| Title: | The genetic architecture of human cortical folding |
| Journal: | Science Advances |
| Published: | 15 Dec 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34910505/ |
| DOI: | https://doi.org/10.1126/sciadv.abj9446 |
| URL: | https://www.duo.uio.no/bitstream/10852/90662/1/The%2bgenetic%2barchitecture%2bof%2bhuman%2bcortical%2bfolding.pdf |
Publication 10870
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
The folding of the human cerebral cortex is a highly genetically regulated process that allows for a much larger surface area to fit into the cranial vault and optimizes functional organization. Sulcal depth is a robust yet understudied measure of localized folding, previously associated with multiple neurodevelopmental disorders. Here, we report the first genome-wide association study of sulcal depth. Through the multivariate omnibus statistical test (MOSTest) applied to vertex-wise measures from 33,748 U.K. Biobank participants (mean age, 64.3 years; 52.0% female), we identified 856 genome-wide significant loci (P < 5 × 10−8). Comparisons with cortical thickness and surface area indicated that sulcal depth has higher locus yield, heritability, and effective sample size. There was a large amount of genetic overlap between these traits, with gene-based analyses indicating strong associations with neurodevelopmental processes. Our findings demonstrate sulcal depth is a promising neuroimaging phenotype that may enhance our understanding of cortical morphology.</p>
17 Authors
- Dennis van der Meer
- Tobias Kaufmann
- Alexey A. Shadrin
- Carolina Makowski
- Oleksandr Frei
- Daniel Roelfs
- Jennifer Monereo-Sánchez
- David E. J. Linden
- Jaroslav Rokicki
- Dag Alnæs
- Christiaan de Leeuw
- Wesley K. Thompson
- Robert Loughnan
- Chun Chieh Fan
- Lars T. Westlye
- Ole A. Andreassen
- Anders M. Dale
1 Application
| Application ID | Title |
|---|---|
| 27412 | Boosting the power of GWAS using novel statistical tools |
Enabling scientific discoveries that improve human health