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.

Genome-wide DNA methylation analysis of peripheral blood cells derived from patients with first-episode schizophrenia in the Chinese Han population

A Correction to this article was published on 04 January 2021

This article has been updated

Abstract

Schizophrenia is a severe neuropsychiatric disorder with core features including hallucinations, delusions, and cognition deficits. Accumulating evidence has implicated abnormal DNA methylation in the development of schizophrenia. However, the mechanisms by which DNA methylation changes alter the risk for schizophrenia remain largely unknown. We recently carried out a DNA methylome study of peripheral blood samples from 469 first-episode patients with schizophrenia and 476 age- and gender-matched healthy controls of Han Chinese origin. Genomic DNA methylation patterns were quantified using an Illumina Infinium Human MethylationEPIC BeadChip. We identified multiple differentially methylated positions (DMPs) and regions between patients and controls. The most significant DMPs were annotated to genes C17orf53, THAP1 and KCNQ4 (KV7.4), with Bonferroni-adjusted P values of \({\mathrm{1}}{\mathrm{.34}} \times {\mathrm{10}}^{{\mathrm{ - 12}}}\), \({\mathrm{1}}{\mathrm{.15}} \times {\mathrm{10}}^{{\mathrm{ - 11}}}\), and \({\mathrm{3}}{\mathrm{.11}} \times {\mathrm{10}}^{{\mathrm{ - 11}}}\), respectively. In particular, KCNQ4 encodes a voltage-gated potassium channel of the KV7 family, which is linked to neuronal excitability. The genes associated with top-ranked DMPs also included many genes involved in nervous system development, such as LIMK2 and TMOD2. Gene ontology analysis of the differentially methylated genes further identified strong enrichment of neuronal networks, including neuron projection extension, axonogenesis and neuron apoptotic process. Finally, we provided evidence that schizophrenia-associated epigenetic alterations co-localize with genetic susceptibility loci. By focusing on first-episode schizophrenia patients, our investigation lends particularly strong support for an important role of DNA methylation in schizophrenia pathogenesis unconfounded by the effects of long-term antipsychotic medication or disease progression. The observed DNA methylation aberrations in schizophrenia patients could potentially provide a valuable resource for identifying diagnostic biomarkers and developing novel therapeutic targets to benefit schizophrenia patients.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1: Differentially methylated positions between FESZ patients and healthy controls.
Fig. 2: Violin plot of important DMPs between patients and controls.
Fig. 3: Regional Manhattan plot, genomic annotation and co-methylation pattern surrounding HPCAL1.
Fig. 4: Significantly enriched GO terms and network of DMP-related genes.
Fig. 5: This figure illustrates the colocalization process.

Similar content being viewed by others

Data availability

Code used in the analyses is available to download from https://github.com/Mingrui-Li1992/SZ_methylation. All data can be viewed in NODE (http://www.biosino.org/node) by pasting the accession OEP001178 into the text search box or through the URL: http://www.biosino.org/node/project/detail/ OEP001178.

Change history

References

  1. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789–858.

    Google Scholar 

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

    PubMed  Google Scholar 

  3. Ripke S, Neale BM, Corvin A, Walters JTR, Farh K-H, Holmans PA, et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    CAS  PubMed Central  Google Scholar 

  4. Bigdeli TB, Genovese G, Georgakopoulos P, Meyers JL, Peterson RE, Iyegbe CO, et al. Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry. Mol Psychiatry. 2020;25:2455–67.

    CAS  PubMed  Google Scholar 

  5. Warland A, Kendall KM, Rees E, Kirov G, Caseras X. Schizophrenia-associated genomic copy number variants and subcortical brain volumes in the UK Biobank. Mol Psychiatry. 2020;25:854–62.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003;33:245–54.

    CAS  PubMed  Google Scholar 

  8. Cholewa-Waclaw J, Bird A, von Schimmelmann M, Schaefer A, Yu H, Song H, et al. The role of epigenetic mechanisms in the regulation of gene expression in the nervous system. J Neurosci. 2016;36:11427–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Tang J, Fan Y, Li H, Xiang Q, Zhang D-F, Li Z, et al. Whole-genome sequencing of monozygotic twins discordant for schizophrenia indicates multiple genetic risk factors for schizophrenia. J Genet Genom. 2017;44:295–306.

    Google Scholar 

  10. Greenberg MVC, Bourc’his D. The diverse roles of DNA methylation in mammalian development and disease. Nat Rev Mol Cell Biol. 2019;20:590–607.

    CAS  PubMed  Google Scholar 

  11. Montano C, Taub MA, Jaffe A, Briem E, Feinberg JI, Trygvadottir R, et al. Association of DNA methylation differences with schizophrenia in an epigenome-wide association study. JAMA Psychiatry. 2016;73:506–14.

    PubMed  PubMed Central  Google Scholar 

  12. Hannon E, Dempster E, Viana J, Burrage J, Smith AR, Macdonald R, et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome Biol. 2016;17:176.

    PubMed  PubMed Central  Google Scholar 

  13. Aberg KA, McClay JL, Nerella S, Clark S, Kumar G, Chen W, et al. Methylome-wide association study of schizophrenia: identifying blood biomarker signatures of environmental insults. JAMA psychiatry. 2014;71:255–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19:40–7.

    CAS  PubMed  Google Scholar 

  15. Duan J, Sanders AR, Gejman PV. From schizophrenia genetics to disease biology: harnessing new concepts and technologies. J Psychiatr Brain Sci. 2019;4:e190014.

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  17. Mansell G, Gorrie-Stone TJ, Bao Y, Kumari M, Schalkwyk LS, Mill J, et al. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genom. 2019;20:366.

    Google Scholar 

  18. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Triche TJ Jr., Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of illumina infinium DNA methylation bead arrays. Nucleic Acids Res. 2013;41:e90–e90.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Fortin J-P, Labbe A, Lemire M, Zanke BW, Hudson TJ, Fertig EJ, et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014;15:503–503.

    PubMed  PubMed Central  Google Scholar 

  21. Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 2016;45:e22–e22.

    PubMed Central  Google Scholar 

  22. Nordlund J, Bäcklin CL, Wahlberg P, Busche S, Berglund EC, Eloranta M-L, et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol. 2013;14:r105.

    PubMed  PubMed Central  Google Scholar 

  23. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29:189–96.

    CAS  PubMed  Google Scholar 

  24. Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33:3982–4.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  26. Xu Z, Niu L, Li L, Taylor JA. ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res. 2016;44:e20–e20.

    PubMed  Google Scholar 

  27. Rahmani E, Yedidim R, Shenhav L, Schweiger R, Weissbrod O, Zaitlen N, et al. GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data. Bioinformatics. 2017;33:1870–2.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, et al. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods. 2016;13:443–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord VR, et al. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin. 2015;8:6.

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  31. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D613.

    CAS  PubMed  Google Scholar 

  33. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Lam M, Chen C-Y, 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Wulff H, Castle NA, Pardo LA. Voltage-gated potassium channels as therapeutic targets. Nat Rev Drug Discov. 2009;8:982–1001.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Peng H, Bian X-L, Ma F-C, Wang K-W. Pharmacological modulation of the voltage-gated neuronal Kv7/KCNQ/M-channel alters the intrinsic excitability and synaptic responses of pyramidal neurons in rat prefrontal cortex slices. Acta Pharm Sin. 2017;38:1248–56.

    CAS  Google Scholar 

  37. Mao R, Deng R, Wei Y, Han L, Meng Y, Xie W, et al. LIMK1 and LIMK2 regulate cortical development through affecting neural progenitor cell proliferation and migration. Mol Brain. 2019;12:67.

    PubMed  PubMed Central  Google Scholar 

  38. Omotade OF, Rui Y, Lei W, Yu K, Hartzell HC, Fowler VM, et al. Tropomodulin isoform-specific regulation of dendrite development and synapse formation. J Neurosci. 2018;38:10271.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Peykov S, Berkel S, Schoen M, Weiss K, Degenhardt F, Strohmaier J, et al. Identification and functional characterization of rare SHANK2 variants in schizophrenia. Mol Psychiatry. 2015;20:1489–98.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Tao R, Davis KN, Li C, Shin JH, Gao Y, Jaffe AE, et al. GAD1 alternative transcripts and DNA methylation in human prefrontal cortex and hippocampus in brain development, schizophrenia. Mol Psychiatry. 2018;23:1496–505.

    CAS  PubMed  Google Scholar 

  41. Zhang Y, Fang X, Fan W, Tang W, Cai J, Song L, et al. Interaction between BDNF and TNF-α genes in schizophrenia. Psychoneuroendocrinology. 2018;89:1–6.

    PubMed  Google Scholar 

  42. Zhang XY, Tan YL, Chen DC, Tan SP, Yang FD, Wu HE, et al. Interaction of BDNF with cytokines in chronic schizophrenia. Brain Behav Immun. 2016;51:169–75.

    CAS  PubMed  Google Scholar 

  43. Meffre D, Grenier J, Bernard S, Courtin F, Dudev T, Shackleford GG, et al. Wnt and lithium: a common destiny in the therapy of nervous system pathologies? Cell Mol Life Sci. 2014;71:1123–48.

    CAS  PubMed  Google Scholar 

  44. Yu Z, Cheng C, Liu Y, Liu N, Lo EH, Wang X. Neuroglobin promotes neurogenesis through Wnt signaling pathway. Cell Death Dis. 2018;9:945–945.

    PubMed  PubMed Central  Google Scholar 

  45. Zhang S, Zhang H, Zhou Y, Qiao M, Zhao S, Kozlova A, et al. Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Science. 2020;369:561.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Jentsch TJ. Neuronal KCNQ potassium channels:physislogy and role in disease. Nat Rev Neurosci. 2000;1:21–30.

    CAS  PubMed  Google Scholar 

  47. Carment L, Dupin L, Guedj L, Térémetz M, Krebs M-O, Cuenca M, et al. Impaired attentional modulation of sensorimotor control and cortical excitability in schizophrenia. Brain. 2019;142:2149–64.

    PubMed  PubMed Central  Google Scholar 

  48. Cuberos H, Vallée B, Vourc’h P, Tastet J, Andres CR, Bénédetti H. Roles of LIM kinases in central nervous system function and dysfunction. FEBS Lett. 2015;589(24, Part B):3795–806.

    CAS  PubMed  Google Scholar 

  49. Datta D, Arion D, Corradi JP, Lewis DA. Altered expression of CDC42 signaling pathway components in cortical layer 3 pyramidal cells in schizophrenia. Biol Psychiatry. 2015;78:775–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Sharp BM, Chen H, Gong S, Wu X, Liu Z, Hiler K, et al. Gene expression in accumbens GABA neurons from inbred rats with different drug-taking behavior. Genes Brain Behav. 2011;10:778–88.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Walton E, Hass J, Liu J, Roffman JL, Bernardoni F, Roessner V, et al. Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research. Schizophr Bull. 2016;42:406–14.

    PubMed  Google Scholar 

  52. Xu R-H, Wei W, Krawczyk M, Wang W, Luo H, Flagg K, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat Mater. 2017;16:1155–61.

    CAS  PubMed  Google Scholar 

  53. Taryma-Leśniak O, Sokolowska KE, Wojdacz TK. Current status of development of methylation biomarkers for in vitro diagnostic IVD applications. Clin Epigenetics. 2020;12:100.

    PubMed  PubMed Central  Google Scholar 

  54. Duruisseaux M, Martínez-Cardús A, Calleja-Cervantes ME, Moran S, Castro de Moura M, Davalos V, et al. Epigenetic prediction of response to anti-PD-1 treatment in non-small-cell lung cancer: a multicentre, retrospective analysis. Lancet Respir Med. 2018;6:771–81.

    CAS  PubMed  Google Scholar 

  55. Zou D, Qiu Y, Li R, Meng Y, Wu Y. A novel schizophrenia diagnostic model based on statistically significant changes in gene methylation in specific brain regions. BioMed Res Int. 2020;2020:8047146.

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to the patients, healthy volunteers, and their families who contributed to this study. This study was funded by School of Life Sciences of Fudan University, as a start-up capital for Dr. Yin Yao, and was partially funded by Beijing HuiLongGuan Hospital. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

ML performed the DNA methylation analysis, result interpretation and discussion, and wrote the manuscript; YL contributed to sample recruitment and preparation; HQ participated in the data analysis pipeline construction, result discussion and interpretation; JDT helped revise the manuscript and provided insightful thoughts to the data interpretation; ML performed the co-localization analysis; CQ contributed to literature review and experimental procedure supervision; JL and QL contributed to literature review; FF, MG, JH, JT, and FY participated in the sample recruitment; YY and YT conceived the study, co-supervised study manuscript writing, interpreted the results and finalized the manuscript. All authors approved the final manuscript.

Corresponding authors

Correspondence to Yunlong Tan or Yin Yao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Li, Y., Qin, H. et al. Genome-wide DNA methylation analysis of peripheral blood cells derived from patients with first-episode schizophrenia in the Chinese Han population. Mol Psychiatry 26, 4475–4485 (2021). https://doi.org/10.1038/s41380-020-00968-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-020-00968-0

This article is cited by

Search

Quick links