| Title: | Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior |
| Journal: | Communications Biology |
| Published: | 12 Oct 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34642422/ |
| DOI: | https://doi.org/10.1038/s42003-021-02712-y |
| URL: | https://www.nature.com/articles/s42003-021-02712-y.pdf |
| Citations: | 9 (4 in last 2 years) as of 8 Aug 2024 |
Publication 5446
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
Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.7 Authors
- Ronald de Vlaming
- Eric A. W. Slob
- Philip R. Jansen
- Alain Dagher
- Philipp D. Koellinger
- Patrick J. F. Groenen
- Cornelius A. Rietveld
Enabling scientific discoveries that improve human health