Leveraging models of cell regulation and GWAS data in integrative network-based association studies

Nature Genetics

Volumn 44, Issue 8, 2012, Pages 841-847

  Califano, Andrea ^a   Butte, Atul J ^b,c   Friend, Stephen ^d   Ideker, Trey ^e,f,g   Schadt, Eric ^h  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c STANFORD UNIVERSITY   (United States)

^d SAGE BIONETWORKS   (United States)

^e UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^f UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^g UNIVERSITY OF CALIFORNIA   (United States)

^h MOUNT SINAI SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMMUNOGLOBULIN ENHANCER BINDING PROTEIN; TRANSCRIPTION FACTOR; UNTRANSLATED RNA;

CELL COMPARTMENTALIZATION; CHROMATIN IMMUNOPRECIPITATION; COHORT ANALYSIS; EPIGENETICS; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENE REARRANGEMENT; GENE REGULATORY NETWORK; GENETIC ASSOCIATION; GENETIC RISK; GENETIC TRAIT; GENETIC TRANSCRIPTION; GENETIC VARIABILITY; GENOMICS; GLIOMA; HIGH THROUGHPUT SCREENING; HUMAN; LYMPHOMA; MASS SPECTROMETRY; NON INSULIN DEPENDENT DIABETES MELLITUS; NONHUMAN; PENETRANCE; PHENOTYPE; POSTTRANSCRIPTIONAL GENE SILENCING; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; REVIEW; RNA INTERFERENCE; SIGNAL TRANSDUCTION; SINGLE NUCLEOTIDE POLYMORPHISM; SOLID TUMOR; SOMATIC MUTATION; TWO HYBRID SYSTEM;

DATABASES, GENETIC; GENE EXPRESSION; GENE REGULATORY NETWORKS; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MODELS, GENETIC; REVERSE GENETICS; SYSTEMS BIOLOGY;

EID: 84864402194     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/ng.2355     Document Type: Review

References (88)

1. Stranger, B.E., Stahl, E.A. & Raj, T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187, 367-383 (2011).
2. Kraft, P. & Hunter, D.J. Genetic risk prediction-are we there yet? N. Engl. J. Med. 360, 1701-1703 (2009).
3. Hardy J, 360A. Genomewide association studies and human disease. N. Engl. J. Med. 360, 1759-1768 (2009).
4. Goldstein, D.B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696-1698 (2009).
5. Zeggini, E. et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40, 638-645 (2008).
6. Lyssenko, V. et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N. Engl. J. Med. 359, 2220-2232 (2008).
7. Altshuler, D., Daly, M.J. & Lander, E.S. Genetic mapping in human disease. Science 322, 881-888 (2008).
8. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747-753 (2009).
9. Narayanan, M., Vetta, A., Schadt, E.E. & Zhu, J. Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Comput. Biol. 6, e1000742 (2010).
10. Stephens, P.J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer. Cell 144, 27-40 (2011).
11. Zhong, H., Yang, X., Kaplan, L.M., Molony, C. & Schadt, E.E. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am. J. Hum. Genet. 86, 581-591 (2010).
12. 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).
13. Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29-34 (1999).
14. Wang, K., Li, M. & Hakonarson, H. Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet. 11, 843-854 (2010).
15. Dobrin, R. et al. Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol. 10, R55 (2009).
16. Guttman, M. et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 477, 295-300 (2011).
17. Sumazin, P. et al. An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell 147, 370-381 (2011).
18. ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799-816 (2007).
19. Margolin, A.A. et al. ChIP-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes. Proc. Natl. Acad. Sci. USA 106, 244-249 (2009).
20. Lefebvre, C. et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol. Syst. Biol. 6, 377 (2010).
21. Ravasi, T. et al. An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140, 744-752 (2010).
22. Wang, K. et al. Dissecting the interface between signaling and transcriptional regulation in human B cells. Pac. Symp. Biocomput. 264-275 (2009).
23. Rual, J.F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173-1178 (2005).
24. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429-435 (2008).
25. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423-428 (2008).
26. Schadt, E.E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710-717 (2005).
27. Yang, X. et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat. Genet. 41, 415-423 (2009).
28. Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet. 40, 854-861 (2008).
29. Zhong, H. et al. Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes. PLoS Genet. 6, e1000932 (2010).
30. Kinzler, K.W. & Vogelstein, B. Landscaping the cancer terrain. Science 280, 1036-1037 (1998).
31. Ferron, M. et al. Insulin signaling in osteoblasts integrates bone remodeling and energy metabolism. Cell 142, 296-308 (2010).
32. Schadt, E.E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218-223 (2009).
33. Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382-390 (2005).
34. Wang, K. et al. Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nat. Biotechnol. 27, 829-839 (2009).
35. Mani, K.M. et al. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol. Syst. Biol. 4, 169 (2008).
36. Luscombe, N.M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308-312 (2004).
37. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385-1389 (2010).
38. Pan, W. Network-based model weighting to detect multiple loci influencing complex diseases. Hum. Genet. 124, 225-234 (2008).
39. Chen, G.K. & Thomas, D.C. Using biological knowledge to discover higher order interactions in genetic association studies. Genet. Epidemiol. 34, 863-878 (2010).
40. Calvano, S.E. et al. A network-based analysis of systemic inflammation in humans. Nature 437, 1032-1037 (2005).
41. Quayle, A.P., Siddiqui, A.S. & Jones, S.J. Perturbation of interaction networks for application to cancer therapy. Cancer Inform. 5, 45-65 (2007).
42. Birtwistle, M.R. et al. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol. Syst. Biol. 3, 144 (2007).
43. Nelander, S. et al. Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4, 216 (2008).
44. Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166-176 (2003).
45. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799-805 (2004).
46. Gardner, T.S., di Bernardo, D., Lorenz, D. & Collins, J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102-105 (2003).
47. Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415-1426 (2007).
48. Bandyopadhyay, S. et al. A human MAP kinase interactome. Nat. Methods 7, 801-805 (2010).
49. Huang, Y. et al. A study of miRNAs targets prediction and experimental validation. Protein Cell 1, 979-986 (2010).
50. Shlomi, T., Cabili, M.N., Herrgard, M.J., Palsson, B.O. & Ruppin, E. Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol. 26, 1003-1010 (2008).
51. Zhu, J. et al. An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet. Genome Res. 105, 363-374 (2004).
52. Yang, X. et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat. Genet. 41, 415-423 (2009).
53. Carro, M.S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318-325 (2010).
54. Zhao, X. et al. The N-Myc-DLL3 cascade is suppressed by the ubiquitin ligase Huwe1 to inhibit proliferation and promote neurogenesis in the developing brain. Dev. Cell 17, 210-221 (2009).
55. Yang, X. et al. Identification and validation of genes affecting aortic lesions in mice. J. Clin. Invest. 120, 2414-2422 (2010).
56. König, R. et al. Human host factors required for influenza virus replication. Nature 463, 813-817 (2010).
57. Jansen, R. et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449-453 (2003).
58. Sharan, R. et al. Conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. USA 102, 1974-1979 (2005).
59. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929-1935 (2006).
60. Wang, W., Cherry, J.M., Botstein, D. & Li, H. A systematic approach to reconstructing transcription networks in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA 99, 16893-16898 (2002).
61. Pe'er, D., Regev, A., Elidan, G. & Friedman, N. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 (suppl. 1), S215-S224 (2001).
62. di Bernardo, D. et al. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23, 377-383 (2005).
63. Chen, B.J. et al. Harnessing gene expression to identify the genetic basis of drug resistance. Mol. Syst. Biol. 5, 310 (2009).
64. Zhu, J. et al. Characterizing dynamic changes in the human blood transcriptional network. PLoS Comput. Biol. 6, e1000671 (2010).
65. Leonardson, A.S. et al. The effect of food intake on gene expression in human peripheral blood. Hum. Mol. Genet. 19, 159-169 (2010).
66. Chautard, E., Thierry-Mieg, N. & Ricard-Blum, S. Interaction networks: from protein functions to drug discovery. A review. Pathol. Biol. (Paris) 57, 324-333 (2009).
67. Jia, J. et al. Mechanisms of drug combinations: interaction and network perspectives. Nat. Rev. Drug Discov. 8, 111-128 (2009).
68. Xie, L., Li, J. & Bourne, P.E. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput. Biol. 5, e1000387 (2009).
69. Pandey, G. et al. An integrative multi-network and multi-classifier approach to predict genetic interactions. PLoS Comput. Biol. 6 pii: e1000928 (2010).
70. Compagno, M. et al. Mutations of multiple genes cause deregulation of NF-κB in diffuse large B-cell lymphoma. Nature 459, 717-721 (2009).
71. Lenz, G. et al. Stromal gene signatures in large-B-cell lymphomas. N. Engl. J. Med. 359, 2313-2323 (2008).
72. Feldman, I., Rzhetsky, A. & Vitkup, D. Network properties of genes harboring inherited disease mutations. Proc. Natl. Acad. Sci. USA 105, 4323-4328 (2008).
73. Ideker, T., Ozier, O., Schwikowski, B. & Siegel, A.F. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 (suppl. 1), S233-S240 (2002).
74. Zien, A., Kuffner, R., Zimmer, R. & Lengauer, T. Analysis of gene expression data with pathway scores. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 407-417 (2000).
75. Faust, K., Dupont, P., Callut, J. & van Helden, J. Pathway discovery in metabolic networks by subgraph extraction. Bioinformatics 26, 1211-1218 (2010).
76. Ulitsky, I., Krishnamurthy, A., Karp, R.M. & Shamir, R. DEGAS: de novo discovery of dysregulated pathways in human diseases. PLoS ONE 5, e13367 (2010).
77. Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309-316 (2007).
78. Jia, P., Zheng, S., Long, J., Zheng, W. & Zhao, Z. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics 27, 95-102 (2011).
79. Hannum, G. et al. Genome-wide association data reveal a global map of genetic interactions among protein complexes. PLoS Genet. 5, e1000782 (2009).
80. Lee, S.I., Pe'er, D., Dudley, A.M., Church, G.M. & Koller, D. Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc. Natl. Acad. Sci. USA 103, 14062-14067 (2006).
81. Akavia, U.D. et al. An integrated approach to uncover drivers of cancer. Cell 143, 1005-1017 (2010).
82. Rhodes, D.R. et al. Mining for regulatory programs in the cancer transcriptome. Nat. Genet. 37, 579-583 (2005).
83. Orozco, G. et al. Analysis of a functional BTNL2 polymorphism in type 1 diabetes, rheumatoid arthritis, and systemic lupus erythematosus. Hum. Immunol. 66, 1235-1241 (2005).
84. Sirota, M., Schaub, M.A., Batzoglou, S., Robinson, W.H. & Butte, A.J. Autoimmune disease classification by inverse association with SNP alleles. PLoS Genet. 5, e1000792 (2009).
85. TCGA-Consortium. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061-1068 (2008).
86. Phillips, H.S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157-173 (2006).
87. Verhaak, R.G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98-110 (2010).
88. Marko, N.F., Quackenbush, J. & Weil, R.J. Why is there a lack of consensus on molecular subgroups of glioblastoma? Understanding the nature of biological and statistical variability in glioblastoma expression data. PLoS ONE 6, e20826 (2011).