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.

  • Original Article
  • Published:

Novel loci for major depression identified by genome-wide association study of Sequenced Treatment Alternatives to Relieve Depression and meta-analysis of three studies

Molecular Psychiatry volume 16, pages 202–215 (2011)Cite this article

Subjects

Abstract

We report a genome-wide association study (GWAS) of major depressive disorder (MDD) in 1221 cases from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study and 1636 screened controls. No genome-wide evidence for association was detected. We also carried out a meta-analysis of three European-ancestry MDD GWAS data sets: STAR*D, Genetics of Recurrent Early-onset Depression and the publicly available Genetic Association Information Network–MDD data set. These data sets, totaling 3957 cases and 3428 controls, were genotyped using four different platforms (Affymetrix 6.0, 5.0 and 500 K, and Perlegen). For each of 2.4 million HapMap II single-nucleotide polymorphisms (SNPs), using genotyped data where available and imputed data otherwise, single-SNP association tests were carried out in each sample with correction for ancestry-informative principal components. The strongest evidence for association in the meta-analysis was observed for intronic SNPs in ATP6V1B2 (P=6.78 × 10−7), SP4 (P=7.68 × 10−7) and GRM7 (P=1.11 × 10−6). Additional exploratory analyses were carried out for a narrower phenotype (recurrent MDD with onset before age 31, N=2191 cases), and separately for males and females. Several of the best findings were supported primarily by evidence from narrow cases or from either males or females. On the basis of previous biological evidence, we consider GRM7 a strong MDD candidate gene. Larger samples will be required to determine whether any common SNPs are significantly associated with MDD.

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

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

Accession codes

Accessions

GenBank/EMBL/DDBJ

References

  1. World Health Organization. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020; Summary. Published by the Harvard School of Public Health on behalf of the World Health Organization and the World Bank; Distributed by Harvard University Press: Cambridge, MA, 1996 p. 43.

  2. Belmaker RH, Agam G . Major depressive disorder. N Engl J Med 2008; 358: 55–68.

    Article  CAS  PubMed  Google Scholar 

  3. Sullivan PF, Neale MC, Kendler KS . Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry 2000; 157: 1552–1562.

    CAS  PubMed  Google Scholar 

  4. Shi J, Potash JB, Knowles JA, Weissman MM, Coryell W, Scheftner WA et al. Genomewide association study of recurrent early-onset major depressive disorder. Molecular Psychiatry (in press).

  5. Sullivan PF, de Geus EJ, Willemsen G, James MR, Smit JH, Zandbelt T et al. Genome-wide association for major depressive disorder: a possible role for the presynaptic protein piccolo. Mol Psychiatry 2009; 14: 359–375.

    Article  CAS  PubMed  Google Scholar 

  6. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 2007; 39: 1181–1186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Fava M, Rush AJ, Trivedi MH, Nierenberg AA, Thase ME, Sackeim HA et al. Background and rationale for the sequenced treatment alternatives to relieve depression (STAR*D) study. Psychiatr Clin North Am 2003; 26: 457–494, x.

    Article  PubMed  Google Scholar 

  8. Rush AJ, Fava M, Wisniewski SR, Lavori PW, Trivedi MH, Sackeim HA et al. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Control Clin Trials 2004; 25: 119–142.

    Article  PubMed  Google Scholar 

  9. Sanders AR, Duan J, Levinson DF, Shi J, He D, Hou C et al. No significant association of 14 candidate genes with schizophrenia in a large European ancestry sample: implications for psychiatric genetics. Am J Psychiatry 2008; 165: 497–506.

    Article  PubMed  Google Scholar 

  10. Psychiatric GWAS Consortium Coordinating Committee, Cichon S, Craddock N, Daly M, Faraone SV, Gejman PV et al. Genomewide association studies: history, rationale, and prospects for psychiatric disorders. Am J Psychiatry 2009; 166: 540–556.

    Article  Google Scholar 

  11. Ferreira MAR, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 2008; 40: 1056–1058.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009; 460: 748–752.

    CAS  PubMed  Google Scholar 

  13. 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: 753–757.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D et al. Common variants conferring risk of schizophrenia. Nature 2009; 460: 744–747.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 2009; 459: 528–533.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Muglia P, Tozzi F, Galwey NW, Francks C, Upmanyu R, Kong XQ et al. Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts. Mol Psychiatry 2008 (in press).

  17. Kessler RC, Andrews G, Mroczek D, Ustun TB, Wittchen H-U . The World Health Organization Composite International Diagnostic Interview Short Form (CIDI-SF). Int J Methods Psychiatr Res 1998; 7: 171–185.

    Article  Google Scholar 

  18. Aalto-Setala T, Haarasilta L, Marttunen M, Tuulio-Henriksson A, Poikolainen K, Aro H et al. Major depressive episode among young adults: CIDI-SF versus SCAN consensus diagnoses. Psychol Med 2002; 32: 1309–1314.

    Article  CAS  PubMed  Google Scholar 

  19. Nurnberger Jr JI, Blehar MC, Kaufmann CA, York-Cooler C, Simpson SG, Harkavy-Friedman J et al. Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative. Arch Gen Psychiatry 1994; 51: 849–859.

    Article  PubMed  Google Scholar 

  20. Kendler KS, Gatz M, Gardner CO, Pedersen NL . Clinical indices of familial depression in the Swedish Twin Registry. Acta Psychiatr Scand 2007; 115: 214–220.

    Article  CAS  PubMed  Google Scholar 

  21. Levinson DF, Zubenko GS, Crowe RR, DePaulo RJ, Scheftner WS, Weissman MM et al. Genetics of recurrent early-onset depression (GenRED). Am J Med Genet B NeuropsychiatrGenet 2003; 119: 118–130.

    Article  Google Scholar 

  22. Kendler KS, Gardner CO, Neale MC, Prescott CA . Genetic risk factors for major depression in men and women: similar or different heritabilities and same or partly distinct genes? PsycholMed 2001; 31: 605–616.

    CAS  Google Scholar 

  23. Kendler KS, Gatz M, Gardner CO, Pedersen NL . A Swedish National Twin Study of lifetime major depression. Am J Psychiatry 2006; 163: 109–114.

    Article  PubMed  Google Scholar 

  24. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ge D, Zhang K, Need AC, Martin O, Fellay J, Urban TJ et al. WGAViewer: software for genomic annotation of whole genome association studies. Genome Res 2008; 18: 640–643.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Barrett JC, Fry B, Maller J, Daly MJ . Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 263–265.

    Article  CAS  PubMed  Google Scholar 

  27. Rabbee N, Speed TP . A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 2006; 22: 7–12.

    Article  CAS  PubMed  Google Scholar 

  28. Sklar P, Smoller JW, Fan J, Ferreira MA, Perlis RH, Chambert K et al. Whole-genome association study of bipolar disorder. Mol Psychiatry 2008; 13: 558–569.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Jorgenson E, Witte JS . A gene-centric approach to genome-wide association studies. Nat Rev Genet 2006; 7: 885–891.

    Article  CAS  PubMed  Google Scholar 

  30. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904–909.

    Article  CAS  PubMed  Google Scholar 

  31. Huang L, Li Y, Singleton AB, Hardy JA, Abecasis G, Rosenberg NA et al. Genotype-imputation accuracy across worldwide human populations. Am J Hum Genet 2009; 84: 235–250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Marchini J, Howie B, Myers S, McVean G, Donnelly P . A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 2007; 39: 906–913.

    Article  CAS  PubMed  Google Scholar 

  33. Nothnagel M, Ellinghaus D, Schreiber S, Krawczak M, Franke A . A comprehensive evaluation of SNP genotype imputation. Hum Genet 2009; 125: 163–171.

    Article  CAS  PubMed  Google Scholar 

  34. Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nature genetics 2009; 41: 56–65.

    Article  CAS  PubMed  Google Scholar 

  35. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science (New York, NY) 2007; 316: 1341–1345.

    Article  CAS  Google Scholar 

  36. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 2008; 40: 161–169.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009; 41: 25–34.

    Article  CAS  PubMed  Google Scholar 

  38. Dudbridge F, Gusnanto A . Estimation of significance thresholds for genome-wide association scans. Genet Epidemiol 2008; 32: 227–234.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hoggart CJ, Clark TG, De Iorio M, Whittaker JC, Balding DJ . Genome-wide significance for dense SNP and resequencing data. Genet Epidemiol 2008; 32: 179–185.

    Article  PubMed  Google Scholar 

  40. Pe′er I, Yelensky R, Altshuler D, Daly MJ . Estimation of the multiple testing burden for genome-wide association studies of nearly all common variants. Genet Epidemiol 2008; 32: 381–385.

    Article  PubMed  Google Scholar 

  41. Taylor J, Tyekucheva S, King DC, Hardison RC, Miller W, Chiaromonte F . ESPERR: learning strong and weak signals in genomic sequence alignments to identify functional elements. Genome Res 2006; 16: 1596–1604.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. McCarroll SA, Kuruvilla FG, Korn JM, Cawley S, Nemesh J, Wysoker A et al. Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet 2008; 40: 1166–1174.

    Article  CAS  PubMed  Google Scholar 

  43. Manolio TA, Brooks LD, Collins FS . A HapMap harvest of insights into the genetics of common disease. J Clin Invest 2008; 118: 1590–1605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Lohoff FW, Dahl JP, Ferraro TN, Arnold SE, Gallinat J, Sander T et al. Variations in the vesicular monoamine transporter 1 gene (VMAT1/SLC18A1) are associated with bipolar i disorder. Neuropsychopharmacology 2006; 31: 2739–2747.

    Article  CAS  PubMed  Google Scholar 

  45. Suske G . The Sp-family of transcription factors. Gene 1999; 238: 291–300.

    Article  CAS  PubMed  Google Scholar 

  46. Zhou X, Barrett TB, Kelsoe JR . Promoter variant in the GRK3 gene associated with bipolar disorder alters gene expression. Biol Psychiatry 2008; 64: 104–110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhou X, Tang W, Greenwood TA, Guo S, He L, Geyer MA et al. Transcription factor SP4 is a susceptibility gene for bipolar disorder. PLoS ONE 2009; 4: e5196.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zhou X, Qyang Y, Kelsoe JR, Masliah E, Geyer MA . Impaired postnatal development of hippocampal dentate gyrus in Sp4 null mutant mice. Genes Brain Behav 2007; 6: 269–276.

    Article  PubMed  Google Scholar 

  49. Zhou X, Long JM, Geyer MA, Masliah E, Kelsoe JR, Wynshaw-Boris A et al. Reduced expression of the Sp4 gene in mice causes deficits in sensorimotor gating and memory associated with hippocampal vacuolization. Mol Psychiatry 2004; 10: 393–406.

    Article  Google Scholar 

  50. Supp DM, Witte DP, Branford WW, Smith EP, Potter SS . Sp4, a member of the sp1-family of zinc finger transcription factors, is required for normal murine growth, viability, and male fertility. Develop Biol 1996; 176: 284–299.

    Article  CAS  PubMed  Google Scholar 

  51. Safe S, Kim K . Non-classical genomic estrogen receptor (ER)/specificity protein and ER/activating protein-1 signaling pathways. J Mol Endocrinol 2008; 41: 263–275.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Mao X, Moerman-Herzog AM, Wang W, Barger SW . Differential transcriptional control of the superoxide dismutase-2 kappaB element in neurons and astrocytes. J Biol Chem 2006; 281: 35863–35872.

    Article  CAS  PubMed  Google Scholar 

  53. Mao X, Yang SH, Simpkins JW, Barger SW . Glutamate receptor activation evokes calpain-mediated degradation of Sp3 and Sp4, the prominent Sp-family transcription factors in neurons. J Neurochem 2007; 100: 1300–1314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Pilc A, Chaki S, Nowak G, Witkin JM . Mood disorders: regulation by metabotropic glutamate receptors. Biochem Pharmacol 2008; 75: 997–1006.

    Article  CAS  PubMed  Google Scholar 

  55. Witkin JM, Marek GJ, Johnson BG, Schoepp DD . Metabotropic glutamate receptors in the control of mood disorders. CNSNeurol DisordDrug Targets 2007; 6: 87–100.

    CAS  Google Scholar 

  56. Zhou R, Yuan P, Wang Y, Hunsberger JG, Elkahloun A, Wei Y et al. Evidence for selective microRNAs and their effectors as common long-term targets for the actions of mood stabilizers. Neuropsychopharmacology 2008 08/13/online.

  57. Palucha A, Klak K, Branski P, van der Putten H, Flor P, Pilc A . Activation of the mGlu7 receptor elicits antidepressant-like effects in mice. Psychopharmacology 2007; 194: 555–562.

    Article  CAS  PubMed  Google Scholar 

  58. Wieronska JM, Klak K, Palucha A, Branski P, Pilc A . Citalopram influences mGlu7, but not mGlu4 receptors’ expression in the rat brain hippocampus and cortex. Brain Res 2007; 1184: 88–95.

    Article  CAS  PubMed  Google Scholar 

  59. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007; 447: 661–678.

    Article  Google Scholar 

  60. Ghoussaini M, Song H, Koessler T, Al Olama AA, Kote-Jarai Z, Driver KE et al. Multiple loci with different cancer specificities within the 8q24 gene desert. J Natl Cancer Inst 2008; 100: 962–966.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A et al. Genome-wide association study of blood pressure and hypertension. Nat Genet 2009.

  62. Psychiatric GWAS Consortium. A framework for interpreting genome-wide association studies of psychiatric disorders. Mol Psychiatry 2009; 14: 10–17.

    Article  Google Scholar 

Download references

Acknowledgements

The STAR*D GWAS study acknowledges Shaun Purcell (Broad Institute) for technical assistance and Eric Jorgenson (UCSF) for helpful discussion. Genotyping of STAR*D was supported by an NIMH grant to SPH (MH072802), and made possible by the laboratory of Pui Kwok (UCSF) and the UCSF Institute for Human Genetics. This work was further supported by NIMH training funds to SIS (R25 MH060482 & T32 MH19126) and to HAG (F32 MH082562 & T32 MH19552); a NARSAD Young Investigators Award to HAG (A109584); the State of New York, which provided partial support to PJM for this work. The authors appreciate the efforts of the STAR*D Investigator Team for acquiring, compiling and sharing the STAR*D clinical data set. STAR*D was funded by the National Institute of Mental Health through a contract (N01MH90003) to the University of Texas Southwestern Medical Center at Dallas (A John Rush, principal investigator). The authors thank Stephen Wisniewski, PhD, Director, STAR*D Data Coordinating Center, University of Pittsburgh, for demographic data. The GenRED project is supported by grants from NIMH (see online Supplementary Acknowledgements). We acknowledge the contributions of Dr George S Zubenko and Dr Wendy N Zubenko, Department of Psychiatry, University of Pittsburgh School of Medicine, to the GenRED I project. The NIMH Cell Repository at Rutgers University and the NIMH Center for Collaborative Genetic Studies on Mental Disorders made essential contributions to this project. Genotyping was carried out by the Broad Institute Center for Genotyping and Analysis with support from grant U54 RR020278 (which partially subsidized the genotyping of the GenRED cases) from the National Center for Research Resources. The meta-analysis was supported by grants from NIMH and the National Cancer Institute, and by support from the State of New York. GWAS data for the GAIN–MDD data set were accessed by DFL through the Genetic Association Information Network (GAIN), through dbGaP accession number phs000020.v1.p1 (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000020.v2.p1); samples and associated phenotype data for Major Depression: Stage 1 Genome-wide Association in Population-Based Samples were provided by P Sullivan. Data for Molecular Genetics of Schizophrenia (MGS) control subjects was used here by permission of the MGS project. Collection and quality control analyses of the control data set were supported by grants from NIMH and the National Alliance for Research on Schizophrenia and Depression. Genotyping of the controls was supported by grants from NIMH and by the Genetic Association Information Network (GAIN) (http://www.fnih.org/index.php?option=com_content&task=view&id=338&Itemid=454). Control data are available through dbGAP (http://www.ncbi.nlm.nih.gov/gap). We are grateful to Knowledge Networks, Inc. (Menlo Park, CA, USA) for assistance in collecting the control data set. The authors express their profound appreciation to the individuals who participated in this project, and to the many clinicians who facilitated the referral of participants to the study.

Author information

Author notes

  1. S I Shyn and J Shi: These two authors contributed equally to this work.

Authors and Affiliations

  1. Department of Psychiatry and Institute for Human Genetics, University of California, San Francisco, CA, USA

    S I Shyn, J B Kraft, H A Garriock, J S Yokoyama, E J Peters & S P Hamilton

  2. Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA

    J Shi & D F Levinson

  3. Department of Psychiatry, Johns Hopkins University, Baltimore, MD, USA

    J B Potash & D Jancic

  4. Department of Psychiatry, University of Southern California, Los Angeles, CA, USA

    J A Knowles

  5. Department of Psychiatry, Columbia University College of Physicians and Surgeons and New York State Psychiatric Institute, New York, NY, USA

    M M Weissman & P J McGrath

  6. Department of Psychiatry, Rush University Hospital, Chicago, IL, USA

    W A Scheftner

  7. Department of Psychiatry, University of Iowa, Iowa City, IA, USA

    W Coryell

  8. Department of Psychiatry, Howard University, Washington, DC, USA

    W B Lawson

  9. NorthShore University HealthCare, Evanston, IL, USA

    P V Gejman & A R Sanders

  10. Department of Psychological Medicine, Cardiff University, Cardiff, UK

    P Holmans

  11. Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA,

    S L Slager

Authors
  1. S I Shyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J B Kraft
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J B Potash
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J A Knowles
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M M Weissman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. H A Garriock
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. J S Yokoyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. P J McGrath
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. E J Peters
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. W A Scheftner
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. W Coryell
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. W B Lawson
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. D Jancic
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. P V Gejman
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. A R Sanders
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. P Holmans
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. S L Slager
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. D F Levinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. S P Hamilton
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to D F Levinson or S P Hamilton.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Molecular Psychiatry website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shyn, S., Shi, J., Kraft, J. et al. Novel loci for major depression identified by genome-wide association study of Sequenced Treatment Alternatives to Relieve Depression and meta-analysis of three studies. Mol Psychiatry 16, 202–215 (2011). https://doi.org/10.1038/mp.2009.125

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/mp.2009.125

Keywords

This article is cited by

Search

Advanced search

Quick links