Abstract

There is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here we leverage the unique Integrative Psychiatric Research Consortium (iPSYCH) study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder single-nucleotide polymorphism heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during mid-gestation. This epoch is supported by partitioning cross-disorder single-nucleotide polymorphism heritability, which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings suggest that dysregulation of genes that direct neurodevelopment by common genetic variants may result in general liability for many later psychiatric outcomes.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Code availability

Code and scripts available by request from authors.

Data availability

In accordance with the consent structure of iPSYCH and Danish law, individual level genotype and phenotype data are not able to be shared publicly. Cross-disorder (XDX) GWAS summary statistics are available for download (https://ipsych.au.dk/downloads/). Summary statistics from secondary GWAS of single disorders are available upon request from the corresponding author. BrainSpan RNA data are available in the GEO with the accession code GSE25219. Fetal Brain Hi-C data are available in the GEO with the accession code GSE77565.

Additional information

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

References

  1. 1.

    Robins, E. & Guze, S. B. Establishment of diagnostic validity in psychiatric illness: its application to schizophrenia. Am. J. Psychiatry 126, 983–987 (1970).

  2. 2.

    Kendell, R. & Jablensky, A. Distinguishing between the validity and utility of psychiatric diagnoses. Am. J. Psychiatry 160, 4–12 (2003).

  3. 3.

    Krystal, J. H. & State, M. W. Psychiatric disorders: diagnosis to therapy. Cell 157, 201–214 (2014).

  4. 4.

    O’Donovan, M. C. & Owen, M. J. The implications of the shared genetics of psychiatric disorders. Nat. Med. 22, 1214–1219 (2016).

  5. 5.

    Doherty, J. L. & Owen, M. J. Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice. Genome Med. 6, 29 (2014).

  6. 6.

    Widiger, T. A. & Sankis, L. M. Adult psychopathology: issues and controversies. Annu. Rev. Psychol. 51, 377–404 (2000).

  7. 7.

    Bulik, C. M., Prescott, C. A. & Kendler, K. S. Features of childhood sexual abuse and the development of psychiatric and substance use disorders. Br. J. Psychiatry 179, 444–449 (2001).

  8. 8.

    Brown, G. W., Harris, T. O. & Eales, M. J. Social factors and comorbidity of depressive and anxiety disorders. Br. J. Psychiatry Suppl. 168, 50–57 (1996).

  9. 9.

    Gorman, J. M. & Kent, J. M. SSRIs and SNRIs: broad spectrum of efficacy beyond major depression. J. Clin. Psychiatry 60 (Suppl.4), 33–38 (1999).

  10. 10.

    Polderman, T. J. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

  11. 11.

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

  12. 12.

    Sullivan, P. F. et al. Family history of schizophrenia and bipolar disorder as risk factors for autism. Arch. Gen. Psychiatry 69, 1099–1103 (2012).

  13. 13.

    Larsson, H. et al. Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder. Br. J. Psychiatry 203, 103–106 (2013).

  14. 14.

    Lee, S. H. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

  15. 15.

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

  16. 16.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

  17. 17.

    Hamshere, M. L. et al. Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia. Br. J. Psychiatry 203, 107–111 (2013).

  18. 18.

    Demontis, D. et al. Discovery of the first genome-wide significant risk loci for ADHD. Nat.Genet. 51, 63–75 (2019).

  19. 19.

    Grove, J. et al. Common risk variants identified in autism spectrum disorder. Preprint at bioRxiv https://doi.org/10.1101/224774 (2017).

  20. 20.

    Kirov, G. et al. The penetrance of copy number variations for schizophrenia and developmental delay. Biol. Psychiatry 75, 378–385 (2014).

  21. 21.

    Green Snyder, L. et al. Autism spectrum disorder, developmental and psychiatric features in 16p11.2 duplication. J. Autism. Dev. Disord. 46, 2734–2748 (2016).

  22. 22.

    Schneider, M. et al. Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the International Consortium on Brain and Behavior in 22q11.2 deletion syndrome. Am. J. Psychiatry 171, 627–639 (2014).

  23. 23.

    Rujescu, D. et al. Disruption of the neurexin 1 gene is associated with schizophrenia. Hum. Mol. Genet. 18, 988–996 (2009).

  24. 24.

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

  25. 25.

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

  26. 26.

    Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

  27. 27.

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

  28. 28.

    Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

  29. 29.

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

  30. 30.

    Bender, A. T. & Beavo, J. A. Cyclic nucleotide phosphodiesterases: molecular regulation to clinical use. Pharmacol. Rev. 58, 488–520 (2006).

  31. 31.

    Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1), D1074–D1082 (2018).

  32. 32.

    Munton, R. P., Vizi, S. & Mansuy, I. M. The role of protein phosphatase-1 in the modulation of synaptic and structural plasticity. FEBS Lett. 567, 121–128 (2004).

  33. 33.

    Han, Q. J. et al. IPP5 inhibits neurite growth in primary sensory neurons by maintaining TGF-β/Smad signaling. J. Cell Sci. 126, 542–553 (2013).

  34. 34.

    Kosmicki, J. A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504–510 (2017).

  35. 35.

    Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

  36. 36.

    Fishilevich, S. et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford) 2017 https://doi.org/10.1093/database/bax028 (2017).

  37. 37.

    Glatt, S. J. et al. Similarities and differences in peripheral blood gene-expression signatures of individuals with schizophrenia and their first-degree biological relatives. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 156B, 869–887 (2011).

  38. 38.

    Dela Peña, I. et al. Common prefrontal cortical gene expression profiles between adolescent SHR/NCrl and WKY/NCrl rats which showed inattention behavior. Behav. Brain. Res. 291, 268–276 (2015).

  39. 39.

    Jang, S. et al. Synaptic adhesion molecule IgSF11 regulates synaptic transmission and plasticity. Nat. Neurosci. 19, 84–93 (2016).

  40. 40.

    Breiderhoff, T. et al. Sortilin-related receptor SORCS3 is a postsynaptic modulator of synaptic depression and fear extinction. PLoS ONE 8, e75006 (2013).

  41. 41.

    Cohen, P. & Cohen, J. The clinician’s illusion. Arch. Gen. Psychiatry 41, 1178–1182 (1984).

  42. 42.

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

  43. 43.

    Yap, C. X. et al. Misestimation of heritability and prediction accuracy of male-pattern baldness. Nat. Commun. 9, 2537 (2018).

  44. 44.

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

  45. 45.

    Jakobsen, K. D., Frederiksen, J. N., Parnas, J. & Werge, T. Diagnostic agreement of schizophrenia spectrum disorders among chronic patients with functional psychoses. Psychopathology 39, 269–276 (2006).

  46. 46.

    Lauritsen, M. B. et al. Validity of childhood autism in the Danish Psychiatric Central Register: findings from a cohort sample born 1990-1999. J. Autism. Dev. Disord. 40, 139–148 (2010).

  47. 47.

    Schork, A. et al. Exploring contributors to variability in estimates of SNP-heritability and genetic correlations from the iPSYCH case-cohort and published meta-studies of major psychiatric disorders. Preprint at bioRxiv https://doi.org/10.1101/487116 (2018).

  48. 48.

    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).

  49. 49.

    Wang, K., Gaitsch, H., Poon, H., Cox, N. J. & Rzhetsky, A. Classification of common human diseases derived from shared genetic and environmental determinants. Nat. Genet. 49, 1319–1325 (2017).

  50. 50.

    Mortensen, P. B. et al. Effects of family history and place and season of birth on the risk of schizophrenia. N. Engl. J. Med. 340, 603–608 (1999).

  51. 51.

    Pedersen, C. B. The Danish civil registration system. Scand. J. Public Health 39, 22–25 (2011). Suppl.

  52. 52.

    Lynge, E., Sandegaard, J. L. & Rebolj, M. The Danish national patient register. Scand. J. Public Health 39, 30–33 (2011). Suppl.

  53. 53.

    Mors, O., Perto, G. P. & Mortensen, P. B. The Danish psychiatric central research register. Scand. J. Public Health 39, 54–57 (2011). Suppl.

  54. 54.

    Nørgaard-Pedersen, B. & Hougaard, D. M. Storage policies and use of the Danish newborn screening biobank. J. Inherit. Metab. Dis. 30, 530–536 (2007).

  55. 55.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  56. 56.

    Pedersen, C. B. et al. A comprehensive nationwide study of the incidence rate and lifetime risk for treated mental disorders. JAMA Psychiatry 71, 573–581 (2014).

  57. 57.

    World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders: diagnostic criteria for Research. (WHO: Geneva, 1993). http://www.who.int/iris/handle/10665/37108.

  58. 58.

    World Health Organization. Klassifikation af sygdomme; Udvidet dansk-latinsk udgave af verdenssundhedsorganisationens internationale klassifikation af sygdomme. 8 revision, 1965 [Classification of diseases: Extended Danish-Latin version of the World Health Organization International Classification of Diseases, 8th revision, 1965] (Danish National Board of Health, Copenhagen, 1971).

  59. 59.

    O’Connell, J. et al. Haplotype estimation for biobank-scale data sets. Nat. Genet. 48, 817–820 (2016).

  60. 60.

    Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS. Genet. 5, e1000529 (2009).

  61. 61.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  62. 62.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

  63. 63.

    Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS. Genet. 2, e190 (2006).

  64. 64.

    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

  65. 65.

    Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

  66. 66.

    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).

  67. 67.

    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).

  68. 68.

    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).

  69. 69.

    Neale, B. M. et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49, 884–897 (2010).

  70. 70.

    Ripke, S. et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).

  71. 71.

    Duncan, L. et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am. J. Psychiatry 174, 850–858 (2017).

  72. 72.

    Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol. Autism 8, 21 (2017).

  73. 73.

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

  74. 74.

    Hou, L. et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum. Mol. Genet. 25, 3383–3394 (2016).

  75. 75.

    MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45(D1), D896–D901 (2017).

  76. 76.

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

  77. 77.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  78. 78.

    Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

  79. 79.

    Sun, L., Craiu, R. V., Paterson, A. D. & Bull, S. B. Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies. Genet. Epidemiol. 30, 519–530 (2006).

  80. 80.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. U.SA. 100, 9440–9445 (2003).

  81. 81.

    Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

  82. 82.

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

  83. 83.

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

  84. 84.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

  85. 85.

    Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  86. 86.

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

  87. 87.

    Zambon, A. C. et al. GO-Elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 28, 2209–2210 (2012).

  88. 88.

    Kasprzyk, A. et al. EnsMart: a generic system for fast and flexible access to biological data. Genome Res. 14, 160–169 (2004).

  89. 89.

    Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

  90. 90.

    Pollen, A. A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015).

Download references

Acknowledgments

The iPSYCH Initiative is funded by the Lundbeck Foundation (grant nos. R102-A9118 and R155-2014-1724), the Mental Health Services Capital Region of Denmark, University of Copenhagen, Aarhus University and the university hospital in Aarhus. Genotyping of iPSYCH samples was supported by grants from the Lundbeck Foundation, the Stanley Foundation, the Simons Foundation (SFARI 311789) and NIMH (5U01MH094432-02). The iPSYCH Initiative use the Danish National Biobank resource that is supported by the Novo Nordisk Foundation. iPSYCH data was stored and analysed at the Computerome HPC facility (http://www.computerome.dtu.dk/) and the authors are grateful for continuous support from the HPC team led by A. Syed of DTU Bioinformatics, Technical University of Denmark. The following grants provided support for this work: NIH grant nos. R00MH113823 (H.W.), R01GM104400 (W.K.T.), 1R01MH109912 (D.G.), 1R01MH110927 (D.G.) and 1R01MH094714 (D.G.). Australian National Health and Medical Research Council grant nos. 1113400, 1078901, 1087889 (N.R.W).

Author information

Affiliations

  1. Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark

    • Andrew J. Schork
    • , Vivek Appadurai
    • , Ron Nudel
    • , Alfonso Buil
    • , Wesley K. Thompson
    •  & Thomas Werge
  2. The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen, Denmark

    • Andrew J. Schork
    • , Vivek Appadurai
    • , Ron Nudel
    • , David M. Hougaard
    • , Marie Bækved-Hansen
    • , Jonas Bybjerg-Grauholm
    • , Marianne Giørtz Pedersen
    • , Esben Agerbo
    • , Carsten Bøcker Pedersen
    • , Merete Nordentoft
    • , Ole Mors
    • , Anders D. Børglum
    • , Preben Bo Mortensen
    • , Alfonso Buil
    • , Wesley K. Thompson
    •  & Thomas Werge
  3. Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA

    • Hyejung Won
    • , Mike Gandal
    •  & Daniel H. Geschwind
  4. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    • Hyejung Won
    • , Mike Gandal
    •  & Daniel H. Geschwind
  5. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Hyejung Won
    • , Mike Gandal
    •  & Daniel H. Geschwind
  6. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    • Hyejung Won
  7. UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA

    • Hyejung Won
  8. Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland

    • Olivier Delaneau
  9. Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland

    • Olivier Delaneau
  10. Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland

    • Olivier Delaneau
  11. DTU Bioinformatics, Technical University of Denmark, Lyngby, Denmark

    • Malene Revsbech Christiansen
  12. Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

    • David M. Hougaard
    • , Marie Bækved-Hansen
    •  & Jonas Bybjerg-Grauholm
  13. NCRR - National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark

    • Marianne Giørtz Pedersen
    • , Esben Agerbo
    • , Carsten Bøcker Pedersen
    •  & Preben Bo Mortensen
  14. Centre for Integrated Register-based Research (CIRRAU), Aarhus University, Aarhus, Denmark

    • Marianne Giørtz Pedersen
    • , Esben Agerbo
    • , Carsten Bøcker Pedersen
    •  & Preben Bo Mortensen
  15. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, USA

    • Benjamin M. Neale
    •  & Mark J. Daly
  16. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Benjamin M. Neale
    •  & Mark J. Daly
  17. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Benjamin M. Neale
    •  & Mark J. Daly
  18. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    • Naomi R. Wray
  19. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    • Naomi R. Wray
  20. Copenhagen Mental Health Center, Mental Health Services Capital Region of Denmark Copenhagen, Copenhagen, Denmark

    • Merete Nordentoft
  21. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Merete Nordentoft
    •  & Thomas Werge
  22. Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark

    • Ole Mors
  23. Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark

    • Anders D. Børglum
  24. Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark

    • Anders D. Børglum
    •  & Preben Bo Mortensen
  25. Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA

    • Wesley K. Thompson
  26. Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    • Daniel H. Geschwind

Authors

  1. Search for Andrew J. Schork in:

  2. Search for Hyejung Won in:

  3. Search for Vivek Appadurai in:

  4. Search for Ron Nudel in:

  5. Search for Mike Gandal in:

  6. Search for Olivier Delaneau in:

  7. Search for Malene Revsbech Christiansen in:

  8. Search for David M. Hougaard in:

  9. Search for Marie Bækved-Hansen in:

  10. Search for Jonas Bybjerg-Grauholm in:

  11. Search for Marianne Giørtz Pedersen in:

  12. Search for Esben Agerbo in:

  13. Search for Carsten Bøcker Pedersen in:

  14. Search for Benjamin M. Neale in:

  15. Search for Mark J. Daly in:

  16. Search for Naomi R. Wray in:

  17. Search for Merete Nordentoft in:

  18. Search for Ole Mors in:

  19. Search for Anders D. Børglum in:

  20. Search for Preben Bo Mortensen in:

  21. Search for Alfonso Buil in:

  22. Search for Wesley K. Thompson in:

  23. Search for Daniel H. Geschwind in:

  24. Search for Thomas Werge in:

Contributions

D.G. and T.W. conceived of and supervised the study. A.J.S., H.W., D.G. and T.W. designed the analysis plan. A.J.S., V.A., A.B and W.K.T. prepared the data. A.J.S. performed the GWAS, (partitioned) SNP-(co)heritability, fine mapping and replication analyses. H.W. performed the candidate gene identification and enrichment analyses. R.N., M.G. and N.R.W. provided interpretive support. O.D. contributed imputation software and protocols. M.R.C. contributed analytic support. D.M.H., M.B.-H., J.B.-G., M.G.P., E.A., C.B.P., B.M.N., M.J.D., M.N., O.M., A.D.B., P.B.M. and T.W. designed, implemented and/or oversaw the collection and generation of the iPSYCH data. A.J.S., H.W., D.G. and T.W. wrote the manuscript. All authors discussed the results and contributed to the revision of the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Thomas Werge.

Supplementary information

  1. Supplementary Figures 1–25

    Supplementary Figures 1–25

  2. Reporting Summary

  3. Supplementary Tables 1–21

    Supplementary Tables 1–21

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41593-018-0320-0