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

Area of Science:

• Neuroimaging
• Genetics
• Computational Biology

Background:

• Detecting genetic variations linked to brain structures aids understanding neural disorders.
• High dimensionality in imaging genomic association studies presents a significant challenge.

Purpose of the Study:

• To apply parallel independent component analysis with reference (pICA-R) for investigating genomic factors regulating gray matter variation in a healthy population.
• To identify specific genetic and neuroimaging features associated with normal gray matter density variations.

Main Methods:

• Applied pICA-R to analyze gray matter density (GMD) images and single nucleotide polymorphism (SNP) data from 1,256 healthy individuals (Brain Imaging Genetics study).
• Utilized a genetic reference from the GNA14 gene to guide the analysis.
• Validated findings using an independent dataset from the Mind Clinical Imaging Consortium (MCIC).

Main Results:

• Identified a significant SNP-GMD association (r=-0.16, P=2.34×10(-8)) indicating specific genotypes correlate with lower localized GMD.
• Confirmed significant SNP-GMD association (r=-0.25, P=0.02) in the independent MCIC dataset.
• The imaging component highlighted GMD variations in frontal, precuneus, and cingulate regions, while the SNP component was enriched in neuronal function genes (e.g., GRM1, PRKCH, GNA12, CAMK2B).

Conclusions:

• GNA12 and GNA14 play a crucial role in the genetic architecture of normal gray matter density variation.
• pICA-R effectively addresses high dimensionality in imaging genomic association studies.
• Findings provide insights into the genetic underpinnings of brain structure variation in healthy individuals.