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Genetic patterning for child psychopathology is distinct from that for adults and implicates fetal cerebellar development

Nature Neuroscience volume 26, pages 959–969 (2023)Cite this article

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Abstract

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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Fig. 1: Pearson correlations among dimensional psychopathology measures in ABCD and Generation R cohorts (11 CBCL scales, and PQ-BC distress scores), stratified by sex.
Fig. 2: Prediction of dimensional psychopathology in unrelated young adolescents of European ancestry by disorder-specific and gSEM-derived PGSs.
Fig. 3: Spatial and temporal NDV gene expression.
Fig. 4: Tissue-specific effects of NDV pPGS, based on gene sets with prenatal peak, postnatal peak or continuous gene expression, on dimensional psychopathology.
Fig. 5: Association between cerebellar volumes and dimensional psychopathology.

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Data availability

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 datamanagementgenr@erasmusmc.nl. 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 availability

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).

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Acknowledgements

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.

Author information

Author notes

  1. These authors contributed equally: Keiko Kunitoki, Safia Elyounssi, Mannan Luo.

  2. These authors jointly supervised this work: Phil H. Lee, Joshua L. Roffman.

Authors and Affiliations

  1. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Dylan E. Hughes, Safia Elyounssi, Oren M. Bazer, Casey E. Hopkinson & Kevin F. Dowling

  2. Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Keiko Kunitoki, Alysa E. Doyle, Erin C. Dunn, Hamdi Eryilmaz, Jodi M. Gilman, Daphne J. Holt, Eve M. Valera, Jordan W. Smoller & Joshua L. Roffman

  3. Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands

    Mannan Luo

  4. Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

    Mannan Luo

  5. Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Kevin F. Dowling

  6. Medical Scientist Training Program, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA

    Kevin F. Dowling

  7. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Alysa E. Doyle

  8. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Erin C. Dunn, Jordan W. Smoller & Phil H. Lee

  9. Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Erin C. Dunn, Jordan W. Smoller & Phil H. Lee

  10. Center on the Developing Child at Harvard University, Cambridge, MA, USA

    Erin C. Dunn

  11. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA

    Jodi M. Gilman, Daphne J. Holt & Joshua L. Roffman

  12. Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Jordan W. Smoller

  13. Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, the Netherlands

    Charlotte A. M. Cecil & Henning Tiemeier

  14. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands

    Charlotte A. M. Cecil

  15. Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands

    Charlotte A. M. Cecil

  16. Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Henning Tiemeier

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  1. Dylan E. Hughes
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  2. Keiko Kunitoki
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  18. Joshua L. Roffman
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D.E.H., K.K., M.L., C.E.H., K.F.D., P.H.L. and J.L.R. performed genomic data processing and analysis. D.E.H., K.K., S.E., O.M.B., C.E.H. and J.L.R. performed neuroimaging preprocessing and data analysis. D.E.H., K.K., S.E., M.L., O.M.B., P.H.L. and J.L.R. performed behavioral/clinical data curation, processing and analysis. D.E.H., K.K., S.E., M.L., P.H.L. and J.L.R. prepared the manuscript. D.E.H., K.K., S.E., M.L., O.M.B., C.E.H., K.F.D., A.E.D., E.C.D., H.E., J.M.G., D.J.H., E.M.V., J.W.S., C.A.M.C., H.T., P.H.L. and J.L.R. contributed to conceptualization of the study and review of the manuscript.

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Correspondence to Joshua L. Roffman.

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Extended data

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]).

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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 26, 959–969 (2023). https://doi.org/10.1038/s41593-023-01321-8

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