Childhood psychiatric symptoms are often diffuse but can coalesce into discrete mental illnesses during late adolescence. We leveraged polygenic scores (PGSs) to parse genomic risk for childhood symptoms and to uncover related neurodevelopmental mechanisms with transcriptomic and neuroimaging data. In independent samples (Adolescent Brain Cognitive Development, Generation R) a narrow cross-disorder neurodevelopmental PGS, reflecting risk for attention deficit hyperactivity disorder, autism, depression and Tourette syndrome, predicted psychiatric symptoms through early adolescence with greater sensitivity than broad cross-disorder PGSs reflecting shared risk across eight psychiatric disorders, the disorder-specific PGS individually or two other narrow cross-disorder (Compulsive, Mood-Psychotic) scores. Neurodevelopmental PGS-associated genes were preferentially expressed in the cerebellum, where their expression peaked prenatally. Further, lower gray matter volumes in cerebellum and functionally coupled cortical regions associated with psychiatric symptoms in mid-childhood. These findings demonstrate that the genetic underpinnings of pediatric psychiatric symptoms differ from those of adult illness, and implicate fetal cerebellar developmental processes that endure through childhood.
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All ABCD data are available via the NIMH Data Archive. For instructions on gaining access to ABCD data within this repository, refer to this page: https://nda.nih.gov/nda/access-data-info.html. ABCD data created in the current study can also be downloaded from the NDA (https://doi.org/10.15154/1528597). For access to the Generation R dataset, requests can be sent to firstname.lastname@example.org. BrainSpan Atlas of the Developing Brain gene expression data are available through their website (https://www.brainspan.org/static/download.html); 1000 Genomes phase 3 data are available through this site: https://www.internationalgenome.org/data-portal/data-collection; and summary statistics from the Psychiatric Genomics Consortium can be downloaded here: https://www.med.unc.edu/pgc/download-results/. GTEx v.8 RNA-seq data can be analyzed through FUMA’s pipeline (https://fuma.ctglab.nl/) and the raw data downloaded here: https://gtexportal.org/home/datasets.
Code for generation of polygenic scores, spatiotemporal gene expression analyses, imaging analyses and PGS-psychopathology analyses is available on GitHub (https://github.com/hughesdy/ABCD-NDV-CBC).
Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).
Pardinas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
Anderson, J. S., Shade, J., DiBlasi, E., Shabalin, A. A. & Docherty, A. R. Polygenic risk scoring and prediction of mental health outcomes. Curr. Opin. Psychol. 27, 77–81 (2019).
Brandon, N. J. & Sawa, A. Linking neurodevelopmental and synaptic theories of mental illness through DISC1. Nat. Rev. Neurosci. 12, 707–722 (2011).
Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).
Murray, G. K. et al. Could polygenic risk scores be useful in psychiatry?: A review. JAMA Psychiatry 78, 210–219 (2021).
Shaw, D. S. et al. Trajectories and predictors of children’s early-starting conduct problems: child, family, genetic, and intervention effects. Dev. Psychopathol. 31, 1911–1921 (2019).
Posner, J., Biezonski, D., Pieper, S. & Duarte, C. S. Genetic studies of mental illness: are children being left behind? J. Am. Acad. Child Adolesc. Psychiatry 60, 672–674 (2021).
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
Rovira, P. et al. Shared genetic background between children and adults with attention deficit/hyperactivity disorder. Neuropsychopharmacology 45, 1617–1626 (2020).
Reef, J., Diamantopoulou, S., van Meurs, I., Verhulst, F. & van der Ende, J. Child to adult continuities of psychopathology: a 24-year follow-up. Acta Psychiatr. Scand. 120, 230–238 (2009).
Feczko, E. et al. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn. Sci. 23, 584–601 (2019).
Laceulle, O. M., Vollebergh, W. A. M. & Ormel, J. The structure of psychopathology in adolescence: replication of a general psychopathology factor in the TRAILS study. Clin. Psychol. Sci. 3, 850–860 (2015).
Copeland, W., Shanahan, L., Erkanli, A., Costello, E. J. & Angold, A. Indirect comorbidity in childhood and adolescence. Front. Psychiatry https://doi.org/10.3389/fpsyt.2013.00144 (2013).
Vuijk, P. J. et al. Translating discoveries in attention-deficit/hyperactivity disorder genomics to an outpatient child and adolescent psychiatric cohort. J. Am. Acad. Child Adolesc. Psychiatry 59, 964–977 (2020).
Jones, H. J. et al. Phenotypic manifestation of genetic risk for schizophrenia during adolescence in the general population. JAMA Psychiatry 73, 221–228 (2016).
Marsman, A. et al. Do current measures of polygenic risk for mental disorders contribute to population variance in mental health? Schizophr. Bull. 46, 1353–1362 (2020).
Lee, P. H., Feng, Y.-C. A. & Smoller, J. W. Pleiotropy and cross-disorder genetics among psychiatric disorders. Biol. Psychiatry 89, 20–31 (2021).
Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482.e11 (2019).
Smoller, J. W. et al. Psychiatric genetics and the structure of psychopathology. Mol. Psychiatry 24, 409–420 (2019).
Steenkamp, L. R. et al. Psychotic experiences and future school performance in childhood: a population-based cohort study. J. Child Psychol. Psychiatry 62, 357–365 (2021).
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).
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).
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).
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).
Lee, P. H. et al. Genetic association of attention-deficit/hyperactivity disorder and major depression with suicidal ideation and attempts in children: the Adolescent Brain Cognitive Development Study. Biol. Psychiatry 92, 236–245 (2021).
Akingbuwa, W. A. et al. Genetic associations between childhood psychopathology and adult depression and associated traits in 42998 individuals: a meta-analysis. JAMA Psychiatry 77, 715–728 (2020).
Neumann, A. et al. Combined polygenic risk scores of different psychiatric traits predict general and specific psychopathology in childhood. J. Child Psychol. Psychiatry 63, 636–645 (2022).
Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).
Ge, T., Chen, C. Y., Ni, Y., Feng, Y. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).
Clark, D. A. et al. The general factor of psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study: a comparison of alternative modeling approaches. Clin. Psychol. Sci. 9, 169–182 (2021).
Achenbach, T. M. & Rescorla, L. Manual for the ASEBA School-Age Forms & Profiles: An Integrated System of Multi-Informant Assessment (ASEBA, 2001).
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).
Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234.e4 (2019).
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
Carithers, L. J. et al. A novel approach to high-quality postmortem tissue procurement: the GTEx project. Biopreserv. Biobank. 13, 311–319 (2015).
Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–479 (2011).
Tolosa, A. et al. FOXP2 gene and language impairment in schizophrenia: association and epigenetic studies. BMC Med. Genet. 11, 114 (2010).
Han, S., Carass, A., He, Y. & Prince, J. L. Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization. Neuroimage 218, 116819 (2020).
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C. & Yeo, B. T. T. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345 (2011).
Waszczuk, M. A. et al. General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study. Psychol. Med. 53, 1937–1946 (2021).
Sieradzka, D. et al. Are genetic risk factors for psychosis also associated with dimension-specific psychotic experiences in adolescence? PLoS ONE 9, e94398 (2014).
Kember, R. L. et al. Polygenic risk of psychiatric disorders exhibits cross-trait associations in electronic health record data from European ancestry individuals. Biol. Psychiatry 89, 236–245 (2021).
Bitsko, R. H. et al. Mental health surveillance among children – United States, 2013–2019. MMWR Suppl. 71, 1–42 (2022).
Rice, F. et al. Characterizing developmental trajectories and the role of neuropsychiatric genetic risk variants in early-onset depression. JAMA Psychiatry 76, 306–313 (2019).
Tubbs, J. D., Ding, J., Baum, L. & Sham, P. C. Systemic neuro-dysregulation in depression: evidence from genome-wide association. Eur. Neuropsychopharmacol. 39, 1–18 (2020).
Hariri, A. R. The emerging importance of the cerebellum in broad risk for psychopathology. Neuron 102, 17–20 (2019).
Kelly, E. et al. Regulation of autism-relevant behaviors by cerebellar–prefrontal cortical circuits. Nat. Neurosci. 23, 1102–1110 (2020).
Moberget, T. et al. Cerebellar gray matter volume is associated with cognitive function and psychopathology in adolescence. Biol. Psychiatry 86, 65–75 (2019).
Romer, A. L. et al. Structural alterations within cerebellar circuitry are associated with general liability for common mental disorders. Mol. Psychiatry 23, 1084–1090 (2018).
Valera, E. M., Faraone, S. V., Murray, K. E. & Seidman, L. J. Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biol. Psychiatry 61, 1361–1369 (2007).
Diedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E. & Ramnani, N. A probabilistic MR atlas of the human cerebellum. Neuroimage 46, 39–46 (2009).
Schmahmann, J. D., Weilburg, J. B. & Sherman, J. C. The neuropsychiatry of the cerebellum – insights from the clinic. Cerebellum 6, 254–267 (2007).
Luking, K. R. et al. Timing and type of early psychopathology symptoms predict longitudinal change in cortical thickness from middle childhood into early adolescence. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 7, 397–405 (2022).
Schmahmann, J. D. & Caplan, D. Cognition, emotion and the cerebellum. Brain 129, 290–292 (2006).
Khan, A. J. et al. Cerebro-cerebellar resting-state functional connectivity in children and adolescents with autism spectrum disorder. Biol. Psychiatry 78, 625–634 (2015).
Brady, R. O. Jr. et al. Cerebellar-prefrontal network connectivity and negative symptoms in schizophrenia. Am. J. Psychiatry 176, 512–520 (2019).
Wang, S. S., Kloth, A. D. & Badura, A. The cerebellum, sensitive periods, and autism. Neuron 83, 518–532 (2014).
Sathyanesan, A. et al. Emerging connections between cerebellar development, behaviour and complex brain disorders. Nat. Rev. Neurosci. 20, 298–313 (2019).
Miquel, M., Nicola, S. M., Gil-Miravet, I., Guarque-Chabrera, J. & Sanchez-Hernandez, A. A working hypothesis for the role of the cerebellum in impulsivity and compulsivity. Front. Behav. Neurosci. 13, 99 (2019).
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
Szucs, D. & Ioannidis, J. P. Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals. Neuroimage 221, 117164 (2020).
Kozareva, V. et al. A transcriptomic atlas of mouse cerebellar cortex comprehensively defines cell types. Nature 598, 214–219 (2021).
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
Jernigan, T. L., Brown, S. A. & ABCD Consortium Coordinators. Introduction. Dev. Cogn. Neurosci. 32, 1–3 (2018).
Karcher, N. R. et al. Assessment of the Prodromal Questionnaire–Brief Child Version for measurement of self-reported psychoticlike experiences in childhood. JAMA Psychiatry 75, 853–861 (2018).
Auchter, A. M. et al. A description of the ABCD organizational structure and communication framework. Dev. Cogn. Neurosci. 32, 8–15 (2018).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Choi, S. W. & O’Reilly, P. F. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience 8, giz082 (2019).
Mi, H. et al. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 49, D394–D403 (2021).
Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Penzes, P., Cahill, M. E., Jones, K. A., VanLeeuwen, J.-E. & Woolfrey, K. M. Dendritic spine pathology in neuropsychiatric disorders. Nat. Neurosci. 14, 285–293 (2011).
De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).
Medina-Gomez, C. et al. Challenges in conducting genome-wide association studies in highly admixed multi-ethnic populations: the Generation R Study. Eur. J. Epidemiol. 30, 317–330 (2015).
Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).
Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010).
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
Rosen, A. F. G. et al. Quantitative assessment of structural image quality. Neuroimage 169, 407–418 (2018).
Carass, A. et al. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage 183, 150–172 (2018).
Presented in part at the American College of Neuropsychopharmacology 2021 Annual Meeting, December 5–8, San Juan, PR. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award nos. 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/federal-partners.html. A listing of participating sites and a complete listing 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. We thank the investigators and staff at the ABCD sites and coordinating centers, as well as study participants and their families, for their essential contributions to this work. The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and Faculty of Social Sciences at Erasmus University Rotterdam, the Municipal Health Service Rotterdam area, the Rotterdam Homecare Foundation and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR-MDC), Rotterdam. We acknowledge the contribution of children and parents, general practitioners, hospitals, midwives and pharmacies in Rotterdam. The Generation R website contains details of ongoing data collection: http://generationr.nl/researchers/data-collection/. J.L.R. is supported by grant no. R01MH124694, grant no. R01MH120402 and the Mass General Early Brain Development Initiative; P.H.L. is supported in part by grant nos. R01MH119243, R01MH124694, R01MH116037, R01GM148494 and R01MH120219; A.E.D. is supported by grant no. R01MH120402; J.M.G. is supported by grant no. K02DA052684; C.A.M.C. is supported by the European Union’s Horizon 2020 Research and Innovation Programme (EarlyCause; grant agreement no. 848158), the HorizonEurope Research and Innovation Programme (FAMILY; grant agreement no. 101057529) and the European Research Council (TEMPO; grant agreement no. 101039672); H.T. is supported by an NWO-VICI grant (grant no. NWO-ZonMW: 016. VICI.170.200); and E.C.D. is supported by grant no. R01MH113930.
The authors declare no competing interests.
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Extended Data Fig. 1 Pearson correlations among dimensional psychopathology measures in ABCD genotyped subjects only.
(a, b) correlation matrix of psychopathology in genotyped males (top right of matrices) and females (bottom left of matrices) at ages 9- 10 (n = 4,459; A) and 11–12 (n = 3,360; B).
Extended Data Fig. 2 Relationship between gSEM-derived PGS and psychopathology in the Generation R cohort.
(a, b) Heatmaps showing uncorrected p-values from linear regression models regressing psychopathology on PGS covarying for age, sex, and top 5 principal components at age 9 (n = 1,850; A) and 13 (n = 1,791; B). Asterisks indicate p < 0.05 after False Discovery Rate correction for 36 comparisons (3 PGS x 12 measures of psychopathology). (c, d) Variance in CBCL Total accounted for by each gSEM-derived PGS. Uncorrected p-values (shown within the figure in black text near the y-max) represent the significance of the R2 change after adding NDV scores to base linear regression models including the respective PGS while covarying for age, sex, and top 5 principal components (Pt =1). All regressions represented are two-sided.
Extended Data Fig. 3 Odds of clinical-range psychopathology (CBCL Total score ≥64) comparing the top to the bottom quintiles of PGS.
Red represents odds of clinical-range psychopathology scores at baseline (age 9–10; n = 4,462). Blue represents odds of clinical-range psychopathology scores at year 2 (age 11–12) but not baseline (age 9–10; n = 3,152). Linear mixed effects regressions (two-sided) are adjusted for age, sex, and the top 5 genetic PCs as fixed effects, and site as a random effect. Points represent estimated odds ratios and error bars indicate 95% confidence intervals around those estimates.
Extended Data Fig. 4 Regional gene expression patterns across the lifespan.
Depicted are expression patterns of 12 of the most significant NDV genes (q < 0.009) using gene expression data from BrainSpan. Each plotted line represents expression across the lifetime within 1 of 6 regions (one color per region; black represents expression in the cerebellum). Vertical black line represents the delineation between prenatal and postnatal timepoints. Abbreviations: AMY, amygdala; CBC, cerebellar cortex; HP, hippocampus; MD, mediodorsal thalamus; NCX, neocortex; STR, striatum. (a, SORCS3; b, DUSP6; c, SEMA6D; d, CUBN; e, CCDC71; f, SLC30A9; g, CCDC36; h, STGAL3; i, KLHDC8B; j, LAMB2; k, FOXP2; l, VSIG10).
Extended Data Fig. 5 Effects of cortical ROI volumes on dimensions of psychopathology.
Linear mixed effects regressions (two-sided) are adjusted for age, sex, intracranial volume, and Euler number as fixed effects, and site, scanner, and family ID as random effects. Warmer colors represent more significant associations. P-values are corrected at the False Discovery Rate (number of comparisons = 272 [68 regions × 4 scales]).
Supplementary Figs. 1–5 and Tables 1–20.
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Hughes, D.E., Kunitoki, K., Elyounssi, S. et al. Genetic patterning for child psychopathology is distinct from that for adults and implicates fetal cerebellar development. Nat Neurosci (2023). https://doi.org/10.1038/s41593-023-01321-8