and e, PME models. f, Box plot of sparsity of eGenes with significant CD4 interactions (q < 0.05) in GLMM or LMM. P value is from a two-sided Wilcoxon rank-sum test (nGLMM = 612 genes, nLMM = 1214 genes). Each point represents a gene and box plots show median (horizontal bar), 25th and 75th percentiles (lower and upper bounds of the box, respectively) and 1.5 times the IQR (or minimum/maximum values if they fall within that range; end of whiskers). g, rs2214911 eQTL for THAP5 and h, rs4135092 eQTL for TDG in CD4+ (orange) and CD8+ (beige) cells. Box plots show the eQTL effects as per-cell gene expression decreased from 100% to 50, 20, and 10 percent in CD4+ cells (left to right). Each point represents the average log2(UMI counts + 1) across all cells in the indicated subset of cells in a donor (n = 259), grouped by genotype. Box plots show median (horizontal bar), 25th and 75th percentiles (lower and upper bounds of the box, respectively) and 1.5 times the IQR (or minimum/maximum values if they fall within that range; end of whiskers).
Extended Data Fig. 4 Concordance between eQTL interactions with continuous and discrete states.
a, Distribution of CV1 scores for cells in CD4+ and CD8+ gates. Dashed line represents CV1 = 0. b-e, Dot plots of eGenes’ Wald z scores of genotype interactions with b, CV1 and CD4+, c, CV2 and Treg, d, CV1 and Treg, or CV2 and CD4+. Dashed line represents the identity line. Only eGenes with significant interaction (LRT q < 0.05) are plotted in b and c. Black dots represent eGenes significantly interacting with both continuous and discrete states, red dots are only significantly interacting with continuous state, and blue dots are only significantly interacting with discrete state. r is calculated as the Pearson correlation coefficient.
Extended Data Fig. 5 Cell-state-dependent eQTL interactions with continuous CVs.
a, Number of significant eGenes (LRT q < 0.05) detected by PME interaction models with increasing numbers of CVs. b, Number of eGenes with significant interaction with each CV in a multivariate PME model with 7 CVs. c, Heat map of the number of eGenes with significant interactions with pairs of CVs in the multivariate model. Boxes along the diagonal reflect the total number of eGenes interacting with the corresponding CV. d, Proportion of eGenes in c with the same direction of effect. e, eGenes with significant interactions with either CV1 or CV2 plotted based on Wald z scores with CV2 and CV1. f, eGenes with significant interactions with either CV1 or CV3 plotted based on Wald z scores with CV3 and CV1.
Extended Data Fig. 6 Opposite interaction directions for independent variants in a locus.
a, Bar plot of the number of eGenes for which the lead and secondary variants have opposite directions of interaction effect for each of the seven CVs. b, Comparison of interaction effect direction for lead and secondary variants for each of 436 eGenes with 2+ independent eQTLs. Each plot corresponds to one CV, from CV1 to CV7. Each point represents an eGene. For eGenes in grey, neither lead nor secondary variant was significantly dependent on the given CV state. For eGenes in black, either only one of the two eQTLs was significantly dependent on the CV, or both were significantly state-dependent with the same direction of effect. For eGenes in red, both lead and secondary variants were significantly state-dependent but with different directions of interaction with the given CV.
Extended Data Fig. 7 Enrichment of eQTLs in disease-associated variants.
a, Dot plot of traits from the GWAS catalogue plotted based on the −log(Fisher p value) and odds ratio of the enrichment test comparing the proportion of GWAS variants colocalizing with memory-T cell eQTLs for one trait compared to all other traits. Labelled traits have p < 10−5. b, Dot plot of traits from the GWAS catalogue plotted based on the number of GWAS variants overlapping state-dependent eQTLs compared to the total number of GWAS variants overlapping eQTLs. Overlap was defined by r2 > 0.5. The dashed line represents the overall proportion of state-dependent eQTLs (2,117/6,511 = 0.33) and labelled traits have Fisher p < 0.005. c, Dot plot of traits plotted based on the number of GWAS variants colocalizing with state-dependent eQTLs compared to the total number of GWAS variants colocalizing with eQTLs under the Bayesian coloc model. The dashed line represents the overall proportion of state-dependent eQTLs (2,117/6,511 = 0.33) and traits outlined in red are significantly enriched at Fisher p < 0.01. AD=Atopic dermatitis, AS=Ankylosing spondylitis, SLE=Systemic lupus erythematosus, T(1/2)D=Type 1/2 diabetes, RA=Rheumatoid arthritis, CAD=Coronary artery disease, MS=Multiple sclerosis, IBD=Inflammatory bowel disease, CD=Crohn’s disease.
Extended Data Fig. 8 Additional regulatory region enrichment of eQTL effects.
a, We calculated the enrichment of lead effects or independent secondary (conditional) effects in promoter or T-cell-specific regulatory regions. Analysis was limited to loci that were also significant eGenes in Peruvian analysis and where at least one variant had PIP > = 0.5. b, We calculated the enrichment of lead effects and c, secondary effects in ATAC-seq peaks from Calderon, et al. 2019. Peaks were binarized as present or not in each sample at a threshold of > 5 CPM. In all plots, the height of the grey bar corresponds to the average enrichment calculated across all loci containing a variant with PIP < 0.05, red bar corresponds to the subset with significant cell-state interaction (LRT q < 0.05 in multivariate model with 7 CVs), and the blue bar corresponds to the subset without significant cell-state interaction. Bars with p > = 0.001 (limit of 1,000 permutations) are labelled with their one-sided p-value and with corresponding asterisks. Each pair of interacting/non-interacting bars is labelled with a one-sided permutation p value for the difference (interacting minus non-interacting; only labelled if p < 0.1 for the ATAC-seq analysis). The grey dotted line indicates enrichment statistic = 1.
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Nathan, A., Asgari, S., Ishigaki, K. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 606, 120–128 (2022). https://doi.org/10.1038/s41586-022-04713-1
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DOI: https://doi.org/10.1038/s41586-022-04713-1
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