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 meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

Abstract

Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.

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: Manhattan plot of the observed –log10 P values of each variant for an association with depression in the meta-analysis (n =807,553; 246,363 cases and 561,190 controls).
Fig. 2: Significance of enrichment using a partitioned heritability approach.
Fig. 3: Chord diagram of genes significantly associated (P < 2.80 × 10−6) with depression and the second-level Anatomical Therapeutic Chemical classifications to which interacting drugs have been assigned.

Code availability

All code is available upon reasonable request from the corresponding author.

Data availability

Summary statistics for 10,000 genetic variants from the meta-analysis of 23andMe_307k, UK Biobank and PGC_139k and the summary statistics for all assessed genetic variants for the meta-analysis of depression in UK Biobank and PGC_139k are available from: https://doi.org/10.7488/ds/2458. To access the summary statistics for all assessed genetic variants for the meta-analysis of depression in 23andMe_307k, UK Biobank and PGC_139k, a data transfer agreement is required from 23andMe (dataset-request@23andMe.com) before a request is made to the corresponding author (D.Howard@ed.ac.uk). The raw genetic and phenotypic UK Biobank data used in this study, which were used under license, are available from: http://www.ukbiobank.ac.uk/. The genome-wide summary statistics for the Hyde et al. analysis of 23andMe, Inc. data were obtained under a data transfer agreement. Further information about obtaining access to the 23and Me, Inc. summary statistics are available from: https://research.23andme.com/collaborate/. The genome-wide summary statistics for the Wray et al. analysis of PGC data were obtained under secondary analysis proposals #60 and #76. Further information about obtaining access to the PGC summary statistics are available from: http://www.med.unc.edu/pgc/statgen.

References

  1. World Health Organization. Depression and other common mental disorders. World Health Organization http://apps.who.int/iris/bitstream/handle/10665/254610/WHOMSD?sequence=1 (2017).

  2. Kessler, R. C. et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62, 593–602 (2005).

    Article  PubMed  Google Scholar 

  3. Sullivan, P. F., Neale, M. C. & Kendler, K. S. Genetic epidemiology of major depression: review and meta-analysis. Am. J. Psychiatry 157, 1552–1562 (2000).

    Article  CAS  PubMed  Google Scholar 

  4. Ripke, S. et al. Major depressive disorder working group of the psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Hek, K. et al. A genome-wide association study of depressive symptoms. Biol. Psychiatry 73, 667–678 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. CONVERGE consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).

    Article  PubMed Central  CAS  Google Scholar 

  8. Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. McGee, S. L., Fairlie, E., Garnham, A. P. & Hargreaves, M. Exercise-induced histone modifications in human skeletal muscle. J. Physiol. (Lond.) 587, 5951–5958 (2009).

    Article  CAS  Google Scholar 

  13. Agudelo, L. Z. et al. Skeletal muscle PGC-1α1 modulates kynurenine metabolism and mediates resilience to stress-induced depression. Cell 159, 33–45 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

    Article  PubMed Central  CAS  Google Scholar 

  17. Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, 360 (2018).

    Article  Google Scholar 

  18. Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Quarto, T. et al. Interaction between DRD2 variation and sound environment on mood and emotion-related brain activity. Neuroscience 341, 9–17 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  PubMed Central  CAS  Google Scholar 

  22. Whitmer, A. J. & Gotlib, I. H. Depressive rumination and the C957T polymorphism of the DRD2 gene. Cogn. Affect. Behav. Neurosci. 12, 741–747 (2012).

    Article  PubMed  Google Scholar 

  23. Wagnon, J. L. et al. CELF4 regulates translation and local abundance of a vast set of mRNAs, including genes associated with regulation of synaptic function. PLoS Genet. 8, e1003067 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Halgren, C. et al. Haploinsufficiency of CELF4 at 18q12.2 is associated with developmental and behavioral disorders, seizures, eye manifestations, and obesity.Eur.J. Hum. Genet. 20, 1315–1319 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fogel, B. L. et al. RBFOX1 regulates both splicing and transcriptional networks in human neuronal development. Hum. Mol. Genet. 21, 4171–4186 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chang, H. et al. Further evidence of VRK2 rs2312147 associated with schizophrenia. World J. Biol. Psychiatry 17, 457–466 (2016).

    Article  PubMed  Google Scholar 

  27. Li, M. et al. Meta-analysis and brain imaging data support the involvement of VRK2 (rs2312147) in schizophrenia susceptibility. Schizophr. Res. 142, 200–205 (2012).

    Article  PubMed  Google Scholar 

  28. Potkin, S. G. et al. Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia. Mol. Psychiatry 14, 416–428 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. Mossakowska-Wójcik, J., Orzechowska, A., Talarowska, M., Szemraj, J. & Gałecki, P. The importance of TCF4 gene in the etiology of recurrent depressive disorders. Prog. Neuropsychopharmacol. Biol. Psychiatry 80, 304–308 (2018).

    Article  PubMed  CAS  Google Scholar 

  30. Rannals, M. D. et al. Psychiatric risk gene transcription factor 4 regulates intrinsic excitability of prefrontal neurons via repression of SCN10a and KCNQ1. Neuron 90, 43–55 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chronis-Tuscano, A. et al. Very early predictors of adolescent depression and suicide attempts in children with attention-deficit/hyperactivity disorder. Arch. Gen. Psychiatry 67, 1044–1051 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lesch, K.-P. et al. Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J. Neural Transm. (Vienna) 115, 1573–1585 (2008).

    Article  CAS  Google Scholar 

  33. Wilson, P. M., Fryer, R. H., Fang, Y. & Hatten, M. E. Astn2, a novel member of the astrotactin gene family, regulates the trafficking of ASTN1 during glial-guided neuronal migration. J. Neurosci. 30, 8529–8540 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mota, N. R. et al. NCAM1-TTC12-ANKK1-DRD2 gene cluster and the clinical and genetic heterogeneity of adults with ADHD.Am. J. Med. Genet. B Neuropsychiatr. Genet. 168, 433–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Kaltiala-Heino, R., Kosunen, E. & Rimpelä, M. Pubertal timing, sexual behaviour and self-reported depression in middle adolescence. J. Adolesc. 26, 531–545 (2003).

    Article  PubMed  Google Scholar 

  36. Sequeira, M.-E., Lewis, S. J., Bonilla, C., Smith, G. D. & Joinson, C. Association of timing of menarche with depressive symptoms and depression in adolescence: Mendelian randomisation study. Br. J. Psychiatry 210, 39–46 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ong, K. K. et al. Genetic variation in LIN28B is associated with the timing of puberty. Nat. Genet. 41, 729–733 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yu, J. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Wei, Y. B. et al. Elevation of Il6 is associated with disturbed let-7 biogenesis in a genetic model of depression. Transl. Psychiatry 6, e869 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Boden, J. M., Fergusson, D. M. & Horwood, L. J. Cigarette smoking and depression: tests of causal linkages using a longitudinal birth cohort. Br. J. Psychiatry 196, 440–446 (2010).

    Article  PubMed  Google Scholar 

  41. Wootton, R. E. et al. Causal effects of lifetime smoking on risk for depression and schizophrenia: Evidence from a Mendelian randomisation study. Preprint at bioRxiv https://doi.org/10.1101/381301 (2018).

  42. Wium-Andersen, M. K., Ørsted, D. D. & Nordestgaard, B. G. Tobacco smoking is causally associated with antipsychotic medication use and schizophrenia, but not with antidepressant medication use or depression. Int. J. Epidemiol. 44, 566–577 (2015).

    Article  PubMed  Google Scholar 

  43. Munafò, M. R. & Araya, R. Cigarette smoking and depression: a question of causation. Br. J. Psychiatry 196, 425–426 (2010).

    Article  PubMed  Google Scholar 

  44. Bonci, A. & Hopf, F. W. The dopamine D2 receptor: new surprises from an old friend. Neuron 47, 335–338 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Bi, L.-L. et al. Amygdala NRG1-ErbB4 is critical for the modulation of anxiety-like behaviors. Neuropsychopharmacology 40, 974–986 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Fuxe, K. & Borroto-Escuela, D. O. Basimglurant for treatment of major depressive disorder: a novel negative allosteric modulator of metabotropic glutamate receptor 5. Expert. Opin. Investig. Drugs. 24, 1247–1260 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. Espallergues, J., Temsamani, J., Laruelle, C. & Urani, A. & Maurice, T. The antidepressant-like effect of the 3β-hydroxysteroid dehydrogenase inhibitor trilostane involves a regulation of β-type estrogen receptors. Psychopharmacology (Berl.) 214, 455–463 (2011).

    Article  CAS  Google Scholar 

  48. Estrada-Camarena, E., Fernández-Guasti, A. & López-Rubalcava, C. Antidepressant-like effect of different estrogenic compounds in the forced swimming test. Neuropsychopharmacology 28, 830–838 (2003).

    Article  CAS  PubMed  Google Scholar 

  49. Dunn, E. C. et al. Genetic determinants of depression: recent findings and future directions. Harv. Rev. Psychiatry 23, 1–18 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Biernacka, J. M. et al. The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Transl. Psychiatry 5, e553 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at https://doi.org/10.1101/166298 (2017).

  52. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Smith, D. J. et al. Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants. PLoS One 8, e75362 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Allen, N. E., Sudlow, C., Peakman, T. & Collins, R. UK biobank data: come and get it. Sci. Transl. Med. 6, 224ed4 (2014).

    Article  PubMed  Google Scholar 

  56. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 1–34 (2014).

    Google Scholar 

  59. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012). S1–S3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Smith, B. H. et al. Cohort Profile: generation scotland: scottish family health study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness. Int. J. Epidemiol. 42, 689–700 (2013).

    Article  PubMed  Google Scholar 

  61. Teismann, H. et al. Establishing the bidirectional relationship between depression and subclinical arteriosclerosis—rationale, design, and characteristics of the BiDirect Study. BMC Psychiatry 14, 174 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Nagy, R. et al. Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants. Genome Med. 9, 23 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. W. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). (BiometricsResearch, New York State Psychiatric Institute, New York, NY, USA, 2002).

    Google Scholar 

  64. Fernandez-Pujals, A. M. et al. Epidemiology and heritability of major depressive disorder, stratified by age of onset, sex, and illness course in generation scotland: scottish family health study (GS:SFHS). PLoS One 10, e0142197 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    Article  CAS  PubMed  Google Scholar 

  66. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  68. Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  69. van den Berg, S. M. et al. Harmonization of neuroticism and extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of item response theory. Behav. Genet. 44, 295–313 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Hagenaars, S. P., Gale, C. R., Deary, I. J. & Harris, S. E. Cognitive ability and physical health: a Mendelian randomization study. Sci. Rep. 7, 2651 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  CAS  Google Scholar 

  74. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  76. Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Auton, A. et al. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  CAS  Google Scholar 

  81. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. The Gene Ontology Consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res. 45(D1), D331–D338 (2017).

    Article  CAS  Google Scholar 

  83. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This research was conducted using the UK Biobank resource, application number 4844. We are grateful to the UK Biobank and all its voluntary participants. The UK Biobank study was conducted under generic approval from the NHS National Research Ethics Service (approval letter dated June 17, 2011, Ref 11/NW/0382). All participants gave full informed written consent. The authors acknowledge the help, advice and support from all members of the UK Biobank Psychiatric Genetics Group. The BiDirect cohort and the Münster cohort were approved by the ethics committee of the University of Münster and the Westphalian Chamber of Physicians in Münster, North-Rhine-Westphalia, Germany, and written informed consent was obtained from all participants. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL) Reference 104036/Z/14/Z). Ethics approval for the Generation Scotland was given by the NHS Tayside committee on research ethics (reference 15/ES/0040), and all participants provided written informed consent for the use of their data. The study protocol used by 23andMe was approved by an external AAHRPP-accredited institutional review board. Details of the manuscripts containing approvals for the 35 PGC cohorts are listed in Supplementary Table 1 and Supplementary Table 2 of Wray et al9. We are grateful to the participants and research teams behind the Psychiatric Genomics Consortium, UK Biobank and 23andMe. We thank the following members of the 23andMe Research Team: M. Agee, B. Alipanahi, A. Auton, R. K. Bell, K. Bryc, S. L. Elson, P. Fontanillas, N. A. Furlotte, B. Hicks, K. E. Huber, E. M. Jewett, Y. Jiang, A. Kleinman, K.-Han. Lin, N. K. Litterman, M. H. McIntyre, J. L. Mountain, E. S. Noblin, C. A.M. Northover, S. J. Pitts, G. D. Poznik, J. F. Sathirapongsasuti, O. V. Sazonova, J. F. Shelton, S. Shringarpure, J. Y. Tung, V. Vacic, X. Wang and C. H. Wilson. We would like to thank N. Skene for his advice on analyzing the expression-weighted enrichment for brain cell types and N. Martin for his suggestions on polygenic risk scores. A.M.McI and I.J.D. acknowledge support from the Wellcome Trust (Wellcome Trust Strategic Award ‘STratifying Resilience and Depression Longitudinally’ (STRADL) Reference 104036/Z/14/Z and the Dr Mortimer and Theresa Sackler Foundation. I.J.D. is supported by the Centre for Cognitive Ageing and Cognitive Epidemiology, which is funded by the Medical Research Council and the Biotechnology and Biological Sciences Research Council (MR/K026992/1). This investigation represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. G.H. is supported by the Wellcome Trust (208806/Z/17/Z). D.J.S. is supported by the Lister Institute Prize Fellowship 2016-2021. N.R.W. acknowledges NMHRC grants 1078901 and 1087889. The BiDirect Study is supported by grants of the German Ministry of Research and Education (BMBF) to the University of Münster (01ER0816 and 01ER1506). The Münster cohort was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to U.D.; SFB-TRR58, Projects C09 and Z02 to U.D.) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to U.D.). The PGC has received major funding from the US National Institute of Mental Health and the US National Institute of Drug Abuse (U01 MH109528 and U01 MH1095320).

Author information

Authors and Affiliations

Authors

Consortia

Contributions

D.M.H., D.J.S., G.B., C.M.L., and A.M.McI. conceived the research project. M.J.A., J.R.I.C., J.W., D.J.S., G.B., and A.M.McI. determined the variables that formed the depression phenotypes within UK Biobank. D.M.H., M.J.A., J.R.I.C., R.E.M. and G.D. applied quality control to the UK Biobank data. M.J.A. ran the association analysis in UK Biobank. 23andMe Research Team provided the summary statistics from the Hyde, et al.8 analysis. Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, M.T., S.R., P.F.S., N.R.W. and C.M.L. provided the summary statistics from the Wray, et al.9 analysis with additional removal of overlapping cohorts. D.M.H. ran the meta-analysis of the three contributing cohorts. C.T. and D.A.H conducted the replication analysis in 23andMe for the significant variants in the meta-analysis. T.-K.C., S.P.H and E.Y.X. conducted the polygenic risk score analysis. K.B., H.T. and R.R. ascertained the BiDirect cohort, and the Münster cohort was ascertained by V.A., B.T.B., U.D. and K.D. T.-K.C. and D.M.H calculated the genetic correlations and partitioned the heritability component of depression. D.M.H., and G.H. performed the Mendelian randomization analyses. D.M.H. ran the MAGMA-based analyses with J.W., M.S., E.M.W., C.A., J.G., X.S. and M.C.B. examining the genes and gene sets identified. J.D.H., D.M.H. and A.M.McI. conducted the gene–drug interaction analysis. I.J.D., D.J.P., H.C.W., C.S.H., S.R., D.J.S., P.F.S., N.R.W., E.M.B., G.B., C.M.L. and A.M.McI. provided expertise of association study methodology and statistical analysis. D.M.H. oversaw the research project and serves as the primary contact for all communication. All authors commented on the manuscript.

Corresponding author

Correspondence to David M. Howard.

Ethics declarations

Competing interests

I.J.D. is a participant in UK Biobank. C.T., D.A.H., and Members of the 23andMe Research Team are employees of 23andMe, Inc. The authors report no other conflicts of interest.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Quantile–quantile plot of the observed P values on those expected for each genetic variants association with depression in our meta-analysis (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests.

Supplementary Figure 2

Odds ratios and 95% confidence intervals for major depressive disorder (MDD) in Generation Scotland (n = 6,946) based on polygenic risk score (PRS) deciles calculated from the current meta-analysis of depression (n = 765,884 individuals) and from summary statistics from the genome-wide association study of major depression conducted by Wray, et al.9 (n = 453,779 individuals).

Supplementary Figure 3 Significant genetic correlations (rG; P < 0.01, after false discovery rate correction for multiple testing) between depression (n = 807,553 individuals) and other behavioral and disease related traits using LD score regression.

A negative rG indicates that an earlier or lower value of a continuous trait (that is earlier father’s age of death or lower subjective well-being was associated with depression. A positive rG indicates that a later or higher value of a continuous trait (that is higher triglyceride level) was associated with depression. P-values were calculated by testing whether rG was significantly different from 0 using a two-sided test. The center values are the genetic correlation for each trait and the error bars are the respective standard error. LD score regression implemented in LD Hub software (http://ldsc.broadinstitute.org/).

Supplementary Figure 4 Mendelian randomization test for a putative causal effect of depression (n = 629,934 individuals; 56 instrumental variables) on neuroticism (n = 29,496 twin pairs) using inverse weighted regression, MR Egger and a weighted median test.

Center values reflect the effect of each instrumental variable on each trait with the respective error bars reflecting the standard error of the variable’s effect on each trait.

Supplementary Figure 5 Mendelian randomization analysis for a putative causal effect of depression (n = 629,934 individuals; 49 instrumental variables) on ever vs. never smoked (n = 69,409 individuals) using inverse weighted regression.

Center values reflect the effect of each instrumental variable on each trait with the respective error bars reflecting the standard error of the variable’s effect on each trait.

Supplementary Figure 6 Mendelian randomization test for a putative causal effect of neuroticism (n = 329,821 individuals; 65 instrumental variables) on depression (n = 537,701 individuals) using inverse weighted regression, MR Egger and a weighted median test.

Center values reflect the effect of each instrumental variable on each trait with the respective error bars reflecting the standard error of the variable’s effect on each trait.

Supplementary Figure 7 Contribution of functional annotation of 24 categories to the heritability of depression (n = 807,553 individuals) based on the variants within each category.

The enrichment of each functional category, shown on the y-axis, is calculated as the proportion of heritability assigned to a functional categories divided by the proportion of variants in that category (Pr(h2)/Pr(SNPs)). Error bars represent jackknife standard errors for each the estimate of enrichment, and an asterisk indicates significant enrichment after Bonferroni correction for multiple testing (Conserved P-value = 2.55 × 10−17; H3K4me1 P-value = 0.0015; Intron P-value = 0.0014). P-values were calculated using a one-sided test and tested whether there the estimate of enrichment was significantly different from zero enrichment. The dashed line represents the threshold for no enrichment.

Supplementary Figure 8 Enrichment estimate (β) of significantly enriched brain cell regions using GTEx overlaid on physical representation of brain anatomy (n = 807,553 individuals).

The pseudo-coloring highlights the coefficients of the brain regions in red that were significantly enriched (P < 0.05) for depression variants compared to no enrichment using a one-sided test and after Bonferroni correction for multiple testing.

Supplementary Figure 9 Stratified LD score regression analyses showing significance of enrichment estimates for 3 brain cell types in depression (n = 807,553 individuals).

The dashed line represents the Bonferroni threshold for significance for multiple testing correction (P = 0.0167) and * indicates significant enrichment for that brain cell type (Neuron P-value = 4.52 × 10−4). P-values were calculated based on evidence of enrichment compared to no enrichment using a one-sided test.

Supplementary Figure 10 Regional visualization plot centered on the independently-associated variant, rs1021363, close to the Sortilin-related VPS10 domain containing receptor 3 (SORCS3) gene on chromosome 10 (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests. Recombination rates used in the plots are based on the European 1000 Genomes panel from November 2014.

Supplementary Figure 11 Regional visualization plots centered on independently-associated variants (A. rs2568958 and B. rs10890020) close to the neuronal growth regulator 1 (NEGR1) gene on chromosome 1 (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests. Recombination rates used in the plots are based on the European 1000 Genomes panel from November 2014.

Supplementary Figure 12 Regional visualization plots centred on independently-associated variants (A. rs62091461, B. rs12966052, and C. rs12967143) close to the transcription factor 4 (TCF4) and RAB27B genes on chromosome 18 (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests. Recombination rates used in the plots are based on the European 1000 Genomes panel from November 2014.

Supplementary information

Supplementary Figures 1–12

Supplementary Note

Reporting Summary

Supplementary Table 1

Variants with a P-value < 5 × 10-8 for an association with depression (n = 807,553 individuals) in the meta-analysis of PGC_139k, 23andMe_307k and UK Biobank. Effects sizes and allele frequencies are reported for the A1 allele.

Supplementary Table 2

The direction of effect of previously reported significant variants for depression across the studies contributing to the meta-analysis.

Supplementary Table 3

Genetic correlations between depression (n = 807,553 individuals) and other behavioural and disease related traits using LD score regression implemented in LD Hub software (http://ldsc.broadinstitute.org/).

Supplementary Table 4

Mendelian randomization analysis between depression and other traits using MR Egger test for directional horizontal pleiotropy, inverse variance weighted (IVW) test for variant heterogeneity and IVW regression, weighted median and MR-Egger tests for a causal effect.

Supplementary Table 5

Heritability partitioned by functional annotation enrichment (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0021).

Supplementary Table 6

Partitioning of the heritability estimate by cell type enrichment (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0056).

Supplementary Table 7

Enrichment estimates for brain regions using GTEx (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0038).

Supplementary Table 8

Enrichment estimates for brain cell types (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0167).

Supplementary Table 9

Genome-wide significant gene-based hits (P < 2.80 x 10-6) in the meta-analysis of depression using MAGMA. NSNPS is the number of SNPs in the gene; NiSNPs is the number of independent SNPs in the gene.

Supplementary Table 10

Number and proportion of gene overlap within the Gene Ontology Consortium gene-sets associated (Pcorrected < 0.05) with depression.

Supplementary Table 11

Drug x gene interactions for the genes identified as significantly associated with depression with interactions obtained from the drug gene interaction database (http://dgidb.genome.wustl.edu/). The Anatomical Therapeutic Chemical (ATC) classification for each drug is provided along with the type of interaction and its source.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Howard, D.M., Adams, M.J., Clarke, TK. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 22, 343–352 (2019). https://doi.org/10.1038/s41593-018-0326-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0326-7

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing