English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

G-Protein genomic association with normal variation in gray matter density

MPS-Authors
/persons/resource/persons4427

Fisher,  Simon E.
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Chen_HBM_2015.pdf
(Publisher version), 354KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Chen, J., Calhoun, V. D., Arias-Vasquez, A., Zwiers, M. P., Van Hulzen, K., Fernández, G., et al. (2015). G-Protein genomic association with normal variation in gray matter density. Human Brain Mapping, 36(11), 4272-4286. doi:10.1002/hbm.22916.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0028-42F3-D
Abstract
While detecting genetic variations underlying brain structures helps reveal mechanisms of neural disorders, high data dimensionality poses a major challenge for imaging genomic association studies. In this work, we present the application of a recently proposed approach, parallel independent component analysis with reference (pICA-R), to investigate genomic factors potentially regulating gray matter variation in a healthy population. This approach simultaneously assesses many variables for an aggregate effect and helps to elicit particular features in the data. We applied pICA-R to analyze gray matter density (GMD) images (274,131 voxels) in conjunction with single nucleotide polymorphism (SNP) data (666,019 markers) collected from 1,256 healthy individuals of the Brain Imaging Genetics (BIG) study. Guided by a genetic reference derived from the gene GNA14, pICA-R identified a significant SNP-GMD association (r = −0.16, P = 2.34 × 10−8), implying that subjects with specific genotypes have lower localized GMD. The identified components were then projected to an independent dataset from the Mind Clinical Imaging Consortium (MCIC) including 89 healthy individuals, and the obtained loadings again yielded a significant SNP-GMD association (r = −0.25, P = 0.02). The imaging component reflected GMD variations in frontal, precuneus, and cingulate regions. The SNP component was enriched in genes with neuronal functions, including synaptic plasticity, axon guidance, molecular signal transduction via PKA and CREB, highlighting the GRM1, PRKCH, GNA12, and CAMK2B genes. Collectively, our findings suggest that GNA12 and GNA14 play a key role in the genetic architecture underlying normal GMD variation in frontal and parietal regions