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

Nature Neuroscience

Volumn 22, Issue 3, 2019, Pages 343-352

  Howard, David M ^a   Adams, Mark J ^a   Clarke, Toni Kim ^a   Hafferty, Jonathan D ^a   Gibson, Jude ^a   Shirali, Masoud ^a   Coleman, Jonathan R I ^b,c   Hagenaars, Saskia P ^b,c   Ward, Joey ^d   Wigmore, Eleanor M ^a   Alloza, Clara ^a   Shen, Xueyi ^a   Barbu, Miruna C ^a   Xu, Eileen Y ^a   Whalley, Heather C ^a   Marioni, Riccardo E ^a   Porteous, David J ^a   Davies, Gail ^a   Deary, Ian J ^a   Hemani, Gibran ^e   Berger, Klaus ^f   Teismann, Henning ^f   Rawal, Rajesh ^f   Arolt, Volker ^f   Baune, Bernhard T ^g   Dannlowski, Udo ^f   Domschke, Katharina ^h   Tian, Chao ⁱ   Hinds, David A ⁱ   Trzaskowski, Maciej ^j   Byrne, Enda M ^j   Ripke, Stephan ^k,l,m   Smith, Daniel J ^d   Sullivan, Patrick F ^n,o   Wray, Naomi R ^j   Breen, Gerome ^b,c   Lewis, Cathryn M ^b,c   McIntosh, Andrew M ^a   more..

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c SOUTH LONDON AND MAUDSLEY NHS FOUNDATION TRUST   (United Kingdom)

^d UNIVERSITY OF GLASGOW   (United Kingdom)

^e UNIVERSITY OF BRISTOL   (United Kingdom)

^f UNIVERSITY OF MÜNSTER   (Germany)

^g UNIVERSITY OF MELBOURNE   (Australia)

^h UNIVERSITY OF FREIBURG   (Germany)

ⁱ University of Freiburg ^*  (United States)

^j UNIVERSITY OF QUEENSLAND   (Australia)

^k CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^l BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^m MASSACHUSETTS GENERAL HOSPITAL   (United States)

ⁿ KAROLINSKA INSTITUTE   (Sweden)

^o UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BRAIN FUNCTION; DEPRESSION; GENE FUNCTION; GENE IDENTIFICATION; GENE INTERACTION; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC VARIABILITY; GENOME-WIDE ASSOCIATION STUDY; HERITABILITY; HUMAN; META ANALYSIS; NEUROTRANSMISSION; PREFRONTAL CORTEX; PRIORITY JOURNAL; SYNAPTIC TRANSMISSION; COHORT ANALYSIS; FEMALE; GENETIC PREDISPOSITION; GENETIC VARIATION; GENETICS; MAJOR DEPRESSION; MALE; METABOLISM; MULTIFACTORIAL INHERITANCE; SINGLE NUCLEOTIDE POLYMORPHISM;

COHORT STUDIES; DEPRESSION; DEPRESSIVE DISORDER, MAJOR; FEMALE; GENETIC PREDISPOSITION TO DISEASE; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MALE; MULTIFACTORIAL INHERITANCE; POLYMORPHISM, SINGLE NUCLEOTIDE; PREFRONTAL CORTEX;

EID: 85061198174     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/s41593-018-0326-7     Document Type: Article

References (85)

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).
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).
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).
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).
6. Hek, K. et al. A genome-wide association study of depressive symptoms. Biol. Psychiatry 73, 667–678 (2013).
7. CONVERGE consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).
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).
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).
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).
11. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).
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).
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).
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).
15. Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
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).
17. Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, 360 (2018).
18. Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018).
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).
20. Quarto, T. et al. Interaction between DRD2 variation and sound environment on mood and emotion-related brain activity. Neuroscience 341, 9–17 (2017).
21. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
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).
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).
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).
25. Fogel, B. L. et al. RBFOX1 regulates both splicing and transcriptional networks in human neuronal development. Hum. Mol. Genet. 21, 4171–4186 (2012).
26. Chang, H. et al. Further evidence of VRK2 rs2312147 associated with schizophrenia. World J. Biol. Psychiatry 17, 457–466 (2016).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
37. Ong, K. K. et al. Genetic variation in LIN28B is associated with the timing of puberty. Nat. Genet. 41, 729–733 (2009).
38. Yu, J. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 (2007).
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).
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).
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 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).
43. Munafò, M. R. & Araya, R. Cigarette smoking and depression: a question of causation. Br. J. Psychiatry 196, 425–426 (2010).
44. Bonci, A. & Hopf, F. W. The dopamine D2 receptor: new surprises from an old friend. Neuron 47, 335–338 (2005).
45. Bi, L.-L. et al. Amygdala NRG1-ErbB4 is critical for the modulation of anxiety-like behaviors. Neuropsychopharmacology 40, 974–986 (2015).
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).
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).
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).
49. Dunn, E. C. et al. Genetic determinants of depression: recent findings and future directions. Harv. Rev. Psychiatry 23, 1–18 (2015).
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).
51. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at 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).
53. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
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).
55. Allen, N. E., Sudlow, C., Peakman, T. & Collins, R. UK biobank data: come and get it. Sci. Transl. Med. 6, 224ed4 (2014).
56. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
57. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
58. Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 1–34 (2014).
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.
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).
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).
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).
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).
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).
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).
66. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
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).
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).
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).
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).
71. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
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).
73. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
74. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
75. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
76. Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).
77. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
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).
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).
80. Auton, A. et al. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
81. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).
82. The Gene Ontology Consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res. 45(D1), D331–D338 (2017).
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).
84. Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).
85. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).