Genes encoding heterotrimeric G-proteins are associated with gray matter volume variations in the medial frontal cortex

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

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.

Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p=1.64×10(-6)). The sMRI component showed a significant group difference in loading parameters between patients and controls (p=1.33×10(-15)), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p=0.04) and was predominantly contributed to by 1030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size.

Imaging and genetics of language and cognition in pediatric epilepsy

This paper presents translational aspects of imaging and genetic studies of language and cognition in children with epilepsy of average intelligence. It also discusses current unanswered translational questions in each of these research areas. A brief review of multimodal imaging and language study findings shows that abnormal structure and function, as well as plasticity and reorganization in language-related cortical regions, are found both in children with epilepsy with normal language skills and in those with linguistic deficits. The review on cognition highlights that multiple domains of impaired cognition and abnormalities in brain structure and/or connectivity are evident early on in childhood epilepsy and might be specific for epilepsy syndrome. The description of state-of-the-art genetic analyses that can be used to explain the convergence of language impairment and Rolandic epilepsy includes a discussion of the methodological difficulties involved in these analyses. Two junior researchers describe how their current and planned studies address some of the unanswered translational questions regarding cognition and imaging and the genetic analysis of speech sound disorder, reading, and centrotemporal spikes in Rolandic epilepsy.