Genetic associations of brain structural networks in schizophrenia: a preliminary study

Abstract

Background: Schizophrenia is a complex genetic disorder, with multiple putative risk genes and many reports of reduced cortical gray matter. Identifying the genetic loci contributing to these structural alterations in schizophrenia (and likely also to normal structural gray matter patterns) could aid understanding of schizophrenia's pathophysiology. We used structural parameters as potential intermediate illness markers to investigate genomic factors derived from single nucleotide polymorphism (SNP) arrays.

Method: We used research quality structural magnetic resonance imaging (sMRI) scans from European American subjects including 33 healthy control subjects and 18 schizophrenia patients. All subjects were genotyped for 367 SNPs. Linked sMRI and genetic (SNP) components were extracted to reveal relationships between brain structure and SNPs, using parallel independent component analysis, a novel multivariate approach that operates effectively in small sample sizes.

Results: We identified an sMRI component that significantly correlated with a genetic component (r = -.536, p < .00005); components also distinguished groups. In the sMRI component, schizophrenia gray matter deficits were in brain regions consistently implicated in previous reports, including frontal and temporal lobes and thalamus (p < .01). These deficits were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal central nervous system development, including AKT, PI3K, SLC6A4, DRD2, CHRM2, and ADORA2A.

Conclusions: Despite the small sample size, this novel analysis method identified an sMRI component including brain areas previously reported to be abnormal in schizophrenia and an associated genetic component containing several putative schizophrenia risk genes. Thus, we identified multiple genes potentially underlying specific structural brain abnormalities in schizophrenia.

Figure 1

Illustration of parallel independent component analysis (ICA) and network/canonical pathways implemented in structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. sMRI and SNP data are initialized with specified learning rates: sMRI (λ_f, λcf) and SNP (λ_s λsf). Parallel ICA identified sMRI and SNP component using an optimization algorithm based on entropy terms (H_f and H_s) and correlation term between W_f⁻¹ (sMRI) and W_s⁻¹ (SNP). W_f⁻¹ and W_s⁻¹ a participant-by-component mixing matrix. The loading parameters of the selected sMRI and SNP component were tested for group differences. SNP component that showed group differences and significant correlation with sMRI component were passed into gene pathway analysis.

Figure 2

The structural magnetic resonance imaging Component-A identified by parallel independent component analysis showing gray matter distributions from all patients and control subjects (n = 51) and thresholded at |z| > 2.0

Figure 3

Functional network identified by genes in the single nucleotide polymorphism (SNP) component using independent component analysis. Nodes in the red represent genes in the SNP component. The other molecules found in the network are possible connections and interactions between molecules, based onIngenuity Pathway Analysis knowledge base. Axonal Guidance signaling, G-protein-coupled receptor (GPCR) signaling, and PI3K/AKT signaling were the main signaling networks found. The genes with the colored circles in the network diagram are relevantly expressed in their respective pathways. The shapes of each gene symbol in the network denote the class of that gene as defined by the Ingenuity Pathway Analysis tool.