Molecular network analysis enhances understanding of the biology of mental disorders

BioEssays

Volumn 36, Issue 6, 2014, Pages 606-616

  Grennan, Kay S ^a   Chen, Chao ^a   Gershon, Elliot S ^a   Liu, Chunyu ^a,b  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^b CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Mental disorders; Molecular networks; Neuropsychiatric disease; Systems biology

Indexed keywords

ARTICLE; DISEASE CLASSIFICATION; EPISTASIS; GENE CONTROL; GENE EXPRESSION; GENETIC ASSOCIATION; GENETIC VARIABILITY; MENTAL DISEASE; MOLECULAR INTERACTION; NEUROLOGIC DISEASE; PHENOTYPE; PLEIOTROPY; GENE REGULATORY NETWORK; GENETICS; HUMAN;

GENE REGULATORY NETWORKS; HUMANS; MENTAL DISORDERS; PHENOTYPE;

EID: 84900000180     PISSN: 02659247     EISSN: 15211878     Source Type: Journal    
DOI: 10.1002/bies.201300147     Document Type: Article

References (122)

1. Sullivan PF, Daly MJ, O'Donovan M. 2012. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet 13: 537-51.
2. Camargo LM, Collura V, Rain JC, Mizuguchi K, et al. 2007. Disrupted in schizophrenia 1 interactome: evidence for the close connectivity of risk genes and a potential synaptic basis for schizophrenia. Mol Psychiatry 12: 74-86.
3. Fields S, Song O. 1989. A novel genetic system to detect protein-protein interactions. Nature 340: 245-6.
4. Millar JK, Wilson-Annan JC, Anderson S, Christie S, et al. 2000. Disruption of two novel genes by a translocation co-segregating with schizophrenia. Hum Mol Genet 9: 1415-23.
5. Goh KI, Cusick ME, Valle D, Childs B, et al. 2007. The human disease network. Proc Natl Acad Sci USA 104: 8685-90.
6. Feldman I, Rzhetsky A, Vitkup D. 2008. Network properties of genes harboring inherited disease mutations. Proc Natl Acad Sci USA 105: 4323-8.
7. Wolfe CJ, Kohane IS, Butte AJ. 2005. Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks. BMC Bioinformatics 6: 227.
8. Bogdanov P, Singh AK. 2010. Molecular function prediction using neighborhood features. IEEE/ACM Trans Comput Biol Bioinform 7: 208-17.
9. Gillis J, Pavlidis P. 2012. " Guilt by association" is the exception rather than the rule in gene networks. PLoS Comput Biol 8: e1002444.
10. Milenkovic T, Przulj N. 2012. Topological characteristics of molecular networks. In: Koyuturk M, Subramaniam S, Green E, ed; Functional Coherence of Molecular Networks in Bioinformatics. New York: Springer, p. 15-48.
11. Lima-Mendez G, van Helden J. 2009. The powerful law of the power law and other myths in network biology. Mol Biosyst 5: 1482-93.
12. Zhang B, Horvath S. 2005. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4: 1-45.
13. Chen C, Cheng L, Grennan K, Pibiri F, et al. 2013. Two gene co-expression modules differentiate psychotics and controls. Mol Psychiatry 18: 1308-14.
14. Draisma HH, Reijmers TH, Meulman JJ, van der Greef J, et al. 2013. Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families. Eur J Hum Genet 21: 95-101.
15. Chen G, Sullivan PF, Kosorok MR. 2013. Biclustering with heterogeneous variance. Proc Natl Acad Sci USA 110: 12253-8.
16. Diaz-Uriarte R, Alvarez de Andres S. 2006. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7: 3.
17. Schena M, Shalon D, Davis RW, Brown PO. 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270: 467-70.
18. Weimann M, Grossmann A, Woodsmith J, Ozkan Z, et al. 2013. A Y2H-seq approach defines the human protein methyltransferase interactome. Nat Methods 10: 339-42.
19. Waaijers S, Koorman T, Kerver J, Boxem M. 2013. Identification of human protein interaction domains using an ORFeome-based yeast two-hybrid fragment library. J Proteome Res 12: 3181-92.
20. Fujimori S, Hirai N, Ohashi H, Masuoka K, et al. 2012. Next-generation sequencing coupled with a cell-free display technology for high-throughput production of reliable interactome data. Sci Rep 2: 691.
21. Yu H, Tardivo L, Tam S, Weiner E, et al. 2011. Next-generation sequencing to generate interactome datasets. Nat Methods 8: 478-80.
22. Ewing RM, Chu P, Elisma F, Li H, et al. 2007. Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol Syst Biol 3: 89.
23. Lopes TJ, Schaefer M, Shoemaker J, Matsuoka Y, et al. 2011. Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases. Bioinformatics 27: 2414-21.
24. Bernstein BE, Birney E, Dunham I, Green ED, et al. 2012. An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57-74.
25. Murigneux V, Sauliere J, Roest CH, Le HH. 2013. Transcriptome-wide identification of RNA binding sites by CLIP-seq. Methods 63: 32-40.
26. Zhang C, Darnell RB. 2011. Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat Biotechnol 29: 607-14.
27. Hafner M, Landthaler M, Burger L, Khorshid M, et al. 2010. PAR-CliP - a method to identify transcriptome-wide the binding sites of RNA binding proteins. J Vis Exp pii: 2034.
28. Konig J, Zarnack K, Rot G, Curk T, et al. 2011. iCLIP - transcriptome-wide mapping of protein-RNA interactions with individual nucleotide resolution. J Vis Exp pii: 2638.
29. Helwak A, Kudla G, Dudnakova T, Tollervey D. 2013. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153: 654-65.
30. Li JH, Liu S, Zhou H, Qu LH, et al. 2014. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42: D92-7.
31. Jia P, Kao CF, Kuo PH, Zhao Z. 2011. A comprehensive network and pathway analysis of candidate genes in major depressive disorder. BMC Syst Biol 5: S12.
32. Noh HJ, Ponting CP, Boulding HC, Meader S, et al. 2013. Network topologies and convergent aetiologies arising from deletions and duplications observed in individuals with autism. PLoS Genet 9: e1003523.
33. Lee SA, Tsao TT, Yang KC, Lin H, et al. 2011. Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression. BMC Bioinformatics 12: S20.
34. Luo X, Huang L, Jia P, Li M, et al. 2014. Protein-protein interaction and pathway analyses of top schizophrenia genes reveal schizophrenia susceptibility genes converge on common molecular networks and enrichment of nucleosome (chromatin) assembly genes in schizophrenia susceptibility loci. Schizophr Bull 40: 39-49.
35. Sun J, Jia P, Fanous AH, van den Oord EJ, et al. 2010. Schizophrenia gene networks and pathways and their applications for novel candidate gene selection. PLoS One 5: e11351.
36. Ben-David E, Shifman S. 2012. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet 8: e1002556.
37. Gulsuner S, Walsh T, Watts AC, Lee MK, et al. 2013. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154: 518-29.
38. Gilman SR, Chang J, Xu B, Bawa TS, et al. 2012. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat Neurosci 15: 1723-8.
39. Gilman SR, Iossifov I, Levy D, Ronemus M, et al. 2011. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70: 898-907.
40. Sakai Y, Shaw CA, Dawson BC, Dugas DV, et al. 2011. Protein interactome reveals converging molecular pathways among autism disorders. Sci Transl Med 3: 86ra49.
41. Parikshak NN, Luo R, Zhang A, Won H, et al. 2013. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155: 1008-21.
42. Willsey AJ, Sanders SJ, Li M, Dong S, et al. 2013. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155: 997-1007.
43. Oldham MC, Horvath S, Geschwind DH. 2006. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci USA 103: 17973-8.
44. Voineagu I, Wang X, Johnston P, Lowe JK, et al. 2011. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474: 380-4.
45. Zhang B, Gaiteri C, Bodea LG, Wang Z, et al. 2013. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153: 707-20.
46. Bogdanov P, Macropol K, Singh AK. 2012. Function annotation in gene networks. In: Koyutürk M, Subramaniam S, Grama A, ed; Functional Coherence in Molecular Networks in Bioinformatics. p. 49-68.
47. Franke L, van Bakel H, Fokkens L, de Jong ED, et al. 2006. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am J Hum Genet 78: 1011-25.
48. Lage K, Karlberg EO, Storling ZM, Olason PI, et al. 2007. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25: 309-16.
49. Kohler S, Bauer S, Horn D, Robinson PN. 2008. Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82: 949-58.
50. Vanunu O, Magger O, Ruppin E, Shlomi T, et al. 2010. Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol 6: E1000641.
51. Lee I, Blom UM, Wang PI, Shim JE, et al. 2011. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 21: 1109-21.
52. Doncheva NT, Kacprowski T, Albrecht M. 2012. Recent approaches to the prioritization of candidate disease genes. Wiley Interdiscip Rev Syst Biol Med 4: 429-42.
53. Magger O, Waldman YY, Ruppin E, Sharan R. 2012. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Comput Biol 8: e1002690.
54. Karni S, Soreq H, Sharan R. 2009. A network-based method for predicting disease-causing genes. J Comput Biol 16: 181-9.
55. Miller JA, Horvath S, Geschwind DH. 2010. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci USA 107: 12698-703.
56. Langfelder P, Mischel PS, Horvath S. 2013. When is hub gene selection better than standard meta-analysis? PLoS One 8: e61505.
57. Khatri P, Sirota M, Butte AJ. 2012. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8: e1002375.
58. Poultney CS, Greenfield A, Bonneau R. 2012. Integrated inference and analysis of regulatory networks from multi-level measurements. Methods Cell Biol 110: 19-56.
59. Holzinger ER, Ritchie MD. 2012. Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics 13: 213-22.
60. Costanzo M, Baryshnikova A, Bellay J, Kim Y, et al. 2010. The genetic landscape of a cell. Science 327: 425-31.
61. Ames RM, Macpherson JI, Pinney JW, Lovell SC, et al. 2013. Modular biological function is most effectively captured by combining molecular interaction data types. PLoS One 8: e62670.
62. Zuk O, Hechter E, Sunyaev SR, Lander ES. 2012. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA 109: 1193-8.
63. Hill WG, Goddard ME, Visscher PM. 2008. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4: e1000008.
64. Moore JH. 2003. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56: 73-82.
65. Tyler AL, Asselbergs FW, Williams SM, Moore JH. 2009. Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. BioEssays 31: 220-7.
66. Judy JT, Seifuddin F, Pirooznia M, Mahon PB, et al. 2013. Converging evidence for epistasis between ANK3 and potassium channel gene KCNQ2 in bipolar disorder. Front Genet 4: 87.
67. Qin S, Zhao X, Pan Y, Liu J, et al. 2005. An association study of the N-methyl-d-aspartate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray. Eur J Hum Genet 13: 807-14.
68. Ma DQ, Whitehead PL, Menold MM, Martin ER, et al. 2005. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet 77: 377-88.
69. Combarros O, Cortina-Borja M, Smith AD, Lehmann DJ. 2009. Epistasis in sporadic Alzheimer's disease. Neurobiol Aging 30: 1333-49.
70. Cordell HJ. 2009. Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10: 392-404.
71. Emily M, Mailund T, Hein J, Schauser L, et al. 2009. Using biological networks to search for interacting loci in genome-wide association studies. Eur J Hum Genet 17: 1231-40.
72. Lee I, Lehner B, Vavouri T, Shin J, et al. 2010. Predicting genetic modifier loci using functional gene networks. Genome Res 20: 1143-53.
73. Stearns FW. 2010. One hundred years of pleiotropy: a retrospective. Genetics 186: 767-73.
74. Featherstone DE, Broadie K. 2002. Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network. BioEssays 24: 267-74.
75. Wagner GP, Zhang J. 2011. The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms. Nat Rev Genet 12: 204-13.
76. Hill WG, Zhang XS. 2012. On the pleiotropic structure of the genotype-phenotype map and the evolvability of complex organisms. Genetics 190: 1131-7.
77. Paaby AB, Rockman MV. 2013. Pleiotropy: what do you mean? Reply to Zhang and Wagner. Trends Genet 29: 384.
78. Smoller JW, Craddock N, Kendler K, Lee PH, et al. 2013. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381: 1371-9.
79. Lee SH, Ripke S, Neale BM, Faraone SV, et al. 2013. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 45: 984-94.
80. Rzhetsky A, Wajngurt D, Park N, Zheng T. 2007. Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci USA 104: 11694-9.
81. Vorstman JA, Breetvelt EJ, Thode KI, Chow EW, et al. 2013. Expression of autism spectrum and schizophrenia in patients with a 22q11.2 deletion. Schizophr Res 143: 55-9.
82. Jonas RK, Montojo CA, Bearden CE. 2013. The 22q11.2 deletion syndrome as a window into complex neuropsychiatric disorders over the lifespan. Biol Psychiatry 75: 351-60.
83. Splawski I, Timothy KW, Decher N, Kumar P, et al. 2005. Severe arrhythmia disorder caused by cardiac L-type calcium channel mutations. Proc Natl Acad Sci USA 102: 8089-96.
84. Gershon ES, Grennan K, Busnello J, Badner JA, et al. 2014. A rare mutation of CACNA1C in a patient with bipolar disorder, and decreased gene expression associated with a bipolar-associated common SNP of CACNA1C in brain. Mol Psychiatry, in press, doi: 10.1038/mp.2013.107.
85. Navarrete K, Pedroso I, de Jong S, Stefansson H, et al. 2013. TCF4 (e2-2; ITF2): a schizophrenia-associated gene with pleiotropic effects on human disease. Am J Med Genet B Neuropsychiatr Genet 162B: 1-16.
86. Selbach M, Schwanhausser B, Thierfelder N, Fang Z, et al. 2008. Widespread changes in protein synthesis induced by microRNAs. Nature 455: 58-63.
87. Grennan K, Zhang F, Liu CY. 2012. MicroRNAs and their potential roles in schizophrenia and bipolar disorder. In Yue J, Lu L, ed; miRNAs and Human Diseases. Trivandrum: Research Signpost, p. 131-54.
88. Guella I, Sequeira A, Rollins B, Morgan L, et al. 2013. Analysis of miR-137 expression and rs1625579 in dorsolateral prefrontal cortex. J Psychiatr Res 47: 1215-21.
89. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. 2011. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 43: 969-76.
90. Zhang W, Zhu J, Schadt EE, Liu JS. 2010. A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules. PLoS Comput Biol 6: e1000642.
91. Miguez DG. 2013. Network nonlinearities in drug treatment. Interdiscip Sci 5: 85-94.
92. Isalan M. 2009. Gene networks and liar paradoxes. BioEssays 31: 1110-5.
93. Gerstein MB, Kundaje A, Hariharan M, Landt SG, et al. 2012. Architecture of the human regulatory network derived from ENCODE data. Nature 489: 91-100.
94. Zhang EE, Kay SA. 2010. Clocks not winding down: unravelling circadian networks. Nat Rev Mol Cell Biol 11: 764-76.
95. Shi J, Wittke-Thompson JK, Badner JA, Hattori E, et al. 2008. Clock genes may influence bipolar disorder susceptibility and dysfunctional circadian rhythm. Am J Med Genet B Neuropsychiatr Genet 147B: 1047-55.
96. Kitano H. 2004. Biological robustness. Nat Rev Genet 5: 826-37.
97. Albert R, Jeong H, Barabasi AL. 2000. Error and attack tolerance of complex networks. Nature 406: 378-82.
98. Bergman A, Siegal ML. 2003. Evolutionary capacitance as a general feature of complex gene networks. Nature 424: 549-52.
99. Levy SF, Siegal ML. 2008. Network hubs buffer environmental variation in Saccharomyces cerevisiae. PLoS Biol 6: e264.
100. Alawieh A, Zaraket FA, Li JL, Mondello S, et al. 2012. Systems biology, bioinformatics, and biomarkers in neuropsychiatry. Front Neurosci 6: 187.
101. Craddock N, Kendler K, Neale M, Nurnberger J, et al. 2009. Dissecting the phenotype in genome-wide association studies of psychiatric illness. Br J Psychiatry 195: 97-9.
102. Lyalina S, Percha B, Lependu P, Iyer SV, et al. 2013. Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. J Am Med Inform Assoc 20: e297-305.
103. Fava GA, Tossani E, Bech P, Berrocal C, et al. 2014. Emerging clinical trends and perspectives on comorbid patterns of mental disorders in research. Int J Methods Psychiatr Res 23: 92-101.
104. Gottesman II, Gould TD. 2003. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 160: 636-45.
105. Greenwood TA, Swerdlow NR, Gur RE, Cadenhead KS, et al. 2013. Genome-wide linkage analyses of 12 endophenotypes for schizophrenia from the Consortium on the Genetics of Schizophrenia. Am J Psychiatry 170: 521-32.
106. Geng X, Prom-Wormley EC, Perez J, Kubarych T, et al. 2012. White matter heritability using diffusion tensor imaging in neonatal brains. Twin Res Hum Genet 15: 336-50.
107. Tamminga CA, Ivleva EI, Keshavan MS, Pearlson GD, et al. 2013. Clinical phenotypes of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). Am J Psychiatry 170: 1263-74.
108. Zhang B, Li H, Riggins RB, Zhan M, et al. 2009. Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics 25: 526-32.
109. Dobrin R, Zhu J, Molony C, Argman C, et al. 2009. Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol 10: R55.
110. Xu L, Barker B, Gu Z. 2012. Dynamic epistasis for different alleles of the same gene. Proc Natl Acad Sci USA 109: 10420-5.
111. Foley KS, Young PW. 2013. An analysis of splicing, actin-binding properties, heterodimerization and molecular interactions of the non-muscle alpha-actinins. Biochem J 452: 477-88.
112. Przytycka TM, Singh M, Slonim DK. 2010. Toward the dynamic interactome: it's about time. Brief Bioinform 11: 15-29.
113. Kunieda T, Minamino T, Katsuno T, Tateno K, et al. 2006. Cellular senescence impairs circadian expression of clock genes in vitro and in vivo. Circ Res 98: 532-9.
114. Zhang L, Lin QL, Lu L, Yang CC, et al. 2013. Tissue-specific modification of clock methylation in aging mice. Eur Rev Med Pharmacol Sci 17: 1874-80.
115. Schaefer MH, Lopes TJ, Mah N, Shoemaker JE, et al. 2013. Adding protein context to the human protein-protein interaction network to reveal meaningful interactions. PLoS Comput Biol 9: e1002860.
116. Barshir R, Basha O, Eluk A, Smoly IY, et al. 2013. The TissueNet database of human tissue protein-protein interactions. Nucleic Acids Res 41: D841-4.
117. Bearer EL. 2012. HSV, axonal transport and Alzheimer's disease: in vitro and in vivo evidence for causal relationships. Future Virol 7: 885-99.
118. Hamza TH, Chen H, Hill-Burns EM, Rhodes SL, et al. 2011. Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson's disease modifier gene via interaction with coffee. PLoS Genet 7: e1002237.
119. Purves D, Augustine GJ, Fitzpatrick D, Hall WC, et al. 2008. Neuroscience, 4th edition. Sunderland, MA, USA: Sinauer Associates, Inc.
120. Zhang W, Ota T, Shridhar V, Chien J, et al. 2013. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment. PLoS Comput Biol 9: e1002975.
121. Taylor IW, Linding R, Warde-Farley D, Liu Y, et al. 2009. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 27: 199-204.
122. Bader JS. 2011. Grand network convergence. Genome Biol 12: 306.