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Understanding multicellular function and disease with human tissue-specific networks

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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Figure 1: Regularized Bayesian integration based on tissue ontology.
Figure 2: Predicted IL1B functional interaction partners from the blood vessel network are significantly upregulated after stimulation of blood vessel cells with IL-1β.
Figure 3: Tissue networks capture tissue-specific functional rewiring.
Figure 4: A disease map centered on Parkinson's disease summarizing its molecular associations with other diseases in substantia nigra.
Figure 5: Network reprioritization of hypertension GWAS identifies hypertension-associated genes.

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References

  1. D'Agati, V.D. The spectrum of focal segmental glomerulosclerosis: new insights. Curr. Opin. Nephrol. Hypertens. 17, 271–281 (2008).

    Article  PubMed  Google Scholar 

  2. Cai, J.J. & Petrov, D.A. Relaxed purifying selection and possibly high rate of adaptation in primate lineage-specific genes. Genome Biol. Evol. 2, 393–409 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Winter, E.E., Goodstadt, L. & Ponting, C.P. Elevated rates of protein secretion, evolution, and disease among tissue-specific genes. Genome Res. 14, 54–61 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lage, K. et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc. Natl. Acad. Sci. USA 105, 20870–20875 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  6. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  7. Pandey, A.K., Lu, L., Wang, X., Homayouni, R. & Williams, R.W. Functionally enigmatic genes: a case study of the brain ignorome. PLoS ONE 9, e88889 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Huttenhower, C. et al. Exploring the human genome with functional maps. Genome Res. 19, 1093–1106 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ju, W. et al. Defining cell-type specificity at the transcriptional level in human disease. Genome Res. 23, 1862–1873 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Troyanskaya, O.G., Dolinski, K., Owen, A.B., Altman, R.B. & Botstein, D. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl. Acad. Sci. USA 100, 8348–8353 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Myers, C.L. & Troyanskaya, O.G. Context-sensitive data integration and prediction of biological networks. Bioinformatics 23, 2322–2330 (2007).

    Article  CAS  PubMed  Google Scholar 

  12. Hibbs, M.A. et al. Directing experimental biology: a case study in mitochondrial biogenesis. PLoS Comput. Biol. 5, e1000322 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Park, C.Y. et al. Functional knowledge transfer for high-accuracy prediction of under-studied biological processes. PLoS Comput. Biol. 9, e1002957 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jansen, R. et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003).

    Article  CAS  PubMed  Google Scholar 

  15. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 306, 1555–1558 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C. & Morris, Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9 (suppl. 1), S4 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Hwang, S., Rhee, S.Y., Marcotte, E.M. & Lee, I. Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network. Nat. Protoc. 6, 1429–1442 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Kofler, S., Nickel, T. & Weis, M. Role of cytokines in cardiovascular diseases: a focus on endothelial responses to inflammation. Clin. Sci. 108, 205–213 (2005).

    Article  CAS  Google Scholar 

  19. Liu, J.Z. et al. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Keshava Prasad, T.S. et al. Human Protein Reference Database—2009 update. Nucleic Acids Res. 37, D767–D772 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. Gremse, M. et al. The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources. Nucleic Acids Res. 39, D507–D513 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Britten, R.J. & Davidson, E.H. Gene regulation for higher cells: a theory. Science 165, 349–357 (1969).

    Article  CAS  PubMed  Google Scholar 

  23. Spitz, F. & Furlong, E.E.M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    Article  CAS  PubMed  Google Scholar 

  24. Graf, T. & Enver, T. Forcing cells to change lineages. Nature 462, 587–594 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Stadtfeld, M. & Hochedlinger, K. Induced pluripotency: history, mechanisms, and applications. Genes Dev. 24, 2239–2263 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Goh, K.-I. et al. The human disease network. Proc. Natl. Acad. Sci. USA 104, 8685–8690 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Brunner, H.G. & van Driel, M.A. From syndrome families to functional genomics. Nat. Rev. Genet. 5, 545–551 (2004).

    Article  CAS  PubMed  Google Scholar 

  28. Arce, L., Yokoyama, N.N. & Waterman, M.L. Diversity of LEF/TCF action in development and disease. Oncogene 25, 7492–7504 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. van Amerongen, R. & Nusse, R. Towards an integrated view of Wnt signaling in development. Development 136, 3205–3214 (2009).

    Article  CAS  PubMed  Google Scholar 

  30. Reya, T. et al. Wnt signaling regulates B lymphocyte proliferation through a LEF-1 dependent mechanism. Immunity 13, 15–24 (2000).

    Article  CAS  PubMed  Google Scholar 

  31. Park, S.-K., Son, Y. & Kang, C.-J. A strong promoter activity of pre–B cell stage-specific Crlz1 gene is caused by one distal LEF-1 and multiple proximal Ets sites. Mol. Cells 32, 67–76 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gutierrez, A. et al. LEF-1 is a prosurvival factor in chronic lymphocytic leukemia and is expressed in the preleukemic state of monoclonal B-cell lymphocytosis. Blood 116, 2975–2983 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Erdfelder, F., Hertweck, M., Filipovich, A., Uhrmacher, S. & Kreuzer, K.-A. High lymphoid enhancer-binding factor-1 expression is associated with disease progression and poor prognosis in chronic lymphocytic leukemia. Hematol. Rep. 2, e3 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Gandhirajan, R.K. et al. Small molecule inhibitors of Wnt/β-catenin/lef-1 signaling induces apoptosis in chronic lymphocytic leukemia cells in vitro and in vivo. Neoplasia 12, 326–335 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lee, J.E., Wu, S.-F., Goering, L.M. & Dorsky, R.I. Canonical Wnt signaling through Lef1 is required for hypothalamic neurogenesis. Development 133, 4451–4461 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Wang, X., Lee, J.E. & Dorsky, R.I. Identification of Wnt-responsive cells in the zebrafish hypothalamus. Zebrafish 6, 49–58 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kahler, R.A. et al. Lymphocyte enhancer-binding factor 1 (Lef1) inhibits terminal differentiation of osteoblasts. J. Cell. Biochem. 97, 969–983 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Hoeppner, L.H. et al. Runx2 and bone morphogenic protein 2 regulate the expression of an alternative Lef1 transcript during osteoblast maturation. J. Cell. Physiol. 221, 480–489 (2009).

    Article  CAS  PubMed  Google Scholar 

  39. Noh, T. et al. Lef1 haploinsufficient mice display a low turnover and low bone mass phenotype in a gender- and age-specific manner. PLoS ONE 4, e5438 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Westendorf, J.J., Kahler, R.A. & Schroeder, T.M. Wnt signaling in osteoblasts and bone diseases. Gene 341, 19–39 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Cohen, M.M. Biology of RUNX2 and cleidocranial dysplasia. J. Craniofac. Surg. 24, 130–133 (2013).

    Article  PubMed  Google Scholar 

  42. Duan, D. et al. Submucosal gland development in the airway is controlled by lymphoid enhancer binding factor 1 (LEF1). Development 126, 4441–4453 (1999).

    CAS  PubMed  Google Scholar 

  43. Driskell, R.R. et al. Wnt-responsive element controls Lef-1 promoter expression during submucosal gland morphogenesis. Am. J. Physiol. Lung Cell. Mol. Physiol. 287, L752–L763 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Driskell, R.R. et al. Wnt3a regulates Lef-1 expression during airway submucosal gland morphogenesis. Dev. Biol. 305, 90–102 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Verkman, A.S., Song, Y. & Thiagarajah, J.R. Role of airway surface liquid and submucosal glands in cystic fibrosis lung disease. Am. J. Physiol. Cell Physiol. 284, C2–C15 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. Forno, L.S. Neuropathology of Parkinson's disease. J. Neuropathol. Exp. Neurol. 55, 259–272 (1996).

    Article  CAS  PubMed  Google Scholar 

  47. Veeriah, S. et al. Somatic mutations of the Parkinson's disease–associated gene PARK2 in glioblastoma and other human malignancies. Nat. Genet. 42, 77–82 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Denison, S.R. et al. Alterations in the common fragile site gene Parkin in ovarian and other cancers. Oncogene 22, 8370–8378 (2003).

    Article  CAS  PubMed  Google Scholar 

  49. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. O'Seaghdha, C.M. & Fox, C.S. Genome-wide association studies of chronic kidney disease: what have we learned? Nat. Rev. Nephrol. 8, 89–99 (2012).

    Article  CAS  Google Scholar 

  51. Ridker, P.M. et al. Rationale, design, and methodology of the Women's Genome Health Study: a genome-wide association study of more than 25,000 initially healthy American women. Clin. Chem. 54, 249–255 (2008).

    Article  CAS  PubMed  Google Scholar 

  52. Ho, J.E. et al. Discovery and replication of novel blood pressure genetic loci in the Women's Genome Health Study. J. Hypertens. 29, 62–69 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Oldham, P.D., Pickering, G., Roberts, J.A. & Sowry, G.S. The nature of essential hypertension. Lancet 1, 1085–1093 (1960).

    Article  CAS  PubMed  Google Scholar 

  54. Guyton, A.C. Blood pressure control—special role of the kidneys and body fluids. Science 252, 1813–1816 (1991).

    Article  CAS  PubMed  Google Scholar 

  55. Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A. & McKusick, V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514–D517 (2005).

    Article  CAS  PubMed  Google Scholar 

  56. Wishart, D.S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Thorn, C.F., Klein, T.E. & Altman, R.B. PharmGKB: the Pharmacogenomics Knowledge Base. Methods Mol. Biol. 1015, 311–320 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Qin, C. et al. Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res. 42, D1118–D1123 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Davis, A.P. et al. The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res. 41, D1104–D1114 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Bostock, M., Ogievetsky, V. & Heer, J. D3: Data-Driven Documents. IEEE Trans. Vis. Comput. Graph. 17, 2301–2309 (2011).

    Article  PubMed  Google Scholar 

  61. Forrest, A.R.R. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Hoffmann, R. & Valencia, A. Life cycles of successful genes. Trends Genet. 19, 79–81 (2003).

    Article  CAS  PubMed  Google Scholar 

  63. Köhler, S., Bauer, S., Horn, D. & Robinson, P.N. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82, 949–958 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Denny, J.C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26, 1205–1210 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–D823 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Kerrien, S. et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40, D841–D846 (2012).

    Article  CAS  PubMed  Google Scholar 

  67. Licata, L. et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 40, D857–D861 (2012).

    Article  CAS  PubMed  Google Scholar 

  68. Mewes, H.W. et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 27, 44–48 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Portales-Casamar, E. et al. JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res. 38, D105–D110 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Bailey, T.L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Huber, B.R. & Bulyk, M.L. Meta-analysis discovery of tissue-specific DNA sequence motifs from mammalian gene expression data. BMC Bioinformatics 7, 229 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Barrett, T. et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 41, D991–D995 (2013).

    Article  CAS  PubMed  Google Scholar 

  75. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001).

    Article  CAS  PubMed  Google Scholar 

  76. 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  PubMed  Google Scholar 

  77. Myers, C.L., Barrett, D.R., Hibbs, M.A., Huttenhower, C. & Troyanskaya, O.G. Finding function: evaluation methods for functional genomic data. BMC Genomics 7, 187 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Bossi, A. & Lehner, B. Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Ramsköld, D., Wang, E.T., Burge, C.B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Burkard, T.R. et al. Initial characterization of the human central proteome. BMC Syst. Biol. 5, 17 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28, 1248–1250 (2010).

    Article  CAS  PubMed  Google Scholar 

  82. Huttenhower, C., Schroeder, M., Chikina, M.D. & Troyanskaya, O.G. The Sleipnir library for computational functional genomics. Bioinformatics 24, 1559–1561 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Schmid, P.R., Palmer, N.P., Kohane, I.S. & Berger, B. Making sense out of massive data by going beyond differential expression. Proc. Natl. Acad. Sci. USA 109, 5594–5599 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Aronson, A.R. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc. AMIA Symp. 2001, 17–21 (2001).

    Google Scholar 

  85. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  PubMed  Google Scholar 

  86. Dai, M. et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Smyth, G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).

    Article  PubMed  Google Scholar 

  88. Meigs, J.B. et al. Genome-wide association with diabetes-related traits in the Framingham Heart Study. BMC Med. Genet. 8 (suppl. 1), S16 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Randall, J.C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 9, e1003500 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Fritsche, L.G. et al. Seven new loci associated with age-related macular degeneration. Nat. Genet. 45, 433–439 (2013).

    Article  CAS  PubMed  Google Scholar 

  91. Mailman, M.D. et al. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet. 39, 1181–1186 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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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).

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Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to Olga G Troyanskaya.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Integrating the entire data compendium with hierarchy-aware tissue-specific knowledge generates networks that better capture tissue-specific interactions than limiting the integration to tissue-specific data (P = 1.3 × 10−9).

For each tissue, two networks—one integrating the entire data compendium and the other integrating only tissue-labeled data—were generated, and their performance was measured using area under the receiver operator curve (AUC) on the basis of cross-validation. The scatterplot shows that the performance for 64 tissues (points) with tissue-labeled data (x axis) and all data (y axis), with 62 of 64 performing better with all data (above the diagonal line; P = 3.2 × 10−12). The remaining 80 tissues did not have sufficient tissue-specific data (fewer than 5 data sets) available to perform a tissue-restricted integration. The performance of our Bayesian integration for these tissues is shown on the disconnected axis.

Supplementary Figure 2 The blood vessel and cardiovascular system networks show the best correspondence with the experiment, over and above the tissue-naive network and the bulk of other unrelated tissue networks.

For each network, genes were ranked on the basis of their connectivity to IL-1β in that network. Then, at each rank, the precision of the predictions up to that rank was calculated as the fraction of genes that are differentially expressed in the experiment. Plotted in each of the three graphs is the precision (y axis) at incremental sets of top-ranked genes (1–100; x axis). The precision for the blood vessel and tissue-naive networks is plotted in solid blue and dashed dark gray, respectively. The median precision at each rank for the cardiovascular system of tissues and all tissues are plotted in dotted blue and gray, respectively. Further, the gray band around the all_tissues median represents the interquartile range of precision values at each rank calculated across all tissues. The three plots correspond to different choices of differentially expressed genes (DEGs) from the microarray, with (a) 500 genes, (b) 250 genes and (c) 1,000 genes. The results in the main text are based on choosing genes from (a) at rank 20.

Supplementary Figure 3 We analyzed publicly available gene expression data sets that included IL-1β treatment and found that genes connected to IL-1β in tissue-specific networks for the corresponding tissue responded significantly to treatment.

Each plot shows the mean log2 fold change after IL-1β treatment of the 20 genes most tightly connected to IL-1β in the network listed on the x axis, and error bars represent the standard error (s.e.). Also plotted alongside as controls are the mean and s.e. of 20 random genes from the data set. The first plot (GSE59671) corresponds to the blood vessel experiment elaborated in the main text (Fig. 2). The cell type and GEO identifier of the other data sets from which gene expression data were extracted is listed above the plot. Of these data sets, only GSE7216 (epidermal keratinocytes) is included in the data compendium used for integration. The rest are independent of the integration.

Supplementary Figure 4 Single-tissue query of LEF-1 in the GIANT interface.

(a) LEF-1’s functional network neighborhood in B lymphocytes. (b) The functional enrichment of LEF-1’s neighborhood. (c) A table of the most connected genes to LEF-1 in the network.

Supplementary Figure 5 This stacked bar plot shows the results of our blinded literature evaluation.

Only 10% of randomly selected diseases were associated strongly to Parkinson’s disease in the literature, while more than 75% of disease map–selected diseases were associated.

Supplementary Figure 6 Tissue-specific networks can capture additional disease associations.

(a) Alzheimer’s disease in the temporal lobe network (z score ≥ 2.25), (b) glycogen metabolism disorder in liver (z score ≥ 1.75) and (c) glomerulonephritis in renal glomerulus (z score ≥ 1.5). The appropriate tissue network was chosen on the basis of connectivity of diseases in their relevant tissues (see “Network connectivity in tissue-specific processes” in the Online Methods).

Supplementary Figure 7 Relevant tissue networks show the best performance in reprioritizing hypertension GWAS and are enriched with targets of antihypertensive drugs.

To evaluate the choice of tissues for reprioritization, we evaluated all tissue networks (along with the tissue-naive network) in the same setting we used for the kidney network. (a) The distribution of performance (measured using AUC) shows that the right tissue network, kidney, and other relevant tissues, heart and liver, are among the best, while the tissue-naive network sits amidst tissue networks that provide an average performance. (b) Top-ranked genes by NetWAS are significantly enriched with targets of antihypertensive drugs. Drug targets were obtained from four databases—DrugBank, TTD, PharmGKB and CTD—which curate this information using different criteria. We evaluated both the original GWAS (gray) and NetWAS using the kidney network (dark red) for enrichment of drug targets from each of these sources among the top-ranked genes. Enrichment was measures using z scores (Online Methods), with higher scores indicating greater enrichment near the top of the list. In nearly all cases—target data sources and phenotypic end points—NetWAS reprioritization resulted in significant top ranking of therapeutic targets, over the original GWAS.

Supplementary Figure 8 NetWAS reprioritization is effective across studies, phenotypes and relevant networks.

Each bar shows the performance of NetWAS reprioritization as measured by the area under the curve (AUC) of documented disease associations with the disease specified in the label above the plot. The horizontal axis shows relevant networks (colored bars) and GWAS alone (gray bars), and the horizontal axis label describes the GWAS phenotype from which associations were obtained.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Note. (PDF 6544 kb)

Supplementary Table 1

Tissue model weights of expression data sets. (XLSX 2437 kb)

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. (XLSX 45 kb)

Supplementary Table 3

Top 20 genes tightly connected to IL1B in the blood vessel network. (XLSX 29 kb)

Supplementary Table 4

NetWAS results for combined hypertension phenotypes. (XLSX 1856 kb)

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. (XLSX 41 kb)

Supplementary Table 6

Expert-curated GO terms used to generate a global functional interaction standard. (XLSX 37 kb)

Supplementary Table 7

HPRD tissues were linked by direct text matching to terms in the BTO. (XLSX 41 kb)

Supplementary Table 8

This table contains the pruned BTO terms. (XLSX 55 kb)

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. (XLSX 136 kb)

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Greene, C., Krishnan, A., Wong, A. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet 47, 569–576 (2015). https://doi.org/10.1038/ng.3259

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