Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Identification and prioritization of gene sets associated with schizophrenia risk by co-expression network analysis in human brain

Molecular Psychiatry volume 25, pages 791–804 (2020)Cite this article

Subjects

Abstract

Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. The main purpose of this study was to identify and prioritize co-expressed gene sets in a hierarchical manner, based on the strength of the relationships with clinical diagnosis and with polygenic risk for schizophrenia. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to RNA-quality-adjusted DLPFC RNA-Seq data from the LIBD Postmortem Human Brain Repository (90 controls, 74 schizophrenia cases; all Caucasians) to construct co-expression networks and detect “modules” of co-expressed genes. After multiple internal and external validation procedures, modules of selected interest were tested for enrichment in biological ontologies, for association with schizophrenia polygenic risk scores (PRSs) and with diagnosis, and also for enrichment in genes within the significant GWAS loci reported by the Psychiatric Genomic Consortium (PGC2). The association between schizophrenia genetic signals and modules of co-expression converged on one module showing not only a significant association with both diagnosis and PRS but also significant overlap with 36 PGC2 loci genes, deemed the strongest candidates for drug targets. This module contained many genes involved in synaptic signaling and neuroplasticity. Fifty-three PGC2 genes were in modules associated only with diagnosis and 59 in modules unrelated to diagnosis or PRS. Our study highlights complex relationships between gene co-expression networks in the brain and clinical state and polygenic risk for SCZ and provides a strategy for using this information in selecting and prioritizing potentially targetable gene sets for therapeutic drug development.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    Article  Google Scholar 

  2. Jaffe AE, Straub RE, Shin JH, Tao R, Gao Y, Torres LC, et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat Neurosci. 2018;21:1117–25.

    Article  CAS  Google Scholar 

  3. Hannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci. 2016;19:48–54.

    Article  CAS  Google Scholar 

  4. Webber C. Epistasis in neuropsychiatric disorders. Trends Genet. 2017;33:256–65.

    Article  CAS  Google Scholar 

  5. Birnbaum R, Weinberger DR. Genetic insights into the neurodevelopmental origins of schizophrenia. Nat Rev Neurosci. 2017;18:727–40.

    Article  CAS  Google Scholar 

  6. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559.

    Article  Google Scholar 

  7. Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, et al. Functional organization of the transcriptome in human brain. Nat Neurosci. 2008;11:1271–82.

    Article  CAS  Google Scholar 

  8. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011;474:380–4.

    Article  CAS  Google Scholar 

  9. Chen C, Cheng L, Grennan K, Pibiri F, Zhang C, Badner JA, et al. Two gene co-expression modules differentiate psychotics and controls. Mol Psychiatry. 2013;18:1308–14.

    Article  CAS  Google Scholar 

  10. Torkamani A, Dean B, Schork NJ, Thomas EA. Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. Genome Res. 2010;20:403–12.

    Article  CAS  Google Scholar 

  11. Roussos P, Katsel P, Davis KL, Siever LJ, Haroutunian V. A system-level transcriptomic analysis of schizophrenia using postmortem brain tissue samples. Arch Gen Psychiatry. 2012;69:1205–13.

    Article  Google Scholar 

  12. Kim Y, Giusti-Rodriguez P, Crowley JJ, Bryois J, Nonneman RJ, Ryan AK, et al. Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects. Mol Psychiatry. 2017;23:708–712.

  13. Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359:693–7.

    Article  CAS  Google Scholar 

  14. Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19:1442–53.

    Article  CAS  Google Scholar 

  15. Pergola G, Di Carlo P, D’Ambrosio E, Gelao B, Fazio L, Papalino M, et al. DRD2 co-expression network and a related polygenic index predict imaging, behavioral and clinical phenotypes linked to schizophrenia. Transl Psychiatry. 2017;7:e1006.

    Article  CAS  Google Scholar 

  16. BrainSeq A. Human Brain Genomics Consortium. BrainSeq: neurogenomics to drive novel target discovery for neuropsychiatric disorders. Neuron. 2015;88:1078–83.

    Article  Google Scholar 

  17. TopHat: https://ccb.jhu.edu/software/tophat/index.shtml

  18. Ensembl: http://www.ensembl.org/index.html

  19. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.

    Article  Google Scholar 

  20. Delaneau O, Coulonges C, Zagury JF. Shape-IT: new rapid and accurate algorithm for haplotype inference. BMC Bioinforma. 2008;9:540.

    Article  Google Scholar 

  21. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  Google Scholar 

  22. Chen Q, Ursini G, Romer AL, Knodt AR, Mezeivtch K, Xiao E, et al. Schizophrenia polygenic risk score predicts mnemonic hippocampal activity. Brain. 2018;141:1218–1228.

  23. WGCNA manual: https://cran.r-project.org/web/packages/WGCNA/WGCNA.pdf

  24. WGCNA tutorials: https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html

  25. Horvath, S. Networks and fundamental concepts. Weighted network analysis. Applications in systems biology. Springer: New York, Dordrecht, Heidelberg, London; 2011. p.1–34.

  26. Jaffe AE, Tao R, Norris AL, Kealhofer M, Nellore A, Shin JH, et al. qSVA framework for RNA quality correction in differential expression analysis. Proc Natl Acad Sci USA. 2017;114:7130–5.

    Article  CAS  Google Scholar 

  27. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.

    Article  Google Scholar 

  28. Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module preserved and reproducible? PLoS Comput Biol. 2011;7:e1001057.

    Article  CAS  Google Scholar 

  29. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.

    Article  CAS  Google Scholar 

  30. Cahoy JD, Emery B, Kaushal A, Foo LC, Zamanian JL, Christopherson KS, et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci. 2008;28:264–78.

    Article  CAS  Google Scholar 

  31. Poduri A, Evrony GD, Cai X, Elhosary PC, Beroukhim R, Lehtinen MK, et al. Somatic activation of AKT3 causes hemispheric developmental brain malformations. Neuron. 2012;74:41–8.

    Article  CAS  Google Scholar 

  32. GeneCards: www.genecards.org

  33. Chang H, Xiao X, Li M. The schizophrenia risk gene ZNF804A: clinical associations, biological mechanisms and neuronal functions. Mol Psychiatry. 2017;22:944–53.

    Article  CAS  Google Scholar 

  34. Manning BD, Toker A. AKT/PKB signaling: navigating the network. Cell. 2017;169:381–405.

    Article  CAS  Google Scholar 

  35. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRINGv10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):447.

    Article  Google Scholar 

  36. Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell . 2017;169:1177–86.

    Article  CAS  Google Scholar 

  37. Tao R, Cousijn H, Jaffe AE, Burnet PW, Edwards F, Eastwood SL, et al. Expression of ZNF804A in human brain and alterations in schizophrenia, bipolar disorder, and major depressive disorder: a novel transcript fetally regulated by the psychosis risk variant rs1344706. JAMA Psychiatry. 2014;71:1112–20.

    Article  Google Scholar 

  38. Hill MJ, Jeffries AR, Dobson RJ, Price J, Bray NJ. Knockdown of the psychosis susceptibility gene ZNF804A alters expression of genes involved in cell adhesion. Hum Mol Genet. 2012;21:1018–24.

    Article  CAS  Google Scholar 

  39. Chen J, Lin M, Hrabovsky A, Pedrosa E, Dean J, Jain S, et al. ZNF804A transcriptional networks in differentiating neurons derived from induced pluripotent stem cells of human origin. PLoS One. 2015;10:e0124597.

    Article  Google Scholar 

  40. Paterson C, Wang Y, Hyde TM, Weinberger DR, Kleinman JE, Law AJ. Temporal, diagnostic, and tissue-specific regulation of NRG3 isoform expression in human brain development and affective disorders. Am J Psychiatry. 2017;174:256–65.

    Article  Google Scholar 

  41. Farrell MS, Werge T, Sklar P, Owen MJ, Ophoff RA, O’Donovan MC, et al. Evaluating historical candidate genes for schizophrenia. Mol Psychiatry. 2015;20:555–62.

    Article  CAS  Google Scholar 

  42. Kabir ZD, Martinez-Rivera A, Rajadhyaksha AM. From gene to behavior: L-type calcium channel mechanisms underlying neuropsychiatric symptoms. Neurotherapeutics. 2017;14:588–613.

    Article  CAS  Google Scholar 

  43. Lencz T, Malhotra AK. Targeting the schizophrenia genome: a fast track strategy from GWAS to clinic. Mol Psychiatry. 2015;20:820–6.

    Article  CAS  Google Scholar 

  44. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful for the generosity of the Lieber and Maltz families in establishing an institute dedicated to understanding the basis of developmental brain disorders. We would like to gratefully acknowledge the families of the subjects whose donations made this research possible. This research was funded by the Lieber Institute for Brain Development.

Author information

Authors and Affiliations

  1. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA

    Eugenia Radulescu, Andrew E. Jaffe, Richard E. Straub, Qiang Chen, Joo Heon Shin, Thomas M. Hyde, Joel E Kleinman & Daniel R. Weinberger

  2. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA

    Andrew E. Jaffe

  3. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA

    Andrew E. Jaffe

  4. Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA

    Andrew E. Jaffe

  5. Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA

    Thomas M. Hyde & Joel E Kleinman

  6. Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA

    Thomas M. Hyde & Daniel R. Weinberger

  7. Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA

    Daniel R. Weinberger

  8. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA

    Daniel R. Weinberger

Authors
  1. Eugenia Radulescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew E. Jaffe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard E. Straub
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joo Heon Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas M. Hyde
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joel E Kleinman
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daniel R. Weinberger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel R. Weinberger.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Radulescu, E., Jaffe, A.E., Straub, R.E. et al. Identification and prioritization of gene sets associated with schizophrenia risk by co-expression network analysis in human brain. Mol Psychiatry 25, 791–804 (2020). https://doi.org/10.1038/s41380-018-0304-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-018-0304-1

This article is cited by

Search

Advanced search

Quick links