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.
| Original language | English |
|---|---|
| Pages (from-to) | 1483-1493 |
| Number of pages | 11 |
| Journal | Nature Genetics |
| Volume | 55 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 17 Aug 2023 |
Bibliographical note
Funding Information: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.
Funding Information:
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.
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.