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Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells

Nature Genetics volume 54, pages 603–612 (2022)Cite this article

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Abstract

Genome-wide association studies (GWASs) have uncovered hundreds of autoimmune disease-associated loci; however, the causal genetic variants within each locus are mostly unknown. Here, we perform high-throughput allele-specific reporter assays to prioritize disease-associated variants for five autoimmune diseases. By examining variants that both promote allele-specific reporter expression and are located in accessible chromatin, we identify 60 putatively causal variants that enrich for statistically fine-mapped variants by up to 57.8-fold. We introduced the risk allele of a prioritized variant (rs72928038) into a human T cell line and deleted the orthologous sequence in mice, both resulting in reduced BACH2 expression. Naive CD8 T cells from mice containing the deletion had reduced expression of genes that suppress activation and maintain stemness and, upon acute viral infection, displayed greater propensity to become effector T cells. Our results represent an example of an effective approach for prioritizing variants and studying their physiologically relevant effects.

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Fig. 1: Prioritizing GWAS variants using high-throughput reporter assays in Jurkat T cells.
Fig. 2: T-GWAS emVars in T cell-accessible chromatin enrich highly for fine-mapped variants.
Fig. 3: Putative causal variants in a BACH2 intron and upstream of TAGAP.
Fig. 4: Base editing of the BACH2 emVar (rs72928038) reduces BACH2 expression.
Fig. 5: Naive T cells from mice with a deletion overlapping orthologous rs72928038 have reduced transcriptional features of stemness.
Fig. 6: Bach218del CD8 T cells are more prone to effector T cell differentiation after acute viral infection.

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Data availability

Data generated in this study from all manuscript figures are available in NCBI GEO (GSE197539). The 1000 Genomes Phase 3 reference panel was obtained from ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. DHS data across 733 samples were obtained from https://zenodo.org/record/3838751#.X_IA7-lKg6U. Histone chromatin immunoprecipitation sequencing data were downloaded from ENCODE (encodeproject.org); the specific files utilized are listed in Supplementary Table 20. CAGE-based enhancer annotations were downloaded from https://fantom.gsc.riken.jp/5/datafiles/latest/extra/Enhancers/. ChromHMM was obtained from https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/core_K27ac/jointModel/final/. HOCOMOCO TF position-weighted matrices were obtained from https://hocomoco11.autosome.ru/downloads_v10. ATAC-seq allelic skew data were obtained from Calderon et al.5 (https://www.nature.com/articles/s41588-019-0505-9; Supplementary Table 1, “significant_ASCs” tab). Chromatin accessibility QTLs were downloaded from Gate et al.65 (https://www.nature.com/articles/s41588-018-0156-2; Supplementary Table 6). DeltaSVM precomputed weights for naive CD4 T cells and Jurkat cells were obtained from http://www.beerlab.org/deltasvm_models/downloads/deltasvm_models_e2e.tar.gz. The EMBL GWAS catalog54 (https://www.ebi.ac.uk/gwas/) was accessed on 10 August 2020. T1D GWAS fine-mapping results were obtained from Onengut-Gumuscu et al.15 (https://www.nature.com/articles/ng.3245; Supplementary Table 1). pcHiC data were obtained from Javierre et al.18 (https://osf.io/u8tzp/). The ImmunoSigDB immunologic signatures database (v7.2) was downloaded from http://www.gsea-msigdb.org/gsea/msigdb/. Tscm Bach2 guide RNA perturbed mouse RNA-seq data were obtained from NCBI GEO (GSE152379). The GRCm38 mouse transcriptome index for Kallisto RNA-seq alignments were obtained from https://github.com/pachterlab/kallisto-transcriptome-indices/releases. The Bach218del (stock 35028) mouse strain is available at The Jackson Laboratory.

Code availability

Code supporting this manuscript is available at https://doi.org/10.5281/zenodo.6302248 (ref. 66) (MPRA analysis), https://doi.org/10.5281/zenodo.6299905 (ref. 67) (base-editing analysis) and https://doi.org/10.5281/zenodo.6038725 (ref. 68) (single-cell RNA-seq analysis). Data visualization, exploratory data analysis and processing were performed using R v3.6.2.

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Acknowledgements

We gratefully acknowledge the contribution of R. Maser and Genetic Engineering Technologies Service, J. Kelmenson and Transgenic Genotyping Service, W. Schott and Flow Cytometry Service and R. Lynch and Genome Technologies Service at The Jackson Laboratory; A. McCaffrey and J. Henderson at Trilink Biotechnologies; the Broad Institute vivarium, Flow Cytometry Core and Genomics Core; and the Benaroya Research Institute vivarium, Flow Cytometry Core, Genomics Core and Bioinformatics Core for expert assistance with the work described in this manuscript. We thank P. Gregersen and B. Diamond for providing genotyped human PBMCs and S. Malkiel for help with processing human PBMCs for ATAC-seq and review of the manuscript. We thank M. Dufort and S. Pribitzer for contributions to single-cell RNA-seq analysis. We thank B. Doughty for discussion on strategies for the base-editing experiments. We thank J.C. Ulirsch and V.M. Green for their critical review of the manuscript. This work is funded by National Institutes of Health grant R25NS065745 (M.H.G.); a Canadian Institutes of Health Research fellowship (C.G.D.) and National Institutes of Health grants K99HG009920. (C.G.D.) and T32AR007108 (M.M.L.); a Helen Hay Whitney postdoctoral fellowship (G.A.N.); EMBO Long-Term Fellowship ALTF486-2018 (M.G.); Cancer Research Institute/Bristol-Myers Squibb Fellow CRI2993 (M.G.); and National Institutes of Health grants U01AI142756 (D.R.L.), RM1HG009490 (D.R.L.), R01AI124693 (D.J.C), NHGRI R01HG008131 (N.H.), P50HG006193 (N.H.), R00HG008179 (R.T.), R35HG011329 (R.T.), R01AI151051 (R.T.), F32AI129249 (J.P.R.) and K22AI153648 (J.P.R.).

Author information

Author notes

  1. These authors contributed equally: Kousuke Mouri, Michael H. Guo.

Authors and Affiliations

  1. The Jackson Laboratory, Bar Harbor, ME, USA

    Kousuke Mouri & Ryan Tewhey

  2. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Michael H. Guo

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Michael H. Guo, Carl G. de Boer, Gregory A. Newby, Matteo Gentili, David R. Liu, Nir Hacohen & John P. Ray

  4. School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada

    Carl G. de Boer

  5. Fundamental Immunology, Benaroya Research Institute, Seattle, WA, USA

    Michelle M. Lissner & Daniel J. Campbell

  6. Division of Rheumatology, Department of Medicine, University of Washington, Seattle, WA, USA

    Michelle M. Lissner

  7. Systems Immunology, Benaroya Research Institute, Seattle, WA, USA

    Ingrid A. Harten, Hannah A. DeBerg, Winona F. Platt & John P. Ray

  8. Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Gregory A. Newby & David R. Liu

  9. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Gregory A. Newby & David R. Liu

  10. Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA

    Gregory A. Newby & David R. Liu

  11. Department of Immunology, University of Washington, Seattle, WA, USA

    Daniel J. Campbell & John P. Ray

  12. Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA

    Nir Hacohen

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Contributions

J.P.R., R.T., K.M. and M.H.G. conceived the study. J.P.R. performed MPRA, ATAC-seq on human CD4 T cells, base-editing experiments and luciferase, with the help of M.G. K.M. created the Bach218del mouse line and performed RNA-seq on mouse naive CD4 and CD8 T cells. M.M.L and I.A.H., with the help of W.F.P. and D.J.C., performed VSV-OVA in vivo mouse experiments. M.H.G, C.G.D., H.A.D., K.M., R.T. and J.P.R. performed data analysis. G.A.N. and D.R.L. provided essential base-editing reagents and designed base-editing strategies. J.P.R. and M.H.G. wrote the manuscript with the help of K.M., R.T. and N.H. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Nir Hacohen, Ryan Tewhey or John P. Ray.

Ethics declarations

Competing interests

G.A.N. and D.R.L. have filed patent applications on genome-editing agents. D.R.L. is a consultant and equity owner of Beam Therapeutics, Prime Medicine and Pairwise Plants, companies that use genome editing. N.H. holds equity in BioNTech and consults for Related Sciences. The other authors declare no competing interests.

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Peer review information

Nature Genetics thanks Patrick Gaffney, Naganari Ohkura, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 T-GWAS MPRA workflow and quality metrics.

a) Extended MPRA workflow. i) Oligonucleotide synthesis of elements containing variants and 200 bp surrounding genomic region; ii) barcode elements through PCR; iii) sequence barcoded elements to link barcodes to elements; iv) insert minimal promoter and GFP between element and barcode; v) transfect library into Jurkat T cells; vi) harvest RNA and pull down GFP mRNA; vii) create cDNA and plasmid sequencing libraries and sequence, comparing the prevalence of barcodes in cDNA to their prevalence in plasmid libraries; viii) compare alleles for differential reporter expression. b) Correlation of barcode prevalence in separate replicates of plasmid libraries (left), biological replicates of cDNA libraries (middle), and between a cDNA library and a plasmid library (right). c) Plot of MPRA expression fold change (log2 RNA/plasmid; y axis) against normalized plasmid tag counts for elements with putative cis-regulatory activity (active; blue) and inactive elements (black) within the T-GWAS library. d) Plot of MPRA expression fold change (log2 RNA/plasmid; y axis) against normalized plasmid tag counts for positive controls and negative controls compared to T-GWAS elements.

Extended Data Fig. 2 Variant locations relative to cis-regulatory features.

a) Location relative to TSSs of all MPRA tested variants, active elements (pCRE), and emVars. b) Enrichment of variants within pCREs (light blue) and emVars (dark blue) within chromHMM-defined genomic regions in human T cells. * for P < 0.05; *** P < 1.4 ×10−3 (Bonferroni-corrected for 36 independent tests). c) Functional enrichment of variants within pCREs and emVars. *** for P < 6.3 ×10−3 (nominal p-value threshold of 0.05 Bonferroni-corrected for 8 independent tests). d) Proportion of inactive element and pCRE variants and emVars that have allelic bias in ATAC-seq. e) Scatter plot comparing MPRA log2 allelic bias (y axis) with allelic bias in ATAC-seq from hematopoietic cells (x axis)5. Red dots are emVars (n = 5) and gray dots are pCRE variants (n = 45). f) Proportion of MPRA inactive and pCRE variants, and emVars that are chromatin accessibility QTLs (caQTLs) from T cells65. g) Scatter plot comparing caQTL effect size (beta; x axis) and MPRA log2 allelic bias (y axis). Red dots are emVars (n = 6) and gray dots are pCREs (n = 22). h) Scatter plot comparing deltaSVM score (x axis) with MPRA log2 allelic bias (y axis) (n = 278). i) Proportion of MPRA inactive and pCRE variants and emVars that overlap TF motifs. j) Scatter plot comparing allele-specific TF binding scores (y axis) and MPRA allelic bias (x axis) for emVars predicted to perturb TF binding (n = 11). Enrichment of emVars for TF ChIP-seq (-log10P on y axis). Calculations for (b and c) are risk ratios (see Methods) with Fisher’s exact test P values and Bonferroni correction (see Supplementary Tables 5 and 6 for exact P values). (d, f, and i) P values calculated using two-sided two proportions z test with no multiple comparisons adjustment. (e, g, h and j) P values are from linear regression F statistic. (k) P values are from a two-sided binomial test.

Extended Data Fig. 3 MPRA prioritizes variants in hundreds of loci.

a) Total number of GWAS loci tested (green) and number of loci with at least one emVar identified (orange) for each disease GWAS. b) Histogram of the number of emVars within each GWAS locus. c) Bar plot showing enrichment (from all loci tested) of DHS alone (left), emVars (middle), and emVars in T cell DHS (right) for PICS fine-mapped variants, with the minimum posterior probability threshold indicated on the x axis and fold enrichment shown on the y axis and bars with darker shades of blue as probability increases. Details of PICS enrichment results are shown in Supplementary Table 10. d) Bar plot showing enrichment of emVars for fine-mapped T1D GWAS loci from Onengut-Gumuscu et al. Statistical fine-mapping posterior probability threshold is shown on the x axis and fold enrichment shown on the y axis and with darker shades of blue as probability increases. For both c) and d), gray numbers below each bar show the number of emVars that are statistically fine-mapped at a given PICS probability threshold. Purple numbers above each bar show the -log10 of the enrichment P value. Enrichment in (c) and (d) were calculated as a risk ratio (see Methods), and P values were determined through a two-sided Fisher’s exact test.

Extended Data Fig. 4 Putative causal variants in the promoters of IRF5 and RASGRP.

a, b) Dotplots showing DHS signal (DHS score) and statistical significance of allelic bias (log10FDR of MPRA allelic bias) for MPRA variants in the region; all tested variants on haplotype (black), significant emVars in DHS (red) (top). Position of variants that are emVars, pCREs, variants tested in MPRA, and disease-associated variants for CD, MS, psoriasis, RA, T1D, and UC from the GWAS Catalog54 (middle). Genes in the locus are shown along with chromatin accessibility profiles (from in Jurkat and specific T cell subsets) and T cell pcHiC loops anchored on the region containing the emVar. Gray line depicts position of the prioritized emVar position with respect to all data types. Statistical significance of allelic biases in (a) and (b) were calculated using a paired Student’s two-sided t-test as described in Methods.

Extended Data Fig. 5 rs72928038 reduces luciferase reporter expression and contacts the BACH2 promoter.

a) Luciferase reporter activity of rs72928038 alleles (n = 3, two independent experiments). b) Promoter capture HiC (pcHiC18) conducted in naïve T cells anchored on the region containing the rs72928038. For (a), statistical significance was calculated using a Student’s two-sided t-test, central tendency is shown as median, and all points are plotted to show dispersion with error bars representing standard deviation. (c) i) Schematic of installing rs72928038 using the evoCDAmax cytosine base editor, achieving 95% base editing. We also created a second condition, separately combining the 95% base-edited cells with WT base-edited cells (combined 50/50) post-nucleofection. (ii) We performed PrimeFlow, staining BACH2 mRNA, and sorted cells based on high and low BACH2 expression. iii) We sequenced the amplicon containing rs72928038 in all sorted populations. iv) Mock data of expected ratios of risk vs. nonrisk alleles in high and low bins of BACH2 expression. If rs72928038 reduces BACH2 expression, one would anticipate the edited risk allele to enrich in low BACH2 expression bins.

Extended Data Fig. 6 Orthologous rs72928038 region binds STATs and ETS1 and deletion of the region in CD8 T cells partially recapitulates transcriptional phenotypes of Bach2-deficient Tscms.

a) STAT and ETS1 TF ChIP-seq peaks30 overlapping mouse rs72928038 ortholog. b) PCA on RNA-seq of naïve CD4 T cells from WT and Bach218del mice. c) Bach2 expression in WT and Bach218del naïve CD4 T cells from RNA-seq normalized counts. d) GSEA enrichment of Bach218del vs. WT naïve CD8 (left) and CD4 (right) T cells. Depicted GSEA results for a gene set derived from genes upregulated in empty vector vs. Bach2 sgRNA-transduced Tscms (d left and right) and genes upregulated in Bach2 sgRNA-transduced Tscms vs. empty vector (d middle). Full GSEA results are shown in Supplementary Table 16. e) Expression of genes in Tscms that have been transduced with empty vector or a Bach2 sgRNA (same experiment as in d) for differentially expressed genes in Bach218del vs. WT naïve CD8 T cells. Genes upregulated in Bach218del T cells as compared to WT are on the left and downregulated are on the right. Normalized enrichment score (NES) in (d) was calculated based on observed enrichment as compared to enrichments from permuted data as previously described and statistical significance shown as the false discovery rate (q). P value in (b) was determined by a two-sided Wald test from normalized counts and adjusted P value was determined using Benjamini Hochberg adjustment. For (b-e), n = 3 independent animals.

Extended Data Fig. 7 Bach218del CD8 T cells have reduced memory-precursor and enhancer effector phenotypes.

(a–e) Flow cytometry histograms depicting WT (orange) and Bach218del (turquoise) expression of CD62L (a), Eomes (b), CD127 (c), KLRG1 (d), and CX3CR1 (e). For (a–e), n = 10 independent animals per experiment analyzed over 2 experiments, and statistical significance was calculated using a Student’s paired two-sided t-test with no adjustments for multiple testing.

Extended Data Fig. 8 Single-cell RNA-seq of WT and Bach218del CD8 T cells at 8 dpi with VSV-OVA.

a) UMAP plots depicting the composition of cells from different pools (left) and between mice (right). b) Heatmap depicting the top genes representing each cluster in Fig. 6f. c) UMAP plot indicating regions from Fig. 6h with significant enrichment of WT or Bach218del cells. d) Line plots depicting the relative frequencies of WT and Bach218del cells, for each of 5 replicates/group, within each cluster depicted in Fig. 6f. Statistical significance for (c) was assessed using a two-sided permutation test with N = 5,000 permutations, identifying cellular enrichment outside of the 99% confidence interval (see methods). (d) was calculated using a Student’s paired two-sided t-test with no adjustments for multiple testing.

Extended Data Fig. 9 Flow cytometry gating strategy for Bach218del and WT OTI cotransfer VSV-OVA experiment.

Cells are gated on the lymphocyte population and single cells, followed by gating out dead cells, gating on activated CD8 T cells, and identifying cells from each genotype using CD45.1.2 (WT) and CD45.2 (Bach218del). Cells were further assessed for their prevalence in effector (KLRG1 + ) or memory precursor (CD127 + ) populations.

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Mouri, K., Guo, M.H., de Boer, C.G. et al. Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells. Nat Genet 54, 603–612 (2022). https://doi.org/10.1038/s41588-022-01056-5

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