Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

Article metrics

Abstract

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

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: Results of genome-wide association meta-analysis of seven cohorts for major depression.
Fig. 2: Genetic risk score prediction analyses into PGC29 MDD target samples.
Fig. 3: Analyses exploring enrichment of major depression association results based on different SNP annotations.
Fig. 4: Generative topographic mapping of the 19 significant pathway results.

References

  1. 1.

    Kessler, R. C. & Bromet, E. J. The epidemiology of depression across cultures. Annu. Rev. Public Health 34, 119–138 (2013).

  2. 2.

    Judd, L. L. The clinical course of unipolar major depressive disorders. Arch. Gen. Psychiatry 54, 989–991 (1997).

  3. 3.

    Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T. & Murray, C. J. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet 367, 1747–1757 (2006).

  4. 4.

    Wittchen, H. U. et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur. Neuropsychopharmacol. 21, 655–679 (2011).

  5. 5.

    Ferrari, A. J. et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 10, e1001547 (2013).

  6. 6.

    Angst, F., Stassen, H. H., Clayton, P. J. & Angst, J. Mortality of patients with mood disorders: follow-up over 34–38 years. J. Affect. Disord. 68, 167–181 (2002).

  7. 7.

    Gustavsson, A. et al. Cost of disorders of the brain in Europe 2010. Eur. Neuropsychopharmacol. 21, 718–779 (2011).

  8. 8.

    Murray, C. J. et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2197–2223 (2012).

  9. 9.

    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).

  10. 10.

    Rice, F., Harold, G. & Thapar, A. The genetic aetiology of childhood depression: a review. J. Child Psychol. Psychiatry 43, 65–79 (2002).

  11. 11.

    Viktorin, A. et al. Heritability of perinatal depression and genetic overlap with nonperinatal depression. Am. J. Psychiatry 73, 158–165 (2016).

  12. 12.

    Levinson, D. F. et al. Genetic studies of major depressive disorder: why are there no GWAS findings, and what can we do about it. Biol. Psychiatry 76, 510–512 (2014).

  13. 13.

    Major Depressive Disorder Working Group of the PGC. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).

  14. 14.

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

  15. 15.

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

  16. 16.

    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).

  17. 17.

    Sullivan, P. F. et al. Psychiatric genomics. An update and an agenda. Am. J. Psychiatry 175, 15–27 (2018).

  18. 18.

    Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

  19. 19.

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

  20. 20.

    Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).

  21. 21.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

  22. 22.

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

  23. 23.

    Wray, N. R. et al. Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Mol. Psychiatry 17, 36–48 (2012).

  24. 24.

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

  25. 25.

    Meier, S. M. et al. High loading of polygenic risk in cases with chronic schizophrenia. Mol. Psychiatry 21, 969–974 (2016).

  26. 26.

    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).

  27. 27.

    Wray, N. R. & Maier, R. Genetic basis of complex genetic disease: the contribution of disease heterogeneity to missing heritability. Curr. Epidemiol. Rep. 1, 220–227 (2014).

  28. 28.

    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).

  29. 29.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

  30. 30.

    Berndt, S. I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45, 501–512 (2013).

  31. 31.

    Bradfield, J. P. et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat. Genet. 44, 526–531 (2012).

  32. 32.

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

  33. 33.

    Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25–34 (2009).

  34. 34.

    Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).

  35. 35.

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

  36. 36.

    Gehman, L. T. et al. The splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain. Nat. Genet. 43, 706–711 (2011).

  37. 37.

    Pariante, C. M. & Lightman, S. L. The HPA axis in major depression: classical theories and new developments. Trends Neurosci. 31, 464–468 (2008).

  38. 38.

    Choi, Y. et al. SALM5 trans-synaptically interacts with LAR-RPTPs in a splicing-dependent manner to regulate synapse development. Sci. Rep. 6, 26676 (2016).

  39. 39.

    Mah, W. et al. Selected SALM (synaptic adhesion-like molecule) family proteins regulate synapse formation. J. Neurosci. 30, 5559–5568 (2010).

  40. 40.

    Zhu, Y. et al. Neuron-specific SALM5 limits inflammation in the CNS via its interaction with HVEM. Sci. Adv. 2, e1500637 (2016).

  41. 41.

    Amiel, J. et al. Mutations in TCF4, encoding a class I basic helix-loop-helix transcription factor, are responsible for Pitt–Hopkins syndrome, a severe epileptic encephalopathy associated with autonomic dysfunction. Am. J. Hum. Genet. 80, 988–993 (2007).

  42. 42.

    Akbarian, S. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).

  43. 43.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  44. 44.

    Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22, 900–909 (2017).

  45. 45.

    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).

  46. 46.

    Finucane, H. K. et al. Partitioning heritability by functional category using GWAS summary statistics. Nat. Genet. 47, 1228–1235 (2015).

  47. 47.

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

  48. 48.

    Simonti, C. N. et al. The phenotypic legacy of admixture between modern humans and Neandertals. Science 351, 737–741 (2016).

  49. 49.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

  50. 50.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

  51. 51.

    Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

  52. 52.

    Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

  53. 53.

    Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

  54. 54.

    Martin, J. S. et al. HUGIn. Hi-C Unifying Genomic Interrogator. Bioinformatics 33, 3793–3795 (2017).

  55. 55.

    Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).

  56. 56.

    De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

  57. 57.

    Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).

  58. 58.

    Gaspar, H. A. & Breen, G. Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach. Sci. Rep. 7, 12460 (2017).

  59. 59.

    Breen, G. et al. Translating genome-wide association findings into new therapeutics for psychiatry. Nat. Neurosci. 19, 1392–1396 (2016).

  60. 60.

    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).

  61. 61.

    Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).

  62. 62.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

  63. 63.

    Nikpay, M. et al. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

  64. 64.

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

  65. 65.

    Wray, N. R., Lee, S. H. & Kendler, K. S. Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. Eur. J. Hum. Genet. 20, 668–674 (2012).

  66. 66.

    Hippocrates. The Aphorisms of Hippocrates (Collins & Co., New York, 1817).

  67. 67.

    Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. (in the press).

  68. 68.

    Yang, X. et al. Widespread expansion of protein interaction capabilities by alternative splicing. Cell 164, 805–817 (2016).

  69. 69.

    Zhang, X. et al. Cell-type-specific alternative splicing governs cell fate in the developing cerebral cortex. Cell 166, 1147–1162 (2016).

  70. 70.

    Kessler, R. C. et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). J. Am. Med. Assoc. 289, 3095–3105 (2003).

  71. 71.

    Hasin, D. S., Goodwin, R. D., Stinson, F. S. & Grant, B. F. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch. Gen. Psychiatry 62, 1097–1106 (2005).

  72. 72.

    Kendler, K. S. et al. The structure of genetic and environmental risk factors for syndromal and subsyndromal common DSM-IV axis I and all axis II disorders. Am. J. Psychiatry 168, 29–39 (2011).

  73. 73.

    Kendler, K. S., Prescott, C. A., Myers, J. & Neale, M. C. The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Arch. Gen. Psychiatry 60, 929–937 (2003).

  74. 74.

    Robinson, E. B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).

  75. 75.

    Middeldorp, C. M. et al. A genome-wide association meta-analysis of attention-deficit/hyperactivity disorder symptoms in population-based pediatric cohorts. J. Am. Acad. Child. Adolesc. Psychiatry 55, 896–905 (2016).

  76. 76.

    Kendell, R. E. The classification of depressions: a review of contemporary confusion. Br. J. Psychiatry 129, 15–28 (1976).

  77. 77.

    Verduijn, J. et al. Using clinical characteristics to identify which patients with major depressive disorder have a higher genetic load for three psychiatric disorders. Biol. Psychiatry 81, 316–324 (2017).

  78. 78.

    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).

  79. 79.

    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).

  80. 80.

    Banda, Y. et al. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200, 1285–1295 (2015).

  81. 81.

    Pedersen, C. B. et al. The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).

  82. 82.

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

  83. 83.

    Demontis, D. et al. Discovery of the first genome-wide significant risk loci for ADHD. Preprint at bioRxiv https://www.biorxiv.org/content/early/2017/06/03/145581 (2017).

  84. 84.

    Boraska, V. et al. A genome-wide association study of anorexia nervosa. Mol. Psychiatry 19, 1085–1094 (2014).

  85. 85.

    Otowa, T. et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry 21, 1391–1399 (2016).

  86. 86.

    Grove, J. et al. Common risk variants identified in autism spectrum disorder. Preprint at bioRxiv https://www.biorxiv.org/content/early/2017/11/27/224774 (2017).

  87. 87.

    Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

  88. 88.

    Deary, V. et al. Genetic contributions to self-reported tiredness. Mol. Psychiatry 23, 609–620 (2018).

  89. 89.

    Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

  90. 90.

    Lu, Y. et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat. Commun. 7, 10495 (2016).

  91. 91.

    Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

  92. 92.

    Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

  93. 93.

    Perry, J. R. et al. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature 514, 92–97 (2014).

  94. 94.

    Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).

  95. 95.

    Pilling, L. C. et al. Human longevity is influenced by many genetic variants: evidence from 75,000 UK Biobank participants. Aging 8, 547–560 (2016).

  96. 96.

    Patel, Y. M. et al. Novel association of genetic markers affecting CYP2A6 activity and lung cancer risk. Cancer Res. 76, 5768–5776 (2016).

  97. 97.

    World Health Organization. International Classification of Diseases (World Health Organization, Geneva, 1978).

  98. 98.

    World Health Organization. International Classification of Diseases (World Health Organization, Geneva, 1992).

  99. 99.

    American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, Washington, DC, 1994).

  100. 100.

    Abecasis, G. R. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  101. 101.

    ENCODE Project Consortium. A user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 9, e1001046 (2011).

  102. 102.

    Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  103. 103.

    Vernot, B. et al. Excavating Neandertal and Denisovan DNA from the genomes of Melanesian individuals. Science 352, 235–239 (2016).

  104. 104.

    Bryois, J. et al. Evaluation of chromatin accessibility in prefrontal cortex of schizophrenia cases and controls. Nat. Commun. (in the press).

  105. 105.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  106. 106.

    Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).

  107. 107.

    Finucane, H. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. https://dx.doi.org/10.1038/s41588-018-0081-4 (2018).

  108. 108.

    Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).

  109. 109.

    Jansen, R. et al. Conditional eQTL analysis reveals allelic heterogeneity of gene expression. Hum. Mol. Genet. 26, 1444–1451 (2017).

  110. 110.

    Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

  111. 111.

    Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).

  112. 112.

    Hannon, E., Weedon, M., Bray, N., O’Donovan, M. & Mill, J. Pleiotropic effects of trait-associated genetic variation on DNA methylation: utility for refining GWAS loci. Am. J. Hum. Genet. 100, 954–959 (2017).

  113. 113.

    de Leeuw, C. A., Neale, B. M., Heskes, T. & Posthuma, D. The statistical properties of gene-set analysis. Nat. Rev. Genet. 17, 353–364 (2016).

  114. 114.

    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).

  115. 115.

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

  116. 116.

    Turner, T. N. et al. denovo-db: a compendium of human de novo variants. Nucleic Acids Res. 45 (D1), D804–D811 (2017).

  117. 117.

    Pirooznia, M. et al. High-throughput sequencing of the synaptome in major depressive disorder. Mol. Psychiatry 21, 650–655 (2016).

  118. 118.

    Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

  119. 119.

    Wagner, A. H. et al. DGIdb 2.0: mining clinically relevant drug–gene interactions. Nucleic Acids Res. 44 (D1), D1036–D1044 (2016).

  120. 120.

    Roth, B. L., Kroeze, W. K., Patel, S. & Lopez, E. The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrasment of riches? Neuroscientist 6, 252–262 (2000).

  121. 121.

    Olier, I., Vellido, A. & Giraldo, J. Kernel generative topographic mapping. in ESANN 2010 Proc. 28–30 (2010).

  122. 122.

    Smith, G. D. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

  123. 123.

    Wooldridge, J. M. Introductory Econometrics: A Modern Approach (Cengage Learning, Boston, MA, 2015).

  124. 124.

    Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

Download references

Acknowledgements

Full acknowledgments are in the Supplementary Note. We are deeply indebted to the investigators who comprise the PGC, and to the hundreds of thousands of subjects who have shared their life experiences with PGC investigators. A full list of funding is in the Supplementary Note. Major funding for the PGC is from the US National Institutes of Health (U01 MH109528 and U01 MH109532). Statistical analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org/) hosted by SURFsara. The iPSYCH team acknowledges funding from the Lundbeck Foundation (grants R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, the European Research Council (project 294838), the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. This research has been conducted using the UK Biobank Resource (see URLs), including applications 4844 and 6818. Finally, we thank the members of the eQTLGen Consortium for allowing us to use their very large eQTL database ahead of publication. Its members are listed in Supplementary Table 14.

Some data used in this study were obtained from dbGaP (see URLs). dbGaP accession phs000021: funding support for the Genome-Wide Association of Schizophrenia Study was provided by the National Institute of Mental Health (R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01 MH46318, U01 MH79469, and U01 MH79470), and the genotyping of samples was provided through the Genetic Association Information Network (GAIN). Samples and associated phenotype data for the Genome-Wide Association of Schizophrenia Study were provided by the Molecular Genetics of Schizophrenia Collaboration (principal investigator P. V. Gejman, Evanston Northwestern Healthcare (ENH) and Northwestern University, Evanston, IL, USA). dbGaP accession phs000196: this work used in part data from the NINDS dbGaP database from the CIDR:NGRC PARKINSON’S DISEASE STUDY. dbGaP accession phs000187: High-Density SNP Association Analysis of Melanoma: Case–Control and Outcomes Investigation. Research support to collect data and develop an application to support this project was provided by P50 CA093459, P50 CA097007, R01 ES011740, and R01 CA133996 from the NIH.

Author information

Writing group: G.B., A.D.B., D.F.L., C.M.L., S.R., P.F.S., N.R.W. PGC MDD PI group: V.A., B.T.B., K.B., D.I.B., G.B., A.D.B., S.C., U.D., J.R.D., E.D., K.D., T.E., E.J.C.d.G., H.J.G., S.P.H., C. Hayward, A.C.H., D.M.H., K.S.K., S.K., D.F.L., C.M.L., G.L., Q.S.L., S.L., P.A.F.M., P.K.M., N.G.M., A.M.M., A.M., O.M., P.B.M., B.M.-M., M. Nordentoft, M.M.N., M.C.O’D., S.A.P., N.L.P., B.W.P., R.H.P., D.J.P., J.B.P., M.P., M. Rietschel, C.S., T.G.S., J.W.S., K.S., P.F.S., H. Tiemeier, R.U., H.V., M.M.W., T.W., A.R.W., N.R.W. Bioinformatics: 23andMe Research Team, M.J.A., S.V.d.A., G.B., J.B., A.D.B., E.C., J.H.C., T.-K.C., J.R.I.C., L.C.-C., eQTLGen Consortium, G.E.C., C.A.C., G.D., E.M.D., T.E., A.J.F., H.A.G., P.G.-R., J.G., L.S.H., E.H., T.F.H., C. Hayward, M.H., R.J., F.J., Z.K., Q.S.L., Yihan Li, P.A.L., X.L., L.L., D.J.M., S.E.M., E.M., Y.M., J. Mill, J.N.P., B.W.P., W.J.P., G.P., P.Q., L.S., S.I.S., C.A.S., P.F.S., K.E.T., A.T., P.A.T., A.G.U., Y. Wang, S.M.W., N.R.W., H.S.X. Clinical: E.A., T.M.A., V.A., B.T.B., A.T.F.B., K.B., E.B.B., D.H.R.B., H.N.B., A.D.B., N. Craddock, U.D., J.R.D., N.D., K.D., M.G., F.S.G., H.J.G., A.C.H., A.M.v.H., I.B.H., M.I., S.K., J. Krogh, D.F.L., S.L., D.J.M., D.F.M., P.A.F.M., W.M., N.G.M., P. McGrath, P. McGuffin, A.M.M., A.M., C.M.M., S.S.M., F.M.M., O.M., P.B.M., D.R.N., H.O., M.J.O., C.B.P., M.G.P., J.B.P., J.A.Q., J.P.R., M. Rietschel, C.S., R. Schoevers, E.S., G.C.B.S., D.J.S., F.S., J. Strohmaier, D.U., M.M.W., J.W., T.W., G.W. Genomic assays: G.B., H.N.B., J.B.-G., M.B.-H., A.D.B., S.C., T.-K.C., F.D., A.J.F., S.P.H., C.S.H., A.C.H., P.H., G.H., C. Horn, J.A.K., P.A.F.M., L.M., G.W.M., M. Nauck, M.M.N., M. Rietschel, M. Rivera, E.C.S., T.G.S., S.I.S., H.S., F.S., T.E.T., J.T., A.G.U., S.H.W. Obtained funding for primary MDD samples: B.T.B., K.B., D.H.R.B., D.I.B., G.B., H.N.B., A.D.B., S.C., J.R.D., I.J.D., E.D., T.C.E., T.E., H.J.G., S.P.H., A.C.H., D.M.H., I.S.K., D.F.L., C.M.L., G.L., Q.S.L., S.L., P.A.F.M., W.M., N.G.M., P. McGuffin, A.M.M., A.M., G.W.M., O.M., P.B.M., M. Nordentoft, D.R.N., M.M.N., P.F.O’R., B.W.P., D.J.P., J.B.P., M.P., M. Rietschel, C.S., T.G.S., G.C.B.S., J.H.S., D.J.S., H.S., K.S., P.F.S., T.E.T., H. Tiemeier, A.G.U., H.V., M.M.W., T.W., N.R.W. Statistical analysis: 23andMe Research Team, A.A., M.J.A., T.F.M.A., S.V.d.A., S.-A.B., K.B., T.B.B., G.B., E.M.B., A.D.B., N. Cai, T.-K.C., J.R.I.C., B.C.-D., H.S.D., G.D., N.D., C.V.D., E.C.D., N.E., V.E.-P., T.E., H.K.F., J.F., H.A.G., S.D.G., J.G., L.S.H., C. Hayward, A.C.H., S.H., D.A.H., J.-J.H., C.L.H., M.I., E.J., F.F.H.K., J. Kraft, W.W.K., Z.K., J.M.L., C.M.L., Q.S.L., Yun Li, D.J.M., P.A.F.M., R.M.M., J. Marchini, M.M., H.M., A.M.M., S.E.M., D.M., E.M., Y.M., S.S.M., S.M., N.M., B.M.-M., B.N., M.G.N., D.R.N., P.F.O’R., R.E.P., E.P., W.J.P., G.P., D.P., S.M.P., B.P.R., S.R., M. Rivera, R. Saxena, C.S., L.S., J. Shi, S.I.S., H.S., S.S., P.F.S., K.E.T., H. Teismann, A.T., W.T., P.A.T., T.E.T., C.T., M. Traylor, V.T., M. Trzaskowski, A.V., P.M.V., Y. Wang, B.T.W., J.W., T.W., N.R.W., Y. Wu, J.Y., F.Z.

Correspondence to Naomi R. Wray or Patrick F. Sullivan.

Ethics declarations

Competing interests

A.T.F.B. is on speaker’s bureaus for Lundbeck and GlaxoSmithKline. G.C. is a cofounder of Element Genomics. E.D. was an employee of Hoffmann–La Roche at the time this study was conducted and a consultant to Roche and Pierre-Fabre. N.E. is employed by 23andMe, Inc., and owns stock in 23andMe, Inc. D.H. is an employee of and owns stock options in 23andMe, Inc. S.P. is an employee of Pfizer, Inc. C.L.H. is an employee of Pfizer, Inc. A.R.W. was a former employee and stockholder of Pfizer, Inc. J.A.Q. was an employee of Hoffmann–La Roche at the time this study was conducted. H.S. is an employee of deCODE Genetics/Amgen. K.S. is an employee of deCODE Genetics/Amgen. S.S. is an employee of deCODE Genetics/Amgen. P.F.S. is on the scientific advisory board for Pfizer, Inc., and the advisory committee for Lundbeck. T.E.T. is an employee of deCODE Genetics/Amgen. C.T. is an employee of and owns stock options in 23andMe, Inc.

Additional information

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

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Note

Reporting Summary

Supplementary Tables

Supplementary Tables 1–15

Supplementary Data 1

Regional association plots of the 44 regions with genome-wide significant loci associated with major depression. Association test from meta-analysis of 135,458 major depression cases and 344,901 controls.

Supplementary Data 2

Regional association plots of genomic regions identified from SMR analysis of major depression genome-wide association and eQTL results. SMR analysis helps to prioritize specific genes in a region of association for follow-up functional studies. Figures appear in the same order as the results reported in Supplementary Table 9. In the top plot, gray dots represent the major depression genome-wide association P values, diamonds show P values for probes from the SMR test, and triangles are probes without a cis-eQTL (at PeQTL < 5 × 10–8). Genes that pass SMR and heterogeneity tests (designed to remove loci with more than one causal association) are highlighted in red. The eQTL P values of SNPs are shown in the bottom plot.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wray, N.R., Ripke, S., Mattheisen, M. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50, 668–681 (2018) doi:10.1038/s41588-018-0090-3

Download citation