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The overlapping genetic architecture of psychiatric disorders and cortical brain structure

Nature Mental Health volume 3, pages 1020–1036 (2025)Cite this article

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Abstract

Both psychiatric vulnerability and cortical structure are shaped by the cumulative effect of common genetic variants across the genome. However, the shared genetic underpinnings between psychiatric disorders and brain structural phenotypes, such as thickness and surface area of the cerebral cortex, remain elusive. Here we use pleiotropy-informed conjunctional false discovery rate analysis to investigate shared loci across genome-wide association scans of regional cortical thickness, surface area and eight psychiatric disorders in individuals of European ancestry. Aggregating regional measures, we identified 55 independent genetic loci shared between psychiatric disorders and surface area, as well as 29 independent genetic loci shared with cortical thickness. Risk alleles exhibited bidirectional effects on both cortical thickness and surface area, such that some risk alleles for each disorder were associated with increased regional brain size while other risk alleles were associated with decreased regional brain size. Due to bidirectional effects, in many cases we observed extensive pleiotropy between an imaging phenotype and a psychiatric disorder even in the absence of a significant genetic correlation between them. The impact of genetic risk for psychiatric disorders on regional brain structure did exhibit a consistent pattern across highly comorbid psychiatric disorders, with 80% of the independent genetic loci shared across multiple disorders displaying consistent directions of effect. Cortical patterning of genetic overlap revealed a hierarchical genetic architecture, with the association cortex and sensorimotor cortex representing two extremes of shared genetic influence on psychiatric disorders and brain structural variation. Integrating multiscale functional annotations and transcriptomic profiles, we observed that shared genetic loci were enriched in active genomic regions, converged on neurobiological and metabolic pathways and showed differential expression in postmortem brain tissue from individuals with psychiatric disorders. Cumulatively, these findings provide a significant advance in our understanding of the overlapping polygenic architecture between psychopathology and cortical brain structure.

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Fig. 1: Shared genetic loci between psychiatric disorders and CT and SA measures.
Fig. 2: Genetic overlap of psychiatric disorders with the regional brain structure and its association with anatomical hierarchy.
Fig. 3: Effect direction of shared independent genetic loci between psychiatric disorders and brain structure.
Fig. 4: Overlapping genetic effect of psychiatric disorders on brain structures.
Fig. 5: Functional annotation and differential expression of shared genes between psychiatric disorders and brain structures.

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Data availability

All GWAS results of cortical brain structures are available at https://portal.camide.cam.ac.uk/overview/483. GWAS summary statistics on subcortical structures are publicly available for download via the ENIGMA and CHARGE consortia at https://enigma.ini.usc.edu/research/download-enigma-gwas-results/. GWAS summary statistics on ADHD, AUD, ANX, ASD, BD, MDD, PTSD and SCZ are publicly available for download via psychiatric genomics consortium website at https://pgc.unc.edu/for-researchers/download-results/.

Code availability

This study used openly available software and codes, specifically pleioFDR (https://github.com/precimed/pleiofdr), FUMA v1.6.2 (https://fuma.ctglab.nl/), MAGMA v1.10 (https://ctg.cncr.nl/software/magma, also implemented in FUMA) and ConsensusPathDB v35 (http://cpdb.molgen.mpg.de/).

References

  1. Hyman, S. E. Can neuroscience be integrated into the DSM-V? Nat. Rev. Neurosci. 8, 725–732 (2007).

    Article  PubMed  Google Scholar 

  2. Grotzinger, A. D. et al. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat. Genet. 54, 548–559 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  4. Mallard, T. T. et al. Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities. Cell Genom. 2, 100140 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  6. Romero, C. et al. Exploring the genetic overlap between twelve psychiatric disorders. Nat. Genet. 54, 1795–1802 (2022).

    Article  PubMed  Google Scholar 

  7. Thompson, P. M. et al. ENIGMA and global neuroscience: a decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl. Psychiatry 10, 100 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Thompson, P. M. et al. The enhancing neuroimaging genetics through meta-analysis consortium: 10 years of global collaborations in human brain mapping. Hum. Brain Mapp. 43, 15–22 (2022).

    Article  PubMed  Google Scholar 

  9. Hoogman, M. et al. Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: the ENIGMA adventure. Hum. Brain Mapp. 43, 37–55 (2022).

    Article  PubMed  Google Scholar 

  10. Ching, C. R. et al. What we learn about bipolar disorder from large‐scale neuroimaging: findings and future directions from the ENIGMA Bipolar Disorder Working Group. Hum. Brain Mapp. 43, 56–82 (2022).

    Article  PubMed  Google Scholar 

  11. Bas-Hoogendam, J. M. et al. ENIGMA-anxiety working group: rationale for and organization of large-scale neuroimaging studies of anxiety disorders. Hum. Brain Mapp. 43, 83–112 (2022).

    Article  PubMed  Google Scholar 

  12. Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22, 900–909 (2017).

    Article  PubMed  Google Scholar 

  13. Wang, X. et al. Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis. Mol. Psychiatry 26, 4331–4343 (2021).

    Article  PubMed  Google Scholar 

  14. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Moran, M. E., Hulshoff Pol, H. & Gogtay, N. A family affair: brain abnormalities in siblings of patients with schizophrenia. Brain 136, 3215–3226 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  16. McDonald, C. et al. Regional brain morphometry in patients with schizophrenia or bipolar disorder and their unaffected relatives. Am. J. Psychiatry 163, 478–487 (2006).

    Article  PubMed  Google Scholar 

  17. Carballedo, A. et al. Early life adversity is associated with brain changes in subjects at family risk for depression. World J. Biol. Psychiatry 13, 569–578 (2012).

    Article  PubMed  Google Scholar 

  18. Stauffer, E.-M. et al. Grey and white matter microstructure is associated with polygenic risk for schizophrenia. Mol. Psychiatry 26, 7709–7718 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Maglanoc, L. A. et al. Brain connectome mapping of complex human traits and their polygenic architecture using machine learning. Biol. Psychiatry 87, 717–726 (2020).

    Article  PubMed  Google Scholar 

  20. Kirschner, M. et al. Schizophrenia polygenic risk during typical development reflects multiscale cortical organization. Biol. Psychiatry Glob. Open Sci. 3, 1083–1093 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367, eaay6690 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Warrier, V. et al. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat. Genet. 55, 1483–1493 (2023).

  24. Panizzon, M. S. et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728–2735 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Sullivan, P. F., Daly, M. J. & O’Donovan, M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat. Rev. Genet. 13, 537–551 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chambers, J. C. et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat. Genet. 43, 1131–1138 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: Polygenic Risk Score software. Bioinformatics 31, 1466–1468 (2015).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  34. Doan, N. T. et al. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. Neuroimage Clin. 15, 719–731 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  35. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Andreassen, O. A., Thompson, W. K. & Dale, A. M. Boosting the power of schizophrenia genetics by leveraging new statistical tools. Schizophr. Bull. 40, 13–17 (2014).

    Article  PubMed  Google Scholar 

  37. Andreassen, O. A. et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 9, e1003455 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  39. 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).

    Article  PubMed  Google Scholar 

  40. Yokoyama, J. S. et al. Association between genetic traits for immune-mediated diseases and Alzheimer disease. JAMA Neurol. 73, 691–697 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  42. Zhou, H. et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat. Neurosci. 23, 809–818 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Otowa, T. et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry 21, 1485 (2016).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  46. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Stein, M. B. et al. Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program. Nat. Genet. 53, 174–184 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Garcia-Marin, L. M. et al. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries. Nat. Genet. 56, 2333–2344 (2024).

    Article  PubMed  Google Scholar 

  51. Wilson, D. J. The harmonic mean p-value for combining dependent tests. Proc. Natl Acad. Sci. USA 116, 1195–1200 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  53. Burt, J. B. et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat. Neurosci. 21, 1251–1259 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Garcia-Cabezas, M. A., Zikopoulos, B. & Barbas, H. The Structural Model: a theory linking connections, plasticity, pathology, development and evolution of the cerebral cortex. Brain Struct. Funct. 224, 985–1008 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Hilgetag, C. C., Beul, S. F., van Albada, S. J. & Goulas, A. An architectonic type principle integrates macroscopic cortico-cortical connections with intrinsic cortical circuits of the primate brain. Netw. Neurosci. 3, 905–923 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Sydnor, V. J. et al. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hilgetag, C. C. & Goulas, A. ‘Hierarchy’ in the organization of brain networks. Philos. Trans. R. Soc. Lond. B 375, 20190319 (2020).

    Article  Google Scholar 

  58. Mesulam, M. M. From sensation to cognition. Brain 121, 1013–1052 (1998).

    Article  PubMed  Google Scholar 

  59. Di Biase, M. A. et al. Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia. Mol. Psychiatry 27, 2052–2060 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res. 41, D793–D800 (2013).

    Article  PubMed  Google Scholar 

  61. Rivero, O. et al. Haploinsufficiency of the attention-deficit/hyperactivity disorder risk gene St3gal3 in mice causes alterations in cognition and expression of genes involved in myelination and sialylation. Front. Genet. 12, 688488 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Ford, T. J. L., Jeon, B. T., Lee, H. & Kim, W. Y. Dendritic spine and synapse pathology in chromatin modifier-associated autism spectrum disorders and intellectual disability. Front. Mol. Neurosci. 15, 1048713 (2022).

    Article  PubMed  Google Scholar 

  63. Ferreira, M. A. et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat. Genet. 40, 1056–1058 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Yoon, S. et al. Usp9X controls ankyrin-repeat domain protein homeostasis during dendritic spine development. Neuron 105, 506–521 e507 (2020).

    Article  PubMed  Google Scholar 

  65. Zhang, Y. et al. The emerging role of furin in neurodegenerative and neuropsychiatric diseases. Transl. Neurodegener. 11, 39 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Sudre, G. et al. Mapping the cortico-striatal transcriptome in attention deficit hyperactivity disorder. Mol. Psychiatry 28, 792–800 (2023).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  68. Jaffe, A. E. et al. Decoding shared versus divergent transcriptomic signatures across cortico-amygdala circuitry in PTSD and depressive disorders. Am. J. Psychiatry 179, 673–686 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Willis, C. et al. Gene expression differences associated with alcohol use disorder in human brain. Mol. Psychiatry https://doi.org/10.1038/s41380-024-02777-1 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kaczkurkin, A. N. et al. Evidence for dissociable linkage of dimensions of psychopathology to brain structure in youths. Am J Psychiatry 176, 1000–1009 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Sha, Z., Wager, T. D., Mechelli, A. & He, Y. Common dysfunction of large-scale neurocognitive networks across psychiatric disorders. Biol. Psychiatry 85, 379–388 (2019).

    Article  PubMed  Google Scholar 

  72. Barack, D. L. & Krakauer, J. W. Two views on the cognitive brain. Nat. Rev. Neurosci. 22, 359–371 (2021).

    Article  PubMed  Google Scholar 

  73. Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Bearden, C. E. & Freimer, N. B. Endophenotypes for psychiatric disorders: ready for primetime? Trends Genet. 22, 306–313 (2006).

    Article  PubMed  Google Scholar 

  75. Ferland, R. J., Cherry, T. J., Preware, P. O., Morrisey, E. E. & Walsh, C. A. Characterization of Foxp2 and Foxp1 mRNA and protein in the developing and mature brain. J. Comp. Neurol. 460, 266–279 (2003).

    Article  PubMed  Google Scholar 

  76. Lai, C. S., Gerrelli, D., Monaco, A. P., Fisher, S. E. & Copp, A. J. FOXP2 expression during brain development coincides with adult sites of pathology in a severe speech and language disorder. Brain 126, 2455–2462 (2003).

    Article  PubMed  Google Scholar 

  77. den Hoed, J., Devaraju, K. & Fisher, S. E. Molecular networks of the FOXP2 transcription factor in the brain. EMBO Rep. 22, e52803 (2021).

    Article  Google Scholar 

  78. French, C. A. et al. Differential effects of Foxp2 disruption in distinct motor circuits. Mol. Psychiatry 24, 447–462 (2019).

    Article  PubMed  Google Scholar 

  79. Vargha-Khadem, F., Gadian, D. G., Copp, A. & Mishkin, M. FOXP2 and the neuroanatomy of speech and language. Nat. Rev. Neurosci. 6, 131–138 (2005).

    Article  PubMed  Google Scholar 

  80. Chand, G. B. et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 143, 1027–1038 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Haruvi-Lamdan, N., Horesh, D. & Golan, O. PTSD and autism spectrum disorder: co-morbidity, gaps in research, and potential shared mechanisms. Psychol. Trauma 10, 290–299 (2018).

    Article  PubMed  Google Scholar 

  82. Van Rooij, D. et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: results from the ENIGMA ASD Working Group. Am. J. Psychiatry 175, 359–369 (2018).

    Article  PubMed  Google Scholar 

  83. Sha, Z. et al. The genetic architecture of structural left-right asymmetry of the human brain. Nat. Hum. Behav. 5, 1226–1239 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Attwaters, M. Connecting noncoding variants to human traits. Nat. Rev. Genet. 24, 489 (2023).

    Article  PubMed  Google Scholar 

  85. Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  87. Nelson, C. E., Hersh, B. M. & Carroll, S. B. The regulatory content of intergenic DNA shapes genome architecture. Genome Biol. 5, R25 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Gandal, M. J. et al. Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD. Nature 611, 532–539 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Fusar-Poli, P. et al. Transdiagnostic psychiatry: a systematic review. World Psychiatry 18, 192–207 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Plana-Ripoll, O. et al. Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 76, 259–270 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Merikangas, K. R. et al. Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). J. Am. Acad. Child Adolesc. Psychiatry 49, 980–989 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L. & Kraft, P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am. J. Hum. Genet. 96, 329–339 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  93. International Obsessive Compulsive Disorder Foundation Genetics Collaborative & OCD Collaborative Genetics Association Studies. Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018).

  94. 1000 Genomes Project Consortium A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  95. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Article  Google Scholar 

  96. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  99. Giusti-Rodríguez, P. et al. Using three-dimensional regulatory chromatin interactions from adult and fetal cortex to interpret genetic results for psychiatric disorders and cognitive traits. Preprint at bioRxiv https://doi.org/10.1101/406330 (2018).

  100. Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Roadmap Epigenomics Consortiumet al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed Central  Google Scholar 

  102. Glasser, M. F. & Van Essen, D. C. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 31, 11597–11616 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Vasa, F. et al. Adolescent tuning of association cortex in human structural brain networks. Cereb. Cortex 28, 281–294 (2018).

    Article  PubMed  Google Scholar 

  104. Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178, 540–551 (2018).

    Article  PubMed  Google Scholar 

  105. Alexander-Bloch, A., Raznahan, A., Bullmore, E. & Giedd, J. The convergence of maturational change and structural covariance in human cortical networks. J. Neurosci. 33, 2889–2899 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Koskinen, M. K. & Hovatta, I. Genetic insights into the neurobiology of anxiety. Trends Neurosci. 46, 318–331 (2023).

    Article  PubMed  Google Scholar 

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Acknowledgements

This research was conducted using summary statistics that used the UK Biobank resource (application no. 20904), with V.W. as the principal applicant. The research was funded by R01MH132934 (A.F.A.-B.) and R01MH133843 (A.F.A.-B.).

Author information

Authors and Affiliations

  1. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

    Zhiqiang Sha, Kevin Y. Sun, Ruben C. Gur, Raquel E. Gur, Michael J. Gandal, Jakob Seidlitz & Aaron F. Alexander-Bloch

  2. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Zhiqiang Sha, Kevin Y. Sun, Jakob Seidlitz & Aaron F. Alexander-Bloch

  3. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Varun Warrier

  4. Department of Psychology, University of Cambridge, Cambridge, UK

    Varun Warrier & Richard A. I. Bethlehem

  5. Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Laura M. Schultz, Alison Merikangas & Laura Almasy

  6. Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA

    Laura M. Schultz, Alison Merikangas, Kevin Y. Sun, Ruben C. Gur, Raquel E. Gur, Michael J. Gandal, Jakob Seidlitz, Laura Almasy & Aaron F. Alexander-Bloch

  7. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Alison Merikangas, Michael J. Gandal & Laura Almasy

  8. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ruben C. Gur & Raquel E. Gur

  9. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ruben C. Gur & Raquel E. Gur

  10. Penn Statistics in Imaging and Visualization Endeavor, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Russell T. Shinohara

  11. Center for Artificial Intelligence and Data Science for Integrated Diagnostics, Department of Radiology, Perelman School of Medicine, Philadelphia, PA, USA

    Russell T. Shinohara

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

    Ole A. Andreassen

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Contributions

Z.S.: conceptualization, methodology, analysis, visualization, original draft writing, review and editing. V.W.: conceptualization, methodology, analysis, visualization, review and editing. R.A.I.B.: conceptualization, methodology, analysis, visualization, review and editing. L.M.S.: conceptualization, methodology, visualization, review and editing. A.M.: conceptualization, methodology, visualization, review and editing. K.Y.S.: conceptualization, methodology, visualization, review and editing. R.C.G.: conceptualization, methodology, visualization, review and editing. R.E.G.: conceptualization, methodology, visualization, review and editing. R.T.S.: conceptualization, methodology, visualization, review and editing. M.J.G.: conceptualization, methodology, visualization, review and editing. J.S.: conceptualization, methodology, visualization, review and editing. L.A.: conceptualization, methodology, visualization, review and editing. O.A.A.: conceptualization, methodology, visualization, review and editing. A.F.A.-B.: conceptualization, direction, funding acquisition, supervision, original draft writing, review and editing.

Corresponding authors

Correspondence to Zhiqiang Sha or Aaron F. Alexander-Bloch.

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Competing interests

A.F.A.-B. has received consulting income from Octave Bioscience and holds equity in Centile Bioscience. J.S. and R.A.I.B. hold equity in and serve on the board of Centile Bioscience. R.T.S. has received consulting income from Octave Bioscience and compensation for scientific reviewing from the American Medical Association. O.A.A. is a consultant to cortechs.ai and has received speaker’s honorarium from Lundbeck, Janssen, Sunovion. The remaining authors declare no competing interests.

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Sha, Z., Warrier, V., Bethlehem, R.A.I. et al. The overlapping genetic architecture of psychiatric disorders and cortical brain structure. Nat. Mental Health 3, 1020–1036 (2025). https://doi.org/10.1038/s44220-025-00475-7

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