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:

Methylome-wide association findings for major depressive disorder overlap in blood and brain and replicate in independent brain samples

Abstract

We present the first large-scale methylome-wide association studies (MWAS) for major depressive disorder (MDD) to identify sites of potential importance for MDD etiology. Using a sequencing-based approach that provides near-complete coverage of all 28 million common CpGs in the human genome, we assay methylation in MDD cases and controls from both blood (N = 1132) and postmortem brain tissues (N = 61 samples from Brodmann Area 10, BA10). The MWAS for blood identified several loci with P ranging from 1.91 × 10−8 to 4.39 × 10−8 and a resampling approach showed that the cumulative association was significant (P = 4.03 × 10−10) with the signal coming from the top 25,000 MWAS markers. Furthermore, a permutation-based analysis showed significant overlap (P = 5.4 × 10−3) between the MWAS findings in blood and brain (BA10). This overlap was significantly enriched for a number of features including being in eQTLs in blood and the frontal cortex, CpG islands and shores, and exons. The overlapping sites were also enriched for active chromatin states in brain including genic enhancers and active transcription start sites. Furthermore, three loci located in GABBR2, RUFY3, and in an intergenic region on chromosome 2 replicated with the same direction of effect in the second brain tissue (BA25, N = 60) from the same individuals and in two independent brain collections (BA10, N = 81 and 64). GABBR2 inhibits neuronal activity through G protein-coupled second-messenger systems and RUFY3 is implicated in the establishment of neuronal polarity and axon elongation. In conclusion, we identified and replicated methylated loci associated with MDD that are involved in biological functions of likely importance to MDD etiology.

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

Access options

Buy this article

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

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Judd LL. The clinical course of unipolar major depressive disorders. Arch Gen Psychiatry. 1997;54:989–91.

    CAS  PubMed  Google Scholar 

  2. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367:1747–57.

    PubMed  Google Scholar 

  3. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289:3095–105.

    PubMed  Google Scholar 

  4. Avenevoli S, Swendsen J, He JP, Burstein M, Merikangas KR. Major depression in the national comorbidity survey-adolescent supplement: prevalence, correlates, and treatment. J Am Acad Child Adolesc Psychiatry. 2015;54:37–44.e32.

    PubMed  Google Scholar 

  5. 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 

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

  7. World Health Organization. The global burden of disease: 2004 update. Geneva: World Health Organization; 2008.

  8. Okbay A, Baselmans BM, De Neve JE, Turley P, Nivard MG, Fontana MA, et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet. 2016;48:624–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016;48:1031–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. PGC MDDWGot. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. http://www.biorxiv.org/content/early/2017/07/24/1675772017.

  11. Miller CA, Sweatt JD. Covalent modification of DNA regulates memory formation. Neuron. 2007;53:857–69.

    CAS  PubMed  Google Scholar 

  12. Kaffman A, Meaney MJ. Neurodevelopmental sequelae of postnatal maternal care in rodents: clinical and research implications of molecular insights. J Child Psychol Psychiatry. 2007;48:224–44.

    PubMed  Google Scholar 

  13. Szyf M, Weaver IC, Champagne FA, Diorio J, Meaney MJ. Maternal programming of steroid receptor expression and phenotype through DNA methylation in the rat. Front Neuroendocrinol. 2005;26:139–62.

    CAS  PubMed  Google Scholar 

  14. Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13:R43.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Aberg KA, Xie LY, McClay JL, Nerella S, Vunck S, Snider S, et al. Testing two models describing how methylome-wide studies in blood are informative for psychiatric conditions. Epigenomics. 2013;5:367–77.

    CAS  PubMed  Google Scholar 

  16. Efstratiadis A. Parental imprinting of autosomal mammalian genes. Curr Opin Genet Dev. 1994;4:265–80.

    CAS  PubMed  Google Scholar 

  17. Sutherland JE, Costa M. Epigenetics and the environment. Ann NY Acad Sci. 2003;983:151–60.

    CAS  PubMed  Google Scholar 

  18. Kerkel K, Spadola A, Yuan E, Kosek J, Jiang L, Hod E, et al. Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet. 2008;40:904–8.

    CAS  PubMed  Google Scholar 

  19. 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. Nucleic Acids Res. 2017;45:e97.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Aberg KA, Chan RF, Shabalin AA, Zhao M, Turecki G, Staunstrup NH, 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 

  21. Wittchen HU. Reliability and validity studies of the WHO—Composite International Diagnostic Interview (CIDI): a critical review. J Psychiatr Res. 1994;28:57–84.

    CAS  PubMed  Google Scholar 

  22. Rush AJ, Giles DE, Schlesser MA, Fulton CL, Weissenburger J, Burns C. The Inventory for Depressive Symptomatology (IDS): preliminary findings. Psychiatry Res. 1986;18:65–87.

    CAS  PubMed  Google Scholar 

  23. Deep-Soboslay A, Iglesias B, Hyde TM, Bigelow LB, Imamovic V, Herman MM, et al. Evaluation of tissue collection for postmortem studies of bipolar disorder. Bipolar Disord. 2008;10:822–8.

    PubMed  PubMed Central  Google Scholar 

  24. Conner KR, Conwell Y, Duberstein PR. The validity of proxy-based data in suicide research: a study of patients 50 years of age and older who attempted suicide. II. Life events, social support and suicidal behavior. Acta Psychiatr Scand. 2001;104:452–7.

    CAS  PubMed  Google Scholar 

  25. Kelly TM, Mann JJ. Validity of DSM-III-R diagnosis by psychological autopsy: a comparison with clinician ante-mortem diagnosis. Acta Psychiatr Scand. 1996;94:337–43.

    CAS  PubMed  Google Scholar 

  26. Dumais A, Lesage AD, Alda M, Rouleau G, Dumont M, Chawky N, et al. Risk factors for suicide completion in major depression: a case-control study of impulsive and aggressive behaviors in men. Am J Psychiatry. 2005;162:2116–24.

    CAS  PubMed  Google Scholar 

  27. 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 

  28. 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 Bioinformatics. 2012;13:86.

    PubMed  PubMed Central  Google Scholar 

  29. 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 

  30. 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 

  31. 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 

  32. 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 

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

    Google Scholar 

  34. 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 

  35. Boomsma DI, Willemsen G, Sullivan PF, Heutnik P, Meijer P, Sondervan D, et al. Genome-wide association of major depression: description of samples for the GAIN major depressive disorder study: NTR and NESDA Biobank Projects. Eur J Hum Genet. 2008;16:335–42.

    CAS  PubMed  Google Scholar 

  36. 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 

  37. Sullivan P, de Geus E, Willemsen G, James MR, Smit JH, Zandbelt T, et al. Genomewide association for major depressive disorder: a possible role for the presynaptic protein piccolo. Mol Psychiatry. 2009;14:359–75.

    CAS  PubMed  Google Scholar 

  38. van den Oord EJ, Bukszar J, Rudolf G, Nerella S, McClay JL, Xie LY, et al. Estimation of CpG coverage in whole methylome next-generation sequencing studies. BMC Bioinformatics. 2013;14:50.

    PubMed  PubMed Central  Google Scholar 

  39. Weissman MM, Klerman GL. Sex differences and the epidemiology of depression. Arch Gen Psychiatry. 1977;34:98–111.

    CAS  PubMed  Google Scholar 

  40. Kessler RC, McGonagle KA, Swartz MS, Blazer DG, Nelson CB. Sex and depression in the National Comorbidity Survey: I. Lifetime prevalence, chronicity and recurrence. J Affect Disord. 1993;29:85–96.

    CAS  PubMed  Google Scholar 

  41. Bebbington P. The origins of sex differences in depressive disorder: bridging the gap. Int Rev Psychiatry. 1996;8:295–332.

    Google Scholar 

  42. Bouma GJ, Hudson QJ, Washburn LL, Eicher EM. New candidate genes identified for controlling mouse gonadal sex determination and the early stages of granulosa and Sertoli cell differentiation. Biol Reprod. 2010;82:380–9.

    CAS  PubMed  Google Scholar 

  43. Hammond GL. Plasma steroid-binding proteins: primary gatekeepers of steroid hormone action. J Endocrinol. 2016;230:R13–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Soares CN, Zitek B. Reproductive hormone sensitivity and risk for depression across the female life cycle: a continuum of vulnerability? J Psychiatry Neurosci. 2008;33:331–43.

    PubMed  PubMed Central  Google Scholar 

  45. Carrier N, Saland SK, Duclot F, He H, Mercer R, Kabbaj M. The anxiolytic and antidepressant-like effects of testosterone and estrogen in gonadectomized male rats. Biol Psychiatry. 2015;78:259–69.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. McHenry J, Carrier N, Hull E, Kabbaj M. Sex differences in anxiety and depression: role of testosterone. Front Neuroendocrinol. 2014;35:42–57.

    CAS  PubMed  Google Scholar 

  47. Gibbons AS, Brooks L, Scarr E, Dean B. AMPA receptor expression is increased post-mortem samples of the anterior cingulate from subjects with major depressive disorder. J Affect Disord. 2012;136:1232–7.

    CAS  PubMed  Google Scholar 

  48. Kamburov A, Wierling C, Lehrach H, Herwig R. ConsensusPathDB—a database for integrating human functional interaction networks. Nucleic Acids Res. 2009;37(Database issue):D623–8.

    CAS  PubMed  Google Scholar 

  49. Reynolds LM, Magid HS, Chi GC, Lohman K, Barr RG, Kaufman JD, et al. Secondhand tobacco smoke exposure associations with DNA methylation of the aryl hydrocarbon receptor repressor. Nicotine Tob Res. 2017;19:442–51.

    CAS  PubMed  Google Scholar 

  50. Philibert R, Hollenbeck N, Andersen E, McElroy S, Wilson S, Vercande K, et al. Reversion of AHRR demethylation is a quantitative biomarker of smoking cessation. Front Psychiatry. 2016;7:55.

    PubMed  PubMed Central  Google Scholar 

  51. Edelmann E, Lessmann V, Brigadski T. Pre- and postsynaptic twists in BDNF secretion and action in synaptic plasticity. Neuropharmacology. 2014;76:610–27. Pt C

    CAS  PubMed  Google Scholar 

  52. Park H, Poo MM. Neurotrophin regulation of neural circuit development and function. Nat Rev Neurosci. 2013;14:7–23.

    CAS  PubMed  Google Scholar 

  53. Krishnan V, Nestler EJ. The molecular neurobiology of depression. Nature. 2008;455:894–902.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Vialou V, Feng J, Robison AJ, Nestler EJ. Epigenetic mechanisms of depression and antidepressant action. Annu Rev Pharmacol Toxicol. 2013;53:59–87.

    CAS  PubMed  Google Scholar 

  55. Boulle F, van den Hove DL, Jakob SB, Rutten BP, Hamon M, van Os J, et al. Epigenetic regulation of the BDNF gene: implications for psychiatric disorders. Mol Psychiatry. 2012;17:584–96.

    CAS  PubMed  Google Scholar 

  56. Ikegame T, Bundo M, Murata Y, Kasai K, Kato T, Iwamoto K. DNA methylation of the BDNF gene and its relevance to psychiatric disorders. J Hum Genet. 2013;58:434–8.

    CAS  PubMed  Google Scholar 

  57. Converge Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature. 2015;523:588–91.

    PubMed Central  Google Scholar 

  58. Schizophrenia Psychiatric Genome-Wide Association Study Consortium, Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, et al. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011;43:969–76.

    Google Scholar 

  59. Pehrson AL, Sanchez C. Altered gamma-aminobutyric acid neurotransmission in major depressive disorder: a critical review of the supporting evidence and the influence of serotonergic antidepressants. Drug Des Dev Ther. 2015;9:603–24.

    CAS  Google Scholar 

  60. Fatemi SH, Folsom TD, Thuras PD. Deficits in GABA(B) receptor system in schizophrenia and mood disorders: a postmortem study. Schizophr Res. 2011;128:37–43.

    PubMed  PubMed Central  Google Scholar 

  61. Mori T, Wada T, Suzuki T, Kubota Y, Inagaki N. Singar1, a novel RUN domain-containing protein, suppresses formation of surplus axons for neuronal polarity. J Biol Chem. 2007;282:19884–93.

    CAS  PubMed  Google Scholar 

  62. Honda A, Ito Y, Takahashi-Niki K, Matsushita N, Nozumi M, Tabata H, et al. Extracellular signals induce glycoprotein M6a clustering of lipid rafts and associated signaling molecules. J Neurosci. 2017;37:4046–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Wei Z, Sun M, Liu X, Zhang J, Jin Y. Rufy3, a protein specifically expressed in neurons, interacts with actin-bundling protein Fascin to control the growth of axons. J Neurochem. 2014;130:678–92.

    CAS  PubMed  Google Scholar 

  64. Uddin M, Koenen KC, Aiello AE, Wildman DE, de los Santos R, Galea S. Epigenetic and inflammatory marker profiles associated with depression in a community-based epidemiologic sample. Psychol Med. 2011;41:997–1007.

    CAS  PubMed  Google Scholar 

  65. Davies MN, Krause L, Bell JT, Gao F, Ward KJ, Wu H, et al. Hypermethylation in the ZBTB20 gene is associated with major depressive disorder. Genome Biol. 2014;15:R56.

    PubMed  PubMed Central  Google Scholar 

  66. Dempster EL, Wong CC, Lester KJ, Burrage J, Gregory AM, Mill J, et al. Genome-wide methylomic analysis of monozygotic twins discordant for adolescent depression. Biol Psychiatry. 2014;76:977–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Nagy C, Suderman M, Yang J, Szyf M, Mechawar N, Ernst C, et al. Astrocytic abnormalities and global DNA methylation patterns in depression and suicide. Mol Psychiatry. 2015;20:320–8.

    CAS  PubMed  Google Scholar 

  68. Sabunciyan S, Aryee MJ, Irizarry RA, Rongione M, Webster MJ, Kaufman WE, et al. Genome-wide DNA methylation scan in major depressive disorder. PLoS ONE. 2012;7:e34451.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Murphy TM, Crawford B, Dempster EL, Hannon E, Burrage J, Turecki G, et al. Methylomic profiling of cortex samples from completed suicide cases implicates a role for PSORS1C3 in major depression and suicide. Transl Psychiatry. 2017;7:e989.

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Haghighi F, Xin Y, Chanrion B, O’Donnell AH, Ge Y, Dwork AJ, et al. Increased DNA methylation in the suicide brain. Dialog Clin Neurosci. 2014;16:430–8.

    Google Scholar 

  71. Oh G, Wang SC, Pal M, Chen ZF, Khare T, Tochigi M, et al. DNA modification study of major depressive disorder: beyond locus-by-locus comparisons. Biol Psychiatry. 2015;77:246–55.

    CAS  PubMed  Google Scholar 

  72. Philibert R, Hollenbeck N, Andersen E, Osborn T, Gerrard M, Gibbons FX, et al. A quantitative epigenetic approach for the assessment of cigarette consumption. Front Psychol. 2015;6:656.

    PubMed  PubMed Central  Google Scholar 

  73. Andersen AM, Dogan MV, Beach SR, Philibert RA. Current and future prospects for epigenetic biomarkers of substance use disorders. Genes (Basel). 2015;6:991–1022.

    CAS  Google Scholar 

  74. Zhang Y, Elgizouli M, Schottker B, Holleczek B, Nieters A, Brenner H. Smoking-associated DNA methylation markers predict lung cancer incidence. Clin Epigenet. 2016;8:127.

    Google Scholar 

  75. Baglietto L, Ponzi E, Haycock P, Hodge A, Bianca Assumma M, Jung CH, et al. DNA methylation changes measured in pre-diagnostic peripheral blood samples are associated with smoking and lung cancer risk. Int J Cancer. 2017;140:50–61.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The current methylation study was supported by grant R01MH099110 from the National Institute of Mental Health. Tissues were received from five brain banks, including the Victorian Brain Bank, supported by The Florey Institute of Neuroscience and Mental Health, The Alfred and Victorian Forensic Institute of Medicine and funded by Australia’s National Health & Medical Research Council and Parkinson’s Victoria; the Stanley Medical Research Institute; The Netherlands Brain Bank, Netherlands Institute of Neuroscience, Amsterdam; the Harvard Brain Tissue Resource Center; and The Douglas – Bell Canada Brain Bank, Douglas Institute Research Center, Canada. Funding for NESDA was obtained from the Netherlands Organization for Scientific Research (Geestkracht program grant 10-000-1002); the Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University’s Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by BiG Grid, the Dutch e-Science Grid, which is financially supported by NWO.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Karolina A. Aberg.

Ethics declarations

Conflict of interest

B.W.J.H.P. has received research funding (non-related) from Jansen Research and Boehringer Ingelheim. The remaining authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aberg, K.A., Dean, B., Shabalin, A.A. et al. Methylome-wide association findings for major depressive disorder overlap in blood and brain and replicate in independent brain samples. Mol Psychiatry 25, 1344–1354 (2020). https://doi.org/10.1038/s41380-018-0247-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-018-0247-6

This article is cited by

Search

Quick links