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The polygenic architecture of schizophrenia — rethinking pathogenesis and nosology

Nature Reviews Neurology volume 16, pages 366–379 (2020)Cite this article

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Abstract

Schizophrenia is a severe psychiatric disorder with considerable morbidity and mortality. Although the past two decades have seen limited improvement in the treatment of schizophrenia, research into the genetic causes of this condition has made important advances that offer new insights into the aetiology of schizophrenia. This Review summarizes the evidence for a polygenic architecture of schizophrenia that involves a large number of risk alleles across the whole range of population frequencies. These genetic risk loci implicate biological processes related to neurodevelopment, neuronal excitability, synaptic function and the immune system in the pathogenesis of schizophrenia. Mathematical models also suggest a substantial overlap between schizophrenia and psychiatric, behavioural and cognitive traits, a situation that has implications for understanding its clinical epidemiology, psychiatric nosology and pathobiology. Looking ahead, further genetic discoveries are expected to lead to clinically relevant predictive approaches for identifying high-risk individuals, improved diagnostic accuracy, increased yield from drug development programmes and improved stratification strategies to address the heterogeneous disease course and treatment responses observed among affected patients.

Key points

  • Schizophrenia is characterized by ‘positive’ psychotic symptoms (including hallucinations and delusions) and ‘negative’ symptoms (including blunted affect, apathy and social impairment); this disorder is associated with considerable morbidity and mortality.

  • In the past decade, important advances have been made in our understanding of the genetics of schizophrenia.

  • The polygenic architecture of schizophrenia is accounted for by thousands of common genetic variants with small effect sizes and a few rare variants with large effect sizes.

  • These genetic risk variants implicate dysregulation of biological processes linked to neurodevelopment, neuronal excitability, synaptic function and the immune system in schizophrenia.

  • Genetic risk factors associated with schizophrenia transcend diagnostic boundaries and form a continuum with normal psychosocial traits, which challenges current psychiatric nosology.

  • Although increasingly larger sample sizes will accelerate the discovery of genetic variants, novel statistical methodologies could also improve the efficiency of analyses, render discoveries clinically relevant and facilitate precision medicine approaches.

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Fig. 1: The aetiology of schizophrenia and its relationship to other psychiatric disorders.
Fig. 2: A comparison of genetic overlap and genetic correlation.
Fig. 3: The proportions of causal variants shared between schizophrenia and other phenotypes.
Fig. 4: Statistical power calculations for current and future GWAS.

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References

  1. Charlson, F. J. et al. Global epidemiology and burden of schizophrenia: findings from the Global Burden of Disease study 2016. Schizophr. Bull. 44, 1195–1203 (2018).

    PubMed  PubMed Central  Google Scholar 

  2. Whiteford, H. A. et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382, 1575–1586 (2013).

    PubMed  Google Scholar 

  3. Laursen, T. M., Nordentoft, M. & Mortensen, P. B. Excess early mortality in schizophrenia. Annu. Rev. Clin. Psychol. 10, 425–448 (2014).

    PubMed  Google Scholar 

  4. Fusar-Poli, P. et al. Cognitive functioning in prodromal psychosis: a meta-analysis. Arch. Gen. Psychiatry 69, 562–571 (2012).

    PubMed  Google Scholar 

  5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5). (APA, 2013).

  6. World Health Organization. International classification of diseases for mortality and morbidity statistics (11th revision). https://icd.who.int/browse11/l-m/en. (WHO, 2018).

  7. Lieberman, J. A. et al. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N. Engl. J. Med. 353, 1209–1223 (2005).

    CAS  PubMed  Google Scholar 

  8. Kahn, R. S. & Keefe, R. S. Schizophrenia is a cognitive illness: time for a change in focus. JAMA Psychiatry 70, 1107–1112 (2013).

    PubMed  Google Scholar 

  9. Thornicroft, G. et al. Global pattern of experienced and anticipated discrimination against people with schizophrenia: a cross-sectional survey. Lancet 373, 408–415 (2009).

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  11. Demyttenaere, K. et al. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys. JAMA 291, 2581–2590 (2004).

    PubMed  Google Scholar 

  12. Gottesman, I. I. & Wolfgram, D. L. Schizophrenia Genesis: The Origins of Madness. (Freeman, 1991).

  13. Howes, O. D. & Kapur, S. The dopamine hypothesis of schizophrenia: version III — the final common pathway. Schizophr. Bull. 35, 549–562 (2009).

    PubMed  PubMed Central  Google Scholar 

  14. Moghaddam, B. & Javitt, D. From revolution to evolution: the glutamate hypothesis of schizophrenia and its implication for treatment. Neuropsychopharmacology 37, 4–15 (2012).

    CAS  PubMed  Google Scholar 

  15. Nakazawa, K. et al. GABAergic interneuron origin of schizophrenia pathophysiology. Neuropharmacology 62, 1574–1583 (2012).

    CAS  PubMed  Google Scholar 

  16. Lieberman, J. A. & First, M. B. Psychotic disorders. N. Engl. J. Med. 379, 270–280 (2018).

    CAS  PubMed  Google Scholar 

  17. Carlsson, A. & Lindqvist, M. Effect of chlorpromazine or haloperidol on formation of 3methoxytyramine and normetanephrine in mouse brain. Acta Pharmacol. Toxicol. 20, 140–144 (1963).

    CAS  Google Scholar 

  18. Carlsson, A., Lindqvist, M. & Magnusson, T. 3,4-Dihydroxyphenylalanine and 5-hydroxytryptophan as reserpine antagonists. Nature 180, 1200 (1957).

    CAS  PubMed  Google Scholar 

  19. Howes, O., McCutcheon, R. & Stone, J. Glutamate and dopamine in schizophrenia: an update for the 21st century. J. Psychopharmacol. 29, 97–115 (2015).

    PubMed  PubMed Central  Google Scholar 

  20. Javitt, D. C. & Zukin, S. R. Recent advances in the phencyclidine model of schizophrenia. Am. J. Psychiatry 148, 1301–1308 (1991).

    CAS  PubMed  Google Scholar 

  21. Krystal, J. H. et al. Subanesthetic effects of the noncompetitive NMDA antagonist, ketamine, in humans. Psychotomimetic, perceptual, cognitive, and neuroendocrine responses. Arch. Gen. Psychiatry 51, 199–214 (1994).

    CAS  PubMed  Google Scholar 

  22. Olney, J. W. & Farber, N. B. Glutamate receptor dysfunction and schizophrenia. Arch. Gen. Psychiatry 52, 998–1007 (1995).

    CAS  PubMed  Google Scholar 

  23. Dalmau, J. et al. An update on anti-NMDA receptor encephalitis for neurologists and psychiatrists: mechanisms and models. Lancet Neurol. 18, 1045–1057 (2019).

    CAS  PubMed  Google Scholar 

  24. Akbarian, S. et al. Gene expression for glutamic acid decarboxylase is reduced without loss of neurons in prefrontal cortex of schizophrenics. Arch. Gen. Psychiatry 52, 258–266 (1995).

    CAS  PubMed  Google Scholar 

  25. Lewis, D. A., Hashimoto, T. & Volk, D. W. Cortical inhibitory neurons and schizophrenia. Nat. Rev. Neurosci. 6, 312–324 (2005).

    CAS  PubMed  Google Scholar 

  26. Gonzalez-Burgos, G., Cho, R. Y. & Lewis, D. A. Alterations in cortical network oscillations and parvalbumin neurons in schizophrenia. Biol. Psychiatry 77, 1031–1040 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Paulus, M. P. & Thompson, W. K. The challenges and opportunities of small effects: the new normal in academic psychiatry. JAMA Psychiatry 76, 353–354 (2019).

    PubMed  Google Scholar 

  28. Murray, R. M. & Lewis, S. W. Is schizophrenia a neurodevelopmental disorder? Br. Med. J. 295, 681–682 (1987).

    CAS  Google Scholar 

  29. Weinberger, D. R. Implications of normal brain development for the pathogenesis of schizophrenia. Arch. Gen. Psychiatry 44, 660–669 (1987).

    CAS  PubMed  Google Scholar 

  30. Howes, O. D. & Murray, R. M. Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet 383, 1677–1687 (2014).

    PubMed  Google Scholar 

  31. Insel, T. R. Rethinking schizophrenia. Nature 468, 187–193 (2010).

    CAS  PubMed  Google Scholar 

  32. Glantz, L. A. & Lewis, D. A. Decreased dendritic spine density on prefrontal cortical pyramidal neurons in schizophrenia. Arch. Gen. Psychiatry 57, 65–73 (2000).

    CAS  PubMed  Google Scholar 

  33. Glausier, J. R. & Lewis, D. A. Dendritic spine pathology in schizophrenia. Neuroscience 251, 90–107 (2013).

    CAS  PubMed  Google Scholar 

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

    PubMed Central  Google Scholar 

  35. Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Stevens, B. et al. The classical complement cascade mediates CNS synapse elimination. Cell 131, 1164–1178 (2007).

    CAS  PubMed  Google Scholar 

  38. Salter, M. W. & Stevens, B. Microglia emerge as central players in brain disease. Nat. Med. 23, 1018–1027 (2017).

    CAS  PubMed  Google Scholar 

  39. Miller, B. J., Buckley, P., Seabolt, W., Mellor, A. & Kirkpatrick, B. Meta-analysis of cytokine alterations in schizophrenia: clinical status and antipsychotic effects. Biol. Psychiatry 70, 663–671 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Pillinger, T. et al. A meta-analysis of immune parameters, variability, and assessment of modal distribution in psychosis and test of the immune subgroup hypothesis. Schizophr. Bull. 45, 1120–1133 (2019).

    PubMed  Google Scholar 

  41. Haijma, S. V. et al. Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr. Bull. 39, 1129–1138 (2013).

    PubMed  Google Scholar 

  42. van Erp, T. G. M. et al. Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) Consortium. Biol. Psychiatry 84, 644–654 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. van Erp, T. G. et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatry 21, 547–553 (2016).

    PubMed  Google Scholar 

  44. Alnaes, D. et al. Brain heterogeneity in schizophrenia and its association with polygenic risk. JAMA Psychiatry 76, 739–748 (2019).

    PubMed  PubMed Central  Google Scholar 

  45. Fusar-Poli, P. et al. Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies. Neurosci. Biobehav. Rev. 37, 1680–1691 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Cannon, T. D. et al. Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol. Psychiatry 77, 147–157 (2015).

    PubMed  Google Scholar 

  47. Ho, B. C., Andreasen, N. C., Ziebell, S., Pierson, R. & Magnotta, V. Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch. Gen. Psychiatry 68, 128–137 (2011).

    PubMed  PubMed Central  Google Scholar 

  48. Brown, A. S. & Derkits, E. J. Prenatal infection and schizophrenia: a review of epidemiologic and translational studies. Am. J. Psychiatry 167, 261–280 (2010).

    PubMed  PubMed Central  Google Scholar 

  49. Cannon, M., Jones, P. B. & Murray, R. M. Obstetric complications and schizophrenia: historical and meta-analytic review. Am. J. Psychiatry 159, 1080–1092 (2002).

    PubMed  Google Scholar 

  50. Khandaker, G. M., Zimbron, J., Dalman, C., Lewis, G. & Jones, P. B. Childhood infection and adult schizophrenia: a meta-analysis of population-based studies. Schizophr. Res. 139, 161–168 (2012).

    PubMed  PubMed Central  Google Scholar 

  51. van Os, J., Kenis, G. & Rutten, B. P. The environment and schizophrenia. Nature 468, 203–212 (2010).

    PubMed  Google Scholar 

  52. Cantor-Graae, E. & Selten, J. P. Schizophrenia and migration: a meta-analysis and review. Am. J. Psychiatry 162, 12–24 (2005).

    PubMed  Google Scholar 

  53. Kraepelin, E., Barclay, R. M. & Robertson, G. M. Dementia præcox and paraphrenia. (E. & S. Livingstone, 1919).

  54. Sullivan, P. F., Kendler, K. S. & Neale, M. C. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch. Gen. Psychiatry 60, 1187–1192 (2003).

    PubMed  Google Scholar 

  55. Hilker, R. et al. Heritability of schizophrenia and schizophrenia spectrum based on the Nationwide Danish twin register. Biol. Psychiatry 83, 492–498 (2018).

    PubMed  Google Scholar 

  56. Lichtenstein, P. et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 373, 234–239 (2009).

    CAS  PubMed  Google Scholar 

  57. Hall, J. M. et al. Linkage of early-onset familial breast cancer to chromosome 17q21. Science 250, 1684–1689 (1990).

    CAS  PubMed  Google Scholar 

  58. Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).

    CAS  PubMed  Google Scholar 

  59. Ng, M. Y. et al. Meta-analysis of 32 genome-wide linkage studies of schizophrenia. Mol. Psychiatry 14, 774–785 (2009).

    CAS  PubMed  Google Scholar 

  60. Lewis, C. M. et al. Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am. J. Hum. Genet. 73, 34–48 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Corder, E. H. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261, 921–923 (1993).

    CAS  PubMed  Google Scholar 

  62. Gatt, J. M., Burton, K. L., Williams, L. M. & Schofield, P. R. Specific and common genes implicated across major mental disorders: a review of meta-analysis studies. J. Psychiatr. Res. 60, 1–13 (2015).

    PubMed  Google Scholar 

  63. Psychiatric GWAS Consortium Steering Committee. A framework for interpreting genome-wide association studies of psychiatric disorders. Mol. Psychiatry 14, 10–17 (2009).

    Google Scholar 

  64. Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45, 1150–1159 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Li, Z. et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49, 1576–1583 (2017).

    CAS  PubMed  Google Scholar 

  66. Pardinas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat. Genet. 43, 969–976 (2011).

    Google Scholar 

  68. Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Manolio, T. A. et al. New models of collaboration in genome-wide association studies: the Genetic Association Information Network. Nat. Genet. 39, 1045–1051 (2007).

    CAS  PubMed  Google Scholar 

  70. O’Donovan, M. C. et al. Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat. Genet. 40, 1053–1055 (2008).

    PubMed  Google Scholar 

  71. International Schizophrenia, C. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    Google Scholar 

  72. Stefansson, H. et al. Common variants conferring risk of schizophrenia. Nature 460, 744–747 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Lee, S. H. et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat. Genet. 44, 247–250 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat. Commun. 10, 2417 (2019).

    PubMed  PubMed Central  Google Scholar 

  77. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    PubMed  PubMed Central  Google Scholar 

  79. Speed, D. & Balding, D. J. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat. Genet. 51, 277–284 (2019).

    CAS  PubMed  Google Scholar 

  80. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Devor, A. et al. Genetic evidence for role of integration of fast and slow neurotransmission in schizophrenia. Mol. Psychiatry 22, 792–801 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Lips, E. S. et al. Functional gene group analysis identifies synaptic gene groups as risk factor for schizophrenia. Mol. Psychiatry 17, 996–1006 (2012).

    CAS  PubMed  Google Scholar 

  84. Wang, Y. et al. Leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia GWAS. PLoS Genet. 12, e1005803 (2016).

    PubMed  PubMed Central  Google Scholar 

  85. Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Verkerk, A. J. et al. Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell 65, 905–914 (1991).

    CAS  PubMed  Google Scholar 

  87. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J. & Visscher, P. M. Common disease is more complex than implied by the core gene omnigenic model. Cell 173, 1573–1580 (2018).

    CAS  PubMed  Google Scholar 

  89. Schrode, N. et al. Synergistic effects of common schizophrenia risk variants. Nat. Genet. 51, 1475–1485 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  91. Schork, A. J. et al. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 9, e1003449 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Roussos, P. et al. A role for noncoding variation in schizophrenia. Cell Rep. 9, 1417–1429 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Flint, J. & Ideker, T. The great hairball gambit. PLoS Genet. 15, e1008519 (2019).

    PubMed  PubMed Central  Google Scholar 

  94. Maki-Marttunen, T. et al. Biophysical psychiatry – how computational neuroscience can help to understand the complex mechanisms of mental disorders. Front. Psychiatry 10, 534 (2019).

    PubMed  PubMed Central  Google Scholar 

  95. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    CAS  PubMed  Google Scholar 

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

    PubMed Central  Google Scholar 

  98. Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    Google Scholar 

  99. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715.e16 (2018).

    PubMed Central  Google Scholar 

  100. Andreassen, O. A. et al. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am. J. Hum. Genet. 92, 197–209 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Pasman, J. A. et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat. Neurosci. 21, 1161–1170 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Smeland, O. B. et al. Identification of genetic loci jointly influencing schizophrenia risk and the cognitive traits of verbal-numerical reasoning, reaction time, and general cognitive function. JAMA Psychiatry 74, 1065–1075 (2017).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Smeland, O. B. et al. Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence. Mol. Psychiatry 25, 844–853 (2020).

    CAS  PubMed  Google Scholar 

  107. Le Hellard, S. et al. Identification of gene loci that overlap between schizophrenia and educational attainment. Schizophr. Bull. 43, 654–664 (2017).

    PubMed  Google Scholar 

  108. Smeland, O. B. et al. Genetic overlap between schizophrenia and volumes of hippocampus, putamen, and intracranial volume indicates shared molecular genetic mechanisms. Schizophr. Bull. 44, 854–864 (2018).

    PubMed  Google Scholar 

  109. Lee, P. H. et al. Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia. Mol. Psychiatry 21, 1680–1689 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Ohi, K. et al. Genetic correlations between subcortical brain volumes and psychiatric disorders. Br. J. Psychiatry 216, 280–283 (2020).

    PubMed  Google Scholar 

  111. Power, R. A. et al. Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nat. Neurosci. 18, 953–955 (2015).

    CAS  PubMed  Google Scholar 

  112. Lo, M. T. et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat. Genet. 49, 152–156 (2017).

    CAS  PubMed  Google Scholar 

  113. Smeland, O. B. et al. Identification of genetic loci shared between schizophrenia and the Big Five personality traits. Sci. Rep. 7, 2222 (2017).

    PubMed  PubMed Central  Google Scholar 

  114. Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M. & Wray, N. R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Ikeda, M. et al. Re-evaluating classical body type theories: genetic correlation between psychiatric disorders and body mass index. Psychol. Med. 48, 1745–1748 (2018).

    PubMed  PubMed Central  Google Scholar 

  119. Bahrami, S. et al. Shared genetic loci between body mass index and major psychiatric disorders: a genome-wide association study. JAMA Psychiatry 77, 503–512 (2020).

    PubMed  Google Scholar 

  120. Green, M. F. Cognitive impairment and functional outcome in schizophrenia and bipolar disorder. J. Clin. Psychiatry 67(Suppl. 9), 3–8 (2006).

    PubMed  Google Scholar 

  121. Hill, W. D. et al. Age-dependent pleiotropy between general cognitive function and major psychiatric disorders. Biol. Psychiatry 80, 266–273 (2016).

    PubMed  PubMed Central  Google Scholar 

  122. Palmer, B. W. et al. Is it possible to be schizophrenic yet neuropsychologically normal? Neuropsychology 11, 437–446 (1997).

    CAS  PubMed  Google Scholar 

  123. Hibar, D. P. et al. Common genetic variants influence human subcortical brain structures. Nature 520, 224–229 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Franke, B. et al. Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept. Nat. Neurosci. 19, 420–431 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Holland, D. et al. Beyond SNP heritability: polygenicity and discoverability of phenotypes estimated with a univariate gaussian mixture model. PLoS Genet. https://doi.org/10.1371/journal.pgen.1008612 (2019).

  126. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Power, R. A. et al. Fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa, or substance abuse vs their unaffected siblings. JAMA Psychiatry 70, 22–30 (2013).

    PubMed  Google Scholar 

  128. Keller, M. C. & Miller, G. Resolving the paradox of common, harmful, heritable mental disorders: which evolutionary genetic models work best? Behav. Brain Sci. 29, 385–404 (2006).

    PubMed  Google Scholar 

  129. Jarvik, L. F. & Deckard, B. S. The Odyssean personality. A survival advantage for carriers of genes predisposing to schizophrenia? Neuropsychobiology 3, 179–191 (1977).

    CAS  PubMed  Google Scholar 

  130. Mullins, N. et al. Reproductive fitness and genetic risk of psychiatric disorders in the general population. Nat. Commun. 8, 15833 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Escott-Price, V. et al. The relationship between common variant schizophrenia liability and number of offspring in the UK Biobank. Am. J. Psychiatry 176, 661–666 (2019).

    PubMed  Google Scholar 

  132. Crow, T. J. Is schizophrenia the price that Homo sapiens pays for language? Schizophr. Res. 28, 127–141 (1997).

    CAS  PubMed  Google Scholar 

  133. Srinivasan, S. et al. Genetic markers of human evolution are enriched in schizophrenia. Biol. Psychiatry 80, 284–292 (2016).

    CAS  PubMed  Google Scholar 

  134. Xu, K., Schadt, E. E., Pollard, K. S., Roussos, P. & Dudley, J. T. Genomic and network patterns of schizophrenia genetic variation in human evolutionary accelerated regions. Mol. Biol. Evol. 32, 1148–1160 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Charlesworth, B. The effects of deleterious mutations on evolution at linked sites. Genetics 190, 5–22 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. McClellan, J. M., Susser, E. & King, M. C. Schizophrenia: a common disease caused by multiple rare alleles. Br. J. Psychiatry 190, 194–199 (2007).

    PubMed  Google Scholar 

  137. Marshall, C. R. et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat. Genet. 49, 27–35 (2017).

    CAS  PubMed  Google Scholar 

  138. Singh, T. et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat. Neurosci. 19, 571–577 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Steinberg, S. et al. Truncating mutations in RBM12 are associated with psychosis. Nat. Genet. 49, 1251–1254 (2017).

    CAS  PubMed  Google Scholar 

  140. Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N. & Geschwind, D. H. The road to precision psychiatry: translating genetics into disease mechanisms. Nat. Neurosci. 19, 1397–1407 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Singh, T. et al. The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability. Nat. Genet. 49, 1167–1173 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Sullivan, P. F. & Geschwind, D. H. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell 177, 162–183 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. Stefansson, H. et al. Large recurrent microdeletions associated with schizophrenia. Nature 455, 232–236 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Murphy, K. C., Jones, L. A. & Owen, M. J. High rates of schizophrenia in adults with velo-cardio-facial syndrome. Arch. Gen. Psychiatry 56, 940–945 (1999).

    CAS  PubMed  Google Scholar 

  149. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Huckins, L. M. et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat. Genet. 51, 659–674 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. Mancuso, N. et al. Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits. Am. J. Hum. Genet. 100, 473–487 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Gamazon, E. R., Zwinderman, A. H., Cox, N. J., Denys, D. & Derks, E. M. Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits. Nat. Genet. 51, 933–940 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. de la Torre-Ubieta, L. et al. The dynamic landscape of open chromatin during human cortical neurogenesis. Cell 172, 289–304 (2018).

    PubMed  PubMed Central  Google Scholar 

  158. Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  161. Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N. Engl. J. Med. 368, 107–116 (2013).

    CAS  PubMed  Google Scholar 

  162. Ursini, G. et al. Convergence of placenta biology and genetic risk for schizophrenia. Nat. Med. 24, 792–801 (2018).

    CAS  PubMed  Google Scholar 

  163. Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. Magnus, P. et al. Cohort profile: the Norwegian Mother and Child Cohort Study (MoBa). Int. J. Epidemiol. 35, 1146–1150 (2006).

    PubMed  Google Scholar 

  165. Hess, J. L. et al. A polygenic resilience score moderates the genetic risk for schizophrenia. Mol. Psychiatry https://doi.org/10.1038/s41380-019-0463-8 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  166. Smoller, J. W. et al. Psychiatric genetics and the structure of psychopathology. Mol. Psychiatry 24, 409–420 (2019).

    PubMed  Google Scholar 

  167. Allardyce, J. et al. Association between schizophrenia-related polygenic liability and the occurrence and level of mood-incongruent psychotic symptoms in bipolar disorder. JAMA Psychiatry 75, 28–35 (2018).

    PubMed  Google Scholar 

  168. Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    PubMed  Google Scholar 

  169. Richards, A. L. et al. The relationship between polygenic risk scores and cognition in schizophrenia. Schizophr. Bull. 46, 336–344 (2020).

    PubMed  Google Scholar 

  170. Frank, J. et al. Identification of increased genetic risk scores for schizophrenia in treatment-resistant patients. Mol. Psychiatry 20, 913 (2015).

    CAS  PubMed  Google Scholar 

  171. Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed  PubMed Central  Google Scholar 

  172. van der Meer, D. et al. Making the MOSTest of imaging genetics. Biol. Psychiatry 87 (Suppl.), S304–S305

  173. Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    CAS  PubMed  Google Scholar 

  176. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Zhang, Y., Qi, G., Park, J. H. & Chatterjee, N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50, 1318–1326 (2018).

    CAS  PubMed  Google Scholar 

  178. Zhu, X. & Stephens, M. Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. Ann. Appl. Stat. 11, 1561–1592 (2017).

    PubMed  PubMed Central  Google Scholar 

  179. Zeng, J. et al. Signatures of negative selection in the genetic architecture of human complex traits. Nat. Genet. 50, 746–753 (2018).

    CAS  PubMed  Google Scholar 

  180. Smeland, O. B. et al. The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. Psychiatr. Genet. 29, 152–159 (2019).

    PubMed  Google Scholar 

  181. Smeland, O. B. et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum. Genet. 139, 85–94 (2020).

    CAS  PubMed  Google Scholar 

  182. Schork, A. J., Wang, Y., Thompson, W. K., Dale, A. M. & Andreassen, O. A. New statistical approaches exploit the polygenic architecture of schizophrenia-implications for the underlying neurobiology. Curr. Opin. Neurobiol. 36, 89–98 (2016).

    CAS  PubMed  Google Scholar 

  183. Andreassen, O. A. et al. Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci. Mol. Psychiatry 20, 207–214 (2015).

    CAS  PubMed  Google Scholar 

  184. McLaughlin, R. L. et al. Genetic correlation between amyotrophic lateral sclerosis and schizophrenia. Nat. Commun. 8, 14774 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. Smeland, O. B. et al. Genome-wide association analysis of Parkinson’s disease and schizophrenia reveals shared genetic architecture and identifies novel risk loci. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2020.01.026 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors’ research is supported by National Institutes of Health (NIH) grants NS057198 and EB00790 and NIH National Institute on Drug Abuse (NIDA)/National Cancer Institute (NCI) grant U24DA041123 to A.M.D; and Research Council of Norway grants 229129, 213837, 248778, 223273 and 249711, South-East Norway Regional Health Authority grant 2017-112, and funding from K.G. Jebsen Stiftelsen (SKGJ) to O.A.A. The authors also thank N. Karadag for preparation of the original artwork for figure 2.

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Authors and Affiliations

  1. NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

    Olav B. Smeland, Oleksandr Frei & Ole A. Andreassen

  2. Departments of Cognitive Science, Neuroscience and Radiology, University of California, San Diego, La Jolla, CA, USA

    Anders M. Dale

  3. Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA

    Anders M. Dale

  4. The Adolescent Brain Cognitive Development (ABCD) Study Data Analysis and Informatics Center, University of California San Diego, La Jolla, CA, USA

    Anders M. Dale

  5. K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway

    Ole A. Andreassen

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Contributions

O.A.A., O.B.S., O.F. and A.M.D. researched data for the article, contributed substantially to discussions of its content and participated in review or editing of the manuscript before submission. In addition, O.A.A. and O.B.S. wrote the initial draft.

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Correspondence to Ole A. Andreassen.

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Competing interests

O.A.A. declares that he has received a speaker’s honorarium from Lundbeck and is a consultant for HealthLytix. A.M.D. declares that he is a founder of and holds equity interest in CorTechs Labs, that he is a member of the scientific advisory boards of CorTechs Labs and HealthLytix, and receives research funding from General Electric Healthcare. The terms of these arrangements have been reviewed and approved by the University of California San Diego in accordance with its conflict of interest policies. The other authors declare no competing interests.

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Related links

Conditional false discovery rate (FDR) software: https://github.com/precimed/pleiofdr

MiXeR software: https://github.com/precimed/mixer.

Glossary

Polygenic risk score

(PRS). An estimate of overall genetic propensity to develop a given phenotype, derived from the sum of a given individual’s risk alleles weighted by their effect sizes.

SNP-based heritability

The fraction of phenotypic variation attributable to common genetic variants detected in genome-wide association studies. Heritability of continuous traits (such as height) is estimated by comparison with the observed range (observed scale), whereas heritability of binary traits (such as schizophrenia) is estimated as a propensity score that takes into account population prevalence (liability scale).

Linkage disequilibrium

The tendency for genes, alleles or other genetic markers to be non-randomly inherited in association with each other owing to physical proximity to one another on the same chromosome.

Genetic pleiotropy

A genetic variant that affects more than one phenotype.

Protein isoform

Many human genes encode multiple protein variants generated by alternative promoters, alternative mRNA splicing or post-translational modification.

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Smeland, O.B., Frei, O., Dale, A.M. et al. The polygenic architecture of schizophrenia — rethinking pathogenesis and nosology. Nat Rev Neurol 16, 366–379 (2020). https://doi.org/10.1038/s41582-020-0364-0

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