Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk

Nature Genetics volume 54, pages 804–816 (2022)Cite this article

Subjects

An Author Correction to this article was published on 29 June 2022

This article has been updated

Abstract

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across cell types. Genome-wide association studies have identified over 200 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements. Here, we applied single-nucleus assay for transposase-accessible chromatin with sequencing to profile 28,316 nuclei across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell-type-specific elements and transcription factors, and prioritized functional CAD risk variants. We identified elements in smooth muscle cell transition states (for example, fibromyocytes) and functional variants predicted to alter smooth muscle cell- and macrophage-specific regulation of MRAS (3q22) and LIPA (10q23), respectively. We further nominated key driver transcription factors such as PRDM16 and TBX2. Together, this single-nucleus atlas provides a critical step towards interpreting regulatory mechanisms across the continuum of CAD risk.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: snATAC-seq profiling of 28,316 nuclei from human coronary arteries reveals cell-type chromatin accessibility patterns across 41 individuals.
Fig. 2: Human coronary artery cell types display distinct gene regulatory processes.
Fig. 3: Subcluster analysis of SMC accessible chromatin identifies fibromyocyte regulatory programs.
Fig. 4: Single-nucleus chromatin accessibility further resolves mechanisms for functional CAD GWAS loci.
Fig. 5: Identification of genetic variants that regulate chromatin accessibility within coronary artery cell types.
Fig. 6: PRDM16 is a CAD-associated key driver transcriptional regulator in SMCs.

Similar content being viewed by others

Data availability

All raw and processed single-nucleus chromatin accessibility sequencing datasets are made available on the Gene Expression Omnibus (GEO) database (accessions codes GSE175621 and GSE188422). The processed and analyzed snATAC-seq data will also be made available on the PlaqView single-cell data portal (https://www.plaqview.com). All caQTL data are available in the Supplementary Data. Low-pass whole-genome sequencing-based genotyping data are available on dbGaP (accession code phs002855.v1.p1). The human coronary artery scRNA-seq dataset we used in this study from Wirka et al.7 is available through GEO (accession code GSE131778). The mouse atherosclerosis scRNA-seq dataset from Pan et al.9 is available through GEO (accession code GSE155513). The reprocessed and analyzed human and mouse datasets are also available on PlaqView. Gene expression levels, expression quantitative trait locus (eQTL) data and eQTL boxplots were obtained from the Genotype-Tissue Expression (GTEx) v.8 portal website (https://www.gtexportal.org), GEO and HeartBioPortal (www.heartbioportal.com). Gene regulatory network analysis data from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) are available at http://starnet.mssm.edu.

Code availability

Our results make use of published software tools with detailed parameters included in the Methods. All custom scripts used to generate these results are available on GitHub (https://github.com/MillerLab-CPHG/Coronary_snATAC and https://github.com/MillerLab-CPHG/coronary_histology).

Change history

  • 29 June 2022

    In the version of this article initially published, the Reporting Summary linked to this article was incorrect and has now been replaced.

  • 29 June 2022

    A Correction to this paper has been published: https://doi.org/10.1038/s41588-022-01142-8

References

  1. Libby, P. Inflammation in atherosclerosis. Nature 420, 868–874 (2002).

    Article  CAS  PubMed  Google Scholar 

  2. Souilhol, C., Harmsen, M. C., Evans, P. C. & Krenning, G. Endothelial-mesenchymal transition in atherosclerosis. Cardiovasc. Res. 114, 565–577 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Stary, H. C. et al. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation 92, 1355–1374 (1995).

    Article  CAS  PubMed  Google Scholar 

  4. Winkels, H. et al. Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry. Circ. Res. 122, 1675–1688 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cochain, C. et al. Single-cell RNA-seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis. Circ. Res. 122, 1661–1674 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Fernandez, D. M. et al. Single-cell immune landscape of human atherosclerotic plaques. Nat. Med. 25, 1576–1588 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wirka, R. C. et al. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nat. Med. 25, 1280–1289 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Depuydt, M. A. C. et al. Microanatomy of the human atherosclerotic plaque by single-cell transcriptomics. Circ. Res. 127, 1437–1455 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Pan, H. et al. Single-cell genomics reveals a novel cell state during smooth muscle cell phenotypic switching and potential therapeutic targets for atherosclerosis in mouse and human. Circulation 142, 2060–2075 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Alencar, G. F. et al. Stem cell pluripotency genes Klf4 and Oct4 regulate complex SMC phenotypic changes critical in late-stage atherosclerotic lesion pathogenesis. Circulation 142, 2045–2059 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wang, Y. et al. Clonally expanding smooth muscle cells promote atherosclerosis by escaping efferocytosis and activating the complement cascade. Proc. Natl Acad. Sci. USA 117, 15818–15826 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nikpay, M. et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. Koyama, S. et al. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat. Genet. 52, 1169–1177 (2020).

    Article  CAS  PubMed  Google Scholar 

  16. Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Preprint at medRxiv https://doi.org/10.1101/2021.05.24.21257377 (2021).

  17. Erdmann, J., Kessler, T., Munoz Venegas, L. & Schunkert, H. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc. Res. 114, 1241–1257 (2018).

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  22. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Miller, C. L. et al. Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci. Nat. Commun. 7, 12092 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liu, B., Gloudemans, M. J., Rao, A. S., Ingelsson, E. & Montgomery, S. B. Abundant associations with gene expression complicate GWAS follow-up. Nat. Genet. 51, 768–769 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhao, Q. et al. Molecular mechanisms of coronary disease revealed using quantitative trait loci for TCF21 binding, chromatin accessibility, and chromosomal looping. Genome Biol. 21, 135 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Stolze, L. K. et al. Systems genetics in human endothelial cells identifies non-coding variants modifying enhancers, expression, and complex disease traits. Am. J. Hum. Genet. 106, 748–763 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chiou, J. et al. Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk. Nat. Genet. 53, 455–466 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594, 398–402 (2021).

    Article  CAS  PubMed  Google Scholar 

  33. Hocker, J. D. et al. Cardiac cell type-specific gene regulatory programs and disease risk association. Sci. Adv. 7, eabf1444 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Muto, Y. et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat. Commun. 12, 2190 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rai, V. et al. Single-cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol. Metab. 32, 109–121 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Ziffra, R. S. et al. Single-cell epigenomics reveals mechanisms of human cortical development. Nature 598, 205–213 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Örd, T. et al. Single-cell epigenomics and functional fine-mapping of atherosclerosis GWAS loci. Circ. Res. 129, 240–258 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Virmani, R., Burke, A. P., Farb, A. & Kolodgie, F. D. Pathology of the vulnerable plaque. J. Am. Coll. Cardiol. 47, C13–C18 (2006).

    Article  CAS  PubMed  Google Scholar 

  44. Mulligan-Kehoe, M. J. & Simons, M. Vasa vasorum in normal and diseased arteries. Circulation 129, 2557–2566 (2014).

    Article  PubMed  Google Scholar 

  45. Virmani, R. et al. Atherosclerotic plaque progression and vulnerability to rupture: angiogenesis as a source of intraplaque hemorrhage. Arterioscler. Thromb. Vasc. Biol. 25, 2054–2061 (2005).

    Article  CAS  PubMed  Google Scholar 

  46. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Creemers, E. E., Sutherland, L. B., Oh, J., Barbosa, A. C. & Olson, E. N. Coactivation of MEF2 by the SAP domain proteins myocardin and MASTR. Mol. Cell 23, 83–96 (2006).

    Article  CAS  PubMed  Google Scholar 

  48. Maeda, T., Gupta, M. P. & Stewart, A. F. R. TEF-1 and MEF2 transcription factors interact to regulate muscle-specific promoters. Biochem. Biophys. Res. Commun. 294, 791–797 (2002).

    Article  CAS  PubMed  Google Scholar 

  49. Almontashiri, N. A. M. et al. 9p21.3 coronary artery disease risk variants disrupt TEAD transcription factor-dependent transforming growth factor β regulation of p16 expression in human aortic smooth muscle cells. Circulation 132, 1969–1978 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Yoshida, T. et al. Myocardin is a key regulator of CArG-dependent transcription of multiple smooth muscle marker genes. Circ. Res. 92, 856–864 (2003).

    Article  CAS  PubMed  Google Scholar 

  51. Du, K. L. et al. Myocardin is a critical serum response factor cofactor in the transcriptional program regulating smooth muscle cell differentiation. Mol. Cell. Biol. 23, 2425–2437 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Chen, J., Kitchen, C. M., Streb, J. W. & Miano, J. M. Myocardin: a component of a molecular switch for smooth muscle differentiation. J. Mol. Cell. Cardiol. 34, 1345–1356 (2002).

    Article  CAS  PubMed  Google Scholar 

  53. Wang, D.-Z. et al. Potentiation of serum response factor activity by a family of myocardin-related transcription factors. Proc. Natl Acad. Sci. USA 99, 14855–14860 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Meadows, S. M., Myers, C. T. & Krieg, P. A. Regulation of endothelial cell development by ETS transcription factors. Semin. Cell Dev. Biol. 22, 976–984 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Stamatovic, S. M., Keep, R. F., Mostarica-Stojkovic, M. & Andjelkovic, A. V. CCL2 regulates angiogenesis via activation of Ets-1 transcription factor. J. Immunol. 177, 2651–2661 (2006).

    Article  CAS  PubMed  Google Scholar 

  56. Zhang, D. E., Hetherington, C. J., Chen, H. M. & Tenen, D. G. The macrophage transcription factor PU.1 directs tissue-specific expression of the macrophage colony-stimulating factor receptor. Mol. Cell. Biol. 14, 373–381 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Cui, L. et al. Activation of JUN in fibroblasts promotes pro-fibrotic programme and modulates protective immunity. Nat. Commun. 11, 2795 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kitoh, A. et al. Indispensable role of the Runx1-Cbfβ transcription complex for in vivo-suppressive function of FoxP3+ regulatory T cells. Immunity 31, 609–620 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Ono, M. et al. Foxp3 controls regulatory T-cell function by interacting with AML1/Runx1. Nature 446, 685–689 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. Masuda, A. et al. Essential role of GATA transcriptional factors in the activation of mast cells. J. Immunol. 178, 360–368 (2007).

    Article  CAS  PubMed  Google Scholar 

  61. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Nagao, M. et al. Coronary disease-associated gene TCF21 inhibits smooth muscle cell differentiation by blocking the myocardin-serum response factor pathway. Circ. Res. 126, 517–529 (2020).

    Article  CAS  PubMed  Google Scholar 

  63. Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Tabas, I. & Lichtman, A. H. Monocyte-macrophages and T cells in atherosclerosis. Immunity 47, 621–634 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Farrugia, A. J. et al. CDC42EP5/BORG3 modulates SEPT9 to promote actomyosin function, migration, and invasion. J. Cell Biol. 219, e201912159 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Nyati, K. K., Agarwal, R. G., Sharma, P. & Kishimoto, T. Arid5a regulation and the roles of Arid5a in the inflammatory response and disease. Front. Immunol. 10, 2790 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lin, M.-E., Chen, T., Leaf, E. M., Speer, M. Y. & Giachelli, C. M. Runx2 expression in smooth muscle cells is required for arterial medial calcification in mice. Am. J. Pathol. 185, 1958–1969 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lin, M.-E. et al. Runx2 deletion in smooth muscle cells inhibits vascular osteochondrogenesis and calcification but not atherosclerotic lesion formation. Cardiovasc. Res. 112, 606–616 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Nott, A. et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Xu, S. et al. The novel coronary artery disease risk gene JCAD/KIAA1462 promotes endothelial dysfunction and atherosclerosis. Eur. Heart J. 40, 2398–2408 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Beaudoin, M. et al. Myocardial infarction-associated SNP at 6p24 interferes with MEF2 binding and associates with PHACTR1 expression levels in human coronary arteries. Arterioscler. Thromb. Vasc. Biol. 35, 1472–1479 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Nanda, V. et al. Functional regulatory mechanism of smooth muscle cell-restricted LMOD1 coronary artery disease locus. PLoS Genet. 14, e1007755 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Benaglio, P. et al. Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex trait variants using single nucleus ATAC-seq. Preprint at bioRxiv https://doi.org/10.1101/2020.12.03.387894 (2020).

  74. Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51, 1494–1505 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet. 50, 1140–1150 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bryois, J. et al. Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia. Nat. Commun. 9, 3121 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Khetan, S. et al. Type 2 diabetes-associated genetic variants regulate chromatin accessibility in human islets. Diabetes 67, 2466–2477 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Currin, K. W. et al. Genetic effects on liver chromatin accessibility identify disease regulatory variants. Am. J. Hum. Genet. 108, 1169–1189 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  80. Liu, B. et al. Genetic regulatory mechanisms of smooth muscle cells map to coronary artery disease risk loci. Am. J. Hum. Genet. 103, 377–388 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Munz, M. et al. Qtlizer: comprehensive QTL annotation of GWAS results. Sci. Rep. 10, 20417 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Shrikumar, A., Prakash, E. & Kundaje, A. GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs. Bioinformatics 35, i173–i182 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 593, 238–243 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Koplev, S. et al. A mechanistic framework for cardiometabolic and coronary artery diseases. Nat. Cardiovasc. Res. 1, 85–100 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Higgins, E. M. et al. Elucidation of MRAS-mediated Noonan syndrome with cardiac hypertrophy. JCI Insight 2, e91225 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Seale, P. et al. PRDM16 controls a brown fat/skeletal muscle switch. Nature 454, 961–967 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Seale, P. et al. Transcriptional control of brown fat determination by PRDM16. Cell Metab. 6, 38–54 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Kajimura, S. et al. Initiation of myoblast to brown fat switch by a PRDM16-C/EBP-β transcriptional complex. Nature 460, 1154–1158 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Liu, D. et al. PRDM16 upregulation induced by microRNA-448 inhibition alleviates atherosclerosis via the TGF-β signaling pathway inactivation. Front. Physiol. 11, 846 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Warner, D. R. et al. PRDM16/MEL1: a novel Smad binding protein expressed in murine embryonic orofacial tissue. Biochim. Biophys. Acta 1773, 814–820 (2007).

    Article  CAS  PubMed  Google Scholar 

  93. Takahata, M. et al. SKI and MEL1 cooperate to inhibit transforming growth factor-β signal in gastric cancer cells. J. Biol. Chem. 284, 3334–3344 (2009).

    Article  CAS  PubMed  Google Scholar 

  94. Craps, S. et al. Prdm16 supports arterial flow recovery by maintaining endothelial function. Circ. Res. 129, 63–77 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Barron, M. R. et al. Serum response factor, an enriched cardiac mesoderm obligatory factor, is a downstream gene target for Tbx genes. J. Biol. Chem. 280, 11816–11828 (2005).

    Article  CAS  PubMed  Google Scholar 

  96. Shirai, M., Imanaka-Yoshida, K., Schneider, M. D., Schwartz, R. J. & Morisaki, T. T-box 2, a mediator of Bmp-Smad signaling, induced hyaluronan synthase 2 and Tgfβ2 expression and endocardial cushion formation. Proc. Natl Acad. Sci. USA 106, 18604–18609 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Hansson, G. K., Jonasson, L., Holm, J. & Claesson-Welsh, L. Class II MHC antigen expression in the atherosclerotic plaque: smooth muscle cells express HLA-DR, HLA-DQ and the invariant gamma chain. Clin. Exp. Immunol. 64, 261–268 (1986).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e20 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Phanstiel, D. H., Boyle, A. P., Araya, C. L. & Snyder, M. P. Sushi.R: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures. Bioinformatics 30, 2808–2810 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Franceschini, N. et al. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes. Nat. Commun. 9, 5141 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Giri, A. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat. Genet. 51, 51–62 (2019).

    Article  CAS  PubMed  Google Scholar 

  106. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Browning, B. L., Zhou, Y. & Browning, S. R. A one-penny imputed genome from next-generation reference panels. Am. J. Hum. Genet. 103, 338–348 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  113. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Lee, D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics 32, 2196–2198 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  117. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).

    Article  Google Scholar 

  118. Talukdar, H. A. et al. Cross-tissue regulatory gene networks in coronary artery disease. Cell Syst. 2, 196–208 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, e12776 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Shu, L. et al. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC Genom. 17, 874 (2016).

    Article  Google Scholar 

  121. Otsuka, F. et al. Natural progression of atherosclerosis from pathologic intimal thickening to late fibroatheroma in human coronary arteries: a pathology study. Atherosclerosis 241, 772–782 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from: the National Institutes of Health (grant nos. R01HL148239 and R00HL125912 to C.L.M.; grant no. R35GM133712 to C.Z.; grant no. R01HL141425 to A.V.F.; grant no. R01HL125863 to J.L.M.B.; grant nos. R01HL130423, R01HL135093 and R01HL148167-01A1 to J.C.K.; grant nos. R35HL144475, R01HL125224, R01HL134817 and R01HL139478 to T.Q.; and grant no. R01HL123370 to N.J.L.), the American Heart Association (grant no. 20POST35120545 to A.W.T.; grant no. A14SFRN20840000 to J.L.M.B.; grant no. 19EIA34770065 to N.J.L.), the Swedish Research Council and Heart Lung Foundation (grant nos. 2018-02529 and 20170265 to J.L.M.B.) and the Fondation Leducq (grant no. ‘PlaqOmics’ 18CVD02 to N.J.L., J.L.M.B., A.V.F. and C.L.M.). We thank P. Chiu, P. Chang and M. Wong at Stanford University for surgical assistance and research donor heart procurement. We thank T. Koyano at Stanford University for assistance in extracting clinical information. We thank all of the transplant recipients and heart donors, family members, study coordinators and the transplant tissue procurement team at Stanford. We thank B. Liu and N. Kumasaka for helpful discussions on QTL scripts. Finally, we thank P. Pramoonjago and S. VanHoose at the University of Virginia for histological support and all staff of the core facilities for tissue processing, library construction and sequencing assistance.

Author information

Author notes

  1. These authors contributed equally: Adam W. Turner, Shengen Shawn Hu.

Authors and Affiliations

  1. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA

    Adam W. Turner, Shengen Shawn Hu, Jose Verdezoto Mosquera, Wei Feng Ma, Chani J. Hodonsky, Doris Wong, Gaëlle Auguste, Yipei Song, Emily Farber, Suna Onengut-Gumuscu, Chongzhi Zang & Clint L. Miller

  2. Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA

    Jose Verdezoto Mosquera, Doris Wong, Chongzhi Zang & Clint L. Miller

  3. Medical Scientist Training Program, University of Virginia, Charlottesville, VA, USA

    Wei Feng Ma

  4. Department of Pathology, University of Virginia, Charlottesville, VA, USA

    Wei Feng Ma & Katia Sol-Church

  5. Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA

    Chani J. Hodonsky, Doris Wong & Clint L. Miller

  6. Genome Analysis & Technology Core, University of Virginia, Charlottesville, VA, USA

    Katia Sol-Church

  7. Genome Sciences Laboratory, University of Virginia, Charlottesville, VA, USA

    Emily Farber & Suna Onengut-Gumuscu

  8. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Soumya Kundu, Anshul Kundaje & Euan A. Ashley

  9. Department of Computer Science, Stanford University, Stanford, CA, USA

    Anshul Kundaje

  10. Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA, USA

    Nicolas G. Lopez & Nicholas J. Leeper

  11. Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Lijiang Ma & Johan L. M. Björkegren

  12. CVPath Institute, Gaithersburg, MD, USA

    Saikat Kumar B. Ghosh & Aloke V. Finn

  13. Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

    Suna Onengut-Gumuscu, Chongzhi Zang & Clint L. Miller

  14. Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA

    Euan A. Ashley & Thomas Quertermous

  15. Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jason C. Kovacic

  16. Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia

    Jason C. Kovacic

  17. St. Vincent’s Clinical School, University of New South Wales, Sydney, New South Wales, Australia

    Jason C. Kovacic

  18. Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Huddinge, Sweden

    Johan L. M. Björkegren

Authors
  1. Adam W. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengen Shawn Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose Verdezoto Mosquera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Feng Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chani J. Hodonsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Doris Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gaëlle Auguste
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yipei Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Katia Sol-Church
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Emily Farber
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Soumya Kundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Anshul Kundaje
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Nicolas G. Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Lijiang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Saikat Kumar B. Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Suna Onengut-Gumuscu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Euan A. Ashley
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Thomas Quertermous
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Aloke V. Finn
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Nicholas J. Leeper
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jason C. Kovacic
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Johan L. M. Björkegren
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Chongzhi Zang
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Clint L. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.L.M. and C.Z. jointly supervised research primarily related to the study. J.L.M.B., J.C.K., N.J.L., A.V.F. and T.Q. jointly supervised research secondarily related to the study. A.W.T., S.S.H., C.Z. and C.L.M. conceived and designed the experiments. A.W.T., K.S.-C., E.F. and S.K.B.G. performed the experiments. A.W.T., S.S.H., J.V.M. and G.A. performed the statistical analyses. A.W.T., S.S.H., J.V.M. W.F.M., C.J.H., D.W., G.A., Y.S. and C.L.M. analyzed the data. K.S.-C., E.F., S.K., A.K., N.G.L., L.M., S.K.B.G., S.O.-G., E.A.A., T.Q., A.V.F., N.J.L., J.C.K. and J.L.M.B. contributed reagents/materials/analysis tools. A.W.T., S.S.H., J.V.M., W.F.M., C.J.H., D.W., G.A., C.Z. and C.L.M. wrote the paper.

Corresponding authors

Correspondence to Chongzhi Zang or Clint L. Miller.

Ethics declarations

Competing interests

J.L.M.B. is a shareholder in Clinical Gene Network AB who have a vested interest in STARNET. A.V.F. at CVPath also acknowledges receiving financial support from the following entities: 4C Medical, 4Tech, Abbott Vascular, Ablative Solutions, Absorption Systems, Advanced NanoTherapies, Aerwave Medical, Alivas, Amgen, Asahi Medical, Aurios Medical, Avantec Vascular, BD, Biosensors, Biotronik, Biotyx Medical, Bolt Medical, Boston Scientific, Canon, Cardiac Implants, Cardiawave, CardioMech, Cardionomic, Celonova, Cerus, EndoVascular, Chansu Vascular Technologies, Children’s National, Concept Medical, Cook Medical, Cooper Health, Cormaze, CRL, Croivalve, CSI, Dexcom, Edwards Lifesciences, Elucid Bioimaging, eLum Technologies, Emboline, Endotronix, Envision, Filterlex, Imperative Care, Innovalve, Innovative, Cardiovascular Solutions, Intact Vascular, Interface Biolgics, Intershunt Technologies, Invatin, Lahav, Limflow, L&J Bio, Lutonix, Lyra Therapeutics, Mayo Clinic, Maywell, MDS, MedAlliance, Medanex, Medtronic, Mercator, Microport, Microvention, Neovasc, Nephronyx, Nova Vascular, Nyra Medical, Occultech, Olympus, Ohio Health, OrbusNeich, Ossio, Phenox, Pi-Cardia, Polares Medical, Polyvascular, Profusa, ProKidney, LLC, Protembis, Pulse Biosciences, Qool Therapeutics, Recombinetics, Recor Medical, Regencor, Renata Medical, Restore Medical, Ripple Therapeutics, Rush University, Sanofi, Shockwave, SMT, SoundPipe, Spartan Micro, Spectrawave, Surmodics, Terumo Corporation, The Jacobs Institute, Transmural Systems, Transverse Medical, TruLeaf, UCSF, UPMC, Vascudyne, Vesper, Vetex Medical, Whiteswell, WL Gore, Xeltis. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The other authors declare no competing interests.

Peer review

Peer review information

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

Additional information

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

Extended data

Extended Data Fig. 1 Histological characterization of human coronary artery sections.

(a) Representative histology staining of adjacent frozen human coronary artery sections at different disease categories used for snATAC-seq profiling. Category 1 reflects normal to Stary atherosclerosis stage I/II lesions with adaptive intimal thickening and early lipid (Oil Red O (ORO)) and collagen (Sirius Red) accumulation in the subintimal layer. Category 2 reflects Stary stage III/IV early/intermediate atheroma lesions with increased lipid and collagen accumulation and proliferation (Hematoxylin & Eosin (H&E)). Category 3 reflects Stary stage V/VI advanced fibroatheroma or complex lesions with more severe lipid and collagen deposition as well as lipid core and thin media layer. (b) Whole slide quantitative results of ORO area (mm2) normalized to overall tissue area and (c) Sirius Red based quantitation of intima-media thickness (IMT) with maximum intima and average media width captured from >6 automatically defined measurements (Methods). (a-c) Similar results were observed from n = 3, n = 5, and n = 10 independent donor samples per lesion stage, respectively. ANOVA p-values shown for comparisons across lesion stages. Boxplots represent the median and interquartile range (IQR). Scale bar = 1 mm.

Extended Data Fig. 2 Coronary artery cell type marker genes from snATAC-seq gene scores.

(a) Representative UMAP plots of snATAC-seq imputed gene activity scores and integrated RNA scores for SMC and fibromyocyte marker genes. (b) UMAP plots of imputed gene scores for additional cell type marker genes and CAD GWAS genes. (c) Top candidate genes at CAD GWAS loci with cell type enriched chromatin accessibility. Negative Log10 FDR enrichment values shown for CAD GWAS marker genes.

Extended Data Fig. 3 Integration of human coronary artery snATAC-seq data with human coronary artery scRNA-seq (from Wirka et al.7).

(a) UMAP showing projection of scRNA-seq cluster labels onto cells in the snATAC-seq dataset. Colors represent the assigned cellular identities from scRNA-seq label transfer. Detailed parameters of the snATAC-seq/scRNA-seq integration are provided in the Methods section. (b) Heatmap of marker gene scores after ArchR scRNA-seq/snATAC-seq integration highlights 4,649 marker features. (c) Correlation of cell type specific scRNA-seq and snATAC-seq promoter accessibility (pseudo bulk reads from ATAC signal centered on TSS (+/− 3 kb) for each gene). Log2 transformed data is represented as scatter plots and Pearson correlation coefficients are shown for each cell type. White lines represent missing gene counts from scRNA-seq dataset, which is most apparent in the low abundant Mast cells.

Extended Data Fig. 4 Coronary artery snATAC-seq peak cell type and functional annotation.

(a) Pie chart showing genomic annotations of the consensus set of coronary peaks across all cell types (n = 323,767). Peaks were annotated using the ChIPseeker R/Bioconductor package (Yu et al. Bioinformatics 2015). (b) Pie chart of cell type annotation for peaks in the consensus peak set (n = 323,767) according to ArchR (Granja et al.41). Peaks were annotated with a cell type according to the group from which each peak originated according to ArchR’s iterative overlap procedure. (c) Functional enrichment analysis of cell type marker peaks using GREAT.

Extended Data Fig. 5 snATAC-seq co-accessibility and integration with scRNA-seq link putative regulatory elements to target promoters.

(a) Genome browser tracks highlighting CAD-associated SNPs located within peaks linked to the VEGFA promoter peak through co-accessibility. (b) Genome browser tracks highlighting the intronic CAD SNP rs7500448 located in a smooth muscle cell peak in the CDH13 gene linked to the CDH13 promoter peak through co-accessibility. (c) Heatmap summary of ArchR Peak2Gene links (n = 148,617) at 10 kb resolution where chromatin accessibility is highly correlated with target gene expression. Shown on the left are Z-scores for snATAC-seq peak accessibility and on the right are Z-scores for RNA expression.

Extended Data Fig. 6 Additional CAD-associated variants that are coronary artery chromatin accessibility QTLs (caQTLs).

(a-b) Smooth muscle cell caQTL boxplots for variants at the BMP1 (rs73551705) and SMAD3 (rs17293632) CAD loci (n = 40 unique individuals). (c) Macrophage caQTL boxplot for the rs72844419 variant at the GGCX CAD locus (n = 39). Chromatin accessibility reads were normalized using variance stabilizing transformation (vst) in DESeq2. Boxplots represent the median and interquartile range (IQR), while the whisker represent up to 1.5 X IQR. (d-e) Comparison of effect size directions between smooth muscle cell caQTLs (5% FDR) and bulk coronary artery caQTLs (5% FDR), as visualized in scatter plot (d) and donut plot (e). For this analysis, 503 caQTL peaks are shared between both datasets (peaks with a corresponding significant caQTL variant). The rsID reported in the SMC caQTL results (n = 40 individuals) was compared with the rsID reported in the bulk caQTL results (n = 35 individuals). Two variants were considered to be in linkage disequilibrium (LD) if the r2 value between them was between 0.2 and 1 (in EUR population). If variants had an r2 value < 0.2 (in EUR population), the variants were considered to be in low LD (blue). For the caQTL effect size direction, we considered the RASQUAL Pi statistic. The RASQUAL Pi statistic can range from 0–1, where Pi < 0.5 reflects lower peak accessibility for the alternative allele and Pi > 0.5 reflects higher accessibility for the alternative allele. The effect sizes for linked variants go in the same direction (green) if the Pi values in SMCs and bulk coronary artery are both < 0.5 or both > 0.5. Linear regression line and Pearson correlation coefficient shown in (d).

Extended Data Fig. 7 Examples of candidate CAD functional variants within macrophage accessible chromatin.

(a) CAD GWAS locus MAP1S/FCHO1 on chromosome 19 depicting multiple genome-wide significant variants (above dashed line). Highlighted variant rs10418535 is located within a macrophage/immune cell ATAC peak as shown in the genome browser tracks. gkm-SVM importance scores show the predicted effects of the T allele to form a functional binding site, while the C allele (non-effect) is predicted to disrupt TF binding. (b) Genome browser view showing 95% credible CAD SNPs (blue), highlighting rs7296737 located within a strong macrophage marker peak in the first intron of SCARB1 on chr12. (c) Genome browser view highlighting top credible CAD SNP rs17680741 residing in macrophage marker peak in the second intron of TSPAN14 on chr10.

Extended Data Fig. 8 Co-accessibility and gene regulatory analyses prioritize transcriptional regulators TBX2 and PRDM16.

(a) Genome browser track highlighting the association between CAD associated SNPs and SMC marker genes through co-accessibility (peak2gene) detected by snATAC-seq data (Methods). The red loops represent the association between TBX2 promoter and CAD associated SNPs. (b) Network visualization of TBX2 key driver target genes in STARNET atherosclerotic aortic root (AOR) tissue. (c) Clinical trait enrichment for PRDM16 module 28 in STARNET liver tissue. (d) Network visualization of PRDM16 key driver target genes in STARNET mammary artery (MAM) and liver tissues.

Extended Data Fig. 9 Immunostaining of PRDM16 protein in coronary atherosclerosis sections.

(a) Representative negative control (no primary antibody) immunofluoresence (IF) staining in human coronary artery - left anterior descending (LAD). Positive staining of rabbit anti-PRDM16 in vessels in control kidney tissues. Similar results were observed from n = 4 independent donor samples per tissue. Scale bar = 100 um. (b) Representative IF staining of PRDM16 and LMOD1 in atherosclerotic human coronary artery (LAD) segments from normal-Stage II, Stage III-IV, and Stage V-VI lesions based on Stary classification stages. Red = PRDM16 or LMOD1, Green = alpha smooth muscle actin (a-SMA) and blue = DAPI (nuclei). Scale bar = 1 mm (whole slide) or 100 um (highlighted regions of interest). (c) Representative hematoxylin & eosin (H&E) and MOVAT histology staining of distinct human coronary artery segments with similar lesion stages as (b). Scale bar = 1 mm. (b-c) Similar results were observed from n = 4 (Normal-stage II), n = 6 (Stage III-IV), and n = 6 (Stage V-VI) independent donor samples per group.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and methods.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–9.

Supplementary Data 1

Top coronary artery cell type snATAC marker peaks and genes.

Supplementary Data 2

Consensus set of human coronary artery snATAC-seq peaks across all cell types.

Supplementary Data 3

Transcription factor motif enrichment within cell type peaks.

Supplementary Data 4

Differentially accessible regulatory elements and functional annotation between traditional smooth muscle cells and fibromyocytes.

Supplementary Data 5

CAD GWAS variants overlapping coronary artery snATAC-seq accessible chromatin sites.

Supplementary Data 6

Chromatin accessibility QTLs within individual coronary artery cell types, calculated using RASQUAL.

Supplementary Data 7

Chromatin accessibility QTLs from bulk coronary artery ATAC-seq data, calculated using RASQUAL.

Supplementary Data 8

Machine learning prediction and annotation results of functional CAD variants for individual coronary artery cell types.

Supplementary Data 9

Sample size estimations for top fibromyocyte genes comparing traditional smooth muscle cells and fibromyocytes.

Supplementary Data 10

List of PRDM16 and TBX2 eQTLs in atherosclerosis-relevant human gene expression datasets.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Turner, A.W., Hu, S.S., Mosquera, J.V. et al. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat Genet 54, 804–816 (2022). https://doi.org/10.1038/s41588-022-01069-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-022-01069-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing