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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

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

  2. 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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 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).

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 15.

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

  16. 16.

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

  17. 17.

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

  18. 18.

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

  19. 19.

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

  20. 20.

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

  21. 21.

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

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 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).

  30. 30.

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

  31. 31.

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

  32. 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).

  33. 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).

  34. 34.

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

  35. 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).

  36. 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).

  37. 37.

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

  38. 38.

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

  39. 39.

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

  40. 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).

  41. 41.

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

  42. 42.

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

  43. 43.

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

  44. 44.

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

  45. 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).

  46. 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).

  47. 47.

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

  48. 48.

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

  49. 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).

  50. 50.

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

  51. 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).

  52. 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).

  53. 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).

  54. 54.

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

  55. 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).

  56. 56.

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

  57. 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).

  58. 58.

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

  59. 59.

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

  60. 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).

  61. 61.

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

  62. 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).

  63. 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).

  64. 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).

  65. 65.

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

  66. 66.

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

  67. 67.

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

  68. 68.

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

  69. 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).

  70. 70.

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

  71. 71.

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

  72. 72.

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

  73. 73.

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

  74. 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).

  75. 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).

  76. 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).

  77. 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).

  78. 78.

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

  79. 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).

  80. 80.

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

Download references


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.

Author information

Author notes

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


  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


  1. Search for Rachel E. Gate in:

  2. Search for Christine S. Cheng in:

  3. Search for Aviva P. Aiden in:

  4. Search for Atsede Siba in:

  5. Search for Marcin Tabaka in:

  6. Search for Dmytro Lituiev in:

  7. Search for Ido Machol in:

  8. Search for M. Grace Gordon in:

  9. Search for Meena Subramaniam in:

  10. Search for Muhammad Shamim in:

  11. Search for Kendrick L. Hougen in:

  12. Search for Ivo Wortman in:

  13. Search for Su-Chen Huang in:

  14. Search for Neva C. Durand in:

  15. Search for Ting Feng in:

  16. Search for Philip L. De Jager in:

  17. Search for Howard Y. Chang in:

  18. Search for Erez Lieberman Aiden in:

  19. Search for Christophe Benoist in:

  20. Search for Michael A. Beer in:

  21. Search for Chun J. Ye in:

  22. Search for Aviv Regev in:


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.

Competing interests

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

Corresponding authors

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

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–19

  2. Reporting Summary

  3. Supplementary Table 1

    Stimulation response

  4. Supplementary Table 2


  5. Supplementary Table 3

    Covariates and mismatches

  6. Supplementary Table 4

    PC correlation to chromatin accessibility and gene expression

  7. Supplementary Table 5


  8. Supplementary Table 6


  9. Supplementary Table 7

    ATAC heritability

  10. Supplementary Table 8


  11. Supplementary Table 9

    Expression heritability

  12. Supplementary Table 10

    Stimulation response

About this article

Publication history




Issue Date