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Genetic determinants of co-accessible chromatin regions in activated T cells across humans

Nature Genetics volume 50, pages 1140–1150 (2018)Cite this article

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Abstract

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression.

The vast majority of disease-associated loci identified through genome-wide association studies (GWAS)1,2,3 are located in non-coding regions of the genome, often distant from the nearest gene4. Quantitative trait loci (QTLs) studies that associate genetic variants with molecular traits provide a framework for assessing the gene regulatory potential of disease-associated variants. For example, a statistically significant number of GWAS loci are associated with gene expression (expression QTLs—eQTLs) across diverse cell types and states5,6,7,8,9,10, implicating gene regulation in determining disease risk11,12.

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Fig. 1: Changes in chromatin state in human T-cell activation.
Fig. 2: Changes in TF enrichment in response to T-cell activation.
Fig. 3: Inter-individual chromatin co-accessibility.
Fig. 4: Genetic variants that affect chromatin states in human T-cell activation.
Fig. 5: Genetic determinants of co-accessible peaks.
Fig. 6: Association of chromatin accessibility and gene expression.

References

  1. McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356–369 (2008).

    CAS  PubMed  Google Scholar 

  2. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Hirschhorn, J. N. & Daly, M. J. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95–108 (2005).

    CAS  PubMed  Google Scholar 

  4. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Stranger, B. E. et al. Population genomics of human gene expression. Nat. Genet. 39, 1217–1224 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. Ye, C. J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).

    PubMed  PubMed Central  Google Scholar 

  11. Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429.e19 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    PubMed Central  Google Scholar 

  14. Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Roadmap Epigenomics Consortium, et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Google Scholar 

  18. Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    CAS  PubMed  Google Scholar 

  19. Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kasowski, M. et al. Extensive variation in chromatin states across humans. Science 342, 750–752 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. McVicker, G. et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747–749 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Kilpinen, H. et al. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science 342, 744–747 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Waszak, S. M. et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 162, 1039–1050 (2015).

    CAS  PubMed  Google Scholar 

  24. Elinav, E. et al. Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat. Rev. Cancer 13, 759–771 (2013).

    CAS  PubMed  Google Scholar 

  25. Donath, M. Y. & Shoelson, S. E. Type 2 diabetes as an inflammatory disease. Nat. Rev. Immunol. 11, 98–107 (2011).

    CAS  PubMed  Google Scholar 

  26. Ohashi, P. S. T-cell signalling and autoimmunity: molecular mechanisms of disease. Nat. Rev. Immunol. 2, 427–438 (2002).

    CAS  PubMed  Google Scholar 

  27. Kronenberg, M. & Rudensky, A. Regulation of immunity by self-reactive T cells. Nature 435, 598–604 (2005).

    CAS  PubMed  Google Scholar 

  28. Speiser, D. E., Ho, P. C. & Verdeil, G. Regulatory circuits of T cell function in cancer. Nat. Rev. Immunol. 16, 599–611 (2016).

    CAS  PubMed  Google Scholar 

  29. Restifo, N. P., Dudley, M. E. & Rosenberg, S. A. Adoptive immunotherapy for cancer: harnessing the T cell response. Nat. Rev. Immunol. 12, 269–281 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Belkaid, Y. & Rouse, B. T. Natural regulatory T cells in infectious disease. Nat. Immunol. 6, 353–360 (2005).

    CAS  PubMed  Google Scholar 

  31. Feuerer, M., Hill, J. A., Mathis, D. & Benoist, C. Foxp3+ regulatory T cells: differentiation, specification, subphenotypes. Nat. Immunol. 10, 689–695 (2009).

    CAS  PubMed  Google Scholar 

  32. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Kurachi, M. et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 15, 373–383 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Li, P. et al. BATF-JUN is critical for IRF4-mediated transcription in T cells. Nature 490, 543–546 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Murphy, T. L., Tussiwand, R. & Murphy, K. M. Specificity through cooperation: BATF–IRF interactions control immune-regulatory networks. Nat. Rev. Immunol. 13, 499–509 (2013).

    CAS  PubMed  Google Scholar 

  36. Cauchy, P. et al. Dynamic recruitment of Ets1 to both nucleosome-occupied and -depleted enhancer regions mediates a transcriptional program switch during early T-cell differentiation. Nucleic Acids Res. 44, 3567–3585 (2016).

    CAS  PubMed  Google Scholar 

  37. Samstein, R. M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Hollenhorst, P. C. et al. DNA specificity determinants associate with distinct transcription factor functions. PLoS Genet. 5, e1000778 (2009).

    PubMed  PubMed Central  Google Scholar 

  39. Chen, X. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat. Methods 13, 1013–1020 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    CAS  PubMed  Google Scholar 

  42. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kumasaka, N., Knights, A. J. & Gaffney, D. J. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48, 206–213 (2016).

    CAS  PubMed  Google Scholar 

  44. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Ghandi, M., Lee, D., Mohammad-Noori, M. & Beer, M. A. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol. 10, e1003711 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. Ghandi M, et al. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics 32, 2205–2207 (2016).

  48. Lee, D. et al. A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47, 955–961 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Hou, C., Zhao, H., Tanimoto, K. & Dean, A. CTCF-dependent enhancer-blocking by alternative chromatin loop formation. Proc. Natl Acad. Sci. USA 105, 20398–20403 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Phillips, J. E. & Corces, V. G. CTCF: master weaver of the genome. Cell 137, 1194–1211 (2009).

    PubMed  PubMed Central  Google Scholar 

  51. Splinter, E. et al. CTCF mediates long-range chromatin looping and local histone modification in the beta-globin locus. Genes Dev. 20, 2349–2354 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Delisle, J. S. et al. The TGF-beta–Smad3 pathway inhibits CD28-dependent cell growth and proliferation of CD4 T cells. Genes Immun. 14, 115–126 (2013).

    CAS  PubMed  Google Scholar 

  54. Enjyoji, K. et al. Targeted disruption of cd39/ATP diphosphohydrolase results in disordered hemostasis and thromboregulation. Nat. Med. 5, 1010–1017 (1999).

    CAS  PubMed  Google Scholar 

  55. Deaglio, S. et al. Adenosine generation catalyzed by CD39 and CD73 expressed on regulatory T cells mediates immune suppression. J. Exp. Med. 204, 1257–1265 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Plesner, L. Ecto-ATPases: identities and functions. Int. Rev. Cytol. 158, 141–214 (1995).

    CAS  PubMed  Google Scholar 

  57. Sun, X. et al. CD39/ENTPD1 expression by CD4+ Foxp3+ regulatory T cells promotes hepatic metastatic tumor growth in mice. Gastroenterology 139, 1030–1040 (2010).

    CAS  PubMed  Google Scholar 

  58. Hicks, R. & Tingley, D. Causal mediation analysis. Stata J. 11, 605–619 (2011).

  59. Fan, Y. Y. et al. Characterization of an arachidonic acid-deficient (Fads1 knockout) mouse model. J. Lipid Res. 53, 1287–1295 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Barrie, A. et al. Prostaglandin E2 and IL-23 plus IL-1beta differentially regulate the Th1/Th17 immune response of human CD161(+) CD4(+) memory T cells. Clin. Transl. Sci. 4, 268–273 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Sakata, D., Yao, C. & Narumiya, S. Prostaglandin E2, an immunoactivator. J. Pharmacol. Sci. 112, 1–5 (2010).

    CAS  PubMed  Google Scholar 

  62. Stroud, C. K. et al. Disruption of FADS2 gene in mice impairs male reproduction and causes dermal and intestinal ulceration. J. Lipid Res. 50, 1870–1880 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Schmidt, E. M. et al. GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Marigorta, U. M. et al. Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn’s disease. Nat. Genet. 49, 1517–1521 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    CAS  PubMed  Google Scholar 

  69. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    PubMed  PubMed Central  Google Scholar 

  71. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  72. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  74. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the ImmVar participants. We would like to thank J. Buenrostro for critical reading of the manuscript and advice on ATAC-seq analysis, J. Pfiffner and C. Fulco for initial experimental help with ATAC-seq, A. Schep for ATAC-seq nucleosome free caller, N. Asinovski and H.-k. Kwon for help setting up primary T cell cultures and members of the Regev and Ye laboratories for discussions. R.E.G. and C.J.Y. are supported by NIH R01-AR071522 to C.J.Y. M.A.B. and K.L.H. are supported by NIH HG007348 to M.A.B.; H.Y.C. is supported by NIH grant P50-HG007735; C.S.C. is supported by the NIH through a Ruth L. Kirschstein National Research Service Award (F32-DK096822). This work was supported by the Klarman Cell Observatory at the Broad Institute. A.R. is a Howard Hughes Medical Institute Investigator.

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Author notes

  1. These authors contributed equally: Rachel E. Gate, Christine S. Cheng

Authors and Affiliations

  1. Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

    Rachel E. Gate, Dmytro Lituiev, Meena Subramaniam & Chun J. Ye

  2. Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    Rachel E. Gate, M. Grace Gordon & Meena Subramaniam

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

    Christine S. Cheng, Atsede Siba, Marcin Tabaka, Ivo Wortman, Philip L. De Jager & Aviv Regev

  4. Department of Biology, Boston University, Boston, MA, USA

    Christine S. Cheng

  5. Department of Molecular and Human Genetics, the Center for Genome Architecture, Baylor College of Medicine, Houston, TX, USA

    Aviva P. Aiden, Ido Machol, Muhammad Shamim, Su-Chen Huang, Neva C. Durand & Erez Lieberman Aiden

  6. Department of Bioengineering, Rice University, Houston, TX, USA

    Aviva P. Aiden

  7. Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA

    Muhammad Shamim & Erez Lieberman Aiden

  8. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Kendrick L. Hougen & Michael A. Beer

  9. Division of Immunology, Department of Microbiology and Immunology, Harvard Medical School, Boston, MA, USA

    Ting Feng & Christophe Benoist

  10. Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology and Psychiatry, Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Philip L. De Jager

  11. Harvard Medical School, Boston, MA, USA

    Philip L. De Jager

  12. Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA

    Howard Y. Chang

  13. Department of Computer Science, Rice University, Houston, TX, USA

    Erez Lieberman Aiden

  14. Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA

    Erez Lieberman Aiden

  15. Center for Theoretical Biological Physics, Rice University, Houston, TX, USA

    Erez Lieberman Aiden

  16. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    Michael A. Beer

  17. Institute of Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA

    Chun J. Ye

  18. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Chun J. Ye

  19. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

    Chun J. Ye

  20. Howard Hughes Medical Institute, Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aviv Regev

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Contributions

A.R., C.J.Y. and C.S.C. conceived this project. C.S.C. and A.S. performed ATAC-seq and RNA-seq assays. I.W. cultured T cells and collected the fixed pellet for Hi-C assay. A.P.A., I.M., M. Shamim, S.-C.H., N.C.D. and E.L.A. performed and analyzed the Hi-C data set. R.E.G., M.T., D.L., M.G.G. and M. Subramaniam analyzed the ATAC-seq and RNA-seq data sets. K.L.H. and M.A.B. additionally analyzed the ATAC-seq data set. R.E.G. additionally analyzed the Hi-C data set. T.F., P.L.D.J. and C.B. provided the patient samples. H.Y.C. provided helpful comments and discussion. R.E.G., C.S.C., C.J.Y. and A.R. wrote the manuscript.

Corresponding authors

Correspondence to Christine S. Cheng, Chun J. Ye or Aviv Regev.

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Competing interests

A.R. is an SAB member of ThermoFisher Scientific, Syros Pharmaceuticals and Driver group and a founder of Celsius Therapeutics.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19

Reporting Summary

Supplementary Table 1

Stimulation response

Supplementary Table 2

Hi-C

Supplementary Table 3

Covariates and mismatches

Supplementary Table 4

PC correlation to chromatin accessibility and gene expression

Supplementary Table 5

Co-accessibility

Supplementary Table 6

ATAC-QTLs

Supplementary Table 7

ATAC heritability

Supplementary Table 8

eQTLs

Supplementary Table 9

Expression heritability

Supplementary Table 10

Stimulation response

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Gate, R.E., Cheng, C.S., Aiden, A.P. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat Genet 50, 1140–1150 (2018). https://doi.org/10.1038/s41588-018-0156-2

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