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Genetic Architecture of Depression: Where Do We Stand Now?

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Major Depressive Disorder

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1305))

Abstract

The research of depression genetics has been occupied by historical candidate genes which were tested by candidate gene association studies. However, these studies were mostly not replicable. Thus, genetics of depression have remained elusive for a long time. As research moves from candidate gene association studies to GWAS, the hypothesis-free non-candidate gene association studies in genome-wide level, this trend will likely change. Despite the fact that the earlier GWAS of depression were not successful, the recent GWAS suggest robust findings for depression genetics. These altogether will catalyze a new wave of multidisciplinary research to pin down the neurobiology of depression.

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Abbreviations

5-HTTLPR:

Serotonin-transporter-linked polymorphic region

BDNF:

Brain-derived neurotrophic factor

COMT:

Catechol-O-methyltransferase

DSM-5:

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

GWAS:

Genome-wide association studies

LD:

Linkage disequilibrium

MAF:

Minor allele frequency

MD:

Major depression

PRS:

Polygenic risk score

RDoc:

research domain criteria

SNP:

Single-nucleotide polymorphism

TDT:

Transmission disequilibrium test

References

  1. Tatti R, Haley MS, Swanson OK, Tselha T, Maffei A (2017) Neurophysiology and regulation of the balance between excitation and inhibition in neocortical circuits. Biol Psychiatry 81:821–831. Elsevier

    Article  PubMed  Google Scholar 

  2. Li P, Legault J, Litcofsky KA (2014) Neuroplasticity as a function of second language learning: anatomical changes in the human brain. Cortex 58:301–324. Elsevier

    Article  PubMed  Google Scholar 

  3. Pittenger C, Duman RS (2008) Stress, depression, and neuroplasticity: a convergence of mechanisms. Neuropsychopharmacology 33:88–109. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  4. Wittchen H-U, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jönsson B et al (2011) The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol 21:655–679. Elsevier

    Article  CAS  PubMed  Google Scholar 

  5. Kessler RC, Chiu WT, Demler O, Walters EE (2005) Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 62:617–627. American Medical Association

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR et al (2003) The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. American Medical Association 289:3095–3105

    Article  PubMed  Google Scholar 

  7. Sagar R, Dandona R, Gururaj G, Dhaliwal RS, Singh A, Ferrari A et al (2020) The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990–2017. Lancet Psychiatry. Elsevier 7:148–161

    Article  Google Scholar 

  8. Lopez AD, Murray CCJL (1998) The global burden of disease, 1990–2020. Nat Med 4:1241–1243. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  9. Organization WH (2008) The global burden of disease: 2004 update. World Health Organization

    Google Scholar 

  10. Gold SM, O’Connor M, Gill R, Kern KC, Shi Y, Henry RG et al (2014) Detection of altered hippocampal morphology in multiple sclerosis-associated depression using automated surface mesh modeling. Hum Brain Mapp 35:30–37. Wiley Online Library

    Article  PubMed  Google Scholar 

  11. Yuan M, Fang Q, Liu G, Zhou M, Wu J, Pu C (2019) Risk factors for post–acute coronary syndrome depression: a meta-analysis of observational studies. J Cardiovasc Nurs LWW 34:60–70

    Article  Google Scholar 

  12. Sullivan PF, Neale MC, Kendler KS (2000) Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry 157:1552–1562. Am Psychiatric Assoc

    Article  CAS  PubMed  Google Scholar 

  13. Berton O, Nestler EJ (2006) New approaches to antidepressant drug discovery: beyond monoamines. Nat Rev Neurosci 7:137–151. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  14. Aydin O, Aydin PU, Arslan A (2019) Development of neuroimaging-based biomarkers in psychiatry. Front Psychiatry:159–195. Springer

    Google Scholar 

  15. Arslan A (2018) Application of neuroimaging in the diagnosis and treatment of depression. Underst Depress. Springer; pp 69–81

    Google Scholar 

  16. Arslan A (2015) Genes, brains, and behavior: imaging genetics for neuropsychiatric disorders. J Neuropsychiatry Clin Neurosci 27:81–92. Am Neuropsych Assoc

    Article  PubMed  Google Scholar 

  17. Arslan A (2015) The complexity of mental disorders. Period Eng Nat Sci 3

    Google Scholar 

  18. Flint J, Kendler KS (2014) The genetics of major depression. Neuron 81:484–503. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K et al (2010) Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. In: Am Psychiatric Assoc

    Google Scholar 

  20. Cai N, Bigdeli TB, Kretzschmar W, Li Y, Liang J, Song L et al (2015) Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523:588–591. Nature Publishing Group

    Article  CAS  Google Scholar 

  21. Yokoi F, Hiraishi H, Izuhara K (2003) Molecular cloning of a cDNA for the human phospholysine phosphohistidine inorganic pyrophosphate phosphatase. J Biochem 133:607–614. Oxford University Press

    Article  CAS  PubMed  Google Scholar 

  22. Cui L, Gong X, Tang Y, Kong L, Chang M, Geng H et al (2016) Relationship between the LHPP gene polymorphism and resting-state brain activity in major depressive disorder. Neural Plast Hindawi:2016

    Google Scholar 

  23. Cui L, Gong X, Chang M, Yin Z, Geng H, Song Y, et al 2019) Association of LHPP genetic variation (rs35936514) with structural and functional connectivity of hippocampal-corticolimbic neural circuitry. Brain Imaging Behav. Springer;1–9

    Google Scholar 

  24. Peng L, Yuan Z, Ling H, Fukasawa K, Robertson K, Olashaw N et al (2011) SIRT1 deacetylates the DNA methyltransferase 1 (DNMT1) protein and alters its activities. Mol Cell Biol Am Soc Microbiol 31:4720–4734

    Article  CAS  Google Scholar 

  25. Vaquero A, Scher M, Erdjument-Bromage H, Tempst P, Serrano L, Reinberg D (2007) SIRT1 regulates the histone methyl-transferase SUV39H1 during heterochromatin formation. Nature 450:440–444. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  26. Hallows WC, Yu W, Denu JM (2012) Regulation of glycolytic enzyme phosphoglycerate mutase-1 by Sirt1 protein-mediated deacetylation. J Biol Chem 287:3850–3858. ASBMB

    Article  CAS  PubMed  Google Scholar 

  27. Chang H-C, Guarente L (2014) SIRT1 and other sirtuins in metabolism. Trends Endocrinol Metab 25:138–145. Elsevier

    Article  CAS  PubMed  Google Scholar 

  28. Zakhary SM, Ayubcha D, Dileo JN, Jose R, Leheste JR, Horowitz JM et al (2010) Distribution analysis of deacetylase SIRT1 in rodent and human nervous systems. Anat Rec Adv Integr Anat Evol Biol 293:1024–1032. Wiley Online Library

    Article  CAS  Google Scholar 

  29. Ramadori G, Lee CE, Bookout AL, Lee S, Williams KW, Anderson J et al (2008) Brain SIRT1: anatomical distribution and regulation by energy availability. J Neurosci Soc Neuroscience 28:9989–9996

    Article  CAS  Google Scholar 

  30. Burton PR, Tobin MD, Hopper JL (2005) Key concepts in genetic epidemiology. Lancet 366:941–951. Elsevier

    Article  PubMed  Google Scholar 

  31. Teare MD, Barrett JH (2005) Genetic linkage studies. Lancet 366:1036–1044. Elsevier

    Article  CAS  Google Scholar 

  32. McIntosh AM, Sullivan PF, Lewis CM (2019) Uncovering the genetic architecture of major depression. Neuron 102:91–103. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cloninger CR, Van Eerdewegh P, Goate A, Edenberg HJ, Blangero J, Hesselbrock V et al (1998) Anxiety proneness linked to epistatic loci in genome scan of human personality traits. Am J Med Genet 81:313–317. Wiley Online Library

    Article  CAS  PubMed  Google Scholar 

  34. Holmans P, Weissman MM, Zubenko GS, Scheftner WA, Crowe RR, DePaulo MD Jr, Raymond J et al (2007) Genetics of recurrent early-onset major depression (GenRED): final genome scan report. Am J Psychiatry 164:248–258. Am Psychiatric Assoc

    Article  PubMed  Google Scholar 

  35. Camp NJ, Cannon-Albright LA (2005) Dissecting the genetic etiology of major depressive disorder using linkage analysis. Trends Mol Med 11:138–144. Elsevier

    Article  CAS  PubMed  Google Scholar 

  36. Kuo P, Neale MC, Riley BP, Patterson DG, Walsh D, Prescott CA et al (2007) A genome-wide linkage analysis for the personality trait neuroticism in the Irish affected sib-pair study of alcohol dependence. Am J Med Genet Part B Neuropsychiatr Genet 144:463–468. Wiley Online Library

    Article  CAS  Google Scholar 

  37. McGuffin P, Knight J, Breen G, Brewster S, Boyd PR, Craddock N et al (2005) Whole genome linkage scan of recurrent depressive disorder from the depression network study. Hum Mol Genet. Oxford University Press 14:3337–3345

    Article  CAS  PubMed  Google Scholar 

  38. Fullerton J, Cubin M, Tiwari H, Wang C, Bomhra A, Davidson S et al (2003) Linkage analysis of extremely discordant and concordant sibling pairs identifies quantitative-trait loci that influence variation in the human personality trait neuroticism. Am J Hum Genet 72:879–890. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Middeldorp CM, Sullivan PF, Wray NR, Hottenga J, de Geus EJC, van den Berg M et al (2009) Suggestive linkage on chromosome 2, 8, and 17 for lifetime major depression. Am J Med Genet Part B Neuropsychiatr Genet 150:352–358. Wiley Online Library

    Article  CAS  Google Scholar 

  40. Nash MW, Huezo-Diaz P, Williamson RJ, Sterne A, Purcell S, Hoda F et al (2004) Genome-wide linkage analysis of a composite index of neuroticism and mood-related scales in extreme selected sibships. Hum Mol Genet. Oxford University Press 13:2173–2182

    Article  CAS  PubMed  Google Scholar 

  41. Neale BM, Sullivan PF (2005) Kendler KS. A genome scan of neuroticism in nicotine dependent smokers. Am J Med Genet Part B Neuropsychiatr Genet 132:65–69. Wiley Online Library

    Article  Google Scholar 

  42. Nurnberger JI Jr, Foroud T, Flury L, Su J, Meyer ET, Hu K et al (2001) Evidence for a locus on chromosome 1 that influences vulnerability to alcoholism and affective disorder. Am J Psychiatry 158:718–724. Am Psychiatric Assoc

    Article  PubMed  Google Scholar 

  43. Wray NR, Middeldorp CM, Birley AJ, Gordon SD, Sullivan PF, Visscher PM et al (2008) Genome-wide linkage analysis of multiple measures of neuroticism of 2 large cohorts from Australia and the Netherlands. Arch Gen Psychiatry 65:649–658. American Medical Association

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zubenko GS, Maher B, Hughes IIIHB, Zubenko WN, Stiffler JS, Kaplan BB et al (2003) Genome-wide linkage survey for genetic loci that influence the development of depressive disorders in families with recurrent, early-onset, major depression. Am J Med Genet Part B Neuropsychiatr Genet 123:1–18. Wiley Online Library

    Article  Google Scholar 

  45. Douglas JA, Boehnke M, Lange K (2000) A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data. Am J Hum Genet 66:1287–1297. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ioannidis JPA, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG (2001) Replication validity of genetic association studies. Nat Genet 29:306–309. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  47. Sullivan PF (2007) Spurious genetic associations. Biol Psychiatry 61:1121–1126. Elsevier

    Article  CAS  PubMed  Google Scholar 

  48. Sullivan PF (2017) How good were candidate gene guesses in schizophrenia genetics? Biol Psychiatry 82:696. NIH Public Access

    Article  PubMed  PubMed Central  Google Scholar 

  49. Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF et al (2019) No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am J Psychiatry 176:376–387. Am Psychiatric Assoc

    Article  PubMed  PubMed Central  Google Scholar 

  50. Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H et al (2003) Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science (80- ). Am Assoc Adv Sci 301:386–389

    CAS  Google Scholar 

  51. Culverhouse RC, Bowes L, Breslau N, Nurnberger JI Jr, Burmeister M, Fergusson DM et al (2013) Protocol for a collaborative meta-analysis of 5-HTTLPR, stress, and depression. BMC Psychiatry 13:304. BioMed Central

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Fergusson DM, Horwood LJ, Miller AL, Kennedy MA (2011) Life stress, 5-HTTLPR and mental disorder: findings from a 30-year longitudinal study. Br J Psychiatry 198:129–135. Cambridge University Press

    Article  PubMed  PubMed Central  Google Scholar 

  53. Gordon J (2018) Towards a genomic psychiatry: recommendations of the genomics workgroup of the NAMHC. NIMH

    Google Scholar 

  54. Shen H, Liu Y, Liu P, Recker RR, Deng H (2005) Nonreplication in genetic studies of complex diseases—lessons learned from studies of osteoporosis and tentative remedies. J Bone Miner Res 20:365–376. Wiley Online Library

    Article  CAS  PubMed  Google Scholar 

  55. Simundic A-M, Nikolac N, Topic E (2009) Methodological issues in genetic association studies of inherited thrombophilia: original report of recent practice. Clin Appl Thromb 15:327–333. SAGE Publications Sage CA: Los Angeles CA

    Article  Google Scholar 

  56. Abou-Sleiman PM, Hanna MG, Wood NW (2006) Genetic association studies of complex neurological diseases. J Neurol Neurosurg Psychiatry 77:1302–1304. BMJ Publishing Group Ltd

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kocsis I, Györffy B, Németh É, Vásárhelyi B (2004) Examination of Hardy-Weinberg equilibrium in papers of Kidney International: an underused tool. Kidney Int 65:1956–1958. Elsevier

    Article  PubMed  Google Scholar 

  58. Saito YA, Talley NJ, De Andrade M, Petersen GM (2006) Case-control genetic association studies in gastrointestinal disease: review and recommendations. Am J Gastroenterol 101:1379–1389. LWW

    Article  CAS  PubMed  Google Scholar 

  59. Cordell HJ, Clayton DG (2005) Genetic association studies. Lancet 366:1121–1131. Elsevier

    Article  PubMed  Google Scholar 

  60. Lippi G, Favaloro EJ (2009) The missing link between genotype, phenotype and clinics. Biochem medica Biochem medica. Medicinska naklada 19:137–145

    CAS  Google Scholar 

  61. Simundic A-M (2010) Methodological issues of genetic association studies. Clin. Chem Lab Med 48:S115–S118. Walter de Gruyter

    Google Scholar 

  62. Bosker FJ, Hartman CA, Nolte IM, Prins BP, Terpstra P, Posthuma D et al (2011) Poor replication of candidate genes for major depressive disorder using genome-wide association data. Mol Psychiatry 16:516–532. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  63. López-León S, Janssens A, AMG-Z L, Del-Favero J, Claes SJ, Oostra BA et al (2008) Meta-analyses of genetic studies on major depressive disorder. Mol Psychiatry. Nat Publ Group 13:772–785

    Article  PubMed  CAS  Google Scholar 

  64. Arslan A (2018) Mapping the schizophrenia genes by neuroimaging: the opportunities and the challenges. Int J Mol Sci 19:219. Multidisciplinary Digital Publishing Institute

    Article  PubMed Central  CAS  Google Scholar 

  65. Gatt JM, Burton KLO, Williams LM, Schofield PR (2015) Specific and common genes implicated across major mental disorders: a review of meta-analysis studies. J Psychiatr Res 60:1–13. Elsevier

    Article  PubMed  Google Scholar 

  66. Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000) Association mapping in structured populations. Am J Hum Genet 67:170–181. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004. Wiley Online Library

    Article  CAS  PubMed  Google Scholar 

  68. Marchini J, Cardon LR, Phillips MS, Donnelly P (2004) The effects of human population structure on large genetic association studies. Nat Genet 36:512–517. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  69. Chen Y (2004) New approach to association testing in case-parent designs under informative parental missingness. Library 27:131–140. Genet Epidemiol Off Publ Int Genet Epidemiol Soc. Wiley Online

    Google Scholar 

  70. Hattersley AT, McCarthy MI (2005) What makes a good genetic association study? Lancet. Elsevier; 366:1315–1323

    Article  PubMed  Google Scholar 

  71. Palmer LJ, Cardon LR (2005) Shaking the tree: mapping complex disease genes with linkage disequilibrium. Lancet 366:1223–1234. Elsevier

    Article  CAS  PubMed  Google Scholar 

  72. McHugh ML (2008) Power analysis in research. Biochem medica Biochem medica. Medicinska naklada 18:263–274

    Google Scholar 

  73. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA et al (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. Elsevier 101:5–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Browning SR, Browning BL (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet 81:1084–1097. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34:816–834. Wiley Online Library

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  78. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279–1283. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Yang J, Wray NR, Visscher PM (2010) Comparing apples and oranges: equating the power of case-control and quantitative trait association studies. Genet Epidemiol Off Publ Int Genet Epidemiol Soc 34:254–257. Wiley Online Library

    Google Scholar 

  80. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. Oxford University Press 42:D1001–D1006

    Article  CAS  PubMed  Google Scholar 

  81. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE et al (2011) Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet 43:887. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Marigorta UM, Navarro A (2013) High trans-ethnic replicability of GWAS results implies common causal variants. PLoS Genet 9. Public Library of Science

    Google Scholar 

  83. Liu JZ, Mcrae AF, Nyholt DR, Medland SE, Wray NR, Brown KM et al (2010) A versatile gene-based test for genome-wide association studies. Am J Hum Genet 87:139–145. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Li M-X, Gui H-S, Kwan JSH, Sham PC (2011) GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet 88:283–293. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Yang J, Ferreira T, Morris AP, Medland SE, Consortium GI of AnT (GIANT). DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Madden PA, Heath AC, Martin NG, Montgomery GW et al (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex trai. Nat Genet 44:369–75

    Google Scholar 

  86. Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. Nature Publishing Group 38:203–208

    Article  CAS  PubMed  Google Scholar 

  87. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S, Freimer NB et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  89. Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Svishcheva GR, Axenovich TI, Belonogova NM, van Duijn CM, Aulchenko YS (2012) Rapid variance components–based method for whole-genome association analysis. Nat Genet 44:1166. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  91. Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat Genet 46:100. Nature Publishing Group

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Loh P-R, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM et al (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 47:284. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  94. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37:658–665. Wiley Online Library

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  96. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R et al (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47:1228. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Visscher PM, Hill WG, Wray NR (2008) Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet 9:255–266. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  99. Yang J, Lee T, Kim J, Cho M-C, Han B-G, Lee J-Y et al (2013) Ubiquitous polygenicity of human complex traits: genome-wide analysis of 49 traits in Koreans. PLoS Genet 9. Public Library of Science

    Google Scholar 

  100. Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH et al (2015) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet 47:1114. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Wray NR, Goddard ME, Visscher PM (2007) Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res 17:1520–1528. Cold Spring Harbor Lab

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Evans DM, Visscher PM, Wray NR (2009) Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum Mol Genet 18:3525–3531. Oxford University Press

    Article  CAS  PubMed  Google Scholar 

  103. Farh KK-H, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S et al (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518:337–343. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  104. Spain SL, Barrett JC (2015) Strategies for fine-mapping complex traits. Hum Mol Genet 24:R111–R119. Oxford University Press

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Gusev A, Lee SH, Trynka G, Finucane H, Vilhjálmsson BJ, Xu H et al (2014) Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am J Hum Genet 95:535–552. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. He X, Fuller CK, Song Y, Meng Q, Zhang B, Yang X et al (2013) Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am J Hum Genet 92:667–680. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Morris AP (2011) Transethnic meta-analysis of genomewide association studies. Genet Epidemiol.; Wiley Online Library 35:809–822

    Article  PubMed  PubMed Central  Google Scholar 

  109. Mullins N, Lewis CM (2017) Genetics of depression: progress at last. Curr Psychiatry Rep 19:43. Springer

    Article  PubMed  PubMed Central  Google Scholar 

  110. Cohen-Woods S, Craig IW, McGuffin P (2013) The current state of play on the molecular genetics of depression. Psychol Med 43:673–687. Cambridge University Press

    Article  CAS  PubMed  Google Scholar 

  111. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A et al (2018) eQTLGen; 23andMe; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50:668–681

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Pe’er I, Yelensky R, Altshuler D, Daly MJ (2008) Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol Off Publ Int Genet Epidemiol Soc 32:381–385. Wiley Online Library

    Google Scholar 

  113. Conrad DF, Jakobsson M, Coop G, Wen X, Wall JD, Rosenberg NA et al (2006) A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nat Genet 38:1251–1260. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  114. DeGiorgio M, Jakobsson M, Rosenberg NA (2009) Out of Africa: modern human origins special feature: explaining worldwide patterns of human genetic variation using a coalescent-based serial founder model of migration outward from Africa. Proc Natl Acad Sci U S A 106:16057–16062. National Academy of Sciences

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, Fung H-C et al (2008) Genotype, haplotype and copy-number variation in worldwide human populations. Nature 451:998–1003. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  116. Teo Y-Y, Small KS, Kwiatkowski DP (2010) Methodological challenges of genome-wide association analysis in Africa. Nat Rev Genet 11:149–160. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, Walenz BP et al (2007) The diploid genome sequence of an individual human. PLoS Biol 5. Public Library of Science

    Google Scholar 

  118. Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM, Breen G et al (2013) A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry 18:497. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  119. Wray NR, Pergadia ML, Blackwood DHR, Penninx B, Gordon SD, Nyholt DR et al (2012) Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Mol Psychiatry 17:36–48. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  120. Kohli MA, Lucae S, Saemann PG, Schmidt MV, Demirkan A, Hek K et al (2011) The neuronal transporter gene SLC6A15 confers risk to major depression. Neuron. Elsevier 70:252–265

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Quast C, Cuboni S, Bader D, Altmann A, Weber P, Arloth J et al (2013) Functional coding variants in SLC6A15, a possible risk gene for major depression. PLoS One 8. Public Library of Science

    Google Scholar 

  122. Power RA, Tansey KE, Buttenschøn HN, Cohen-Woods S, Bigdeli T, Hall LS et al (2017) Genome-wide association for major depression through age at onset stratification: major depressive disorder working group of the psychiatric genomics consortium. Biol Psychiatry. Elsevier 81:325–335

    Article  PubMed  PubMed Central  Google Scholar 

  123. Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR et al (2016) Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet 48:1031. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M et al (2019) Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 22:343. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A et al (2018) Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50:668. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Zuk O, Schaffner SF, Samocha K, Do R, Hechter E, Kathiresan S et al (2014) Searching for missing heritability: designing rare variant association studies. Proc Natl Acad Sci 111:E455–E464. National Acad Sciences

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Kendall KM, Rees E, Bracher-Smith M, Riglin L, Zammit S, O’donovan MC et al (2018) The role of rare copy number variants in depression. BioRxiv:378307. Cold Spring Harbor Laboratory

    Google Scholar 

  128. Zhang X, Abdellaoui A, Rucker J, de Jong S, Potash JB, Weissman MM et al (2019) Genome-wide burden of rare short deletions is enriched in major depressive disorder in four cohorts. Biol Psychiatry. Elsevier 85:1065–1073

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Maier RM, Visscher PM, Robinson MR, Wray NR (2018) Embracing polygenicity: a review of methods and tools for psychiatric genetics research. Psychol Med 48:1055–1067. Cambridge University Press

    Article  CAS  PubMed  Google Scholar 

  130. Ordóñez AE, Luscher ZI, Gogtay N (2016) Neuroimaging findings from childhood onset schizophrenia patients and their non-psychotic siblings. Schizophr Res [Internet]. [cited 2018 Dec 24];173:124–31. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25819937

  131. Nolte IM, Van Der Most PJ, Alizadeh BZ, PIW DB, Boezen HM, Bruinenberg M et al (2017) Missing heritability: is the gap closing? An analysis of 32 complex traits in the lifelines cohort study. Eur J Hum Genet. Nature Publishing Group 25:877–885

    Article  PubMed  PubMed Central  Google Scholar 

  132. Bosch-Presegué L, Vaquero A (2015) Sirtuin-dependent epigenetic regulation in the maintenance of genome integrity. FEBS J 282:1745–1767. Wiley Online Library

    Article  PubMed  CAS  Google Scholar 

  133. Okbay A, Baselmans BML, De Neve J-E, Turley P, Nivard MG, Fontana MA et al (2016) Genetic variants associated with subjective Well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet 48:624–633. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Wang Y, Ma T, Zhu Y-S, Chu X-F, Yao S, Wang H-F et al (2017) The KSR2-rs7973260 polymorphism is associated with metabolic phenotypes, but not psychological phenotypes, in Chinese elders. Genet Test Mol Biomarkers 21:416–421

    Article  CAS  PubMed  Google Scholar 

  135. Costanzo-Garvey DL, Pfluger PT, Dougherty MK, Stock JL, Boehm M, Chaika O et al (2009) KSR2 is an essential regulator of AMP kinase, energy expenditure, and insulin sensitivity. Cell Metab 10:366–378. Elsevier

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Vosberg DE, Leyton M, Flores C (2019) The Netrin-1/DCC guidance system: dopamine pathway maturation and psychiatric disorders emerging in adolescence. Mol Psychiatry:1–11. Nature Publishing Group

    Google Scholar 

  137. Muench C, Schwandt M, Jung J, Cortes CR, Momenan R, Lohoff FW (2018) The major depressive disorder GWAS-supported variant rs10514299 in TMEM161B-MEF2C predicts putamen activation during reward processing in alcohol dependence. Transl Psychiatry 8:1–10. Nature Publishing Group

    Article  CAS  Google Scholar 

  138. Corfield EC, Yang Y, Martin NG, Nyholt DR (2017) A continuum of genetic liability for minor and major depression. Transl Psychiatry 7:e1131–e1131. Nature Publishing Group

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Li M, Yue W (2018) VRK2, a candidate gene for psychiatric and neurological disorders. Mol neuropsychiatry 4:119–133. Karger Publishers

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Ni H, Xu M, Zhan G-L, Fan Y, Zhou H, Jiang H-Y et al (2018) The GWAS risk genes for depression may be actively involved in Alzheimer’s disease. J Alzheimer’s Dis 64:1149–1161. IOS Press

    Article  CAS  Google Scholar 

  141. Ripke S, Neale BM, Corvin A, Walters JTR, Farh K-H, Holmans PA et al (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427. Nature Publishing Group

    Article  CAS  PubMed Central  Google Scholar 

  142. Lo M-T, Hinds DA, Tung JY, Franz C, Fan C-C, Wang Y et al (2017) Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat Genet 49:152. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  143. Kakeda S, Watanabe K, Katsuki A, Sugimoto K, Ueda I, Igata N et al (2019) Genetic effects on white matter integrity in drug-naive patients with major depressive disorder: a diffusion tensor imaging study of 17 genetic loci associated with depressive symptoms. Neuropsychiatr Dis Treat 15:375. Dove Press

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Breiderhoff T, Christiansen GB, Pallesen LT, Vaegter C, Nykjaer A, Holm MM et al (2013) Sortilin-related receptor SORCS3 is a postsynaptic modulator of synaptic depression and fear extinction. PLoS One 8. Public Library of Science

    Google Scholar 

  145. Agoston Z, Heine P, Brill MS, Grebbin BM, Hau A-C, Kallenborn-Gerhardt W et al (2014) Meis2 is a Pax6 co-factor in neurogenesis and dopaminergic periglomerular fate specification in the adult olfactory bulb. Development 141:28–38. Oxford University Press for The Company of Biologists Limited

    Article  CAS  PubMed  Google Scholar 

  146. Louw JJ, Corveleyn A, Jia Y, Hens G, Gewillig M, Devriendt K (2015) MEIS2 involvement in cardiac development, cleft palate, and intellectual disability. Am J Med Genet Part A. Wiley Online Library 167:1142–1146

    Article  Google Scholar 

  147. Kaneko T, Minohara T, Shima S, Yoshida K, Fukuda A, Iwamori N et al (2019) A membrane protein, TMCO5A, has a close relationship with manchette microtubules in rat spermatids during spermiogenesis. Mol Reprod Dev 86:330–341. Wiley Online Library

    Article  CAS  PubMed  Google Scholar 

  148. Van der Auwera S, Peyrot WJ, Milaneschi Y, Hertel J, Baune B, Breen G et al (2018) Genome-wide gene-environment interaction in depression: a systematic evaluation of candidate genes: the childhood trauma working-group of PGC-MDD. Am J Med Genet Part B Neuropsychiatr Genet. Wiley Online Library 177:40–49

    Article  CAS  Google Scholar 

  149. Perez Y, Menascu S, Cohen I, Kadir R, Basha O, Shorer Z et al (2018) RSRC1 mutation affects intellect and behaviour through aberrant splicing and transcription, downregulating IGFBP3. Brain 141:961–970. Oxford University Press

    Article  PubMed  Google Scholar 

  150. Katsuki A, Kakeda S, Watanabe K, Igata R, Otsuka Y, Kishi T et al (2019) A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder. Neuropsychiatr Dis Treat 15:2425. Dove Press

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Ait-Lounis A, Baas D, Barras E, Benadiba C, Charollais A, Nlend RN et al (2007) Novel function of the ciliogenic transcription factor RFX3 in development of the endocrine pancreas. Diabetes 56:950–959. Am Diabetes Assoc

    Article  CAS  PubMed  Google Scholar 

  152. Bonnafe E, Touka M, AitLounis A, Baas D, Barras E, Ucla C et al (2004) The transcription factor RFX3 directs nodal cilium development and left-right asymmetry specification. Mol Cell Biol 24:4417–4427. Am Soc Microbiol

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Yang C-W, Cheng S-Y, Chang CL (2010) Subcellular localization and mRNA targets of a novel human RNA-binding protein, KIAA0020. AACR

    Google Scholar 

  154. Adams B, Dörfler P, Aguzzi A, Kozmik Z, Urbanek P, Maurer-Fogy I et al (1992) Pax-5 encodes the transcription factor BSAP and is expressed in B lymphocytes, the developing CNS, and adult testis. Genes Dev 6:1589–1607. Cold Spring Harbor Lab

    Article  CAS  PubMed  Google Scholar 

  155. Castillo-Lluva S, Tan CT, Daugaard M, Sorensen PHB, Malliri A (2013) The tumour suppressor HACE1 controls cell migration by regulating Rac1 degradation. Oncogene 32:1735–1742. Nature Publishing Group

    Article  CAS  PubMed  Google Scholar 

  156. Leinonen JT, Chen Y-C, Pennonen J, Lehtonen L, Junna N, Tukiainen T et al (2019) LIN28B affects gene expression at the hypothalamic-pituitary axis and serum testosterone levels. Sci Rep 9:1–13. Nature Publishing Group

    Article  CAS  Google Scholar 

  157. Guo Y, Chen Y, Ito H, Watanabe A, Ge X, Kodama T et al (2006) Identification and characterization of lin-28 homolog B (LIN28B) in human hepatocellular carcinoma. Gene 384:51–61. Elsevier

    Article  CAS  PubMed  Google Scholar 

  158. Küçük C, Hu X, Iqbal J, Gaulard P, Klinkebiel D, Cornish A et al (2013) HACE1 is a tumor suppressor gene candidate in natural killer cell neoplasms. Am J Pathol 182:49–55. Elsevier

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Unal-Aydin, P., Aydin, O., Arslan, A. (2021). Genetic Architecture of Depression: Where Do We Stand Now?. In: Kim, YK. (eds) Major Depressive Disorder. Advances in Experimental Medicine and Biology, vol 1305. Springer, Singapore. https://doi.org/10.1007/978-981-33-6044-0_12

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