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

摘要: 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 recently proposed approach, parallel independent component analysis with reference (pICA-R), to investigate factors potentially regulating gray matter variation in healthy population. This approach simultaneously assesses many variables an aggregate effect and elicit particular features data. We applied pICA-R analyze density (GMD) images (274,131 voxels) conjunction single nucleotide polymorphism (SNP) (666,019 markers) collected from 1,256 individuals Brain Imaging Genetics (BIG) study. Guided by derived gene GNA14, identified significant SNP-GMD (r=-0.16, P=2.34×10(-8)), implying that subjects specific genotypes have lower localized GMD. The components were then projected dataset Mind Clinical Consortium (MCIC) including 89 individuals, obtained loadings again yielded (r=-0.25, P=0.02). reflected GMD frontal, precuneus, cingulate regions. SNP was enriched genes neuronal functions, synaptic plasticity, axon guidance, molecular signal transduction via PKA CREB, highlighting GRM1, PRKCH, GNA12, CAMK2B genes. Collectively, our findings suggest GNA12 GNA14 play key role architecture normal frontal parietal