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.

Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions

Abstract

Major depressive disorder is the most common neuropsychiatric disorder, affecting 11% of veterans. Here we report results of a large meta-analysis of depression using data from the Million Veteran Program, 23andMe, UK Biobank and FinnGen, including individuals of European ancestry (n = 1,154,267; 340,591 cases) and African ancestry (n = 59,600; 25,843 cases). Transcriptome-wide association study analyses revealed significant associations with expression of NEGR1 in the hypothalamus and DRD2 in the nucleus accumbens, among others. We fine-mapped 178 genomic risk loci, and we identified likely pathogenicity in these variants and overlapping gene expression for 17 genes from our transcriptome-wide association study, including TRAF3. Finally, we were able to show substantial replications of our findings in a large independent cohort (n = 1,342,778) provided by 23andMe. This study sheds light on the genetic architecture of depression and provides new insight into the interrelatedness of complex psychiatric traits.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Design of the study and circular Manhattan plot.
Fig. 2: Genetic correlation.
Fig. 3: Tissue-based gene association study (TWAS) and fine mapping.
Fig. 4: gSEM.
Fig. 5: Similar ancestry and trans-ancestry replication analyses.

Data availability

The GWAS summary statistics generated and/or analyzed during this study are available via dbGaP; the dbGaP accession assigned to the Million Veteran Program is phs001672.v1.p.

The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data.

Code availability

No custom code was used in this study. Software and R packages used are discussed in the text.

References

  1. 1.

    Hasin, D. S. et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry 75, 336–346 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Roehrig, C. Mental disorders top the list of the most costly conditions in the United States: $201 billion. Health Affairs 35, 1130–1135 (2016).

    PubMed  Article  Google Scholar 

  3. 3.

    Mullins, N. et al. GWAS of suicide attempt in psychiatric disorders and association with major depression polygenic risk scores. Am. J. Psychiatry 176, 651–660 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Strawbridge, R. J. et al. Identification of novel genome-wide associations for suicidality in UK Biobank, genetic correlation with psychiatric disorders and polygenic association with completed suicide. EBioMedicine 41, 517–525 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Levey, D. F. et al. Genetic associations with suicide attempt severity and genetic overlap with major depression. Transl. Psychiatry 9, 22 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6.

    Docherty, A. R. et al. Genome-wide association study of suicide death and polygenic prediction of clinical antecedents. Am. J. Psychiatry 177, 917–927 (2020).

    PubMed  Article  Google Scholar 

  7. 7.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Howard, D. M. 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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Cai, N. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat. Genet. 52, 437–447 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Willer, C. J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161–169 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Barbeira, A. N. et al. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 15, e1007889 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. 16.

    Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  17. 17.

    Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. 18.

    Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical enrichment tool for animals and plants. Bioinformatics 36, 2628–2629 (2019).

  19. 19.

    Gunther, S. et al. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 36, D919–D922 (2008).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  20. 20.

    Singh, K. et al. Neural cell adhesion molecule Negr1 deficiency in mouse results in structural brain endophenotypes and behavioral deviations related to psychiatric disorders. Sci. Rep. 9, 5457 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

    Noh, K. et al. Negr1 controls adult hippocampal neurogenesis and affective behaviors. Mol. Psychiatry 24, 1189–1205 (2019).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Nestler, E. J. & Carlezon, W. A. Jr. The mesolimbic dopamine reward circuit in depression. Biol. Psychiatry 59, 1151–1159 (2006).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Tye, K. M. et al. Dopamine neurons modulate neural encoding and expression of depression-related behaviour. Nature 493, 537–541 (2013).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Krystal, A. D. et al. A randomized proof-of-mechanism trial applying the ‘fast-fail’ approach to evaluating κ-opioid antagonism as a treatment for anhedonia. Nat. Med. 26, 760–768 (2020).

  25. 25.

    Carlezon, W. A. Jr., Beguin, C., Knoll, A. T. & Cohen, B. M. Kappa-opioid ligands in the study and treatment of mood disorders. Pharmacol. Ther. 123, 334–343 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Gilling, M. et al. A 3.2 Mb deletion on 18q12 in a patient with childhood autism and high-grade myopia. Eur J Hum Genet 16, 312–319 (2008).

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Sun, W. et al. Aberrant sodium channel activity in the complex seizure disorder of Celf4 mutant mice. J. Physiol. 591, 241–255 (2013).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Sakurai, H. et al. Longer-term open-label study of adjunctive riluzole in treatment-resistant depression. J. Affect. Disord. 258, 102–108 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Alt, A., Nisenbaum, E. S., Bleakman, D. & Witkin, J. M. A role for AMPA receptors in mood disorders. Biochem. Pharmacol. 71, 1273–1288 (2006).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Pittenger, C. et al. Riluzole in the treatment of mood and anxiety disorders. CNS Drugs 22, 761–786 (2008).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Chowdhury, G. M. et al. Transiently increased glutamate cycling in rat PFC is associated with rapid onset of antidepressant-like effects. Mol. Psychiatry 22, 120–126 (2017).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Baselmans, B. M. L. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Wani, A. L., Bhat, S. A. & Ara, A. Omega-3 fatty acids and the treatment of depression: a review of scientific evidence. Integr. Med. Res. 4, 132–141 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Hacker, H., Tseng, P. H. & Karin, M. Expanding TRAF function: TRAF3 as a tri-faced immune regulator. Nat. Rev. Immunol. 11, 457–468 (2011).

    PubMed  Article  CAS  Google Scholar 

  35. 35.

    Chiu, W. C., Su, Y. P., Su, K. P. & Chen, P. C. Recurrence of depressive disorders after interferon-induced depression. Transl. Psychiatry 7, e1026 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Descalzi, G. et al. Neuropathic pain promotes adaptive changes in gene expression in brain networks involved in stress and depression. Sci. Signal 10, eaaj1549 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37.

    Nho, K. et al. Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults. J. Alzheimers Dis. 45, 1197–1206 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Gelernter, J. et al. Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nat. Neurosci. 22, 1394–1401 (2019).

  39. 39.

    Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    PubMed  Article  Google Scholar 

  40. 40.

    Harrington, K. M. et al. Gender differences in demographic and health characteristics of the Million Veteran Program cohort. Womens Health Issues 29, S56–S66 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Levey, D. F. et al. Reproducible genetic risk loci for anxiety: results from ~200,000 participants in the Million Veteran Program. Am. J. Psychiatry 177, 223–232 (2020).

  42. 42.

    Kroenke, K., Spitzer, R. L. & Williams, J. B. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med. Care 41, 1284–1292 (2003).

    PubMed  Article  Google Scholar 

  43. 43.

    O’Connor, L. J. & Price, A. L. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nat. Genet. 50, 1728–1734 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B. & Eskin, E. Identifying causal variants at loci with multiple signals of association. Genetics 198, 497–508 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Zhu, Z. H. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Aschard, H., Vilhjalmsson, B. J., Joshi, A. D., Price, A. L. & Kraft, P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am. J. Hum. Genet. 96, 329–339 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Pers, T. H., Timshel, P. & Hirschhorn, J. N. SNPsnap: a web-based tool for identification and annotation of matched SNPs. Bioinformatics 31, 418–420 (2015).

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the participants and investigators of the FinnGen study, 23andMe, the UK Biobank, the PGC and the Million Veteran Program. We would like to thank the research participants and employees of 23andMe for making this work possible. We thank the veterans who participate in the Million Veteran Program. The following members of the 23andMe Research Team contributed to this study: M. Agee, S. Aslibekyan, A. Auton, R. K. Bell, K. Bryc, S. K. Clark, S. L. Elson, K. Fletez-Brant, P. Fontanillas, N. A. Furlotte, P. M. Gandhi, K. Heilbron, B. Hicks, D. A. Hinds, K. E. Huber, E. M. Jewett, Y. Jiang, A. Kleinman, K.-H. Lin, N. K. Litterman, M. K. Luff, J. C. McCreight, M. H. McIntyre, K. F. McManus, J. L. Mountain, S. V. Mozaffari, P. Nandakumar, E. S. Noblin, C. A. M. Northover, J. O’Connell, A. A. Petrakovitz, S. J. Pitts, G. D. Poznik, J. F. Sathirapongsasuti, A. J. Shastri, J. F. Shelton, S. Shringarpure, C. Tian, J. Y. Tung, R. J. Tunney, V. Vacic, X. Wang and A. S. Zare. From the Yale Department of Psychiatry, Division of Human Genetics, we would like to thank and acknowledge the efforts of A. M. Lacobelle, C. Robinson and C. Tyrell. Funding: this work was supported by funding from the Veterans Affairs Office of Research and Development Million Veteran Program grant CX001849-01 (MVP025) and VA Cooperative Studies Program CSP575B. D.F.L. was supported by an NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation.

Author information

Affiliations

Authors

Consortia

Contributions

J.G. and M.B.S. secured funding for this project. D.F.L., M.B.S. and J.G. had primary responsibility for design of the study. J.G., M.B.S. and J.C. conceived, supervised and managed the study. K.R. and M.A. assisted with study administration. D.F.L., F.R.W., G.A.P., H.Z., J.S., S.S. and R.P. contributed to genetic and bioinformatic analyses. R.P. was the senior statistical geneticist. K.H., R.Q. and D.F.L. contributed to phenotyping and phenomic analyses. The initial manuscript was drafted by D.F.L., M.B.S. and J.G. Manuscript contributions and interpretation of results were provided by D.F.L., M.B.S., F.R.W., G.A.P., K.H., G.S., H.Z., Y.Z.N., C.O., R.P., A.M., J.C. and J.G. The remaining authors contributed to other organizational or data processing components of the study. All authors saw, had the opportunity to comment on, and approved the final draft.

Corresponding authors

Correspondence to Murray B. Stein or Joel Gelernter.

Ethics declarations

Competing interests

M.B.S. reports receiving consulting fees in the past 3 years from Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Boehringer Ingelheim, Clexio Biosciences, EmpowerPharm, Engrail Therapeutics, Genentech/Roche, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals and Oxeia Biopharmaceuticals. In the last 12 months, G.S. has provided consulting services to Allergan, Axsome Therapeutics, Biohaven Pharmaceuticals, Boehringer Ingelheim International, Bristol-Myers Squibb, Clexio Biosciences, Epiodyne, Intra-Cellular Therapies, Janssen, Lundbeck, Minerva Pharmaceuticals, Navitor Pharmaceuticals, NeuroRX, Noven Pharmaceuticals, Otsuka, Perception Neuroscience, Praxis Seelos Pharmaceuticals and Vistagen Therapeutics. G.S. has received funds for contracted research from Janssen Pharmaceuticals, Merck and the Usona Institute. G.S. holds equity in Biohaven Pharmaceuticals and has received royalties from Yale University, paid from patent licenses with Biohaven Pharmaceuticals. J.S. and S.S. are employed by and hold stock or stock options in 23andMe, Inc. J.G. is named as co-inventor on Patent Cooperation Treaty application no. 15/878,640 titled ‘Genotype-guided dosing of opioid agonists’, filed on January 24, 2018. All other authors declare that they have no competing financial interests.

Additional information

Peer review information Nature Neuroscience thanks Gerome Breen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplemental Figs. 1–5 and Supplementary Tables 1–5.

Reporting Summary

Supplementary Data 1

A table of genetic correlations.

Supplementary Data 2

Transcriptome-wide association study, variant prioritization and co-localization analysis.

Supplementary Data 3

Additional detail on the precise data sources used in the analysis in Fig. 4.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Levey, D.F., Stein, M.B., Wendt, F.R. et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00860-2

Download citation

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