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Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways

A Corrigendum to this article was published on 25 November 2015

A Corrigendum to this article was published on 26 May 2015

This article has been updated


Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

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Figure 1: Overview of statistical approach for integrative pathway analysis of GWAS data.
Figure 2: Quantile-quantile plot showing P-value distribution for a combined analysis combining results from five pathway analysis methods and six pathway databases.
Figure 3: Multidimensional scaling plot of top 50 pathways with suggestive (<0.
Figure 4: Gene coexpression networks across brain development and regions for genes in all pathways with FDR < 0.1.

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Gene Expression Omnibus

Change history

  • 19 January 2015

    In the version of this article initially published, the affiliation numbers given for several authors were incorrect. Astrid M. Vicente was listed as associated with affiliations 234–236 instead of 233–235, Veronica J. Vieland as 237 instead of 236, Kenneth S. Kendler as 110, 253 and 254 instead of 110, 252 and 253, and Zhaoming Zhao as 255 instead of 254 and 255. The errors have been corrected in the HTML and PDF versions of the article.

  • 28 April 2015

    In the version of this article initially published, the name of author Zhongming Zhao was misspelled Zhaoming Zhao. The error has been corrected in the HTML and PDF versions of the article.


  1. Vos, T. et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2163–2196 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lee, S.H. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Cross-Disorder Group of the Psychiatric Genomics Consortium et al. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  4. 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).

    Article  CAS  PubMed  Google Scholar 

  5. Nam, D. & Kim, S.Y. Gene-set approach for expression pattern analysis. Brief. Bioinform. 9, 189–197 (2008).

    Article  PubMed  Google Scholar 

  6. Ackermann, M. & Strimmer, K. A general modular framework for gene set enrichment analysis. BMC Bioinformatics 10, 47 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Baranzini, S.E. et al. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum. Mol. Genet. 18, 2078–2090 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang, K. et al. Diverse genome-wide association studies associate the IL12/IL23 pathway with Crohn Disease. Am. J. Hum. Genet. 84, 399–405 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Holmans, P. et al. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am. J. Hum. Genet. 85, 13–24 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. O'Dushlaine, C. et al. Molecular pathways involved in neuronal cell adhesion and membrane scaffolding contribute to schizophrenia and bipolar disorder susceptibility. Mol. Psychiatry 16, 286–292 (2011).

    Article  CAS  PubMed  Google Scholar 

  11. Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).

  12. Sullivan, P.F. The psychiatric GWAS consortium: big science comes to psychiatry. Neuron 68, 182–186 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Neale, B.M. et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49, 884–897 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Schizophrenia Psychiatric Genome-Wide Association Study Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat. Genet. 43, 969–976 (2011).

  15. Major Depressive Disorder Working Group of the PGC et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).

  16. Brown, M.B. A method for combining non-independent one-sided tests of significance. Biometrics 31, 987–992 (1975).

    Article  Google Scholar 

  17. McLaren, P.J. et al. Association study of common genetic variants and HIV-1 acquisition in 6,300 infected cases and 7,200 controls. PLoS Pathog. 9, e1003515 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  20. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Schizophrenia Working Group of the Psychiatric Genomics. C. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  23. Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ting, J.T., Peça, J. & Feng, G. Functional consequences of mutations in postsynaptic scaffolding proteins and relevance to psychiatric disorders. Annu. Rev. Neurosci. 35, 49–71 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cruceanu, C. et al. H3K4 tri-methylation in synapsin genes leads to different expression patterns in bipolar disorder and major depression. Int. J. Neuropsychopharmacol. 16, 289–99 (2013).

    Article  CAS  PubMed  Google Scholar 

  30. Jarome, T.J. & Lubin, F.D. Histone lysine methylation: critical regulator of memory and behavior. Rev. Neurosci. 24, 375–387 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. 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 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Parikshak, N.N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    Article  CAS  PubMed  Google Scholar 

  34. Bauman, A.L. et al. Cocaine and antidepressant-sensitive biogenic amine transporters exist in regulated complexes with protein phosphatase 2A. J. Neurosci. 20, 7571–7578 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dieperink, E., Willenbring, M. & Ho, S.B. Neuropsychiatric symptoms associated with hepatitis C and interferon alpha: a review. Am. J. Psychiatry 157, 867–876 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Susser, E., St Clair, D. & He, L. Latent effects of prenatal malnutrition on adult health: the example of schizophrenia. Ann. NY Acad. Sci. 1136, 185–192 (2008).

    Article  PubMed  Google Scholar 

  37. Heijmans, B.T., Tobi, E.W., Lumey, L.H. & Slagboom, P.E. The epigenome: archive of the prenatal environment. Epigenetics 4, 526–531 (2009).

    Article  CAS  PubMed  Google Scholar 

  38. Brykczynska, U. et al. Repressive and active histone methylation mark distinct promoters in human and mouse spermatozoa. Nat. Struct. Mol. Biol. 17, 679–687 (2010).

    Article  CAS  PubMed  Google Scholar 

  39. Huang, H.S. et al. Prefrontal dysfunction in schizophrenia involves mixed-lineage leukemia 1-regulated histone methylation at GABAergic gene promoters. J. Neurosci. 27, 11254–62 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yauch, R.L. & Settleman, J. Recent advances in pathway-targeted cancer drug therapies emerging from cancer genome analysis. Curr. Opin. Genet. Dev. 22, 45–49 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Flicek, P. et al. Ensembl 2013. Nucleic Acids Res. 41, D48–D55 (2013).

    Article  CAS  PubMed  Google Scholar 

  42. Maston, G.A., Evans, S.K. & Green, M.R. Transcriptional regulatory elements in the human genome. Annu. Rev. Genomics Hum. Genet. 7, 29–59 (2006).

    Article  CAS  PubMed  Google Scholar 

  43. Pedroso, I. et al. Common genetic variants and gene-expression changes associated with bipolar disorder are over-represented in brain signaling pathway genes. Biol. Psychiatry 72, 311–317 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Moskvina, V. et al. Evaluation of an approximation method for assessment of overall significance of multiple-dependent tests in a genomewide association study. Genet. Epidemiol. 35, 861–866 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Lee, P.H., O'Dushlaine, C., Thomas, B. & Purcell, S.M. INRICH: interval-based enrichment analysis for genome-wide association studies. Bioinformatics 28, 1797–1799 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Segrè, A.V., Groop, L., Mootha, V.K., Daly, M.J. & Altshuler, D. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Brown, M.B. A method for combining non-independent, one-sided tests of significance. Biometrics 31, 978–992 (1975).

    Google Scholar 

  50. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).

    Article  PubMed  Google Scholar 

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G.B. and S.N. acknowledge funding support for this work from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. P.H.L. is supported by US National Institute of Mental Health (NIMH) grant K99MH101367. The PGC Cross-Disorder Group is supported by NIMH grant U01 MH085520. Statistical analyses were carried out on the Genetic Cluster Computer, which is financially supported by the Netherlands Scientific Organization (NOW; 480-05-003; principal investigator D.P.) along with a supplement from the Dutch Brain Foundation and VU University. Numerous (>100) grants from government agencies along with substantial private and foundation support worldwide enabled the collection of phenotype and genotype data, without which this research would not be possible.

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Authors and Affiliations



Project conception: G.B., P.F.S., P.A.H. Analysis: C.O'D., P.H.L., P.A.H., G.B., S.R., L.R., L.D., S.N. Writing of the manuscript: G.B., C.O'D. P.A.H., P.H.L., L.R., L.D., N.P. Quality control for PGC data: S. Ripke and B.M.N. Revisions to the manuscript: G.B., D.H.G., C.O'D., L.R., P.H.L., N.P. PGC Network and Pathway Analysis Workgroup: S.R., B.M.N., S.M.P., D.H.G., P.A.H., P.H.L., M.M., C.O'D., D.P., L.R., L.D., P.F.S., J.W.S., N.R.W., Z.Z. PGC Workgroup Chairs: M.J.D. (analysis), S.V.F. (ADHD), M.J.D. and B.D. (co-chairs ASD), G.B. and P.A.H. (Network and Pathway Analysis subgroup), J.K. and P. Sklar (co-chairs bipolar disorder), P.F.S. (major depressive disorder), M.C.O'D. (schizophrenia) and J.W.S. and K.S.K. (co-chairs cross-disorder group). Collection, genotyping and analysis for PGC Working Groups. PGC ADHD Working Group: B.M.N., S.V.F., A.T., R.A., P.A., T. Banaschewski, M. Bayés, J.B., J.K.B., M.C., B.C., J.C., A.E.D., R.P.E., J.E., B.F., C.M.F., L. Kent, J.K., K.-P.L., S.K.L., J.M., J.J.M., S.E.M., J.M.S., A. Miranda, S.F.N., R.D.O., J.A.R.-Q., A. Reif, M. Ribasés, H.R., A. Rothenberger, J.A.S., R.S., S.L. Smalley, E.J.S.S.-B., H.-C.S., A.A.T. and N.W. PGC ASD Working Group: R.A., D.E.A., A.J.B., A.B., C.B., J.D. Buxbaum, A. Chakravarti, E.H.C., H.C., M.L.C., G.D., E.D., S.E., E.F., C.M.F., L. Gallagher, D.H.G., M. Gill, D.E.G., J.L.H., H.H., J.H., V.H., S.M.K., L. Klei, D.H. Ledbetter, C. Lord, J.K.L., E.M., S.M.M., C.L.M., W.M.M., A.P.M., D.M.-D.-L., E.M.M., M. Murtha, G.O., A.P., J.R.P., A.D.P., M.A.P.-V., J. Piven, F.P., K. Rehnström, K. Roeder, G.R., S.J.S., S.L. Santangelo, G.D.S., S.W.S., M. State, J.S. Sutcliffe, P. Szatmari, A.M.V., V.J.V., C.A.W., T.H.W., E.M.W., A.J.W., T.W.Y., B.D. and M.J.D. PGC BPD Working Group: S.M.P., D.A., H.A., O.A.A., A.A., L.B., J.A.B., J.D. Barchas, T.B.B., N.B., M. Bauer, F.B., S.E.B., W.B., D.H.R.B., C.S.B., M. Boehnke, G.B., R. Breuer, W.E.B., W.F.B., S. Caesar, K. Chambert, S. Cichon, D.A.C., A. Corvin, W.H.C., D.W.C., R.D., F. Degenhardt, S. Djurovic, F. Dudbridge, H.J.E., B.E., A.E.F., I.N.F., M. Flickinger, T.F., J.F., C.F., L.F., E.S.G., M. Gill, K.G.-S., E.K.G., T.A.G., D.G., W.G., H.G., M.L.H., M. Hautzinger, S. Herms, M. Hipolito, P.A.H., C.M.H., S.J., E.G.J., I.J., L.J., R. Kandaswamy, J.L.K., G.K.K., D.L.K., P.K., M. Landén, N.L., M. Lathrop, J. Lawrence, W.B.L., M. Leboyer, P.H.L., J. Li, P.L., D.-Y.L., C. Liu, F.W.L., S.L., P.B.M., W.M., N.G.M., M. Mattheisen, K.M., M. Mattingsdal, K.A.M., P.M., M.G.M., A. McIntosh, R.M., A.W.M., F.J.M., A. McQuillin, S.M., I.M., F.M., G.W.M., J.L.M., G.M., D.W.M., V. Moskvina, P.M., T.W.M., W.J.M., B.M.-M., R.M.M., C.M.N., I.N., V.N., M.M.N., J.I.N., E.A.N., C.O., U.O., M.J.O., B.S.P., J.B.P., P.P., E.M.Q., S. Raychaudhuri, A. Reif, J.P.R., M. Rietschel, D. Ruderfer, M. Schalling, A.F.S., W.A.S., N.J.S., T.G.S., J. Schumacher, M. Schwarz, E.S., L.J.S., P.D.S., E.N.S., D.S.C., M. Steffens, J.S. Strauss, J. Strohmaier, S.S., R.C.T., F.T., J.T., J.B.V., S.J.W., T.F.W., S.H.W., W.X., A.H.Y., P.P.Z., P.Z., S. Zöllner, J.R.K., P. Sklar, M.J.D., M.C.O. and N.C. PGC MDD Working Group: M.R.B., T. Bettecken, E.B.B., D.H.R.B., D.I.B., G.B., R. Breuer, S. Cichon, W.H.C., I.W.C., D. Czamara, E.J.D.G., F. Degenhardt, A.E.F., J.F., S.D.G., M. Gross, S.P.H., A.C.H., A.K.H., S. Herms, I.B.H., F.H., W.J.H., S. Hoefels, J.-J.H., M.I., I.J., L.J., J.-Y.T., J.A.K., M.A.K., A.K., W.B.L., D.F.L., C.M.L., D.-Y.L., S.L., D.J.M., P.A.F.M., W.M., N.G.M., M. Mattheisen, P.J.M., P.M., A. McIntosh, A.W.M., C.M.M., L.M., G.W.M., P.M., B.M.-M., W.A.N., M.M.N., D.R.N., B.W.P., M.L.P., J.B.P., M. Rietschel, W.A.S., T.G.S., J. Shi, S.I.S., S.L. Slager, J.H.S., M. Steffens, F.T., J.T., M.U., E.J.C.G.v.d.O., G.V.G., M.M.W., G.W., F.G.Z., P.F.S. and N.R.W. PGC SCZ Working Group: S. Ripke, B.M.N., S.M.P., B.J.M., I.A., F.A., O.A.A., M.H.A., N.B., D.W.B., D.H.R.B., R. Bruggeman, N.G.B., W.F.B., W.C., R.M.C., K. Choudhury, S. Cichon, C.R.C., P.C., A. Corvin, D. Curtis, S. Datta, S. Djurovic, G.J.D., J.D., F. Dudbridge, A.F., R.F., N.B.F., M. Friedl, P.V.G., L. Georgieva, I.G., M. Gill, H.G., L.D.H., M.L.H., T.F.H., A.M.H., P.A.H., C.M.H., A.I., A.K.K., R.S.K., M.C.K., E.K., Y.K., G.K.K., B.K., L. Krabbendam, R. Krasucki, J. Lawrence, P.H.L., T.L., D.F.L., J.A.L., D.-Y.L., D.H. Linszen, P.K.E.M., W.M., A.K.M., M. Mattheisen, M. Mattingsdal, S.M., S.A.M., A. McIntosh, A. McQuillin, H.M., I.M., V. Milanova, D.W.M., V. Moskvina, I.M.-G., M.M.N., C.O., A.O., L.O., R.A.O., M.J.O., C.N.P., M.T.P., B.S.P., J. Pimm, D.P., V.P., D.J.Q., H.B.R., M. Rietschel, L.R., D. Ruderfer, D. Rujescu, A.R.S., T.G.S., J. Shi, J.M.S., D.S.C., T.S.S., S.T., J.V.O., P.M.V., T.W., S. Zammit, P. Sklar, M.J.D., M.C.O., N.C., P.F.S. and K.S.K. PGC Cross-Disorder Group Working Group: S.H.L., S. Ripke, B.M.N., S.M.P., R.H.P., A.T., A.F., M.C.N., J.I.N., B.W.P., M. Rietschel, T.G.S., N.C., S.L. Santangelo, P.F.S., J.W.S., K.S.K. and N.R.W. PGC Analysis Working Group: S.H.L., S. Ripke, B.M.N., S.M.P., V.A., E.M.B., P.H.L., S.E.M., M.C.N., D.P., G.B., M.J.D. and N.R.W.

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Correspondence to Peter A Holmans or Gerome Breen.

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Competing interests

G.B. is a consultant in PreClinical Genetics at Eli Lilly.

Integrated supplementary information

Supplementary Figure 1 Observed and expected (shown as ranges representing 95% confidence intervals) number of significant pathways overlapping between different sets of results from different methods.

(a) Analysis conducted using the SCZ dataset, top 25% of each method. (b) Analysis conducted using the NULL dataset, top 25% of each method.

Supplementary Figure 2 Quantile-quantile plots summarizing combined p-values for each method on different phenotypes.

Order is (a) SCZ, (b) BIP, (c) MDD, (d) AUT, (e) ADD, (f) null data from permutations and, (g) HIV.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2, Supplementary Analysis, and Supplementary Tables 1–4, 6 and 12–16 (PDF 280 kb)

Supplementary Methods Checklist (PDF 345 kb)

Supplementary Table 5

Pathway results for each disease/cross-disease dataset (XLS 21847 kb)

Supplementary Table 7

Ranked list of pathways when combined across 3 well-powered disease groups. (XLS 3804 kb)

Supplementary Table 8

Ranked list of pathways when combined across SCZ, HIV and NULL datasets. (XLS 3803 kb)

Supplementary Table 9

Details related to module-level summaries of spatial, temporal, and cell-type specific expression patterns. (XLSX 184 kb)

Supplementary Table 10

Ranked list of pathways when combined across all 5 disease groups. (XLS 3959 kb)

Supplementary Table 11

P values for the SNPs in each gene in each disorder and their pathways membership are presented for all pathways with q < 0.1. (XLSX 639 kb)

Supplementary Table 17

Information related to the supervised network analysis of 797 genes, specifying gene set membership from the 3 major pathways, module membership, and relative connectivity (kME) to each module. (XLSX 171 kb)

Supplementary Data

Source data for Figure 3 (ZIP 1200 kb)

Supplementary Software

PGC pathway network analysis code (TXT 22 kb)

Source data

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The Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci 18, 199–209 (2015).

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