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Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes

Nature Genetics volume 55, pages 1483–1493 (2023)Cite this article

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Abstract

Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.

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Fig. 1: Schematic overview of 13 brain MRI phenotypes and the genetic analyses.
Fig. 2: Manhattan plots of GWAS meta-analysis of 13 global MRI phenotypes.
Fig. 3: Pleiotropy among the 13 global phenotypes demonstrated by genetic/phenotypic correlations, structural equation modeling and colocalization analysis.
Fig. 4: Enrichment of GWAS signals in different cell types during development.
Fig. 5: Signatures of constraint and links to neurodevelopment for the global phenotypes.
Fig. 6: Topographic similarity and principal component structure of cortical phenotypes.

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Data availability

All summary statistics for the GWAS meta-analyses are available for access here: https://portal.ide-cam.org.uk/overview/483. To prevent potential misuse, the data are available under controlled access after approval by the research team for educational and research purposes only. Data from the UKB and ABCD can be applied for and accessed by approved researchers. GWAS summary statistics for other brain imaging phenotypes can be obtained from: The Oxford Brain Imaging Genetics PheWeb (PheWeb (ox.ac.uk)), GWAS catalog (GWAS Catalog (ebi.ac.uk)), GWAS ATLAS (Genome-wide association study ATLAS (ctglab.nl)) and Brain Imaging Genetics Knowledge Portal Brain Imaging Genetics Summary Statistics. The SPARK dataset can be obtained by application to SFARIbase (SFARI | SFARI Base). The DDD dataset can be obtained via EGA (deciphering developmental disorders (DDD)—EGA European Genome-Phenome Archive (ega-archive.org)).

Code availability

Code used are available at https://github.com/ucam-department-of-psychiatry/UKB (ref. 136), https://github.com/ucam-department-of-psychiatry/ABCD (ref. 137), vwarrier/ABCD_geneticQC (github.com; ref. 138) and vwarrier/Imaging_genetics_analyses (github.com; ref. 139).

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Acknowledgements

V.W. is supported by St. Catharine’s College Cambridge, funding from the Wellcome Trust (214322\Z\18\Z) and UKRI (10063472). E.-M.S. is supported by a Ph.D. studentship awarded by the Friends of Peterhouse. E.A.W.S. is supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Center (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. R.A.I.B. is supported by the Autism Research Trust. S.B.C. received funding from the Wellcome Trust (214322\Z\18\Z). S.B.C. also received funding from the Autism Center of Excellence, SFARI, the Templeton World Charitable Fund, the MRC and the NIHR Cambridge Biomedical Research Center. The research was supported by the NIHR Applied Research Collaboration East of England. J.S. was supported by NIMH (T32MH019112-29 and K08MH120564). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH (K08MH120564). R.R.G. was supported by the EMERGIA Junta de Andalucía program (EMERGIA20_00139). S.L.V. was supported by Max Planck Gesellschaft, (Otto Hahn Award) and the Helmholtz Association’s Initiative and Networking Fund under the Helmholtz International Lab grant agreement InterLabs-0015, and the Canada First Research Excellence Fund (CFREF Competition 2, 2015–2016) awarded to the Healthy Brains, Healthy Lives initiative at McGill University, through the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL). G.K.M. was supported by MRC (MR/W020025/1). For the purpose of open access, the authors have applied a CC BY license to any author-accepted manuscript version arising from this submission. We thank L.K. Abraham and J. Asimit for their helpful discussions. Additional acknowledgments are provided in the Supplementary Information.

Author information

Author notes

  1. These authors contributed equally: Hyejung Won, Hilary C. Martin, Edward T. Bullmore.

Authors and Affiliations

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

    Varun Warrier, Eva-Maria Stauffer, Lisa Ronan, Rafael Romero-Garcia, Simon Baron-Cohen, Graham K. Murray, Edward T. Bullmore & Richard A. I. Bethlehem

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

    Varun Warrier, Simon Baron-Cohen & Richard A. I. Bethlehem

  3. Wellcome Trust Sanger Institute, Hinxton, UK

    Qin Qin Huang, Emilie M. Wigdor & Hilary C. Martin

  4. Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK

    Eric A. W. Slob

  5. Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands

    Eric A. W. Slob

  6. Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands

    Eric A. W. Slob

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

    Jakob Seidlitz, Michael J. Gandal & Aaron Alexander-Bloch

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

    Jakob Seidlitz, Michael J. Gandal & Aaron Alexander-Bloch

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

    Jakob Seidlitz & Aaron Alexander-Bloch

  10. Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany

    Sofie L. Valk

  11. Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Sofie L. Valk

  12. Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

    Sofie L. Valk

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

    Travis T. Mallard

  14. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Travis T. Mallard

  15. Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA

    Andrew D. Grotzinger

  16. Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA

    Andrew D. Grotzinger

  17. Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, Spain

    Rafael Romero-Garcia

  18. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

    Daniel H. Geschwind

  19. Program in Neurogenetics, Department of Neurology, University of California, Los Angeles, CA, USA

    Daniel H. Geschwind

  20. Center for Autism Research and Treatment, Jane and TerrySemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA

    Daniel H. Geschwind

  21. Institute of Precision Health, University of California, Los Angeles, CA, USA

    Daniel H. Geschwind

  22. MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, UK

    Madeline A. Lancaster

  23. Cambridgeshire and Peterborough NHS Trust, Cambridge, UK

    Graham K. Murray & Edward T. Bullmore

  24. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    Graham K. Murray

  25. Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Hyejung Won

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  1. Varun Warrier
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Contributions

V.W. and R.A.I.B. designed the study, wrote the first draft of the manuscript and carried out revisions. V.W., R.A.I.B., H.C.M., E.T.B. and H.W. supervised the work. V.W., R.A.I.B., E.S., Q.Q.H., E.M.W., E.A.W.S., J.S. and R.R.G. analyzed the data. T.T.M. and A.D.G. advised on SEM. L.R. and S.V. advised on cortical structure and organization. S.B.C., D.H.G., M.L., G.K.M., M.J.G. and A.B. provided input to various analytical methods and helped interpret the data. All authors edited the manuscript and contributed to critical revisions of the manuscript.

Corresponding authors

Correspondence to Varun Warrier or Richard A. I. Bethlehem.

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Competing interests

A.A.-B. receives consulting income from Octave Biosciences. E.T.B. serves as a consultant for Sosei Heptares, Boehringer Ingelheim, GlaxoSmithKline, Monument Therapeutics and SR One. M.J.G. receives grant support from Mitsubishi Tanabe Pharma, unrelated to the current manuscript. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Consistency in genetic effects between ABCD and UKB.

(a) Correlation in effect size (regression beta from GWAS) between ABCD and UKB cohorts for all 237 genome-wide significant SNPs in the UKB: Pearson’s correlation coefficient, r = 0.54 with 95% confidence interval 0.45–0.63. (b) Genetic correlation (central point) and 95% confidence intervals (error bars) for 12 global phenotypes in the UKB and ABCD cohorts. Given the relatively small size of ABCD, the intercept has been constrained as there is no participant overlap between the UKB (Nmax = 31,797) and ABCD (Nmax = 4,866) and there is no inflation in test statistics due to uncontrolled population stratification. Consequently, estimates of genetic correlation can be above 1.

Extended Data Fig. 2 Mendelian randomization analysis for causal relationships between genetic effects on global brain phenotypes.

Scatter plots for the bidirectional MR effects between surface area and folding index, intrinsic curvature index, and local gyrification index. Slopes of the line (MR regression coefficient) indicate the estimated MR effect by method. Linear a, b, and c are scatter plots where surface area is the exposure, and d, e, and f are scatter plots where surface area is the outcome. All scatter plots are for MR analyses conducted by splitting the UKB into two samples of similar sample sizes. All estimates were statistically significant in scatter plots A,B, and C. Inverse-variance weighted MR failed to reach statistical significance in scatter plots d, e, and f. Number of SNPs included in the MR are provided in Supplementary Table 9. Error bars represent standard errors of the effect size (point estimates).

Extended Data Fig. 3 Forest plots and leave-one-out plots.

Forest plots (a–c) and leave-one-out (d–f) between surface area and folding index (FI, A and D), Intrinsic curvature index (ICI, B and E), and local gyrification index (LGI, C and F). Number of SNPs included in the MR are provided in Supplementary Table 9. Error bars indicate 95% confidence intervals of the effect (point estimates).

Extended Data Fig. 4 Regional heritability.

a. The distribution of the SNP heritability for the 180 bilaterally-averaged regional phenotypes of the 13 neuroimaging modalities. Maximum GWAS sample size = 36,663. Box plots indicate the median value (central line), the interquartile range, and the whiskers indicate the minimum and maximum. b. The cortical spatial topology of SNP heritability for the 13 neuroimaging modalities.

Extended Data Fig. 5 Asymmetry indices and SNP heritability of asymmetry indices for the 13 phenotypes.

a. Asymmetry indices for the 13 phenotypes. Positive values indicate leftward asymmetry. b. SNP heritabilities for asymmetry indices by region and phenotype. SNP heritability was calculated using GCTA–GREML for approximately 9,650 unrelated individuals from the UK Biobank. Significant regions were identified after FDR correction within each of the 13 phenotypes.

Extended Data Fig. 6 Topography of the first phenotypic principal components.

Color scales depict the relative eigenvector ranging from −20 to +29, in all plots the midpoint is defined as 0. It should be noted that the sign is somewhat ambiguous and that the magnitude is relative to its own scaling (in this case within each phenotype for which the PCA is performed). Thus, in this context, the color scale indicates to what extent regions show more homogenous similarity (that is, regions with more similar color have more similar covariance).

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Notes 1–4 and associated figures, and additional acknowledgments.

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Supplementary Tables

Supplementary Tables 1–34.

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Warrier, V., Stauffer, EM., Huang, Q.Q. 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). https://doi.org/10.1038/s41588-023-01475-y

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