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.

Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

Nature Methods volume 13, pages 366–370 (2016)Cite this article

Subjects

Abstract

Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type– and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants—including variants that do not reach genome-wide significance—often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues (http://regulatorycircuits.org).

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

Figure 1: Regulatory circuit inference and GWAS analysis.
Figure 2: Assessment of regulatory circuits.
Figure 3: Network-connectivity enrichment reveals disease-relevant cell types and tissues.

References

  1. Marstrand, T.T. & Storey, J.D. Identifying and mapping cell-type-specific chromatin programming of gene expression. Proc. Natl. Acad. Sci. USA 111, E645–E654 (2014).

    Article  CAS  Google Scholar 

  2. Pers, T.H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    Article  CAS  Google Scholar 

  3. Roy, S. et al. A predictive modeling approach for cell line-specific long-range regulatory interactions. Nucleic Acids Res. 43, 8694–8712 (2015).

    Article  CAS  Google Scholar 

  4. Ward, L.D. & Kellis, M. Interpreting noncoding genetic variation in complex traits and human disease. Nat. Biotechnol. 30, 1095–1106 (2012).

    Article  CAS  Google Scholar 

  5. Parker, S.C.J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl. Acad. Sci. USA 110, 17921–17926 (2013).

    Article  CAS  Google Scholar 

  6. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    Article  CAS  Google Scholar 

  7. Faye, L.L., Machiela, M.J., Kraft, P., Bull, S.B. & Sun, L. Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification. PLoS Genet. 9, e1003609 (2013).

    Article  CAS  Google Scholar 

  8. Pickrell, J.K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

    Article  CAS  Google Scholar 

  9. Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).

    Article  CAS  Google Scholar 

  10. Navlakha, S. & Kingsford, C. The power of protein interaction networks for associating genes with diseases. Bioinformatics 26, 1057–1063 (2010).

    Article  CAS  Google Scholar 

  11. Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    Article  CAS  Google Scholar 

  12. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008).

    Article  CAS  Google Scholar 

  13. Lee, I., Blom, U.M., Wang, P.I., Shim, J.E. & Marcotte, E.M. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21, 1109–1121 (2011).

    Article  CAS  Google Scholar 

  14. Mäkinen, V.-P. et al. Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease. PLoS Genet. 10, e1004502 (2014).

    Article  Google Scholar 

  15. Pierson, E., Koller, D., Battle, A., Mostafavi, S. & the GTEx Consortium. Sharing and specificity of co-expression networks across 35 human tissues. PLoS Comput. Biol. 11, e1004220 (2015).

    Article  Google Scholar 

  16. Greene, C.S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Marbach, D. et al. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks. Genome Res. 22, 1334–1349 (2012).

    Article  CAS  Google Scholar 

  19. Karczewski, K.J., Snyder, M., Altman, R.B. & Tatonetti, N.P. Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. PLoS Genet. 10, e1004122 (2014).

    Article  Google Scholar 

  20. Boyer, L.A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947–956 (2005).

    Article  CAS  Google Scholar 

  21. Ciofani, M. et al. A validated regulatory network for Th17 cell specification. Cell 151, 289–303 (2012).

    Article  CAS  Google Scholar 

  22. Chen, J.C. et al. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks. Cell 159, 402–414 (2014).

    Article  CAS  Google Scholar 

  23. The FANTOM Consortium & the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  24. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  Google Scholar 

  25. Kheradpour, P. et al. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay. Genome Res. 23, 800–811 (2013).

    Article  CAS  Google Scholar 

  26. Kheradpour, P. & Kellis, M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 (2014).

    Article  CAS  Google Scholar 

  27. Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012).

    Article  CAS  Google Scholar 

  28. The GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

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

  30. Lamparter, D., Marbach, D., Rico, R., Kutalik, Z. & Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, e1004714 (2016).

    Article  Google Scholar 

  31. Perez-Costas, E., Melendez-Ferro, M. & Roberts, R.C. Basal ganglia pathology in schizophrenia: dopamine connections and anomalies. J. Neurochem. 113, 287–302 (2010).

    Article  CAS  Google Scholar 

  32. Garrett, A. & Chang, K. The role of the amygdala in bipolar disorder development. Dev. Psychopathol. 20, 1285–1296 (2008).

    Article  Google Scholar 

  33. Cromer, W.E., Mathis, J.M., Granger, D.N., Chaitanya, G.V. & Alexander, J.S. Role of the endothelium in inflammatory bowel diseases. World J. Gastroenterol. 17, 578–593 (2011).

    Article  CAS  Google Scholar 

  34. Swirski, F.K. et al. Identification of splenic reservoir monocytes and their deployment to inflammatory sites. Science 325, 612–616 (2009).

    Article  CAS  Google Scholar 

  35. Chichlowski, M., Westwood, G.S., Abraham, S.N. & Hale, L.P. Role of mast cells in inflammatory bowel disease and inflammation-associated colorectal neoplasia in IL-10-deficient mice. PLoS ONE 5, e12220 (2010).

    Article  Google Scholar 

  36. Wright, H.L., Moots, R.J. & Edwards, S.W. The multifactorial role of neutrophils in rheumatoid arthritis. Nat. Rev. Rheumatol. 10, 593–601 (2014).

    Article  CAS  Google Scholar 

  37. Iadecola, C. Neurovascular regulation in the normal brain and in Alzheimer's disease. Nat. Rev. Neurosci. 5, 347–360 (2004).

    Article  CAS  Google Scholar 

  38. Hor, H. et al. Genome-wide association study identifies new HLA class II haplotypes strongly protective against narcolepsy. Nat. Genet. 42, 786–789 (2010).

    Article  CAS  Google Scholar 

  39. Goodrich, J.K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

    Article  CAS  Google Scholar 

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

  41. Kheradpour, P., Stark, A., Roy, S. & Kellis, M. Reliable prediction of regulator targets using 12 Drosophila genomes. Genome Res. 17, 1919–1931 (2007).

    Article  CAS  Google Scholar 

  42. Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    Article  CAS  Google Scholar 

  43. Marbach, D. et al. Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl. Acad. Sci. USA 107, 6286–6291 (2010).

    Article  CAS  Google Scholar 

  44. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  45. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  46. Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45, 1150–1159 (2013).

    Article  CAS  Google Scholar 

  47. Boraska, V. et al. A genome-wide association study of anorexia nervosa. Mol. Psychiatry 19, 1085–1094 (2014).

    Article  CAS  Google Scholar 

  48. Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

    Article  CAS  Google Scholar 

  49. Simón-Sánchez, J. et al. Genome-wide association study reveals genetic risk underlying Parkinson's disease. Nat. Genet. 41, 1308–1312 (2009).

    Article  Google Scholar 

  50. International Multiple Sclerosis Genetics Consortium. et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).

  51. Anderson, C.A. et al. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47. Nat. Genet. 43, 246–252 (2011).

    Article  CAS  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).

    Article  CAS  Google Scholar 

  53. Stahl, E.A. et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat. Genet. 42, 508–514 (2010).

    Article  CAS  Google Scholar 

  54. Rauch, A. et al. Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study. Gastroenterology 138, 1338–1345 (2010).

    Article  CAS  Google Scholar 

  55. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

    Article  CAS  Google Scholar 

  56. The International Consortium for Blood Pressure Genome-Wide Association Studies. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

  57. Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  58. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  Google Scholar 

  59. Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014).

    Article  Google Scholar 

  60. The DIAbetes Genetxzics Replication and Meta-analysis (DIAGRAM) Consortium. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

  61. Scott, R.A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    Article  CAS  Google Scholar 

  62. Soranzo, N. et al. Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways. Diabetes 59, 3229–3239 (2010).

    Article  CAS  Google Scholar 

  63. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    Article  CAS  Google Scholar 

  64. Strawbridge, R.J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2. Diabetes 60, 2624–2634 (2011).

    Article  CAS  Google Scholar 

  65. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    Article  CAS  Google Scholar 

  66. Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    Article  CAS  Google Scholar 

  67. The AMD Gene Consortium. Seven new loci associated with age-related macular degeneration. Nat. Genet. 45, 433–439 (2013).

  68. Estrada, K. et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

    Article  CAS  Google Scholar 

  69. Clarke, L. et al. The 1000 Genomes Project: data management and community access. Nat. Methods 9, 459–462 (2012).

    Article  CAS  Google Scholar 

  70. Kondor, R.I. & Lafferty, J.D. Diffusion kernels on graphs and other discrete input spaces. in Proc. Nineteenth International Conference on Machine Learning 315–322 (Morgan Kaufmann, 2002).

  71. Smola, A.J. & Kondor, R. Kernels and regularization on graphs. in Learning Theory and Kernel Machines (eds. Schölkopf, B. & Warmuth, M.K.) 144–158 (Springer Berlin Heidelberg, 2003).

  72. Erten, S., Bebek, G., Ewing, R.M. & Koyutürk, M. DADA: degree-aware algorithms for network-based disease gene prioritization. BioData Min. 4, 19 (2011).

    Article  Google Scholar 

  73. Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).

    Article  CAS  Google Scholar 

  74. Maglott, D., Ostell, J., Pruitt, K.D. & Tatusova, T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 39, D52–D57 (2011).

    Article  CAS  Google Scholar 

  75. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43, D470–D478 (2015).

    Article  CAS  Google Scholar 

  76. Rolland, T. et al. A proteome-scale map of the human interactome network. Cell 159, 1212–1226 (2014).

    Article  CAS  Google Scholar 

  77. Neph, S. et al. Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2012).

    Article  CAS  Google Scholar 

  78. Derry, J.M.J. et al. Developing predictive molecular maps of human disease through community-based modeling. Nat. Genet. 44, 127–130 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank P. Kheradpour (MIT) for providing the collection of curated TF binding motifs. This work was supported by the Swiss National Science Foundation (grant FN 310030_152724/1 to S.B. and grant FN 31003A-143914 to Z.K.), SystemsX.ch (grant SysGenetiX to S.B. and grant AgingX to Z.K.), the Swiss Institute of Bioinformatics (Z.K. and S.B.) and the Leenaards Foundation (Z.K.).

Author information

Authors and Affiliations

  1. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

    Daniel Marbach, David Lamparter & Sven Bergmann

  2. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Daniel Marbach, David Lamparter, Zoltán Kutalik & Sven Bergmann

  3. Broad Institute, MIT, Cambridge, Massachusetts, USA

    Gerald Quon & Manolis Kellis

  4. Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA

    Gerald Quon & Manolis Kellis

  5. Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland

    Zoltán Kutalik

Authors
  1. Daniel Marbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Lamparter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerald Quon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manolis Kellis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zoltán Kutalik
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sven Bergmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.M. designed the study, performed analyses and prepared the manuscript. D.L. performed gene scoring and phenotype-label permutation. D.M., D.L., G.Q., M.K., Z.K. and S.B. conceived methods, discussed the results and implications, and commented on the manuscript at all stages.

Corresponding author

Correspondence to Daniel Marbach.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–47 (PDF 2364 kb)

Supplementary Table 1

Sample annotation (XLSX 108 kb)

Supplementary Table 2

ENCODE ChIP-seq experiments (XLSX 47 kb)

Supplementary Table 3

GTEx tissues (XLSX 42 kb)

Supplementary Table 4

Roadmap Epigenomics RNA-seq data (XLSX 46 kb)

Supplementary Table 5

GWAS compendium (XLSX 16 kb)

Supplementary Table 6

Links to external repositories (XLSX 53 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marbach, D., Lamparter, D., Quon, G. et al. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat Methods 13, 366–370 (2016). https://doi.org/10.1038/nmeth.3799

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3799

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research