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:

Integrating cell-type-specific gene expression and genome-wide associations identifies risk genes for schizophrenia

Molecular Psychiatry (2026) Cite this article

Subjects

Abstract

Integrative studies such as transcriptome-wide association studies (TWAS) and Mendelian randomization (MR) have identified multiple risk genes whose expression level is associated with schizophrenia (SCZ). However, the vast majority of integrative studies are based on quantitative trait loci (QTL) data from bulk brain tissues. Given that gene expression and genetic regulatory effects are highly dependent on cell types, it is important to conduct integrative studies using expression data from specific brain cell types. Here, we investigate the causality between cell-type-specific gene expression and SCZ. We first conducted MR by integrating four cell-type-specific expression quantitative trait loci (eQTL) datasets and genome-wide associations of SCZ separately. We then performed a meta-analysis to explore the causal relationships between gene expression in different human brain cell types and SCZ. We identified multiple genes whose cell-type-specific expression levels are causally associated with SCZ, including 148 significant genes in excitatory neurons, 71 in inhibitory neurons, 63 in astrocytes, 48 in microglia, 70 in oligodendrocytes, 39 in oligodendrocyte precursor cells, 20 in endothelial cells, and 7 in pericytes. We also performed MR using eQTL data from brain tissues and identified 206 genes whose expression levels are causally associated with SCZ. By integrating multiple lines of evidence, we prioritized the most plausible causal genes, including the MAU2 and PPP1R13B. Finally, we performed a drug target analysis to evaluate the therapeutic potential of these genes. Our study reveals the causal relationships between cell-type-specific gene expression and SCZ, providing promising targets for mechanistic investigation and therapeutic interventions.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Significant genes identified in excitatory and inhibitory neurons.
The alternative text for this image may have been generated using AI.
Fig. 2: Significant genes identified in astrocytes and microglia.
The alternative text for this image may have been generated using AI.
Fig. 3: Significant genes identified in oligodendrocytes and oligodendrocyte precursor cells.
The alternative text for this image may have been generated using AI.
Fig. 4: Enrichment analysis of the significant genes.
The alternative text for this image may have been generated using AI.
Fig. 5: Overlapping genes identified in cell-type-specific datasets and bulk brain tissues.
The alternative text for this image may have been generated using AI.
Fig. 6: Prioritization of risk genes through integration of multiple lines of evidence.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

The genome-wide summary statistics of the Psychiatric Genomics Consortium Phase3 SCZ GWAS (PGC3) were downloaded from https://pgc.unc.edu/for-researchers/download-results/. Cell-type-specific eQTL datasets were obtained from the following sources: the first dataset (Emani et al.) from http://brainscope.psychencode.org/; the second dataset (Fujita et al.) from https://www.synapse.org/Synapse:syn52335807; the third dataset (Bryois et al.) from https://doi.org/10.5281/zenodo.5543734; and the fourth dataset (Lopes et al.) from https://doi.org/10.5281/zenodo.4118605. The BrainMeta V2 eQTL data were downloaded from https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata. The 1000 Genome Project data were obtained from https://www.internationalgenome.org/data/. Differential expression gene data were obtained from https://www.science.org/doi/10.1126/science.adg5136.

References

  1. Geschwind DH, Flint J. Genetics and genomics of psychiatric disease. Science. 2015;349:1489–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Correll CU, Solmi M, Croatto G, Schneider LK, Rohani-Montez SC, Fairley L, et al. Mortality in people with schizophrenia: a systematic review and meta-analysis of relative risk and aggravating or attenuating factors. World Psychiatry. 2022;21:248–71.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Notaras M, Lodhi A, Dundar F, Collier P, Sayles NM, Tilgner H, et al. Schizophrenia is defined by cell-specific neuropathology and multiple neurodevelopmental mechanisms in patient-derived cerebral organoids. Mol Psychiatry. 2022;27:1416–34.

    Article  CAS  PubMed  Google Scholar 

  4. Woo TU. Neurobiology of schizophrenia onset. Curr Top Behav Neurosci. 2014;16:267–95.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Thaker GK, Carpenter WT Jr. Advances in schizophrenia. Nat Med. 2001;7:667–71.

    Article  CAS  PubMed  Google Scholar 

  6. Jauhar S, Johnstone M, McKenna PJ. Schizophrenia. Lancet. 2022;399:473–86.

    Article  CAS  PubMed  Google Scholar 

  7. Kessler RC, Amminger GP, Aguilar-Gaxiola S, Alonso J, Lee S. Ustun TB. Age of onset of mental disorders: a review of recent literature. Curr Opin Psychiatry. 2007;20:359–64.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388:86–97.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kahn RS, Sommer IE, Murray RM, Meyer-Lindenberg A, Weinberger DR, Cannon TD, et al. Schizophrenia. Nat Rev Dis Primers. 2015;1:15067.

    Article  PubMed  Google Scholar 

  10. Schultz SK, Andreasen NC. Schizophrenia. Lancet. 1999;353:1425–30.

    Article  CAS  PubMed  Google Scholar 

  11. Murray CJ, Lopez AD. Global mortality, disability, and the contribution of risk factors: Global Burden of Disease Study. Lancet. 1997;349:1436–42.

    Article  CAS  PubMed  Google Scholar 

  12. Crown WH, Neslusan C, Russo PA, Holzer S, Ozminkowski R, Croghan T. Hospitalization and total medical costs for privately insured persons with schizophrenia. Adm Policy Ment Health. 2001;28:335–51.

    Article  CAS  PubMed  Google Scholar 

  13. Jungbauer J, Wittmund B, Dietrich S, Angermeyer MC. Subjective burden over 12 months in parents of patients with schizophrenia. Arch Psychiatr Nurs. 2003;17:126–34.

    Article  PubMed  Google Scholar 

  14. Sullivan PF, Yao S, Hjerling-Leffler J. Schizophrenia genomics: genetic complexity and functional insights. Nat Rev Neurosci. 2024;25:611–24.

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  16. Li Z, Chen J, Yu H, He L, Xu Y, Zhang D, et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat Genet. 2017;49:1576–83.

    Article  CAS  PubMed  Google Scholar 

  17. Pardinas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018;50:381–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lam M, Chen CY, Li Z, Martin AR, Bryois J, Ma X, et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat Genet. 2019;51:1670–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Trubetskoy V, Pardinas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:502–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dang X, Teng Z, Yang Y, Li W, Liu J, Hui L, et al. Gene-level analysis reveals the genetic aetiology and therapeutic targets of schizophrenia. Nat Hum Behav. 2025;9:609–24.

    Article  PubMed  Google Scholar 

  21. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

    Article  CAS  PubMed  Google Scholar 

  22. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardinas AF, et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat Genet. 2019;51:659–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hormozdiari F, van de Bunt M, Segre AV, Li X, Joo JWJ, Bilow M, et al. Colocalization of GWAS and eQTL signals detects target genes. Am J Hum Genet. 2016;99:1245–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dobbyn A, Huckins LM, Boocock J, Sloofman LG, Glicksberg BS, Giambartolomei C, et al. Landscape of conditional eQTL in dorsolateral prefrontal cortex and co-localization with schizophrenia GWAS. Am J Hum Genet. 2018;102:1169–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu S, Won H, Clarke D, Matoba N, Khullar S, Mu Y, et al. Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex. Genome Med. 2022;14:133.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zeng B, Bendl J, Kosoy R, Fullard JF, Hoffman GE, Roussos P. Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nat Genet. 2022;54:161–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science. 2018;362:eaat8464.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Consortium GT. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318–30.

    Article  Google Scholar 

  30. Ng B, White CC, Klein HU, Sieberts SK, McCabe C, Patrick E, et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat Neurosci. 2017;20:1418–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018;50:538–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Aldridge S, Teichmann SA. Single cell transcriptomics comes of age. Nat Commun. 2020;11:4307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Network BICC. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature. 2021;598:86–102.

    Article  Google Scholar 

  35. Luo C, Liu H, Xie F, Armand EJ, Siletti K, Bakken TE, et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genom. 2022;2:100107.

    Article  CAS  Google Scholar 

  36. Nott A, Holtman IR, Coufal NG, Schlachetzki JCM, Yu M, Hu R, et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science. 2019;366:1134–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jerber J, Seaton DD, Cuomo ASE, Kumasaka N, Haldane J, Steer J, et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat Genet. 2021;53:304–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bryois J, Calini D, Macnair W, Foo L, Urich E, Ortmann W, et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat Neurosci. 2022;25:1104–12.

    Article  CAS  PubMed  Google Scholar 

  39. Fujita M, Gao Z, Zeng L, McCabe C, White CC, Ng B, et al. Cell subtype-specific effects of genetic variation in the Alzheimer’s disease brain. Nat Genet. 2024;56:605–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, et al. Single-cell genomics and regulatory networks for 388 human brains. Science. 2024;384:eadi5199.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Skene NG, Bryois J, Bakken TE, Breen G, Crowley JJ, Gaspar HA, et al. Genetic identification of brain cell types underlying schizophrenia. Nat Genet. 2018;50:825–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Neavin D, Nguyen Q, Daniszewski MS, Liang HH, Chiu HS, Wee YK, et al. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biol. 2021;22:76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Aygun N, Elwell AL, Liang D, Lafferty MJ, Cheek KE, Courtney KP, et al. Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis. Am J Hum Genet. 2021;108:1647–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Werling DM, Pochareddy S, Choi J, An JY, Sheppard B, Peng M, et al. Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex. Cell Rep. 2020;31:107489.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–R98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Gormley M, Dudding T, Sanderson E, Martin RM, Thomas S, Tyrrell J, et al. A multivariable Mendelian randomization analysis investigating smoking and alcohol consumption in oral and oropharyngeal cancer. Nat Commun. 2020;11:6071.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pavlides JM, Zhu Z, Gratten J, McRae AF, Wray NR, Yang J. Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med. 2016;8:84.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Sun Y, Liu Y, Dian Y, Zeng F, Deng G, Lei S. Association of glucagon-like peptide-1 receptor agonists with risk of cancers-evidence from a drug target Mendelian randomization and clinical trials. Int J Surg. 2024;110:4688–94.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Lopes KP, Snijders GJL, Humphrey J, Allan A, Sneeboer MAM, Navarro E, et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022;54:4–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Qi T, Wu Y, Fang H, Zhang F, Liu S, Zeng J, et al. Genetic control of RNA splicing and its distinct role in complex trait variation. Nat Genet. 2022;54:1355–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:e34408.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34:2926–40.

    Article  Google Scholar 

  55. Kappelmann N, Arloth J, Georgakis MK, Czamara D, Rost N, Ligthart S, et al. Dissecting the association between inflammation, metabolic dysregulation, and specific depressive symptoms: a genetic correlation and 2-sample mendelian randomization study. JAMA Psychiatry. 2021;78:161–70.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yao S, Zhang M, Dong SS, Wang JH, Zhang K, Guo J, et al. Bidirectional two-sample Mendelian randomization analysis identifies causal associations between relative carbohydrate intake and depression. Nat Hum Behav. 2022;6:1569–76.

    Article  PubMed  Google Scholar 

  58. Storm CS, Kia DA, Almramhi MM, Bandres-Ciga S, Finan C, International Parkinson’s Disease Genomics C, et al. Finding genetically-supported drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome. Nat Commun. 2021;12:7342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Zuber V, Grinberg NF, Gill D, Manipur I, Slob EAW, Patel A, et al. Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches. Am J Hum Genet. 2022;109:767–82.

    Article  CAS  PubMed  Google Scholar 

  61. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51:D638–D646.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ruzicka WB, Mohammadi S, Fullard JF, Davila-Velderrain J, Subburaju S, Tso DR, et al. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science. 2024;384:eadg5136.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. International Schizophrenia C, Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748–52.

    Article  Google Scholar 

  64. Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe’er I, et al. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. 2009;460:753–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, et al. Common variants conferring risk of schizophrenia. Nature. 2009;460:744–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Batiuk MY, Tyler T, Dragicevic K, Mei S, Rydbirk R, Petukhov V, et al. Upper cortical layer-driven network impairment in schizophrenia. Sci Adv. 2022;8:eabn8367.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lewis DA, Curley AA, Glausier JR, Volk DW. Cortical parvalbumin interneurons and cognitive dysfunction in schizophrenia. Trends Neurosci. 2012;35:57–67.

    Article  CAS  PubMed  Google Scholar 

  68. Heckers S, Konradi C. GABAergic mechanisms of hippocampal hyperactivity in schizophrenia. Schizophr Res. 2015;167:4–11.

    Article  PubMed  Google Scholar 

  69. Collado-Torres L, Burke EE, Peterson A, Shin J, Straub RE, Rajpurohit A, et al. Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia. Neuron. 2019;103:203–16.e8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Benjamin KJM, Chen Q, Jaffe AE, Stolz JM, Collado-Torres L, Huuki-Myers LA, et al. Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes. Nat Neurosci. 2022;25:1559–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Jaffe AE, Hoeppner DJ, Saito T, Blanpain L, Ukaigwe J, Burke EE, et al. Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk. Nat Neurosci. 2020;23:510–9.

    Article  CAS  PubMed  Google Scholar 

  72. Donovan MKR, D’Antonio-Chronowska A, D’Antonio M, Frazer KA. Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants. Nat Commun. 2020;11:955.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Yao DW, O’Connor LJ, Price AL, Gusev A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet. 2020;52:626–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kim YA, Siddiqui T, Blaze J, Cosacak MI, Winters T, Kumar A, et al. RNA methyltransferase NSun2 deficiency promotes neurodegeneration through epitranscriptomic regulation of tau phosphorylation. Acta Neuropathol. 2023;145:29–48.

    Article  CAS  PubMed  Google Scholar 

  75. Blanco S, Dietmann S, Flores JV, Hussain S, Kutter C, Humphreys P, et al. Aberrant methylation of tRNAs links cellular stress to neuro-developmental disorders. EMBO J. 2014;33:2020–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Sheikh BN, Guhathakurta S, Akhtar A. The non-specific lethal (NSL) complex at the crossroads of transcriptional control and cellular homeostasis. EMBO Rep. 2019;20:e47630.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Lewerissa EI, Nadif Kasri N, Linda K. Epigenetic regulation of autophagy-related genes: Implications for neurodevelopmental disorders. Autophagy. 2024;20:15–28.

    Article  CAS  PubMed  Google Scholar 

  78. Linda K, Lewerissa EI, Verboven AHA, Gabriele M, Frega M, Klein Gunnewiek TM, et al. Imbalanced autophagy causes synaptic deficits in a human model for neurodevelopmental disorders. Autophagy. 2022;18:423–42.

    Article  CAS  PubMed  Google Scholar 

  79. Khandaker GM, Cousins L, Deakin J, Lennox BR, Yolken R, Jones PB. Inflammation and immunity in schizophrenia: implications for pathophysiology and treatment. Lancet Psychiatry. 2015;2:258–70.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Al Mamun A, Chauhan A, Qi S, Ngwa C, Xu Y, Sharmeen R, et al. Microglial IRF5-IRF4 regulatory axis regulates neuroinflammation after cerebral ischemia and impacts stroke outcomes. Proc Natl Acad Sci USA. 2020;117:1742–52.

    Article  PubMed  Google Scholar 

  81. D’Aversa TG, Weidenheim KM, Berman JW. CD40-CD40L interactions induce chemokine expression by human microglia: implications for human immunodeficiency virus encephalitis and multiple sclerosis. Am J Pathol. 2002;160:559–67.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. J Genet Genomics. 2023;50:925–33.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Luo J, Wu X, Cheng Y, Chen G, Wang J, Song X. Expression quantitative trait locus studies in the era of single-cell omics. Front Genet. 2023;14:1182579.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Wang L, Gao S, Chen S, Markus H, Wang G, Carrel L, et al. Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits. Nat Commun. 2025;16:10606.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Maness PF, Schachner M. Neural recognition molecules of the immunoglobulin superfamily: signaling transducers of axon guidance and neuronal migration. Nat Neurosci. 2007;10:19–26.

    Article  CAS  PubMed  Google Scholar 

  86. Leshchyns’ka I, Sytnyk V. Reciprocal interactions between cell adhesion molecules of the immunoglobulin superfamily and the cytoskeleton in neurons. Front Cell Dev Biol. 2016;4:9.

    PubMed  PubMed Central  Google Scholar 

  87. Clarin JD, Reddy N, Alexandropoulos C, Gao WJ. The role of cell adhesion molecule IgSF9b at the inhibitory synapse and psychiatric disease. Neurosci Biobehav Rev. 2024;156:105476.

    Article  CAS  PubMed  Google Scholar 

  88. Woo J, Kwon SK, Nam J, Choi S, Takahashi H, Krueger D, et al. The adhesion protein IgSF9b is coupled to neuroligin 2 via S-SCAM to promote inhibitory synapse development. J Cell Biol. 2013;201:929–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Lewis DA. The chandelier neuron in schizophrenia. Dev Neurobiol. 2011;71:118–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Gonzalez-Burgos G, Lewis DA. GABA neurons and the mechanisms of network oscillations: implications for understanding cortical dysfunction in schizophrenia. Schizophr Bull. 2008;34:944–61.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Kathuria A, Lopez-Lengowski K, Watmuff B, McPhie D, Cohen BM, Karmacharya R. Synaptic deficits in iPSC-derived cortical interneurons in schizophrenia are mediated by NLGN2 and rescued by N-acetylcysteine. Transl Psychiatry. 2019;9:321.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Davidson IF, Bauer B, Goetz D, Tang W, Wutz G, Peters JM. DNA loop extrusion by human cohesin. Science. 2019;366:1338–45.

    Article  CAS  PubMed  Google Scholar 

  93. Fujita Y, Pather SR, Ming GL, Song H. 3D spatial genome organization in the nervous system: From development and plasticity to disease. Neuron. 2022;110:2902–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Schittenhelm MM, Kaiser M, Gyorffy B, Kampa-Schittenhelm KM. Evaluation of apoptosis stimulating protein of TP53-1 (ASPP1/PPP1R13B) to predict therapy resistance and overall survival in acute myeloid leukemia (AML). Cell Death Dis. 2024;15:25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Devor A, Andreassen OA, Wang Y, Maki-Marttunen T, Smeland OB, Fan CC, et al. Genetic evidence for role of integration of fast and slow neurotransmission in schizophrenia. Mol Psychiatry. 2017;22:792–801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Wang D, Li D, Dang X, Mu C, Liu C, Zeng Y, et al. Mendelian randomization reveals causalities between DNA methylation and schizophrenia. Biol Psychiatry. 2025;98:712–23.

    Article  CAS  PubMed  Google Scholar 

  97. Setter SM, Iltz JL, Thams J, Campbell RK. Metformin hydrochloride in the treatment of type 2 diabetes mellitus: a clinical review with a focus on dual therapy. Clin Ther. 2003;25:2991–3026.

    Article  CAS  PubMed  Google Scholar 

  98. Yu J, Liao X, Zhong Y, Wu Y, Lai X, Jiao H, et al. The candidate schizophrenia risk gene Tmem108 regulates glucose metabolism homeostasis. Front Endocrinol (Lausanne). 2021;12:770145.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Huang YS, Lin X, Xu YJ, Chen GB Investigating antidiabetic drug targets as potential therapeutic modulators for schizophrenia. Psychopharmacology (Berl) 2025; e-pub ahead of print 10 November 2025; https://doi.org/10.1007/s00213-025-06951-3. Online ahead of print.

  100. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18:41–58.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

X.-J.L. discloses support for this work from the National Natural Science Foundation of China (Youth Science Fund Project, Category A) [grant number 82525026], the Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project [grant number 2025ZD0216000], the Innovative Drug Research and Development National Science and Technology Major Project, and startup funds from Southeast University [grant number RF1028623032]. W.L. and X.D. declare no relevant funding. We thank Miss. Yinuo Cao for her technical assistance. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Wenqi Lou, Xinglun Dang.

Authors and Affiliations

  1. Medical Research Center, Nantong First People’s Hospital, State Key Laboratory of Digital Medical Engineering, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing, Jiangsu, 210096, China

    Wenqi Lou, Xinglun Dang & Xiong-Jian Luo

Authors
  1. Wenqi Lou
    View author publications

    Search author on:PubMed Google Scholar

  2. Xinglun Dang
    View author publications

    Search author on:PubMed Google Scholar

  3. Xiong-Jian Luo
    View author publications

    Search author on:PubMed Google Scholar

Contributions

XJL conceived, designed, and supervised the whole study. WQL performed the analyses and drafted the manuscript. XLD contributed to this in study design, interpretation of data and manuscript writing. All authors revised the manuscript critically and approved the final version.

Corresponding author

Correspondence to Xiong-Jian Luo.

Ethics declarations

Competing interests

The authors declare no competing interest.

Ethics Approval and Consent to Participate

This study used data derived from publicly available databases and did not involve live vertebrate animals or human participants.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lou, W., Dang, X. & Luo, XJ. Integrating cell-type-specific gene expression and genome-wide associations identifies risk genes for schizophrenia. Mol Psychiatry (2026). https://doi.org/10.1038/s41380-026-03652-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41380-026-03652-x

Search

Advanced search

Quick links