1932

Abstract

In the second half of the twentieth century, twin and family studies established beyond a reasonable doubt that all forms of psychopathology are substantially heritable and highly polygenic. These conclusions were simultaneously an important theoretical advance and a difficult methodological obstacle, as it became clear that heritability is universal and undifferentiated across forms of psychopathology, and the radical polygenicity of genetic effects limits the biological insight provided by genetically informed studies at the phenotypic level. The paradigm-shifting revolution brought on by the Human Genome Project has recapitulated the great methodological promise and the profound theoretical difficulties of the twin study era. We review these issues using the rubric of genetic architecture, which we define as a search for specific genetic insight that adds to the general conclusion that psychopathology is heritable and polygenic. Although significant problems remain, we see many promising avenues for progress.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-clinpsy-081219-091234
2022-05-09
2024-12-04
Loading full text...

Full text loading...

/deliver/fulltext/clinpsy/18/1/annurev-clinpsy-081219-091234.html?itemId=/content/journals/10.1146/annurev-clinpsy-081219-091234&mimeType=html&fmt=ahah

Literature Cited

  1. Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ et al. 2015. The PsychENCODE project. Nat. Neurosci. 18:121707–12
    [Google Scholar]
  2. Assary E, Vincent JP, Keers R, Pluess M. 2018. Gene–environment interaction and psychiatric disorders: review and future directions. Semin. Cell Dev. Biol. 77:133–43
    [Google Scholar]
  3. Balbona JV, Kim Y, Keller MC 2021. Estimation of parental effects using polygenic scores. Behav. Genet. 51:3264–78
    [Google Scholar]
  4. Barr PB, Ksinan A, Su J, Johnson EC, Meyers JL et al. 2020. Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples. Transl. Psychiatry 10:196
    [Google Scholar]
  5. Border R, Johnson EC, Evans LM, Smolen A, Berley N 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 176:5376–87
    [Google Scholar]
  6. Brainstorm Consort Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK et al. 2018. Analysis of shared heritability in common disorders of the brain. Science 360:6395eaap8757
    [Google Scholar]
  7. Castle WE. 1918. Continuation of experimental studies of heredity in small mammals. Yearbook Carnegie Inst. Wash. 17:320
    [Google Scholar]
  8. Cong L, Ran FA, Cox D, Lin S, Barretto R et al. 2013. Multiplex genome engineering using CRISPR/Cas systems. Science 339:6121819–23
    [Google Scholar]
  9. CONVERGE Consort 2015. Sparse whole genome sequencing identifies two loci for major depressive disorder. Nature 523:7562588–91
    [Google Scholar]
  10. Cross-Disord. Group Psychiatr. Genom. Consort 2019. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179:71469–82.e11
    [Google Scholar]
  11. Crow TJ. 2011. The missing genes: What happened to the heritability of psychiatric disorders?. Mol. Psychiatry4362–64
    [Google Scholar]
  12. Dekker J, Belmont AS, Guttman M, Leshyk VO, Lis JT et al. 2017. The 4D Nucleome project. Nature 549:7671219–26
    [Google Scholar]
  13. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD et al. 2019. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51:63–75
    [Google Scholar]
  14. Dickinson D, Zaidman SR, Giangrande EJ, Eisenberg DP, Gregory MD, Berman KF 2019. Distinct polygenic score profiles in schizophrenia subgroups with different trajectories of cognitive development. Am. J. Psychiatry 177:4298–307
    [Google Scholar]
  15. Dobzhansky T. 1955. A review of some fundamental concepts and problems of population genetics. Cold Spring Harb. Symp. Quant. Biol. 20:1–15
    [Google Scholar]
  16. Doherty JL, Owen MJ. 2014. Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice. Genome Med 6:429
    [Google Scholar]
  17. Faraone SV, Tsuang MT. 1985. Quantitative models of the genetic transmission of schizophrenia. Psychol. Bull. 98:141–66
    [Google Scholar]
  18. Frank J, Lang M, Witt SH, Strohmaier J, Rujescu D et al. 2015. Identification of increased genetic risk scores for schizophrenia in treatment-resistant patients. Mol. Psychiatry 20:2150–51
    [Google Scholar]
  19. Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G et al. 2018a. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359:6376693–97
    [Google Scholar]
  20. Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C et al. 2018b. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362:6420eaat8127
    [Google Scholar]
  21. García-González J, Tansey KE, Hauser J, Henigsberg N, Maier W et al. 2017. Pharmacogenetics of antidepressant response: a polygenic approach. Prog. Neuropsychopharmacol. Biol. Psychiatry 75:128–34
    [Google Scholar]
  22. Grove J, Ripke S, Als TD, Mattheisen M, Walters RK et al. 2019. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51:3431–44
    [Google Scholar]
  23. Hernandez LM, Kim M, Hoftman GD, Haney JR, de la Torre-Ubieta L et al. 2021. Transcriptomic insight into the polygenic mechanisms underlying psychiatric disorders. Biol. Psychiatry 89:154–64
    [Google Scholar]
  24. Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J et al. 2019. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22:3343–52
    [Google Scholar]
  25. Jonas KG, Lencz T, Li K, Malhotra AK, Perlman G et al. 2019. Schizophrenia polygenic risk score and 20-year course of illness in psychotic disorders. Transl. Psychiatry 9:300
    [Google Scholar]
  26. Jourdon A, Scuderi S, Capauto D, Abyzov A, Vaccarino FM 2020. PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids. Neuropsychopharmacology 46:170–85
    [Google Scholar]
  27. Kendall KM, Rees E, Bracher-Smith M, Legge S, Riglin L et al. 2019. Association of rare copy number variants with risk of depression. JAMA Psychiatry 76:8818–25
    [Google Scholar]
  28. Kendler KS, Neale MC. 2010. Endophenotype: a conceptual analysis. Mol. Psychiatry 15:789–97
    [Google Scholar]
  29. Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI et al. 2018. The nature of nurture: effects of parental genotypes. Science 359:6374424–28
    [Google Scholar]
  30. Kotov R, Waszczuk MA, Krueger RF, Forbes MK, Watson D et al. 2017. The hierarchical taxonomy of psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J. Abnorm. Psychol. 126:4454–77
    [Google Scholar]
  31. Kowalec K, Lu Y, Sariaslan A, Song J, Ploner A et al. 2021. Increased schizophrenia family history burden and reduced premorbid IQ in treatment-resistant schizophrenia: a Swedish National Register and Genomic Study. Mol. Psychiatry 26:4487–95
    [Google Scholar]
  32. Larsson H, Rydén E, Boman M, Långström N, Lichtenstein P, Landén M. 2013. Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder. Br. J. Psychiatry 203:2103–6
    [Google Scholar]
  33. Legge SE, Dennison CA, Pardiñas AF, Rees E, Lynham AJ et al. 2020. Clinical indicators of treatment-resistant psychosis. Br. J. Psychiatry 216:5259–66
    [Google Scholar]
  34. Levey DF, Stein MB, Wendt FR, Pathak GA, Zhou H et al. 2020a. GWAS of depression phenotypes in the Million Veteran Program and meta-analysis in more than 1.2 million participants yields 178 independent risk loci. MedRxiv 2020.05.18.20100685. https://doi.org/10.1101/2020.05.18.20100685
    [Crossref]
  35. Levey DF, Gelernter J, Polimanti R, Zhou H, Cheng Z et al. 2020b. Reproducible genetic risk loci for anxiety: results from ∼200,000 participants in the Million Veteran Program. Am. J. Psychiatry 177:3223–32
    [Google Scholar]
  36. Lewis CM, Vassos E. 2020. Polygenic risk scores: from research tools to clinical instruments. Genome Med 12:44
    [Google Scholar]
  37. Liu C-C, Kanekiyo T, Xu H, Bu G. 2013. Apolipoprotein E and Alzheimer disease: risk, mechanisms, and therapy. Nat. Rev. Neurol. 9:210618
    [Google Scholar]
  38. Mackay TFC. 2001. The genetic architecture of quantitative traits. Annu. Rev. Genet. 35:303–39
    [Google Scholar]
  39. Marshall CR, Howrigan DP, Merico D, Thiruvahindrapuram B, Wu W et al. 2017. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat. Genet. 49:127–35
    [Google Scholar]
  40. Martin AK, Mowry B. 2016. Increased rare duplication burden genomewide in patients with treatment-resistant schizophrenia. Psychol. Med. 46:3469–76
    [Google Scholar]
  41. Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM et al. 2017. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100:4635–49
    [Google Scholar]
  42. Matoba N, Liang D, Sun H, Aygün N, McAfee JC et al. 2020. Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. Transl. Psychiatry 10:265
    [Google Scholar]
  43. Matthews LJ, Turkheimer E. 2019. Across the great divide: pluralism and the hunt for missing heritability. Synthese 198:2297–311
    [Google Scholar]
  44. McDonald-McGinn DM, Sullivan KE, Marino B, Philip N et al. 2015. 22q11.2 deletion syndrome. Nat. Rev. Dis. Primers 1:15071
    [Google Scholar]
  45. Meier SM, Agerbo E, Maier R, Pedersen CB, Lang M et al. 2016. High loading of polygenic risk in cases with chronic schizophrenia. Mol. Psychiatry 21:7969–74
    [Google Scholar]
  46. Mullins N, Forstner AJ, O'Connell KS, Coombes B, Coleman JRI et al. 2021. Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology. MedRxiv 2020.09.17.20187054. https://doi.org/10.1101/2020.09.17.20187054
    [Crossref]
  47. Natarajan P, Young R, Stitziel NO, Padmanabhan S, Baber U et al. 2017. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135:222091–101
    [Google Scholar]
  48. Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen C-Y et al. 2019. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat. Commun. 10:4558
    [Google Scholar]
  49. Parikshak NN, Gandal MJ, Geschwind DH. 2015. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet. 16:8441–58
    [Google Scholar]
  50. Perkins DO, Loohuis LO, Barbee J, Ford J, Jeffries CD et al. 2019. Polygenic risk score contribution to psychosis prediction in a target population of persons at clinical high risk. Am. J. Psychiatry 177:2155–63
    [Google Scholar]
  51. Pinto D, Delaby E, Merico D, Barbosa M, Merikangas A et al. 2014. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94:5677–94
    [Google Scholar]
  52. Plomin R, Crabbe J. 2000. DNA. Psychol. Bull. 126:6806–28
    [Google Scholar]
  53. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A et al. 2015. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47:7702–9
    [Google Scholar]
  54. Rees E, Kirov G. 2021. Copy number variation and neuropsychiatric illness. Curr. Opin. Genet. Dev. 68:57–63
    [Google Scholar]
  55. Ruzzo EK, Pérez-Cano L, Jung JY, Wang L-K, Kashef-Haghighi D et al. 2019. Inherited and de novo genetic risk for autism impacts shared networks. Cell 178:4850–66.e26
    [Google Scholar]
  56. Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE et al. 2015. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87:61215–33
    [Google Scholar]
  57. Sanders SJ, Neale BM, Huang H, Werling DM, An JY et al. 2017. Whole genome sequencing in psychiatric disorders: the WGSPD Consortium. Nat. Neurosci. 20:121661–68
    [Google Scholar]
  58. Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S et al. 2020. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180:3568–84.e23
    [Google Scholar]
  59. Satterstrom FK, Walters RK, Singh T, Wigdor EM, Lescai F et al. 2019. Autism spectrum disorder and attention deficit hyperactivity disorder have a similar burden of rare protein-truncating variants. Nat. Neurosci. 22:121961–65
    [Google Scholar]
  60. Schizophr. Work. Group Psychiatr. Genom. Consort., Ripke S, Walters JT, O'Donovan MC 2020. Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. MedRxiv 2020.09.12.20192922. https://doi.org/10.1101/2020.09.12.20192922
    [Crossref]
  61. Singh T, Neale BM, Daly MJ, SCHEMA Consort. 2020. Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia. MedRxiv 2020.09.18.20192815
  62. Sul JH, Service SK, Huang AY, Ramensky V, Hwang S-G et al. 2020. Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates. Transl. Psychiatry 10:74
    [Google Scholar]
  63. Sullivan PF, Geschwind DH. 2019. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell 177:1162–83
    [Google Scholar]
  64. Sullivan PF, Magnusson C, Reichenberg A, Boman M, Dalman C et al. 2012. Family history of schizophrenia and bipolar disorder as risk factors for autism. Arch. Gen. Psychiatry 69:111099–103
    [Google Scholar]
  65. Tammimies K, Marshall CR, Walker S, Kaur G, Thiruvahindrapuram B et al. 2015. Molecular diagnostic yield of chromosomal microarray analysis and whole-exome sequencing in children with autism spectrum disorder. JAMA 314:9895–903
    [Google Scholar]
  66. Torkamani A, Wineinger NE, Topol EJ. 2018. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19:9581–90
    [Google Scholar]
  67. Turkheimer E. 1998. Heritability and biological explanation. Psychol. Rev. 105:4782–91
    [Google Scholar]
  68. Turkheimer E, Harden KP 2014. Behavior genetic research methods: testing quasi-causal hypotheses using multivariate twin data. Handbook of Research Methods in Social and Personality Psychology HT Reis, CM Judd 159–87 Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  69. Turkheimer E, Pettersson E, Horn EE 2014. A phenotypic null hypothesis for the genetics of personality. Annu. Rev. Psychol. 65:515–40
    [Google Scholar]
  70. Uffelmann E, Posthuma D. 2021. Emerging methods and resources for biological interrogation of neuropsychiatric polygenic signal. Biol. Psychiatry 89:141–53
    [Google Scholar]
  71. Vassos E, Di Forti M, Coleman J, Iyegbe C, Prata D et al. 2017. An examination of polygenic score risk prediction in individuals with first-episode psychosis. Biol. Psychiatry 81:6470–77
    [Google Scholar]
  72. Ward J, Graham N, Strawbridge RJ, Ferguson A, Jenkins G et al. 2018. Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: meta-analysis of three treatment cohorts. PLOS ONE 13:9e0203896
    [Google Scholar]
  73. Watson HJ, Yilmaz Z, Thornton LM, Hübel C, Coleman JRI et al. 2019. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51:81207–14
    [Google Scholar]
  74. Webber C. 2017. Epistasis in neuropsychiatric disorders. Trends Genet. 33:4256–65
    [Google Scholar]
  75. Wimberley T, Gasse C, Meier SM, Agerbo E, MacCabe JH, Horsdal HT. 2017. Polygenic risk score for schizophrenia and treatment-resistant schizophrenia. Schizophr. Bull. 43:51064–69
    [Google Scholar]
  76. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM et al. 2018. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50:5668–81
    [Google Scholar]
  77. Young AI. 2019. Solving the missing heritability problem. PLOS Genet. 15:6e1008222
    [Google Scholar]
  78. Yu D, Sul JH, Tsetsos F, Nawaz MS, Huang AY et al. 2019. Interrogating the genetic determinants of Tourette's syndrome and other tic disorders through genome-wide association studies. Am. J. Psychiatry 176:3217–27
    [Google Scholar]
  79. Yuen RKC, Merico D, Bookman M, L Howe J, Thiruvahindrapuram B et al. 2017. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat. Neurosci. 20:4602–11
    [Google Scholar]
  80. Zhang JP, Robinson D, Yu J, Gallego J, Fleischhacker WW et al. 2019. Schizophrenia polygenic risk score as a predictor of antipsychotic efficacy in first-episode psychosis. Am. J. Psychiatry 176:121–28
    [Google Scholar]
  81. Zhang X, Abdellaoui A, Rucker J, de Jong S, Potash JB et al. 2019. Genome-wide burden of rare short deletions is enriched in major depressive disorder in four cohorts. Biol. Psychiatry 85:121065–73
    [Google Scholar]
/content/journals/10.1146/annurev-clinpsy-081219-091234
Loading
/content/journals/10.1146/annurev-clinpsy-081219-091234
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error