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The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Nature Human Behaviour volume 8, pages 779–793 (2024)Cite this article

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Abstract

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic–parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685–4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure–function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

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Fig. 1: The multivariate framework discovered 23 independent loci significantly associated with the hypothalamus.
Fig. 2: Functional annotation and gene mapping of the 23 loci associated with the hypothalamus.
Fig. 3: Heritability of hypothalamus volumes and their genetic correlation with each other and other regional brain volumes from univariate GWAS.
Fig. 4: Genetic correlations between the hypothalamus and its function-related traits.
Fig. 5: Regional association plots of the ATAMTS8 locus across discovery and replication samples.
Fig. 6: MR analyses for ADAMTS8 expression versus whole hypothalamic volumes.

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Data availability

The full summary statistics of multivariate and univariate GWAS for the hypothalamus can be found at the figshare website (https://figshare.com/projects/GWAS_summary_data_of_hypothalamus/165589). Summary statistics of regional brain measures are available at https://www.med.unc.edu/bigs2/data/gwas-summary-statistics/. Summary statistics of neuropsychiatric traits and disorders for genetic correlation analyses are summarized in Supplementary Tables 38 and 39. Summary statistics of eQTL were obtained through the eQTLGen website (https://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/2019-12-11-cis-eQTLsFDR-ProbeLevel-CohortInfoRemoved-BonferroniAdded.txt.gz). The individual-level imaging and genetic data used in the current study are available through the UKB (https://biobank.ndph.ox.ac.uk/showcase/index.cgi, accession number 19542), the ABCD (https://nda.nih.gov/data_dictionary.html?source=ABCD%2BRelease%2B4.0&submission=ALL) and the IMAGEN (https://imagen2.cea.fr/account_manager). Data were used under licence and can be accessed through applications.

Code availability

This study used openly available software and codes, including MOSTest (https://github.com/precimed/mostest), PLINK (v.2.0; https://www.cog-genomics.org/plink/), FUMA (v.1.3.6; https://fuma.ctglab.nl/), MAGMA (v.1.08; https://ctg.cncr.nl/software/magma/, also implemented in FUMA), GCTA (v.1.93.2; http://cnsgenomics.com/software/gcta/), LDSC (v.1.0.1; https://github.com/bulik/ldsc/), STRING (https://www.stringdb.org/), Michigan Imputation Server (https://imputationserver.sph.umich.edu/), cFDR (https://github.com/precimed/pleiofdr/), METAL (v.2011-03-25; http://www.sph.umich.edu/csg/abecasis/Metal/), SAIGE-GENE+ (https://saigegit.github.io/SAIGE-doc/), R (v.4.0.3), Matlab R2018b and Python (v.3.10). Custom scripts for the analyses in this paper can be found at GitHub (https://github.com/wbs-whuer/GWAS_hypothalamus/).

References

  1. Müller, H. L. et al. Hypothalamic syndrome. Nat. Rev. Dis. Prim. 8, 24 (2022).

    PubMed  Google Scholar 

  2. Yang, T. et al. Social control of hypothalamus-mediated male aggression. Neuron 95, 955–970.e4 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Hu, R. K. et al. An amygdala-to-hypothalamus circuit for social reward. Nat. Neurosci. 24, 831–842 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Riecher-Rössler, A. Oestrogens, prolactin, hypothalamic–pituitary–gonadal axis, and schizophrenic psychoses. Lancet Psychiatry 4, 63–72 (2017).

    PubMed  Google Scholar 

  5. Zheng, Z. et al. Hypothalamus–habenula potentiation encodes chronic stress experience and drives depression onset. Neuron 110, 1400–1415.e6 (2022).

    CAS  PubMed  Google Scholar 

  6. Ishii, M. & Iadecola, C. Metabolic and non-cognitive manifestations of Alzheimer’s disease: the hypothalamus as both culprit and target of pathology. Cell Metab. 22, 761–776 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Breen, D. P. et al. Hypothalamic volume loss is associated with reduced melatonin output in Parkinson’s disease. Mov. Disord. 31, 1062–1066 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 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 

  9. Hibar, D. P. et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol. Psychiatry 23, 932–942 (2018).

    CAS  PubMed  Google Scholar 

  10. Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Satizabal, C. L. et al. Genetic architecture of subcortical brain structures in 38,851 individuals. Nat. Genet. 51, 1624–1636 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Bocchetta, M. et al. Detailed volumetric analysis of the hypothalamus in behavioral variant frontotemporal dementia. J. Neurol. 262, 2635–2642 (2015).

    PubMed  PubMed Central  Google Scholar 

  13. Makris, N. et al. Volumetric parcellation methodology of the human hypothalamus in neuroimaging: normative data and sex differences. NeuroImage 69, 1–10 (2013).

    PubMed  Google Scholar 

  14. Baroncini, M. et al. MRI atlas of the human hypothalamus. NeuroImage 59, 168–180 (2012).

    PubMed  Google Scholar 

  15. Billot, B. et al. Automated segmentation of the hypothalamus and associated subunits in brain MRI. NeuroImage 223, 117287 (2020).

    PubMed  Google Scholar 

  16. van der Meer, D. et al. Understanding the genetic determinants of the brain with MOSTest. Nat. Commun. 11, 3512 (2020).

    PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Bahrami, S. et al. Distributed genetic architecture across the hippocampal formation implies common neuropathology across brain disorders. Nat. Commun. 13, 3436 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Ou, Y. N. et al. The genetic architecture of human amygdala volumes and their overlap with common brain disorders. Transl. Psychiatry 13, 90 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    PubMed  PubMed Central  Google Scholar 

  21. Wain, L. V. et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat. Genet. 43, 1005–1011 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang, X. D. et al. Nectin-3 links CRHR1 signaling to stress-induced memory deficits and spine loss. Nat. Neurosci. 16, 706–713 (2013).

    CAS  PubMed  Google Scholar 

  23. He, L. et al. Exome-wide age-of-onset analysis reveals exonic variants in ERN1 and SPPL2C associated with Alzheimer’s disease. Transl. Psychiatry 11, 146 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Allen, M. et al. Gene expression, methylation and neuropathology correlations at progressive supranuclear palsy risk loci. Acta Neuropathol. 132, 197–211 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Mignot, E., Taheri, S. & Nishino, S. Sleeping with the hypothalamus: emerging therapeutic targets for sleep disorders. Nat. Neurosci. 5, 1071–1075 (2002).

    CAS  PubMed  Google Scholar 

  26. Jones, S. E. et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat. Commun. 10, 343 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Aydogan, G. et al. Genetic underpinnings of risky behaviour relate to altered neuroanatomy. Nat. Hum. Behav. 5, 787–794 (2021).

    PubMed  PubMed Central  Google Scholar 

  28. Floresco, S. B. Learning is a matter of history and relevance for lateral hypothalamus. Nat. Neurosci. 24, 295–296 (2021).

    CAS  PubMed  Google Scholar 

  29. Averbeck, B. B. & Murray, E. A. Hypothalamic interactions with large-scale neural circuits underlying reinforcement learning and motivated behavior. Trends Neurosci. 43, 681–694 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Li, M. M. et al. The paraventricular hypothalamus regulates satiety and prevents obesity via two genetically distinct circuits. Neuron 102, 653–667.e6 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Horvath, T. L. The hardship of obesity: a soft-wired hypothalamus. Nat. Neurosci. 8, 561–565 (2005).

    CAS  PubMed  Google Scholar 

  32. Zhou, J. J., Shao, J. Y., Chen, S. R. & Pan, H. L. Calcineurin controls hypothalamic NMDA receptor activity and sympathetic outflow. Circ. Res. 131, 345–360 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Gruber, T. et al. Obesity-associated hyperleptinemia alters the gliovascular interface of the hypothalamus to promote hypertension. Cell Metab. 33, 1155–1170.e10 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 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 

  35. Mascarell Maričić, L. et al. The IMAGEN study: a decade of imaging genetics in adolescents. Mol. Psychiatry 25, 2648–2671 (2020).

    PubMed  PubMed Central  Google Scholar 

  36. Zhou, W. et al. SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests. Nat. Genet. 54, 1466–1469 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 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 

  38. Hall, J. E. & Hall, M. E. Guyton and Hall Textbook of Medical Physiology (Elsevier, 2020).

  39. Yamagata, T. et al. The hypothalamic link between arousal and sleep homeostasis in mice. Proc. Natl Acad. Sci. USA 118, e2101580118 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Omura, J. et al. ADAMTS8 promotes the development of pulmonary arterial hypertension and right ventricular failure: a possible novel therapeutic target. Circ. Res. 125, 884–906 (2019).

    CAS  PubMed  Google Scholar 

  41. Morihara, T. et al. Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid-β accumulation modifier. Proc. Natl Acad. Sci. USA 111, 2638–2643 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Alsabban, A. H., Morikawa, M., Tanaka, Y., Takei, Y. & Hirokawa, N. Kinesin Kif3b mutation reduces NMDAR subunit NR2A trafficking and causes schizophrenia-like phenotypes in mice. EMBO J. 39, e101090 (2020).

    CAS  PubMed  Google Scholar 

  43. Yin, X., Takei, Y., Kido, M. A. & Hirokawa, N. Molecular motor KIF17 is fundamental for memory and learning via differential support of synaptic NR2A/2B levels. Neuron 70, 310–325 (2011).

    CAS  PubMed  Google Scholar 

  44. Du, J. et al. A kinesin signaling complex mediates the ability of GSK-3β to affect mood-associated behaviors. Proc. Natl Acad. Sci. USA 107, 11573–11578 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Gabery, S. et al. Volumetric analysis of the hypothalamus in Huntington disease using 3T MRI: the IMAGE-HD Study. PLoS ONE 10, e0117593 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. Goldstein, J. M. et al. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol. Psychiatry 61, 935–945 (2007).

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  48. Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).

    PubMed  PubMed Central  Google Scholar 

  49. LeBlanc, M. et al. A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics 19, 494 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. Schumann, G. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry 15, 1128–1139 (2010).

    CAS  PubMed  Google Scholar 

  51. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Jurgens, S. J. et al. Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank. Nat. Genet. 54, 240–250 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Wu, B. S. et al. Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders. NeuroImage 269, 119928 (2023).

    CAS  PubMed  Google Scholar 

  55. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  57. Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    CAS  PubMed  Google Scholar 

  58. Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 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 

  61. 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 

  62. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  64. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 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 

  69. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Maukonen, M. et al. Genetic associations of chronotype in the Finnish general population. J. Biol. Rhythms 35, 501–511 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Dashti, H. S. et al. Genetic determinants of daytime napping and effects on cardiometabolic health. Nat. Commun. 12, 900 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 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 

  73. Mills, M. C. et al. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat. Hum. Behav. 5, 1717–1730 (2021).

    PubMed  PubMed Central  Google Scholar 

  74. Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018).

    PubMed  PubMed Central  Google Scholar 

  75. Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 53, 1616–1621 (2021).

    CAS  PubMed  Google Scholar 

  76. 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 

  77. Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Zhu, Z. et al. Genetic overlap of chronic obstructive pulmonary disease and cardiovascular disease-related traits: a large-scale genome-wide cross-trait analysis. Respir. Res. 20, 64 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).

    PubMed  Google Scholar 

  80. Pardiñas, 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).

    PubMed  PubMed Central  Google Scholar 

  81. 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 

  82. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Meier, S. M. et al. Genetic variants associated with anxiety and stress-related disorders: a genome-wide association study and mouse-model study. JAMA Psychiatry 76, 924–932 (2019).

    PubMed  PubMed Central  Google Scholar 

  84. Duncan, L. E. et al. Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666–673 (2018).

    CAS  PubMed  Google Scholar 

  85. 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 

  86. Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 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 

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

    PubMed  Google Scholar 

  89. Yu, D. et al. Interrogating the genetic determinants of Tourette’s syndrome and other tic disorders through genome-wide association studies. Am. J. Psychiatry 176, 217–227 (2019).

    PubMed  PubMed Central  Google Scholar 

  90. Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Blauwendraat, C. et al. Parkinson’s disease age at onset genome-wide association study: defining heritability, genetic loci, and α-synuclein mechanisms. Mov. Disord. 34, 866–875 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Vaser, R., Adusumalli, S., Leng, S. N., Sikic, M. & Ng, P. C. SIFT missense predictions for genomes. Nat. Protoc. 11, 1–9 (2016).

    CAS  PubMed  Google Scholar 

  95. Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. Chapter 7, Unit7.20 (2013).

    PubMed  Google Scholar 

  96. Chun, S. & Fay, J. C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553–1561 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Schwarz, J. M., Rödelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010).

    CAS  PubMed  Google Scholar 

  98. Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  100. Bowden, J. et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int. J. Epidemiol. 45, 1961–1974 (2016).

    PubMed  PubMed Central  Google Scholar 

  101. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    PubMed  PubMed Central  Google Scholar 

  102. Hartwig, F. P., Davey Smith, G. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int. J. Epidemiol. 46, 1985–1998 (2017).

    PubMed  PubMed Central  Google Scholar 

  103. Burgess, S. & Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755–764 (2011).

    PubMed  Google Scholar 

  104. Burgess, S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int. J. Epidemiol. 43, 922–929 (2014).

    PubMed  PubMed Central  Google Scholar 

  105. Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).

    PubMed  PubMed Central  Google Scholar 

  106. Sehgal, P. B., Yang, Y. M. & Miller, E. J. Hypothesis: neuroendocrine mechanisms (hypothalamus-growth hormone–STAT5 axis) contribute to sex bias in pulmonary hypertension. Mol. Med. 21, 688–701 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Guo, J. et al. Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders. Nat. Neurosci. 25, 1519–1527 (2022).

    CAS  PubMed  Google Scholar 

  108. 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 

  109. Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749–1755 (2019).

    CAS  PubMed  Google Scholar 

  110. Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21, 1656–1669 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Rhodes, C. J. et al. Genetic determinants of risk in pulmonary arterial hypertension: international genome-wide association studies and meta-analysis. Lancet. Respir. Med. 7, 227–238 (2019).

    Google Scholar 

  112. Aung, N. et al. Genome-wide association analysis reveals insights into the genetic architecture of right ventricular structure and function. Nat. Genet. 54, 783–791 (2022).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600 to J.-T.Y.), the National Natural Science Foundation of China (82071201 to J.-T.Y., 82071997 to W.C.), the Shanghai Municipal Science and Technology Major Project (2018SHZDZX01 to J.-F.F.), the Postdoctoral Innovation Talents Support Program (BX20230087 to S.-D.C.), the Research Start-up Fund of Huashan Hospital (2022QD002 to J.-T.Y.), the Excellence 2025 Talent Cultivation Program at Fudan University (3030277001 to J.-T.Y.), the Shanghai Talent Development Funding for The Project (2019074 to J.-T.Y.), the Shanghai Rising-Star Program (21QA1408700 to W.C.), the 111 Project (B18015 to J.-F.F.), the ZhangJiang Laboratory, the Tianqiao and Chrissy Chen Institute, the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, and the Shanghai Center for Brain Science and Brain-Inspired Technology, Fudan University. The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication. Data acquisition and analyses were conducted using the UKB Resource under approved project number 19542. Participation of the UKB participants is gratefully appreciated. We also thank the UKB team for collecting and preparing data for analyses. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federalpartners.html. A list of participating sites and a complete list of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. IMAGEN received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the National Institutes of Health (NIH) (R01DA049238, a decentralized macro and micro gene-by-environment interaction analysis of substance use behaviour and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1), the NIH-funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01), NSFC grant 82150710554 and European Union funded project ‘environMENTAL’, grant number 101057429. Further support was provided by grants from the ANR (ANR-12-SAMA-0004, AAPG2019-GeBra), the Eranet Neuron (AF12-NEUR0008-01-WM2NA; and ANR-18-NEUR00002-01-ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1) and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence.

Author information

Author notes

  1. These authors contributed equally: Shi-Dong Chen, Jia You, Wei Zhang, Bang-Sheng Wu.

Authors and Affiliations

  1. Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China

    Shi-Dong Chen, Bang-Sheng Wu, Yi-Jun Ge, Jing Du, Kevin Kuo, Qiang Dong, Wei Cheng & Jin-Tai Yu

  2. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Jia You, Wei Zhang, Shi-Tong Xiang, Jing Du, Jian-Feng Feng & Wei Cheng

  3. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Tobias Banaschewski, Frauke Nees & Sabina Millenet

  4. Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Gareth J. Barker

  5. Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland

    Arun L. W. Bokde

  6. Institute of Psychiatry, Psychology & Neuroscience, Social, Genetic, Developmental Psychiatry Centre, King’s College London, London, UK

    Sylvane Desrivières

  7. Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Herta Flor & Frauke Nees

  8. Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany

    Herta Flor

  9. NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France

    Antoine Grigis, Dimitri Papadopoulos Orfanos & Herve Lemaitre

  10. Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA

    Hugh Garavan

  11. Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, UK

    Penny Gowland

  12. Department of Psychiatry and Psychotherapy CCM, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany

    Andreas Heinz & Henrik Walter

  13. Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany

    Rüdiger Brühl

  14. Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 “Trajectoires développementales & psychiatrie”, University Paris-Saclay, CNRS, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France

    Jean-Luc Martinot, Marie-Laure Paillère Martinot & Eric Artiges

  15. AP-HP, Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France

    Marie-Laure Paillère Martinot

  16. Psychiatry Department, EPS Barthélémy Durand, Etampes, France

    Eric Artiges

  17. Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany

    Frauke Nees

  18. Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, Bordeaux, France

    Herve Lemaitre

  19. Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hosptalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada

    Tomáš Paus

  20. Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada

    Tomáš Paus

  21. Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany

    Luise Poustka

  22. Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Sarah Hohmann

  23. Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany

    Christian Baeuchl & Michael N. Smolka

  24. Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany

    Nilakshi Vaidya & Gunter Schumann

  25. School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland

    Robert Whelan

  26. Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China

    Gunter Schumann

  27. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

    Jian-Feng Feng & Wei Cheng

  28. Fudan ISTBI–ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China

    Jian-Feng Feng & Wei Cheng

  29. MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Jian-Feng Feng

  30. Zhangjiang Fudan International Innovation Center, Shanghai, China

    Jian-Feng Feng

  31. Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China

    Wei Cheng

Authors
  1. Shi-Dong Chen
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  2. Jia You
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  3. Wei Zhang
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  4. Bang-Sheng Wu
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  5. Yi-Jun Ge
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  6. Shi-Tong Xiang
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  7. Jing Du
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  8. Kevin Kuo
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  9. Tobias Banaschewski
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  10. Gareth J. Barker
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  11. Arun L. W. Bokde
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  12. Sylvane Desrivières
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  13. Herta Flor
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  14. Antoine Grigis
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  15. Hugh Garavan
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  17. Andreas Heinz
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  18. Rüdiger Brühl
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  19. Jean-Luc Martinot
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  20. Marie-Laure Paillère Martinot
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  21. Eric Artiges
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Consortia

IMAGEN Consortium

  • Tobias Banaschewski
  • , Gareth J. Barker
  • , Arun L. W. Bokde
  • , Sylvane Desrivières
  • , Herta Flor
  • , Antoine Grigis
  • , Hugh Garavan
  • , Penny Gowland
  • , Andreas Heinz
  • , Rüdiger Brühl
  • , Jean-Luc Martinot
  • , Marie-Laure Paillère Martinot
  • , Eric Artiges
  • , Frauke Nees
  • , Dimitri Papadopoulos Orfanos
  • , Herve Lemaitre
  • , Tomáš Paus
  • , Luise Poustka
  • , Sarah Hohmann
  • , Sabina Millenet
  • , Christian Baeuchl
  • , Michael N. Smolka
  • , Nilakshi Vaidya
  • , Henrik Walter
  • , Robert Whelan
  •  & Gunter Schumann

Contributions

Concept and design: J.-T.Y., W.C., Q.D. and J.-F.F. Acquisition, analysis or interpretation of data: all authors. Drafting of the manuscript: S.-D.C., B.-S.W., W.Z. and J.Y. Critical revision of the manuscript for important intellectual content: all authors. Statistical analyses: S.-D.C., B.-S.W., J.Y. and W.Z. Obtained funding: J.-T.Y., W.C., J.-F.F. and S.-D.C. Administrative, technical or material support: W.C., J.-F.F., Q.D. and J.-T.Y. Supervision: J.-F.F., W.C., J.-T.Y. and Q.D.

Corresponding authors

Correspondence to Jian-Feng Feng, Qiang Dong, Wei Cheng or Jin-Tai Yu.

Ethics declarations

Competing interests

T.B. served in an advisory or consultancy role for eye level, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg, Roche and Takeda. He received conference support or speaker’s fees from Janssen, Medice and Takeda. He received royalties from Hogrefe, Kohlhammer, CIP Medien and Oxford University Press; the current work is unrelated to these relationships. G.J.B. has received honoraria from General Electric Healthcare for teaching on scanner programming courses. L.P. served in an advisory or consultancy role for Roche and Viforpharm and received speaker’s fees from Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The current work is unrelated to the above grants and relationships. The remaining authors declare no competing interests.

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Chen, SD., You, J., Zhang, W. et al. The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders. Nat Hum Behav 8, 779–793 (2024). https://doi.org/10.1038/s41562-023-01792-6

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