Analysis

Understanding multicellular function and disease with human tissue-specific networks

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Abstract

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.

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Acknowledgements

The first three authors are co-first authors and are listed alphabetically.

We sincerely thank Y. Lee and D. Gorenshteyn for help in curating disease associations and L. Bongo and M. Homilius for help in processing expression data. We are grateful to all members of the Troyanskaya laboratory for help in curating specific GO biological processes and for valuable discussions.

This work was primarily supported by US National Institutes of Health (NIH) grants R01 GM071966 and R01 HG005998 to O.G.T. and U54 HL117798 to G.A.F. C.S.G. was supported in part by US NIH grants T32 CA009528 and P20 GM103534. A.K.W. was supported in part by US NIH grant T32 HG003284. This work was supported in part by US NIH grant P50 GM071508 and by US NIH contract HHSN272201000054C. O.G.T. is a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR).

Author information

Author notes

    • Casey S Greene
    • , Arjun Krishnan
    •  & Aaron K Wong

    These authors contributed equally to this work.

Affiliations

  1. Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.

    • Casey S Greene
    •  & Rene A Zelaya
  2. Dartmouth-Hitchcock Norris Cotton Cancer Center, Lebanon, New Hampshire, USA.

    • Casey S Greene
  3. Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire, USA.

    • Casey S Greene
  4. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.

    • Arjun Krishnan
    • , Kara Dolinski
    •  & Olga G Troyanskaya
  5. Department of Computer Science, Princeton University, Princeton, New Jersey, USA.

    • Aaron K Wong
    •  & Olga G Troyanskaya
  6. Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Emanuela Ricciotti
    • , Garret A FitzGerald
    •  & Tilo Grosser
  7. Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Emanuela Ricciotti
    • , Garret A FitzGerald
    •  & Tilo Grosser
  8. Biology and Medical Informatics, University of California, San Francisco, San Francisco, California, USA.

    • Daniel S Himmelstein
  9. Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.

    • Ran Zhang
  10. Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Boris M Hartmann
    • , Elena Zaslavsky
    •  & Stuart C Sealfon
  11. Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Daniel I Chasman
  12. Simons Center for Data Analysis, Simons Foundation, New York, New York, USA.

    • Olga G Troyanskaya

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Contributions

C.S.G., A.K., A.K.W. and O.G.T. conceived and designed the research. C.S.G., A.K. and A.K.W. performed computational analyses with contributions from D.S.H. and R.Z., and E.R. performed the molecular experiments. A.K.W., R.A.Z. and C.S.G. developed the web interface. D.I.C., B.M.H., E.Z., S.C.S. and K.D. provided data. C.S.G., A.K., A.K.W. and O.G.T. wrote the manuscript with input from E.R., T.G., G.A.F. and K.D. and revisions from all co-authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Olga G Troyanskaya.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8 and Supplementary Note.

Excel files

  1. 1.

    Supplementary Table 1

    Tissue model weights of expression data sets.

  2. 2.

    Supplementary Table 2

    Pathways known to be specifically active in a tissue are tightly connected in the corresponding tissue network. This table provides the list of tissues, their organ system categories (tissue-slim) and attributes of tissue-specific pathways mapped to those tissues.

  3. 3.

    Supplementary Table 3

    Top 20 genes tightly connected to IL1B in the blood vessel network.

  4. 4.

    Supplementary Table 4

    NetWAS results for combined hypertension phenotypes.

  5. 5.

    Supplementary Table 5

    Many lines of evidence in the literature link the top predicted genes to hypertension via mechanistic relationship to known disease genes and pathways or association with hypertension risk factors.

  6. 6.

    Supplementary Table 6

    Expert-curated GO terms used to generate a global functional interaction standard.

  7. 7.

    Supplementary Table 7

    HPRD tissues were linked by direct text matching to terms in the BTO.

  8. 8.

    Supplementary Table 8

    This table contains the pruned BTO terms.

  9. 9.

    Supplementary Table 9

    We used simple text mining followed by manual curation to map biological process (BP) terms in GO to tissue terms in the BTO.