A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data


Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

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Fig. 1: Schematic of the iRIGS framework.
Fig. 2: Discovery of genomic features characteristic of SCZ risk genes.
Fig. 3: Characteristics of predicted risk genes.

Data availability

All the data used in this study are from public resources that are specified in the Methods and the Supplementary Note.

Code availability

The source code and the companying genomics datasets used in this study are available at https://www.vumc.org/cgg.


  1. 1.

    Visscher, P. M. et al. 10 Years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    CAS  Article  Google Scholar 

  3. 3.

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

  4. 4.

    Breen, G. et al. Translating genome-wide association findings into new therapeutics for psychiatry. Nat. Neurosci. 19, 1392–1396 (2016).

    CAS  Article  Google Scholar 

  5. 5.

    Harrison, P. J. Recent genetic findings in schizophrenia and their therapeutic relevance. J. Psychopharmacol. 29, 85–96 (2015).

    Article  Google Scholar 

  6. 6.

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

    CAS  Article  Google Scholar 

  7. 7.

    Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    CAS  Article  Google Scholar 

  8. 8.

    Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    CAS  Article  Google Scholar 

  9. 9.

    Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

    Article  Google Scholar 

  10. 10.

    Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).

    CAS  Article  Google Scholar 

  11. 11.

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

    CAS  Article  Google Scholar 

  12. 12.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  Google Scholar 

  13. 13.

    Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  Google Scholar 

  15. 15.

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

    CAS  Article  Google Scholar 

  16. 16.

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

    Article  Google Scholar 

  17. 17.

    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).

    CAS  Article  Google Scholar 

  18. 18.

    Verrall, L., Burnet, P. W., Betts, J. F. & Harrison, P. J. The neurobiology of D-amino acid oxidase and its involvement in schizophrenia. Mol. Psychiatry 15, 122–137 (2010).

    CAS  Article  Google Scholar 

  19. 19.

    Yang, H. C. et al. The DAO gene is associated with schizophrenia and interacts with other genes in the Taiwan Han Chinese population. PLoS One 8, e60099 (2013).

    CAS  Article  Google Scholar 

  20. 20.

    Jaitner, C. et al. Satb2 determines miRNA expression and long-term memory in the adult central nervous system. eLife 5, e17361 (2016).

    Article  Google Scholar 

  21. 21.

    Whitton, L. et al. Cognitive analysis of schizophrenia risk genes that function as epigenetic regulators of gene expression. Am. J. Med. Genet. B Neuropsychiatr. Genet. 171, 1170–1179 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Barkus, C. et al. What causes aberrant salience in schizophrenia? A role for impaired short-term habituation and the GRIA1 (GluA1) AMPA receptor subunit. Mol. Psychiatry 19, 1060–1070 (2014).

    CAS  Article  Google Scholar 

  23. 23.

    Thomas, K. T. et al. Inhibition of the schizophrenia-associated microRNA miR-137 disrupts Nrg1alpha neurodevelopmental signal transduction. Cell Rep. 20, 1–12 (2017).

    CAS  Article  Google Scholar 

  24. 24.

    Weickert, C. S. et al. Molecular evidence of N-methyl-D-aspartate receptor hypofunction in schizophrenia. Mol. Psychiatry 18, 1185–1192 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Egan, M. F. et al. Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. Proc. Natl Acad. Sci. USA 101, 12604–12609 (2004).

    CAS  Article  Google Scholar 

  26. 26.

    Boks, M. P. et al. Do mood symptoms subdivide the schizophrenia phenotype? Association of the GMP6A gene with a depression subgroup. Am. J. Med. Genet. B Neuropsychiatr. Genet. 147B, 707–711 (2008).

    Article  Google Scholar 

  27. 27.

    Yan, J. et al. Analysis of the neuroligin 3 and 4 genes in autism and other neuropsychiatric patients. Mol. Psychiatry 10, 329–332 (2005).

    CAS  Article  Google Scholar 

  28. 28.

    Shi, L. et al. The functional genetic link of NLGN4X knockdown and neurodevelopment in neural stem cells. Hum. Mol. Genet. 22, 3749–3760 (2013).

    CAS  Article  Google Scholar 

  29. 29.

    Rannals, M. D. et al. Psychiatric risk gene transcription factor 4 regulates intrinsic excitability of prefrontal neurons via repression of SCN10a and KCNQ1. Neuron 90, 43–55 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Quednow, B. B., Brzozka, M. M. & Rossner, M. J. Transcription factor 4 (TCF4) and schizophrenia: integrating the animal and the human perspective. Cell. Mol. Life Sci. 71, 2815–2835 (2014).

    CAS  Article  Google Scholar 

  31. 31.

    Hill, M. J. et al. Knockdown of the schizophrenia susceptibility gene TCF4 alters gene expression and proliferation of progenitor cells from the developing human neocortex. J. Psychiatry Neurosci. 42, 181–188 (2017).

    Article  Google Scholar 

  32. 32.

    Chang, H., Xiao, X. & Li, M. The schizophrenia risk gene ZNF804A: clinical associations, biological mechanisms and neuronal functions. Mol. Psychiatry 22, 944–953 (2017).

    CAS  Article  Google Scholar 

  33. 33.

    Devanna, P. & Vernes, S. C. A direct molecular link between the autism candidate gene RORa and the schizophrenia candidate MIR137. Sci. Rep. 4, 3994 (2014).

    Article  Google Scholar 

  34. 34.

    Hu, V. W., Sarachana, T., Sherrard, R. M. & Kocher, K. M. Investigation of sex differences in the expression of RORA and its transcriptional targets in the brain as a potential contributor to the sex bias in autism. Mol. Autism 6, 7 (2015).

    Article  Google Scholar 

  35. 35.

    Kwon, E., Wang, W. & Tsai, L. H. Validation of schizophrenia-associated genes CSMD1, C10orf26, CACNA1C and TCF4 as miR-137 targets. Mol. Psychiatry 18, 11–12 (2013).

    CAS  Article  Google Scholar 

  36. 36.

    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  Article  Google Scholar 

  37. 37.

    Pocklington, A. J. et al. Novel findings from CNVs implicate inhibitory and excitatory signaling complexes in schizophrenia. Neuron 86, 1203–1214 (2015).

    CAS  Article  Google Scholar 

  38. 38.

    Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).

    CAS  Article  Google Scholar 

  39. 39.

    Harrison, P. J., Lyon, L., Sartorius, L. J., Burnet, P. W. & Lane, T. A. The group II metabotropic glutamate receptor 3 (mGluR3, mGlu3, GRM3): expression, function and involvement in schizophrenia. J. Psychopharmacol. 22, 308–322 (2008).

    CAS  Article  Google Scholar 

  40. 40.

    Saini, S. M. et al. Meta-analysis supports GWAS-implicated link between GRM3 and schizophrenia risk. Transl. Psychiatry 7, e1196 (2017).

    CAS  Article  Google Scholar 

  41. 41.

    Yang, X., Wang, G., Wang, Y. & Yue, X. Association of metabotropic glutamate receptor 3 gene polymorphisms with schizophrenia risk: evidence from a meta-analysis. Neuropsychiatr. Dis. Treat. 11, 823–833 (2015).

    CAS  Article  Google Scholar 

  42. 42.

    Jia, W. et al. Metabotropic glutamate receptor 3 is associated with heroin dependence but not depression or schizophrenia in a Chinese population. PLoS One 9, e87247 (2014).

    Article  Google Scholar 

  43. 43.

    Jablensky, A. et al. Polymorphisms associated with normal memory variation also affect memory impairment in schizophrenia. Genes Brain Behav. 10, 410–417 (2011).

    CAS  Article  Google Scholar 

  44. 44.

    Baune, B. T. et al. Association between genetic variants of the metabotropic glutamate receptor 3 (GRM3) and cognitive set shifting in healthy individuals. Genes Brain Behav. 9, 459–466 (2010).

    CAS  Google Scholar 

  45. 45.

    Uchida, T. et al. A novel epidermal growth factor-like molecule containing two follistatin modules stimulates tyrosine phosphorylation of erbB-4 in MKN28 gastric cancer cells. Biochem. Biophys. Res. Commun. 266, 593–602 (1999).

    CAS  Article  Google Scholar 

  46. 46.

    Kanemoto, N. et al. Expression of TMEFF1 mRNA in the mouse central nervous system: precise examination and comparative studies of TMEFF1 and TMEFF2. Brain Res. Mol. Brain Res. 86, 48–55 (2001).

    CAS  Article  Google Scholar 

  47. 47.

    Horie, M. et al. Identification and characterization of TMEFF2, a novel survival factor for hippocampal and mesencephalic neurons. Genomics 67, 146–152 (2000).

    CAS  Article  Google Scholar 

  48. 48.

    Siegel, D. A., Davies, P., Dobrenis, K. & Huang, M. Tomoregulin-2 is found extensively in plaques in Alzheimer’s disease brain. J. Neurochem. 98, 34–44 (2006).

    CAS  Article  Google Scholar 

  49. 49.

    Lin, H. et al. Tomoregulin ectodomain shedding by proinflammatory cytokines. Life Sci. 73, 1617–1627 (2003).

    CAS  Article  Google Scholar 

  50. 50.

    Psych, E. C. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).

    Article  Google Scholar 

  51. 51.

    Härdle, W. & Simar, L. Applied Multivariate Statistical Analysis (Springer, 2007).

  52. 52.

    Ascano, M. Jr et al. FMRP targets distinct mRNA sequence elements to regulate protein expression. Nature 492, 382–386 (2012).

    CAS  Article  Google Scholar 

  53. 53.

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

    CAS  Article  Google Scholar 

  54. 54.

    Bayes, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011).

    CAS  Article  Google Scholar 

  55. 55.

    Pirooznia, M. et al. SynaptomeDB: an ontology-based knowledgebase for synaptic genes. Bioinformatics 28, 897–899 (2012).

    CAS  Article  Google Scholar 

  56. 56.

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

    Article  Google Scholar 

  57. 57.

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

    CAS  Article  Google Scholar 

  58. 58.

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

    CAS  Article  Google Scholar 

  59. 59.

    Ji, X., Kember, R. L., Brown, C. D. & Bucan, M. Increased burden of deleterious variants in essential genes in autism spectrum disorder. Proc. Natl Acad. Sci. USA 113, 15054–15059 (2016).

    CAS  Article  Google Scholar 

  60. 60.

    Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

    CAS  Article  Google Scholar 

  61. 61.

    Weyn-Vanhentenryck, S. M. et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 6, 1139–1152 (2014).

    CAS  Article  Google Scholar 

  62. 62.

    Smith, C. L., Goldsmith, C.-A. W. & Eppig, J. T. The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol. 6, R7 (2005).

    Article  Google Scholar 

  63. 63.

    Blake, J. A. et al. Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse. Nucleic Acids Res. 45, D723–D729 (2017).

    CAS  Article  Google Scholar 

  64. 64.

    Girard, S. L. et al. Increased exonic de novo mutation rate in individuals with schizophrenia. Nat. Genet. 43, 860–863 (2011).

    CAS  Article  Google Scholar 

  65. 65.

    Xu, B. et al. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat. Genet. 44, 1365–1369 (2012).

    CAS  Article  Google Scholar 

  66. 66.

    GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  67. 67.

    Cabili, M. N. et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 25, 1915–1927 (2011).

    CAS  Article  Google Scholar 

  68. 68.

    Law, V. et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, D1091–D1097 (2014).

    CAS  Article  Google Scholar 

  69. 69.

    Yang, H. et al. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res. 44, D1069–D1074 (2016).

    CAS  Article  Google Scholar 

  70. 70.

    O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminform. 3, 33 (2011).

    Article  Google Scholar 

  71. 71.

    Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).

    CAS  Article  Google Scholar 

  72. 72.

    Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res. 35, D198–D201 (2007).

    CAS  Article  Google Scholar 

  73. 73.

    Pawson, A. J. et al. The IUPHAR/BPS guide to pharmacology: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 42, D1098–D1106 (2014).

    CAS  Article  Google Scholar 

  74. 74.

    The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017).

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The authors are grateful to C. Niswender, B. Stansley, and J. Conn at Vanderbilt University for critical input on portions of this manuscript. This study is supported by US NIH/NHGRI grants U01HG009086 (to Q. Wang, R.C., Q. Wei, Y.J., H.Y., X.Z., R.T., N.J.C., and B.L.), U24HG008956, and R01MH113362 (to J.S., N.J.C., and B.L.). The grant U01HG009086 supports the Vanderbilt Analysis Center for the Genome Sequencing Project (GSP) and U24 HG008956 supports the GSP Coordinating Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information




B.L. conceived the overall design of the study, with Q. Wang and R.C. providing input. Q. Wang and R.C. implemented the algorithm and performed most of the analyses. F.C., Q. Wei, Y.J., H.Y., X.Z., and R.T. provided data integration and analyses. Z.W., J.S.S., C.L., E.H.C., and N.J.C. contributed to the interpretation of the results. Q. Wang, R.C., F.C., and B.L. wrote the manuscript, and all authors participated in the review and revision of the manuscript.

Corresponding author

Correspondence to Bingshan Li.

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The authors declare no competing interests.

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Journal peer review information: Nature Neuroscience thanks Vincent Mooser, Hon-Cheong So, and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Figure 1 The DRE-promoter links of NRGs and LBGs in brain specific Hi-C before incorporating capHiC and FANTOM5 data.

One-sided Wilcoxon rank sum test was used for comparison (n = 104 and 831 for NRGs and LBGs respectively). The box plots show median and the 25th and 75th percentiles. The whiskers extend from the box to the largest and smallest values no further than 1.5 * IQR from the box (where IQR is the inter-quartile range, or distance between the 25th and 75th percentiles).

Supplementary Figure 2 The temporal expression pattern of whole genome background genes (WBGs) in BrainSpan data.

WBGs are not differentially expressed across development stages in BrainSpan data (one-sided Wilcoxon rank sum test using medians of expression at prenatal (n = 3) and medians of expression at postnatal (n = 4) stages). The error bar plot shows the median and the 25th and 75th percentiles.

Supplementary Figure 3 Tissue specificity of nearest non-HRG genes and non-nearest HRGs.

Nearest non-HRG genes are highly expressed in a majority of brain tissues (a) while non-nearest HRGs are not (b) in GTEx data (one-sided Wilcoxon rank sum test, n = 65 and 817 for non-nearest HRGs/nearest non-HRG genes and LBGs respectively). For LBGs, we excluded all the nearest genes, thus the sample size is a little smaller than LBGs (n = 830) used for all HRGs in the main text.

Supplementary Figure 4 The Hi-C links of genes CACNA1C, CACNB2, SOX2, SATB2, GRIN2A, and TCF4 in BrainGZ data.

For each GWAS locus, we colored 3 genes with the highest PPs in the bottom panel and used the same coloring scheme for Hi-C links in the middle panel. The r2 values in the top panel indicate the LD between nearby SNPs and the index SNPs. The pattern is very similar in BrainCP data (figures not shown here).

Supplementary Figure 5 The Hi-C links of NCAM1 and DRD2 in BrainGZ data.

The pattern is very similar in BrainCP data (figure not shown here).

Supplementary Figure 6 Tissue-specific expression pattern of NCAM1 and DRD2 in GTEx data.

DRD2 is highly expressed in basal ganglia caudate, hypothalamus, basal ganglia nucleus accumbens, basal ganglia putamen and substantia nigra, but the expression in cortex and frontal cortex is rather low. NCAM1 is uniformly and highly expressed in all brain tissues. The red vertical lines indicate the brain tissues.

Supplementary Figure 7 The temporal expression pattern of NCAM1 and DRD2 in BrainSpan data.

NACAM1 shows consecutively high expression at all developmental stages and extremely high expression at prenatal stages. While DRD2 shows no obvious pattern of transition between prenatal and postnatal stages. The red vertical lines indicate the prenatal stages.

Supplementary Figure 8 The Hi-C links of PTK2B in BrainGZ data.

As a non-nearest gene, PTK2B has a lot of physical interactions with the index SNP region. The pattern is very similar in BrainCP data (figure not shown here).

Supplementary Figure 9 The identified SCZ risk genes and the drugs that target these genes.

The circles and squares indicate the drug targets and drugs respectively. Different colors show the drug first-level ATC classification and the size of circle shows gene’s expression level in brain.

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Wang, Q., Chen, R., Cheng, F. et al. A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. Nat Neurosci 22, 691–699 (2019). https://doi.org/10.1038/s41593-019-0382-7

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