Skip to main content

Thank you for visiting 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.

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Key Points

  • When applying high-throughput molecular methods to the study of neurodevelopmental disorders, major challenges include the spatial and temporal heterogeneity of the brain, a lack of appropriate tissue available for studies and poorly defined phenotypes.

  • Transcriptomics assays are currently the most widely used functional genomic assays in neurobiology owing to their ability to efficiently capture tissue-specific spatial and temporal heterogeneity in a high-throughput manner. Principles from transcriptomic studies will aid in evaluating additional molecular and cellular levels of regulation.

  • We review the principles of network analysis and describe how gene networks provide a framework to organize, integrate and analyse large-scale genomic data sets in neurobiology.

  • We review representative differential expression and gene network studies in neurodevelopmental disorders and neurodegenerative diseases and identify some next steps in data generation and integration that are necessary for progress in the field.

  • We provide guidelines for designing, analysing and evaluating high-throughput transcriptomic studies in the brain in order to improve study quality and reproducibility.


Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Molecular systems and the neurobiological hierarchy.
Figure 2: Flowchart of transcriptomic analysis and illustration of seeded and genome-wide approaches to network analysis.
Figure 3: Transcriptomic convergence and divergence across central nervous system disorders.


  1. 1

    Gratten, J., Wray, N. R., Keller, M. C. & Visscher, P. M. Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat. Neurosci. 17, 782–790 (2014). A comprehensive review of GWASs and exome studies across major neuropsychiatric disorders. It discusses the role of common variants and rare variants across disorders, and the concept of explaining heritability.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Beyer, A., Bandyopadhyay, S. & Ideker, T. Integrating physical and genetic maps: from genomes to interaction networks. Nat. Rev. Genet. 8, 699–710 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).

    CAS  PubMed  Google Scholar 

  4. 4

    Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Grant, S. Systems biology in neuroscience: bridging genes to cognition. Curr. Opin. Neurobiol. 13, 577–582 (2003).

    CAS  PubMed  Google Scholar 

  6. 6

    Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat. Methods 6, 875–881 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Kandel, E. R., Markram, H., Matthews, P. M., Yuste, R. & Koch, C. Neuroscience thinks big (and collaboratively). Nat. Rev. Neurosci. 14, 659–664 (2013).

    CAS  PubMed  Google Scholar 

  8. 8

    Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2013).

    CAS  PubMed  Google Scholar 

  9. 9

    Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

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

  11. 11

    Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

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

  15. 15

    Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    CAS  Google Scholar 

  16. 16

    Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014). A large eQTL study across multiple brain regions that identified region-specific eQTLs and demonstrated the value and promise of eQTL analysis in the brain.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Dolmetsch, R., Geschwind, D. H. & Geschwind, D. H. The human brain in a dish: the promise of iPSC-derived neurons. Cell 145, 831–834 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Jucker, M. The benefits and limitations of animal models for translational research in neurodegenerative diseases. Nat. Med. 16, 1210–1214 (2010).

    CAS  PubMed  Google Scholar 

  20. 20

    Nelson, S. B., Sugino, K. & Hempel, C. M. The problem of neuronal cell types: a physiological genomics approach. Trends Neurosci. 29, 339–345 (2006).

    CAS  PubMed  Google Scholar 

  21. 21

    DeFelipe, J. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat. Rev. Neurosci. 14, 202–216 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Casey, B. J. et al. DSM-5 and RDoC: progress in psychiatry research? Nat. Rev. Neurosci. 14, 810–814 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Geschwind, D. H. Autism: many genes, common pathways? Cell 135, 391–395 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    PubMed  Google Scholar 

  25. 25

    Carter, S. L., Brechbühler, C. M., Griffin, M. & Bond, A. T. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20, 2242–2250 (2004).

    CAS  PubMed  Google Scholar 

  26. 26

    Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013). A study that constructs genome-wide co-expression networks to identify modules spanning prenatal human brain development and demonstrates how module structure can be validated with multiple data sources and how tissue-specific and temporally specific co-expression modules can provide new biological insights into genetic variants.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Mitra, K., Carvunis, A.-R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Carter, H., Hofree, M. & Ideker, T. Genotype to phenotype via network analysis. Curr. Opin. Genet. Dev. 23, 611–621 (2013).

    CAS  PubMed  Google Scholar 

  30. 30

    Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011). This study identifies common transcriptomic changes in a heterogeneous neuropsychiatric disorder and illustrates how gene network modules can be validated experimentally and how network modules can be related to GWAS findings.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Horvath, S. et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc. Natl Acad. Sci. USA 103, 17402–17407 (2006).

    CAS  PubMed  Google Scholar 

  32. 32

    Winden, K. D. et al. The organization of the transcriptional network in specific neuronal classes. Mol. Syst. Biol. 5, 291 (2009).

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Wolfe, C. J., Kohane, I. S. & Butte, A. J. Systematic survey reveals general applicability of 'guilt-by-association' within gene coexpression networks. BMC Bioinformatics 6, 227 (2005).

    PubMed  PubMed Central  Google Scholar 

  34. 34

    Dougherty, J. D. et al. PBK/TOPK, a proliferating neural progenitor-specific mitogen-activated protein kinase kinase. J. Neurosci. 25, 10773–10785 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011). A description and comparison of multiple metrics for measuring modular structure in networks, which provides a statistical framework for demonstrating module preservation that is included in the R package WGCNA.[Au:OK?]

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Hudson, N. J., Reverter, A. & Dalrymple, B. P. A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput. Biol. 5, e1000382 (2009).

    PubMed  PubMed Central  Google Scholar 

  37. 37

    Choi, J. K., Yu, U., Yoo, O. J. & Kim, S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21, 4348–4355 (2005).

    CAS  PubMed  Google Scholar 

  38. 38

    Song, L., Langfelder, P. & Horvath, S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 13, 328 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Allen, J. D., Xie, Y., Chen, M., Girard, L. & Xiao, G. Comparing statistical methods for constructing large scale gene networks. PLoS ONE 7, e29348 (2012). This study compares multiple approaches for constructing large-scale gene networks, including methods based on correlation and mutual information.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Ballouz, S., Verleyen, W. & Gillis, J. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics (2015). An evaluation of sample size and power for constructing co-expression networks with RNA-seq.

  41. 41

    Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13, 13–24 (2013).

    PubMed  PubMed Central  Google Scholar 

  42. 42

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4,17 (2005). This paper describes the value of weighted co-expression networks over binary co-expression networks and discusses the theory behind the widely used WGCNA package.

  43. 43

    Ramani, A. K. et al. A map of human protein interactions derived from co-expression of human mRNAs and their orthologs. Mol. Syst. Biol. 4, 180 (2008).

    PubMed  PubMed Central  Google Scholar 

  44. 44

    Dong, J. & Horvath, S. Understanding network concepts in modules. BMC Syst. Biol. 1, 24 (2007).

    PubMed  PubMed Central  Google Scholar 

  45. 45

    Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7, S7 (2006).

    PubMed  PubMed Central  Google Scholar 

  46. 46

    Wexler, E. M. et al. Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways. Sci. Signal. 4, ra65 (2011).

    PubMed  Google Scholar 

  47. 47

    Ernst, J. & Bar-Joseph, Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7, 191 (2006).

    PubMed  PubMed Central  Google Scholar 

  48. 48

    Zoppoli, P., Morganella, S. & Ceccarelli, M. TimeDelay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 11, 154 (2010).

    PubMed  PubMed Central  Google Scholar 

  49. 49

    Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012). This paper, from the DREAM challenge on regulatory network reconstruction, describes the results of applying multiple regulatory network inference algorithms to three large data sets from bacteria and yeast.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Hakes, L., Pinney, J. W., Robertson, D. L. & Lovell, S. C. Protein–protein interaction networks and biology — what's the connection? Nat. Biotechnol. 26, 69–72 (2008).

    CAS  PubMed  Google Scholar 

  53. 53

    Hart, G. T., Ramani, A. K. & Marcotte, E. M. How complete are current yeast and human protein-interaction networks? Genome Biol. 7, 120 (2006).

    PubMed  PubMed Central  Google Scholar 

  54. 54

    Lundby, A. et al. Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics. Nat. Methods 11, 868–874 (2014). This study experimentally defines PPIs specific to cardiac tissue for four genes known to cause long QT syndrome and demonstrates how tissue-relevant PPI networks can be used to prioritize genetic association signals.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).

    CAS  PubMed  Google Scholar 

  56. 56

    Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Cotney, J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Tompa, M. et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol. 23, 137–144 (2005).

    CAS  PubMed  Google Scholar 

  60. 60

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Pique-Regi, R. et al. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res. 21, 447–455 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Ropers, H. H. Genetics of intellectual disability. Curr. Opin. Genet. Dev. 18, 241–250 (2008).

    CAS  PubMed  Google Scholar 

  63. 63

    van Bokhoven, H. Genetic and epigenetic networks in intellectual disabilities. Annu. Rev. Genet. 45, 81–104 (2011).

    CAS  PubMed  Google Scholar 

  64. 64

    Matson, J. L. & Shoemaker, M. Intellectual disability and its relationship to autism spectrum disorders. Res. Dev. Disabil. 30, 1107–1114 (2009).

    PubMed  Google Scholar 

  65. 65

    Lubs, H. A., Stevenson, R. E. & Schwartz, C. E. Fragile X and X-linked intellectual disability: four decades of discovery. Am. J. Hum. Genet. 90, 579–590 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012).

    CAS  PubMed  Google Scholar 

  67. 67

    Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012).

    CAS  PubMed  Google Scholar 

  68. 68

    Gilissen, C. et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344–347 (2014).

    CAS  PubMed  Google Scholar 

  69. 69

    Jamain, S. et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat. Genet. 34, 27–29 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    O'Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Abrahams, B. S. & Geschwind, D. H. Advances in autism genetics: on the threshold of a new neurobiology. Nat. Rev. Genet. 9, 341–355 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

    Geschwind, D. H. Genetics of autism spectrum disorders. Trends Cogn. Sci. 15, 409–416 (2011).

    PubMed  PubMed Central  Google Scholar 

  76. 76

    Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Epi4K Consortium & Epilepsy Phenome/Genome Project. De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013).

  79. 79

    Poduri, A. & Lowenstein, D. Epilepsy genetics — past, present, and future. Curr. Opin. Genet. Dev. 21, 325–332 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82

    Xu, B. et al. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat. Genet. 43, 864–868 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Zhu, X., Need, A. C., Petrovski, S. & Goldstein, D. B. One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat. Neurosci. 17, 773–781 (2014).

    CAS  PubMed  Google Scholar 

  84. 84

    Cross-Disorder, Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

  85. 85

    Doherty, J. L. & Owen, M. J. Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice. Genome Med. 6, 29 (2014).

    PubMed  PubMed Central  Google Scholar 

  86. 86

    Hoischen, A., Krumm, N. & Eichler, E. E. Prioritization of neurodevelopmental disease genes by discovery of new mutations. Nat. Neurosci. 17, 764–772 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87

    Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942).

    Google Scholar 

  89. 89

    Masel, J. & Siegal, M. L. Robustness: mechanisms and consequences. Trends Genet. 25, 395–403 (2009). This paper discusses the concept of canalization and its implications for molecular biology and evolution.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Suliman, R., Ben-David, E. & Shifman, S. Chromatin regulators, phenotypic robustness, and autism risk. Front. Genet. 5, 81 (2014).

    PubMed  PubMed Central  Google Scholar 

  91. 91

    Devlin, B. & Scherer, S. W. Genetic architecture in autism spectrum disorder. Curr. Opin. Genet. Dev. 22, 229–237 (2012).

    CAS  PubMed  Google Scholar 

  92. 92

    Purcell, A. E., Jeon, O. H., Zimmerman, A. W., Blue, M. E. & Pevsner, J. Postmortem brain abnormalities of the glutamate neurotransmitter system in autism. Neurology 57, 1618–1628 (2001).

    CAS  PubMed  Google Scholar 

  93. 93

    Garbett, K. et al. Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol. Dis. 30, 303–311 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94

    Ginsberg, M. R., Rubin, R. A., Falcone, T., Ting, A. H. & Natowicz, M. R. Brain transcriptional and epigenetic associations with autism. PLoS ONE 7, e44736 (2012).

    PubMed  PubMed Central  Google Scholar 

  95. 95

    Chow, M. L. et al. Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages. PLoS Genet. 8, e1002592 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96

    Langfelder, P. & Horvath, S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol. 1, 54 (2007).

    PubMed  PubMed Central  Google Scholar 

  97. 97

    Gupta, S. et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat. Commun. 5, 5748 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98

    van Os, J. & Kapur, S. Schizophrenia. Lancet 374, 635–645 (2009).

    CAS  PubMed  Google Scholar 

  99. 99

    Weinberger, D. R. Implications of normal brain development for the pathogenesis of schizophrenia. Arch. Gen. Psychiatry 44, 660–669 (1987).

    CAS  PubMed  Google Scholar 

  100. 100

    Mirnics, K. & Pevsner, J. Progress in the use of microarray technology to study the neurobiology of disease. Nat. Neurosci. 7, 434–439 (2004).

    CAS  PubMed  Google Scholar 

  101. 101

    Mirnics, K., Middleton, F. A., Marquez, A., Lewis, D. A. & Levitt, P. Molecular characterization of schizophrenia viewed by microarray analysis of gene expression in prefrontal cortex. Neuron 28, 53–67 (2000).

    CAS  PubMed  Google Scholar 

  102. 102

    Hashimoto, T. et al. Alterations in GABA-related transcriptome in the dorsolateral prefrontal cortex of subjects with schizophrenia. Mol. Psychiatry 13, 147–161 (2007).

    PubMed  PubMed Central  Google Scholar 

  103. 103

    Hakak, Y. et al. Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia. Proc. Natl Acad. Sci. USA 98, 4746–4751 (2001).

    CAS  PubMed  Google Scholar 

  104. 104

    Altar, C. A. et al. Deficient hippocampal neuron expression of proteasome, ubiquitin, and mitochondrial genes in multiple schizophrenia cohorts. Biol. Psychiatry 58, 85–96 (2005).

    CAS  PubMed  Google Scholar 

  105. 105

    Faludi, G. & Mirnics, K. Synaptic changes in the brain of subjects with schizophrenia. Int. J. Dev. Neurosci. 29, 305–309 (2011).

    PubMed  PubMed Central  Google Scholar 

  106. 106

    Arion, D., Unger, T., Lewis, D. A., Levitt, P. & Mirnics, K. Molecular evidence for increased expression of genes related to immune and chaperone function in the prefrontal cortex in schizophrenia. Biol. Psychiatry 62, 711–721 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107

    Torkamani, A., Dean, B., Schork, N. J. & Thomas, E. A. Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. Genome Res. 20, 403–412 (2010). This work applies mutual information-based co-expression network analysis to transcriptomic data from post-mortem brains of individuals with schizophrenia to identify several schizophrenia-associated modules.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108

    Chen, C. et al. Two gene co-expression modules differentiate psychotics and controls. Mol. Psychiatry 18, 1308–1314 (2012).

    PubMed  PubMed Central  Google Scholar 

  109. 109

    Ben-David, E. & Shifman, S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet. 8, e1002556 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. 110

    Ronan, J. L., Wu, W. & Crabtree, G. R. From neural development to cognition: unexpected roles for chromatin. Nat. Rev. Genet. 14, 347–359 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111

    Basu, S. N., Kollu, R. & Banerjee-Basu, S. AutDB: a gene reference resource for autism research. Nucleic Acids Res. 37, D832–D836 (2009).

    CAS  PubMed  Google Scholar 

  112. 112

    Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113

    Stein, J. L. et al. A quantitative framework to evaluate modeling of cortical development by neural stem cells. Neuron 83, 69–86 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114

    Steinberg, J. & Webber, C. The roles of FMRP-regulated genes in autism spectrum disorder: single- and multiple-hit genetic etiologies. Am. J. Hum. Genet. 93, 825–839 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Gulsuner, S. et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117

    Ronemus, M., Iossifov, I., Levy, D. & Wigler, M. The role of de novo mutations in the genetics of autism spectrum disorders. Nat. Rev. Genet. 15, 133–141 (2014).

    CAS  PubMed  Google Scholar 

  118. 118

    Sugathan, A. et al. CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors. Proc. Natl Acad. Sci. USA 111, E4468–E4477 (2014).

    CAS  PubMed  Google Scholar 

  119. 119

    Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    O'Roak, B. J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121

    Bernier, R. et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122

    Ben-David, E. & Shifman, S. Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism. Mol. Psychiatry 18, 1054–1056 (2012).

    PubMed  Google Scholar 

  123. 123

    Li, J. et al. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol. Syst. Biol. 10, 774–774 (2014).

    PubMed  PubMed Central  Google Scholar 

  124. 124

    Sakai, Y. et al. Protein interactome reveals converging molecular pathways among autism disorders. Sci. Transl Med. 3, 86ra49 (2011).

    PubMed  PubMed Central  Google Scholar 

  125. 125

    Corominas, R. et al. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism. Nat. Commun. 5, 3650 (2014). This study rigorously identifies the interactors of proteins encoded by autism candidate genes using brain-relevant isoforms and identifies interactions among CNV-affected genes.

    PubMed  PubMed Central  Google Scholar 

  126. 126

    Ellis, J. D. et al. Tissue-specific alternative splicing remodels protein-protein interaction networks. Mol. Cell 46, 884–892 (2012).

    CAS  PubMed  Google Scholar 

  127. 127

    Gilman, S. R. et al. 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 (2011). This study applies a rigorous framework to integrate multiple levels of molecular data and evaluates whether genes affected by CNV in autism were functionally interconnected.

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128

    Lee, I. et al. A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat. Genet. 40, 181–188 (2008).

    CAS  PubMed  Google Scholar 

  129. 129

    Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011).

    CAS  PubMed  Google Scholar 

  130. 130

    Noh, H. J. et al. Network topologies and convergent aetiologies arising from deletions and duplications observed in individuals with autism. PLoS Genet. 9, e1003523 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. 131

    Gilman, S. R. et al. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat. Neurosci. 15, 1723–1728 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132

    Chang, J., Gilman, S. R., Chiang, A. H., Sanders, S. J. & Vitkup, D. Genotype to phenotype relationships in autism spectrum disorders. Nat. Neurosci. 18, 191–198 (2015).

    CAS  PubMed  Google Scholar 

  133. 133

    Hormozdiari, F., Penn, O., Borenstein, E. & Eichler, E. E. The discovery of integrated gene networks for autism and related disorders. Genome Res. 25, 142–154 (2015). This study uses a network analysis method that combines gene co-expression and PPIs to identify modules that are highly interconnected in the network but that are also more likely to be mutated in individuals with neurodevelopmental disorders compared with controls.

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134

    Taylor, J. P. Toxic proteins in neurodegenerative disease. Science 296, 1991–1995 (2002).

    CAS  PubMed  Google Scholar 

  135. 135

    Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L. & Greicius, M. D. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42–52 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. 136

    Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L. & Seeley, W. W. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 1216–1227 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137

    Forman, M. S., Trojanowski, J. Q. & Lee, V. M.-Y. Neurodegenerative diseases: a decade of discoveries paves the way for therapeutic breakthroughs. Nat. Med. 10, 1055–1063 (2004).

    CAS  PubMed  Google Scholar 

  138. 138

    Karsten, S. L. et al. A genomic screen for modifiers of tauopathy identifies puromycin-sensitive aminopeptidase as an inhibitor of tau-induced neurodegeneration. Neuron 51, 549–560 (2006).

    CAS  PubMed  Google Scholar 

  139. 139

    Chen-Plotkin, A. S. et al. Variations in the progranulin gene affect global gene expression in frontotemporal lobar degeneration. Hum. Mol. Genet. 17, 1349–1362 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. 140

    Blalock, E. M. et al. Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc. Natl Acad. Sci. USA 101, 2173–2178 (2004).

    CAS  PubMed  Google Scholar 

  141. 141

    Miller, J. A., Woltjer, R. L., Goodenbour, J. M., Horvath, S. & Geschwind, D. H. Genes and pathways underlying regional and cell type changes in Alzheimer's disease. Genome Med. 5, 48 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142

    Kuhn, A., Thu, D., Waldvogel, H. J., Faull, R. L. M. & Luthi-Carter, R. Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat. Methods 8, 945–947 (2011).

    CAS  PubMed  Google Scholar 

  143. 143

    Miller, J. A. & Geschwind, D. H. in Systems Biology for Signaling Networks (ed. Choi, S.) Ch. 25 611–643 (Springer, 2010).

    Google Scholar 

  144. 144

    Liang, W. S. et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc. Natl Acad. Sci. USA 105, 4441–4446 (2008).

    CAS  PubMed  Google Scholar 

  145. 145

    Small, S. A. et al. Model-guided microarray implicates the retromer complex in Alzheimer's disease. Ann. Neurol. 58, 909–919 (2005).

    CAS  PubMed  Google Scholar 

  146. 146

    Muhammad, A. et al. Retromer deficiency observed in Alzheimer's disease causes hippocampal dysfunction, neurodegeneration, and Aβ accumulation. Proc. Natl Acad. Sci. USA 105, 7327–7332 (2008).

    CAS  PubMed  Google Scholar 

  147. 147

    Webster, J. A. et al. Genetic control of human brain transcript expression in Alzheimer disease. Am. J. Hum. Genet. 84, 445–458 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. 148

    Liang, W. S. et al. Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set. Physiol. Genomics 33, 240–256 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149

    Miller, J. A., Oldham, M. C. & Geschwind, D. H. A. Systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J. Neurosci. 28, 1410–1420 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. 150

    Miller, J. A., Horvath, S. & Geschwind, D. H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl Acad. Sci. USA 107, 12698–12703 (2010).

    CAS  PubMed  Google Scholar 

  151. 151

    Ray, M., Ruan, J. & Zhang, W. Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases. Genome Biol. 9, R148 (2008).

    PubMed  PubMed Central  Google Scholar 

  152. 152

    Forabosco, P. et al. Insights into TREM2 biology by network analysis of human brain gene expression data. Neurobiol. Aging 34, 2699–2714 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. 153

    Rosen, E. Y. et al. Functional genomic analyses identify pathways dysregulated by progranulin deficiency, implicating Wnt signaling. Neuron 71, 1030–1042 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. 154

    Wexler, E. M., Paucer, A., Kornblum, H. I., Palmer, T. D. & Geschwind, D. H. Endogenous Wnt signaling maintains neural progenitor cell potency. Stem Cells 27, 1130–1141 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. 155

    Long, J. M., Ray, B. & Lahiri, D. K. MicroRNA-339-5p down-regulates protein expression of β-site amyloid precursor protein-cleaving enzyme 1 (BACE1) in human primary brain cultures and is reduced in brain tissue specimens of Alzheimer disease subjects. J. Biol. Chem. 289, 5184–5198 (2014).

    CAS  PubMed  Google Scholar 

  156. 156

    Lau, P. et al. Alteration of the microRNA network during the progression of Alzheimer's disease. EMBO Mol. Med. 5, 1613–1634 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 157

    Schonrock, N., Matamales, M., Ittner, L. M. & Götz, J. MicroRNA networks surrounding APP and amyloid-β metabolism — implications for Alzheimer's disease. Exp. Neurol. 235, 447–454 (2012).

    CAS  PubMed  Google Scholar 

  158. 158

    Ginsberg, S. D. et al. Single-cell gene expression analysis: implications for neurodegenerative and neuropsychiatric disorders. Neurochem. Res. 29, 1053–1064 (2004).

    CAS  PubMed  Google Scholar 

  159. 159

    Lobo, M. K., Karsten, S. L., Gray, M., Geschwind, D. H. & Yang, X. W. FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains. Nat. Neurosci. 9, 443–452 (2006).

    CAS  PubMed  Google Scholar 

  160. 160

    Doyle, J. P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. 161

    Gong, S. et al. A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature 425, 917–925 (2003).

    CAS  PubMed  Google Scholar 

  162. 162

    Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014). An RNA-seq database of gene expression and splicing differences between major cell types in the mouse CNS that provides cell type-specific profiles that can be used to query cell type specificity in other studies.

    CAS  PubMed  PubMed Central  Google Scholar 

  163. 163

    Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164

    Lim, J. et al. A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 125, 801–814 (2006).

    CAS  PubMed  Google Scholar 

  165. 165

    Lim, J. et al. Opposing effects of polyglutamine expansion on native protein complexes contribute to SCA1. Nature 452, 713–718 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. 166

    Goehler, H. et al. A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease. Mol. Cell 15, 853–865 (2004).

    CAS  PubMed  Google Scholar 

  167. 167

    Shirasaki, D. I. et al. Network organization of the Huntingtin proteomic interactome in mammalian brain. Neuron 75, 41–57 (2012). Illustrates the power of network analysis for defining protein interaction modules across brain regions and time points to understand the huntingtin interactome.

    CAS  PubMed  PubMed Central  Google Scholar 

  168. 168

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. 169

    Civelek, M. & Lusis, A. J. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48 (2013).

    PubMed  PubMed Central  Google Scholar 

  170. 170

    Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013). This study combined network analysis in post-mortem tissue, eQTL mapping and Bayesian causal inference to identify a causal role for the gene TYROBP in Alzheimer disease.

    CAS  PubMed  PubMed Central  Google Scholar 

  171. 171

    Aten, J. E., Fuller, T. F., Lusis, A. J. & Horvath, S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst. Biol. 2, 34 (2008).

    PubMed  PubMed Central  Google Scholar 

  172. 172

    Sham, P. C. & Purcell, S. M. Statistical power and significance testing in large-scale genetic studies. Nat. Rev. Genet. 15, 335–346 (2014).

    CAS  PubMed  Google Scholar 

  173. 173

    Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A. & Kocher, J.-P. Calculating sample size estimates for RNA sequencing data. J. Comput. Biol. 20, 970–978 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. 174

    Robles, J. A. et al. Efficient experimental design and analysis strategies for the detection of differential expression using RNA-sequencing. BMC Genomics 13, 484 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. 175

    Rapaport, F. et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 14, R95 (2013).

    PubMed  PubMed Central  Google Scholar 

  176. 176

    Ching, T., Huang, S. & Garmire, L. X. Power analysis and sample size estimation for RNA-seq differential expression. RNA 20, 1684–1696 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. 177

    Hansen, K. D., Wu, Z., Irizarry, R. A. & Leek, J. T. Sequencing technology does not eliminate biological variability. Nat. Biotechnol. 29, 572–573 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  178. 178

    Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010). A must-read paper prior to pursuing the design or analysis of a high-throughput experiment, it contains advice and analyses for evaluating the contribution of technical and biological variation in data sets.

    CAS  PubMed  Google Scholar 

  179. 179

    Trabzuni, D. et al. Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J. Neurochem. 120, 473–473 (2011).

    Google Scholar 

  180. 180

    Hoen, P. A., Friedländer, M. R. & Almlöf, J. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31, 1015–1022 (2013).

    PubMed  Google Scholar 

  181. 181

    Mostafavi, S. et al. Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge. PLoS ONE 8, e68141 (2013). This study presents a comprehensive framework for thinking about signal and noise in gene expression data and unifies most known methods into one framework.

    CAS  PubMed  PubMed Central  Google Scholar 

  182. 182

    James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning (Springer Science & Business Media, 2013).

    Google Scholar 

  183. 183

    Wang, K., Li, M. & Bucan, M. Pathway-based approaches for analysis of genomewide association studies. Am. J. Hum. Genet. 81, 1278–1283 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. 184

    Shohat, S. & Shifman, S. Bias towards large genes in autism. Nature 512, E1–E2 (2014).

    CAS  PubMed  Google Scholar 

  185. 185

    Wang, L., Jia, P., Wolfinger, R. D., Chen, X. & Zhao, Z. Gene set analysis of genome-wide association studies: methodological issues and perspectives. Genomics 98, 1–8 (2011).

    CAS  PubMed  Google Scholar 

  186. 186

    Raychaudhuri, S. et al. Accurately assessing the risk of schizophrenia conferred by rare copy-number variation affecting genes with brain function. PLoS Genet. 6, e1001097 (2010).

    PubMed  PubMed Central  Google Scholar 

  187. 187

    Sartor, M. A., Leikauf, G. D. & Medvedovic, M. LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics 25, 211–217 (2009).

    CAS  PubMed  Google Scholar 

  188. 188

    Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  189. 189

    Nord, A. S., Pattabiraman, K., Visel, A. & Rubenstein, J. L. R. Genomic perspectives of transcriptional regulation in forebrain development. Neuron 85, 27–47 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  190. 190

    Maze, I. et al. Analytical tools and current challenges in the modern era of neuroepigenomics. Nat. Neurosci. 17, 1476–1490 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  191. 191

    Bayés, À. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2010).

    PubMed  PubMed Central  Google Scholar 

  192. 192

    Bayés, À. et al. Comparative study of human and mouse postsynaptic proteomes finds high compositional conservation and abundance differences for key synaptic proteins. PLoS ONE 7, e46683 (2012).

    PubMed  PubMed Central  Google Scholar 

  193. 193

    Qu, X. A. & Rajpal, D. K. Applications of Connectivity Map in drug discovery and development. Drug Discov. Today 17, 1289–1298 (2012).

    CAS  PubMed  Google Scholar 

  194. 194

    Lamb, J. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    CAS  PubMed  Google Scholar 

  195. 195

    Butte, A. J. & Kohane, I. S. Creation and implications of a phenome-genome network. Nat. Biotechnol. 24, 55–62 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  196. 196

    Blair, D. R. et al. A nondegenerate code of deleterious variants in mendelian loci contributes to complex disease risk. Cell 155, 70–80 (2013).

    CAS  PubMed  Google Scholar 

  197. 197

    Rzhetsky, A., Wajngurt, D., Park, N. & Zheng, T. Probing genetic overlap among complex human phenotypes. Proc. Natl Acad. Sci. USA 104, 11694–11699 (2007).

    CAS  PubMed  Google Scholar 

  198. 198

    Freimer, N. & Sabatti, C. The Human Phenome Project. Nat. Genet. 34, 15–21 (2003).

    CAS  PubMed  Google Scholar 

  199. 199

    Congdon, E., Poldrack, R. A. & Freimer, N. B. Neurocognitive phenotypes and genetic dissection of disorders of brain and behavior. Neuron 68, 218–230 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  200. 200

    Coppola, G. & Geschwind, D. H. Technology Insight: querying the genome with microarrays — progress and hope for neurological disease. Nat. Clin. Pract. Neurol. 2, 147–158 (2006).

    CAS  PubMed  Google Scholar 

  201. 201

    Jaffe, A. E. et al. Developmental regulation of human cortex transcription and its clinical relevance at single base resolution. Nat. Neurosci. 18, 154–161 (2015).

    CAS  PubMed  Google Scholar 

  202. 202

    Dougherty, J. D. et al. The disruption of Celf6, a gene identified by translational profiling of serotonergic neurons, results in autism-related behaviors. J. Neurosci. 33, 2732–2753 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  203. 203

    Xu, X., Wells, A. B., O'Brien, D. R., Nehorai, A. & Dougherty, J. D. Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. J. Neurosci. 34, 1420–1431 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  204. 204

    Heiman, M. et al. Molecular adaptations of striatal spiny projection neurons during levodopa-induced dyskinesia. Proc. Natl Acad. Sci. USA 111, 4578–4583 (2014).

    CAS  PubMed  Google Scholar 

  205. 205

    Dalal, J. et al. Translational profiling of hypocretin neurons identifies candidate molecules for sleep regulation. Genes Dev. 27, 565–578 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  206. 206

    Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015). The first single-cell RNA-seq study of the adult mouse cortex and hippocampus that uses unsupervised clustering to identify dozens of cell types, including many distinct interneuron subtypes.

    CAS  PubMed  Google Scholar 

  207. 207

    Pollen, A. A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  208. 208

    Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  209. 209

    Ebert, D. H. & Greenberg, M. E. Activity-dependent neuronal signalling and autism spectrum disorder. Nature 493, 327–337 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  210. 210

    Crino, P. B. & Eberwine, J. Molecular characterization of the dendritic growth cone: regulated mRNA transport and local protein synthesis. Neuron 17, 1173–1187 (1996).

    CAS  PubMed  Google Scholar 

  211. 211

    Wang, D. O., Martin, K. C. & Zukin, R. S. Spatially restricting gene expression by local translation at synapses. Trends Neurosci. 33, 173–182 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  212. 212

    Butte, A. J. & Kohane, I. S. in Pacific Symposiumon Biocomputing 2000 (eds Altman, R. B. et al.) 418–429 (World Scientific, 2000).

    Google Scholar 

  213. 213

    Horvath, S. Weighted Network Analysis: Applications in Genomics and Systems Biology (Springer, 2011).

    Google Scholar 

  214. 214

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  215. 215

    Lee, I. & Marcotte, E. M. Effects of functional bias on supervised learning of a gene network model. Methods Mol. Biol. 541, 463–475 (2009).

    CAS  PubMed  Google Scholar 

  216. 216

    Auer, P. L. & Doerge, R. W. Statistical design and analysis of RNA sequencing data. Genetics 185, 405–416 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  217. 217

    Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161 (2007).

    PubMed Central  Google Scholar 

  218. 218

    Li, S. et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. 32, 915–925 (2014). A comprehensive evaluation of different sequencing platforms and methodologies that identifies optimal parameters for RNA-seq, including for degraded RNA.

    PubMed  PubMed Central  Google Scholar 

  219. 219

    Liu, Y., Zhou, J. & White, K. P. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304 (2014). A comparison of multiple RNA-seq differential expression methodologies that demonstrated biological replicates are more important than technical replicates and provided guidelines on sequencing depth.

    CAS  PubMed  Google Scholar 

  220. 220

    Soneson, C. & Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14, 91 (2013).

    PubMed  PubMed Central  Google Scholar 

  221. 221

    Allison, D. B., Cui, X., Page, G. P. & Sabripour, M. Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet. 7, 55–65 (2006).

    CAS  PubMed  Google Scholar 

  222. 222

    Tibshirani, R. A simple method for assessing sample sizes in microarray experiments. BMC Bioinformatics 7, 106 (2006).

    PubMed  PubMed Central  Google Scholar 

  223. 223

    Langfelder, P., Mischel, P. S. & Horvath, S. When is hub gene selection better than standard meta-analysis? PLoS ONE 8, e61505 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  224. 224

    Good, P. I. Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer, 2010).

    Google Scholar 

  225. 225

    Narayan, S. et al. Molecular profiles of schizophrenia in the CNS at different stages of illness. Brain Res. 1239, 235–248 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  226. 226

    Berchtold, N. C. et al. Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer's disease. Neurobiol.Aging 34, 1653–1661 (2013).

    CAS  PubMed  Google Scholar 

  227. 227

    Zambon, A. C. et al. GO-Elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 28, 2209–2210 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


The authors thank L. de la Torre-Ubieta and H. Won for assistance with Figure 1, as well as members of the Geschwind laboratory and K. Lage for critical reading of the manuscript. This work is supported by the US National Institute of Mental Health grants (5R37MH060233 and 5R01MH094714, D.H.G.), an Autism Center for Excellence network grant (9R01MH100027), the Simons Foundation (SFARI 206744, D.H.G.), NIMH Training and NRSA Fellowships (T32MH073526 and F30MH099886, N.N.P.), and the Medical Scientist Training Program at University of California, Los Angeles (UCLA).

Author information



Corresponding author

Correspondence to Daniel H. Geschwind.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Supplementary information

Supplementary information S1 (box)

Methodology and analysis for cross-disorder transcriptomic analysis. (PDF 1457 kb)


Genetic architecture

For genetic variants, the relationship among allele frequency, effect size, number of contributing variants and how they quantitatively influence a given trait.

Molecular systems or integrative network approach

Systems biology methods that use high-throughput quantification, analysis and interpretation of the molecular relationships within and across molecular levels, including the genome, transcriptome, epigenome, proteome and other 'omes'.

Systems neuroscience

An area of neuroscience that focuses on short- and long-range circuits that are usually related to specific behavioral or cognitive functions (vision, motor function, attention and so on).

Gene network

A graph consisting of genes as nodes connected by edges that represent relationships between genes.

Differential gene expression analysis

(DGE analysis). An approach commonly used in transcriptomic studies that serially compares thousands of genes between groups (for example, disease and controls) to evaluate the mean difference and its significance for each gene independently.


Also known as clusters, cliques and communities. Highly interconnected subsets of genes in a gene network; for example, genes in a transcriptomic network sharing highly similar patterns of gene expression.


Molecular entities that constitute a network; for example, genes in a gene network or proteins in a protein interaction network.


The relationships between nodes in a network delineating some measure of shared function; for example, correlations or physical interactions.

Mutual information

A measure of dependence between two variables that can capture complex relationships, including nonlinear and nonmonotonic patterns, that could be missed by linear correlation measures.


Genes in a network or module that are highly connected; that is, they have a relatively high number of edges compared with other genes.

RNA sequencing

(RNA-seq). An assay for measuring RNA transcript levels in a genome-wide manner that involves the extraction of RNA followed by construction of cDNA libraries that undergo high-throughput sequencing.

Weighted networks

Networks in which the edges have continuous values, with higher values reflecting an increased strength or probability of connectivity.

Binary networks

Networks in which the edges are all or nothing, either because this is inherent to the edge measurement (for example, physically interacting or not) or because a cut-off or threshold has been applied to a continuous measurement (for example, by applying a rule that all correlation values ≥0.7 are 1, all others are 0).

Signed networks

Networks in which the direction of association is taken into consideration in addition to the magnitude of the correlation; for example, in a signed correlation network, high positive correlations are assigned high edge values, but high negative correlations are assigned low edge values.

Unsigned networks

Networks in which any high magnitude association is assigned a high edge value regardless of the direction of the association.

Topological overlap

A computation on direct edge relationships in a network that transforms them into indirect edge values that reflect the sharing of neighbourhoods between genes.

Seeded (prior-based) networks

Network analysis approaches in which edges are 'grown' around 'seed' genes that are selected on the basis of previous experiments or prior hypotheses, and the network structure is dependent on these seed genes.

Unseeded (genome-wide) networks

Network analysis approaches in which edges are evaluated in a genome-wide manner, and network structure is not dependent on prior knowledge of a particular set of genes.

Adjacency matrix

A matrix of pairwise node–node relationships that quantifies all possible edges in a network. Edge relationships may be determined from one data type or by weighting the contribution from multiple types of data.


An assay for measuring the binding sites of a protein on RNA transcripts in a genome-wide manner that involves crosslinking immunoprecipitation followed by high-throughput sequencing.


An assay for measuring the binding sites of a protein on DNA across the genome that involves chromatin immunoprecipitation followed by high-throughput sequencing.

DNase hypersensitivity or ATAC-seq

Sequencing methods that infer regions of the genome in a particular cell or tissue with open chromatin by exploiting the fact that these regions are preferentially accessible to the DNase I enzyme or a transposase.


Module-level summaries of expression utilized in co-expression networks calculated by taking the first principal component of the expression levels of genes in a module.


A mental state defined by a loss of contact with reality and characterized by exaggerations or distortions of normal perception.

Negative symptoms

Symptoms involving a loss of normal emotional responses, including a lack of motivation, an inability to experience pleasure and reduced expression through speech.

Unsupervised methods

Analysis approaches that use the intrinsic variation in data to define shared patterns without explicit prior knowledge of how the data should be grouped (for example, hierarchical clustering). This can identify novel clusters or groupings of data points.

Expression quantitative trait locus analysis

(eQTL analysis). A specific case of genotype-to-phenotype association that uses RNA transcript levels as the phenotype in order to identify genetic loci that regulate RNA levels.

Selective vulnerability

The relative susceptibility of specific brain regions, cell populations or time points to genetic or environmental insults that result in disease.

Causal anchor

A causal factor, such as genetic variation, that can be used to orient edges to transform an undirected correlational network to a directed causal network.

Gene set enrichment

An analysis approach that assesses the statistical significance of the overlap between two gene sets; one set is usually an annotated reference set, and the other is an unannotated set of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Parikshak, N., Gandal, M. & Geschwind, D. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat Rev Genet 16, 441–458 (2015).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing