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Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence

Nature Human Behaviour volume 7, pages 1001–1017 (2023)Cite this article

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Abstract

Copy number variations (CNVs) are rare genomic deletions and duplications that can affect brain and behaviour. Previous reports of CNV pleiotropy imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, existing studies have primarily examined single CNV loci in small clinical cohorts. It remains unknown, for example, how distinct CNVs escalate vulnerability for the same developmental and psychiatric disorders. Here we quantitatively dissect the associations between brain organization and behavioural differentiation across 8 key CNVs. In 534 CNV carriers, we explored CNV-specific brain morphology patterns. CNVs were characteristic of disparate morphological changes involving multiple large-scale networks. We extensively annotated these CNV-associated patterns with ~1,000 lifestyle indicators through the UK Biobank resource. The resulting phenotypic profiles largely overlap and have body-wide implications, including the cardiovascular, endocrine, skeletal and nervous systems. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.

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Fig. 1: Eight CNVs lead to largely distinct spatial patterns of abnormalities in brain morphology.
Fig. 2: Pattern-learning models extract distinct intermediate brain phenotypes from CNV status.
Fig. 3: Intermediate brain phenotypes track structural changes with distinct impacts on large-scale networks.
Fig. 4: Using intermediate CNV phenotypes as a basis for phenome-wide association analysis.
Fig. 5: Eight different CNVs converge on similar phenome-wide association profiles.
Fig. 6: Detailing aspects convergence in phenome-wide portfolios across different CNVs.

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Data availability

The majority of 16p11.2 data are publicly available (https://www.sfari.org/). For the 22q11.2 sample, raw data are available upon request from the PI (C.E.B., cbearden@mednet.ucla.edu). All derived measures used in this study are available upon request (S.J., sebastien.jacquemont@umontreal.ca). The rest of the CNV carriers’ data cannot be shared as participants did not provide consent. All data from UK Biobank are available to other investigators online (ukbiobank.ac.uk). The Schaefer-Yeo atlas is accessible online (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal). Source data are provided with this paper.

Code availability

The processing scripts and custom analysis software used in this work are available in a publicly accessible GitHub repository along with examples of key visualizations in the paper: https://github.com/dblabs-mcgill-mila/CNV-convergence.

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Acknowledgements

D.B. was supported by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada, the National Institutes of Health (NIH R01 AG068563A, NIH R01 R01DA053301-01A1), the Canadian Institute of Health Research (CIHR 438531, CIHR 470425), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund), Google (Research Award, Teaching Award) and by the CIFAR Artificial Intelligence Chairs programme (Canada Institute for Advanced Research). This research was supported by Calcul Quebec (http://www.calculquebec.ca) and Compute Canada (http://www.computecanada.ca), the Brain Canada Multi-Investigator initiative, the Canadian Institutes of Health Research, CIHR_400528, the Institute of Data Valorization (IVADO) through the Canada First Research Excellence Fund, Healthy Brains for Healthy Lives through the Canada First Research Excellence Fund. S.J. is a recipient of a Canada Research Chair in neurodevelopmental disorders and a chair from the Jeanne et Jean Louis Levesque Foundation. The Cardiff CNV cohort was supported by the Wellcome Trust Strategic Award ‘DEFINE’ and the National Centre for Mental Health with funds from Health and Care Research Wales (code 100202/Z/12/Z). The CHUV cohort was supported by the SNF (Maillard Anne, Project PMPDP3 171331). Data from the UCLA cohort provided by C.E.B. (participants with 22q11.2 deletions or duplications and controls) were supported through grants from the NIH (U54EB020403), NIMH (R01MH085953, R01MH100900, R03MH105808) and the Simons Foundation (SFARI Explorer Award). K.K. was supported by the Institute of Data Valorization (IVADO) Postdoctoral Fellowship programme through the Canada First Research Excellence Fund. I.E.S. was supported by the Research Council of Norway (No. 223273), South-Eastern Norway Regional Health Authority (No. 2020060), European Union’s Horizon2020 Research and Innovation Programme (CoMorMent project; Grant No. 847776) and Kristian Gerhard Jebsen Stiftelsen (SKGJ-MED-021). We thank all of the families participating at the Simons Searchlight sites and the 16p11.2 European Consortium, Simons Searchlight Consortium. We appreciate obtaining access to brain-imaging and phenotypic data on SFARI Base. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

    Jakub Kopal, Karin Saltoun & Danilo Bzdok

  2. Mila - Quebec Artificial Intelligence Institute, Montréal, Quebec, Canada

    Jakub Kopal, Karin Saltoun & Danilo Bzdok

  3. Centre de recherche CHU Sainte-Justine and University of Montréal, Montréal, Quebec, Canada

    Kuldeep Kumar, Guillaume Huguet, Martineau Jean-Louis, Charles-Olivier Martin, Zohra Saci, Nadine Younis, Petra Tamer, Elise Douard, Sarah Lippé & Sébastien Jacquemont

  4. LREN - Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

    Claudia Modenato, Sandra Martin-Brevet & Bogdan Draganski

  5. Human Genetics and Cognitive Functions, CNRS UMR 3571: Genes, Synapses and Cognition, Institut Pasteur, Paris, France

    Clara A. Moreau

  6. Service des Troubles du Spectre de l’Autisme et apparentés, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

    Anne M. Maillard, Borja Rodriguez-Herreros, Aurèlie Pain & Sonia Richetin

  7. Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, UCLA, Los Angeles, CA, USA

    Leila Kushan & Carrie E. Bearden

  8. School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands

    Ana I. Silva & David E. J. Linden

  9. MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK

    Ana I. Silva, Marianne B. M. van den Bree, David E. J. Linden, Michael J. Owen & Jeremy Hall

  10. Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK

    Marianne B. M. van den Bree, Michael J. Owen & Jeremy Hall

  11. Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK

    Marianne B. M. van den Bree & David E. J. Linden

  12. Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

    Bogdan Draganski

  13. NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and University of Oslo, Oslo, Norway

    Ida E. Sønderby & Ole A. Andreassen

  14. Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

    Ida E. Sønderby

  15. KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway

    Ida E. Sønderby & Ole A. Andreassen

  16. Department of Psychiatry, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA

    David C. Glahn

  17. Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA

    Paul M. Thompson

  18. TheNeuro - Montreal Neurological Institute (MNI), McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

    Danilo Bzdok

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  1. Jakub Kopal
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  2. Kuldeep Kumar
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  19. Ana I. Silva
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  20. Marianne B. M. van den Bree
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  21. David E. J. Linden
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  22. Michael J. Owen
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  23. Jeremy Hall
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  26. Ida E. Sønderby
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  27. Ole A. Andreassen
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  32. Danilo Bzdok
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J.K., D.B. and S.J. designed the study, analysed imaging data and drafted the paper. J.K., C.M. and K.K. did all the preprocessing and analysis of neuroimaging data. K.S. provided scripts for the PheWAS analysis. D.B. and S.J. contributed to the interpretation of the results and the editing of the paper. C.M., A.M.M., A.P., S.R. and S.M.-B. recruited and scanned participants in the 16p11.2 European Consortium. S.L., C.-O.M., N.Y., P.T. and E.D. recruited and scanned participants in the Brain Canada cohort. L.K. collected and provided the data for the UCLA cohort. D.E.J.L., M.J.O., M.B.M.v.d.B., J.H. and A.I.S. provided the data for the Cardiff cohort. All authors provided feedback on the paper. D.B. led data analytics.

Corresponding author

Correspondence to Danilo Bzdok.

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The authors declare no competing interests.

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Peer review information

Nature Human Behaviour thanks Toru Takumi, Serafeim Loukas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and consortia members.

Reporting Summary

Source data

Source Data Fig. 1

Cohen’s d brain maps and their similarity, and PC and LDA embedding of CNV carriers.

Source Data Fig. 2

Performance of LDA models, LDA coefficients and their similarity.

Source Data Fig. 3

Significant LDA coefficients and their association with Cohen’s d, sample size, classification accuracy and CNV attributes.

Source Data Fig. 4

LDA expression in clinical dataset and UK Biobank, PheWAS analysis and percentages of associated phenotypes.

Source Data Fig. 5

PheWAS analyses and their similarities.

Source Data Fig. 6

Most commonly and strongly associated phenotypes, additional PheWAS analysis, and comparison of volumetric and phenotypic similarity.

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Kopal, J., Kumar, K., Saltoun, K. et al. Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence. Nat Hum Behav 7, 1001–1017 (2023). https://doi.org/10.1038/s41562-023-01541-9

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