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:

A methylation study of long-term depression risk

Molecular Psychiatry volume 25, pages 1334–1343 (2020)Cite this article

Subjects

This article has been updated

Abstract

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10−16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

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

Access options

Buy this article

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

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

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Change history

  • 15 October 2019

    Following publication of this article, the authors noticed that the Supplementary Tables file was accidentally omitted. This error has now been fixed, and the Supplementary Tables file is available to download from the HTML version of this article.

References

  1. Depression and other common mental disorders: global health estimates. Geneva: World Health Organization; 2017.

  2. Hardeveld F, Spijker J, De Graaf R, Nolen WA, Beekman AT. Prevalence and predictors of recurrence of major depressive disorder in the adult population. Acta Psychiatr Scand. 2009;122:184–91.

    PubMed  Google Scholar 

  3. Mueller TI, Leon AC, Keller MB, Solomon DA, Endicott J, Coryell W, et al. Recurrence after recovery from major depressive disorder during 15 years of observational follow-up. Am J psychiatry. 1999;156:1000–6.

    CAS  PubMed  Google Scholar 

  4. Bonasio R, Tu S, Reinberg D. Molecular signals of epigenetic states. Science. 2010;330:612–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Vialou V, Feng J, Robison AJ, Nestler EJ. Epigenetic mechanisms of depression and antidepressant action. Annu Rev Pharm Toxicol. 2012;53:59–87.

    Google Scholar 

  6. Murgatroyd C, Patchev AV, Wu Y, Micale V, Bockmuhl Y, Fischer D, et al. Dynamic DNA methylation programs persistent adverse effects of early-life stress. Nat Neurosci. 2009;12:1559–66.

    CAS  PubMed  Google Scholar 

  7. Volkow ND, Koob G, Baler R. Biomarkers in substance use disorders. ACS Chem Neurosci. 2015;6:522–5.

    CAS  PubMed  Google Scholar 

  8. Chan RF, Shabalin AA, Xie LY, Adkins DE, Zhao M, Turecki G, et al. Enrichment methods provide a feasible approach to comprehensive and adequately powered investigations of the brain methylome. Nucl Acids Res. 2017;45:e97.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Aberg KA, Chan RF, Shabalin AA, Zhao M, Turecki G, Heine Staunstrup N, et al. A MBD-seq protocol for large-scale methylome-wide studies with (very) low amounts of DNA. Epigenetics. 2017;12:743–50.

    PubMed  PubMed Central  Google Scholar 

  10. Penninx B, Beekman A, Smit J. The Netherlands study of depression and anxiety (NESDA): rationales, objectives and methods. Int J Methods Psychiatr Res. 2008;17:121–40.

    PubMed  PubMed Central  Google Scholar 

  11. Moreland B, Oman K, Curfman J, Yan P, Bundschuh R. Methyl-CpG/MBD2 interaction requires minimum separation and exhibits minimal sequence specificity. Biophys J. 2016;111:2551–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Nair SS, Coolen MW, Stirzaker C, Song JZ, Statham AL, Strbenac D, et al. Comparison of methyl-DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain (MBD) protein capture for genome-wide DNA methylation analysis reveal CpG sequence coverage bias. Epigenetics. 2011;6:34–44.

    CAS  PubMed  Google Scholar 

  13. Aberg KA, McClay JL, Nerella S, Xie LY, Clark SL, Hudson AD, et al. MBD-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case-control samples. Epigenomics. 2012;4:605–21.

    CAS  PubMed  Google Scholar 

  14. Aberg KA, Xie LY, Nerella S, Copeland WE, Costello EJ, van den Oord EJ. High quality methylome-wide investigations through next-generation sequencing of DNA from a single archived dry blood spot. Epigenetics. 2013;8:542–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Aberg KA, Xie L, Chan RF, Zhao M, Pandey AK, Kumar G, et al. Evaluation of methyl-binding domain based enrichment approaches revisited. PLoS ONE. 2015;10:e0132205.

    PubMed  PubMed Central  Google Scholar 

  16. Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet. 2005;37:853–62.

    CAS  PubMed  Google Scholar 

  17. Bock C, Tomazou EM, Brinkman AB, Muller F, Simmer F, Gu H, et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol. 2010;28:1106–14.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Lentini A, Lagerwall C, Vikingsson S, Mjoseng HK, Douvlataniotis K, Vogt H, et al. A reassessment of DNA-immunoprecipitation-based genomic profiling. Nat Methods. 2018;15:499–504.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. McClay JL, Shabalin AA, Dozmorov MG, Adkins DE, Kumar G, Nerella S, et al. High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction. Genome Biol. 2015;16:291.

    PubMed  PubMed Central  Google Scholar 

  20. 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 

  21. Shabalin AA, Hattab MW, Clark SL, Chan RF, Kumar G, Aberg KA, et al. RaMWAS: fast methylome-wide association study pipeline for enrichment platforms. Bioinformatics. 2018;34:2283–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinforma. 2012;13:86.

    Google Scholar 

  23. Hattab MW, Shabalin AA, Clark SL, Zhao M, Kumar G, Chan RF, et al. Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies. Genome Biol. 2017;18:24.

    PubMed  PubMed Central  Google Scholar 

  24. Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R, et al. The reactome pathway knowledgebase. Nucl Acids Res. 2016;44:D481–7.

    CAS  PubMed  Google Scholar 

  25. Cabrera CP, Navarro P, Huffman JE, Wright AF, Hayward C, Campbell H, et al. Uncovering networks from genome-wide association studies via circular genomic permutation. G3 (Bethesda). 2012;2:1067–75.

    CAS  Google Scholar 

  26. Tibshirani R, Bien J, Friedman J, Hastie T, Simon N, Taylor J, et al. Strong rules for discarding predictors in lasso-type problems. J R Stat Soc Ser B Stat Methodol. 2012;74:245–66.

    Google Scholar 

  27. Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw. 2011;39:1–13.

    PubMed  PubMed Central  Google Scholar 

  28. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.

    PubMed  PubMed Central  Google Scholar 

  29. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc B. 2005;67:301–20.

    Google Scholar 

  30. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer Verlag; 2001.

    Google Scholar 

  31. Gerits N, Van Belle W, Moens U. Transgenic mice expressing constitutive active MAPKAPK5 display gender-dependent differences in exploration and activity. Behav Brain Funct. 2007;3:58.

    PubMed  PubMed Central  Google Scholar 

  32. Chen X, Long F, Cai B, Chen X, Chen G. A novel relationship for schizophrenia, bipolar and major depressive disorder part 3: evidence from chromosome 3 high density association screen. J Comp Neurol. 2018;526:59–79.

    CAS  PubMed  Google Scholar 

  33. Garriock HA, Kraft JB, Shyn SI, Peters EJ, Yokoyama JS, Jenkins GD, et al. A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry. 2010;67:133–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Ju XD, Guo Y, Wang NN, Huang Y, Lai MM, Zhai YH, et al. Both myosin-10 isoforms are required for radial neuronal migration in the developing cerebral cortex. Cereb Cortex. 2014;24:1259–68.

    PubMed  Google Scholar 

  35. Tonazzini I, Meucci S, Van Woerden GM, Elgersma Y, Cecchini M. Impaired neurite contact guidance in ubiquitin ligase E3a (Ube3a)-deficient hippocampal neurons on nanostructured substrates. Adv Health Mater. 2016;5:850–62.

    CAS  Google Scholar 

  36. Miyata S, Kurachi M, Okano Y, Sakurai N, Kobayashi A, Harada K, et al. Blood transcriptomic markers in patients with late-onset major depressive disorder. PLoS ONE. 2016;11:e0150262.

    PubMed  PubMed Central  Google Scholar 

  37. Vincent DB, Jean-Loup G, Renaud L, Etienne L. Fast unfolding of communities in large networks. J Stat Mech: Theory Exp. 2008;2008:P10008.

    Google Scholar 

  38. Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Syst. 2006;1695:1–9.

  39. Cancino J, Luini A. Signaling circuits on the Golgi complex. Traffic. 2013;14:121–34.

    CAS  PubMed  Google Scholar 

  40. Hansen MD, Johnsen IB, Stiberg KA, Sherstova T, Wakita T, Richard GM, et al. Hepatitis C virus triggers Golgi fragmentation and autophagy through the immunity-related GTPase M. Proc Natl Acad Sci USA. 2017;114:E3462–471.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Machamer CE. The golgi complex in stress and death. Front Neurosci. 2015;9:421.

    PubMed  PubMed Central  Google Scholar 

  42. Alvarez-Miranda EA, Sinnl M, Farhan H. Alteration of golgi structure by stress: a link to neurodegeneration? Front Neurosci. 2015;9:435.

    PubMed  PubMed Central  Google Scholar 

  43. Jawdekar GW, Henry RW. Transcriptional regulation of human small nuclear RNA genes. Biochimica et Biophysica Acta. 2008;1779:295–305.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Andersen PR, Tirian L, Vunjak M, Brennecke J. A heterochromatin-dependent transcription machinery drives piRNA expression. Nature. 2017;549:54–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Karijolich J, Yu Y-T. Spliceosomal snRNA modifications and their function. RNA Biol. 2010;7:192–204.

    CAS  PubMed  Google Scholar 

  46. Aravin AA, Sachidanandam R, Bourc’his D, Schaefer C, Pezic D, Toth KF, et al. A piRNA pathway primed by individual transposons is linked to de novo DNA methylation in mice. Mol Cell. 2008;31:785–99.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Lin R, Turecki, G. Noncoding RNAs in depression. In: Delgado-Morales R, editor. Neuroepigenomics in aging and disease. Cham: Springer International Publishing; 2017, p. 197–210.

    Google Scholar 

  48. Yang W, Wang Q, Kanes SJ, Murray JM, Nishikura K. Altered RNA editing of serotonin 5-HT2C receptor induced by interferon: implications for depression associated with cytokine therapy. Mol Brain Res. 2004;124:70–8.

    CAS  PubMed  Google Scholar 

  49. Menard C, Pfau ML, Hodes GE, Kana V, Wang VX, Bouchard S, et al. Social stress induces neurovascular pathology promoting depression. Nat Neurosci. 2017;20:1752–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Luster AD, Alon R, von Andrian UH. Immune cell migration in inflammation: present and future therapeutic targets. Nat Immunol. 2005;6:1182.

    CAS  PubMed  Google Scholar 

  51. Barreiro O, De La Fuente H, Mittelbrunn M, Sánchez-Madrid F. Functional insights on the polarized redistribution of leukocyte integrins and their ligands during leukocyte migration and immune interactions. Immunological Rev. 2007;218:147–64.

    CAS  Google Scholar 

  52. Smith GC, Seaman SR, Wood AM, Royston P, White IR. Correcting for optimistic prediction in small data sets. Am J Epidemiol. 2014;180:318–24.

    PubMed  PubMed Central  Google Scholar 

  53. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.

    CAS  PubMed  Google Scholar 

  54. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucl Acids Res. 2017;45:D896–901.

    CAS  PubMed  Google Scholar 

  56. Gamazon ER, Segre AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat Genet. 2018;50:956–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Roadmap Epigenomics C, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317.

    Google Scholar 

  58. Coppo P, Clauvel JP, Bengoufa D, Oksenhendler E, Lacroix C, Lassoued K. Inflammatory myositis associated with anti‐U1‐small nuclear ribonucleoprotein antibodies: a subset of myositis associated with a favourable outcome. Rheumatology. 2002;41:1040–6.

    CAS  PubMed  Google Scholar 

  59. Kattah NH, Kattah MG, Utz PJ. The U1-snRNP complex: structural properties relating to autoimmune pathogenesis in rheumatic diseases. Immunological Rev. 2010;233:126–45.

    CAS  Google Scholar 

  60. Wohleb ES, Franklin T, Iwata M, Duman RS. Integrating neuroimmune systems in the neurobiology of depression. Nat Rev Neurosci. 2016;17:497.

    CAS  PubMed  Google Scholar 

  61. Crawford B, Craig Z, Mansell G, White I, Smith A, Spaull S, et al. DNA methylation and inflammation marker profiles associated with a history of depression. Hum Mol Genet. 2018;27:2840–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Benros ME, Waltoft BL, Nordentoft M, et al. Autoimmune diseases and severe infections as risk factors for mood disorders: a nationwide study. JAMA Psychiatry. 2013;70:812–20.

    PubMed  Google Scholar 

  63. Kappelmann N, Lewis G, Dantzer R, Jones PB, Khandaker GM. Antidepressant activity of anti-cytokine treatment: a systematic review and meta-analysis of clinical trials of chronic inflammatory conditions. Mol Psychiatry. 2016;23:335–43.

    PubMed  PubMed Central  Google Scholar 

  64. van Loo HM, Aggen SH, Gardner CO, Kendler KS. Multiple risk factors predict recurrence of major depressive disorder in women. J Affect Disord. 2015;180:52–61.

    PubMed  PubMed Central  Google Scholar 

  65. Wardenaar KJ, van Loo HM, Cai T, Fava M, Gruber MJ, Li J, et al. The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity. Psychol Med. 2014;44:3289–302.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. van Loo HM, Cai T, Gruber MJ, Li J, de Jonge P, Petukhova M, et al. Major depressive disorder subtypes to predict long-term course. Depress Anxiety. 2014;31:765–77.

    PubMed  PubMed Central  Google Scholar 

  67. Nelson JC, Zhang Q, Deberdt W, Marangell LB, Karamustafalioglu O, Lipkovich IA. Predictors of remission with placebo using an integrated study database from patients with major depressive disorder. Curr Med Res Opin. 2012;28:325–34.

    CAS  PubMed  Google Scholar 

  68. Riedel M, Moller HJ, Obermeier M, Adli M, Bauer M, Kronmuller K, et al. Clinical predictors of response and remission in inpatients with depressive syndromes. J Affect Disord. 2011;133:137–49.

    PubMed  Google Scholar 

  69. Langie SAS, Moisse M, Declerck K, Koppen G, Godderis L, Vanden Berghe W, et al. Salivary DNA methylation profiling: aspects to consider for biomarker identification. Basic Clin Pharm Toxicol. 2017;121(Suppl 3):93–101.

    CAS  Google Scholar 

  70. Tzoulaki I, Liberopoulos G, Ioannidis JP. Assessment of claims of improved prediction beyond the Framingham risk score. JAMA. 2009;302:2345–52.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The NESDA study is supported by the Geestkracht program of the Netherlands Organization for Health Research and Development (Zon-Mw, grant number 10-000-1002) and the participating institutions (VU University Medical Center, Leiden University Medical Center, University Medical Center Groningen. The current methylation project was supported by grant R01MH099110 from the National Institute of Mental Health. The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA

    Shaunna L. Clark, Mohammad W. Hattab, Robin F. Chan, Andrey A. Shabalin, Min Zhao, Lin Ying Xie, Karolina A. Aberg & Edwin J. C. G. van den Oord

  2. Department of Psychiatry, VU University Medical Center/GGZ inGeest, 1081 HV, Amsterdam, The Netherlands

    Laura K. M. Han, Johannes H. Smit, Rick Jansen, Yuri Milaneschi, Gerard van Grootheest & Brenda W. J. H. Penninx

Authors
  1. Shaunna L. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad W. Hattab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robin F. Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrey A. Shabalin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laura K. M. Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Min Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Johannes H. Smit
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rick Jansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yuri Milaneschi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lin Ying Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Gerard van Grootheest
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Brenda W. J. H. Penninx
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Karolina A. Aberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Edwin J. C. G. van den Oord
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edwin J. C. G. van den Oord.

Ethics declarations

Conflict of interest

BWJHP has obtained research funding—not related to current study—from Jansen Research and Boehringer Ingelheim. The other 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

Clark, S.L., Hattab, M.W., Chan, R.F. et al. A methylation study of long-term depression risk. Mol Psychiatry 25, 1334–1343 (2020). https://doi.org/10.1038/s41380-019-0516-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-019-0516-z

This article is cited by

Search

Advanced search

Quick links