Genetic influences of cortical gray matter in language-related regions in healthy controls and schizophrenia

Abstract

Individuals with schizophrenia show a broad range of language impairments, including reading difficulties. A recent structural MRI (sMRI) study linked these difficulties to structural abnormalities in language-related regions (Leonard et al., 2008). Similar regions have been implicated in primary reading disability (RD). Major hypotheses of RD implicate abnormal embryonic neuronal migration in the cortex, and genetic linkage and association studies have identified a number of candidate RD genes that are associated with neuronal migration (Paracchini et al., 2007). Interestingly, evidence suggests at least some individuals with schizophrenia also show impaired neuronal migration in the cortex (Akbarian et al., 1996). Thus the aim of this study was to examine the link between RD-related genes and gray matter volumes in healthy controls and schizophrenia. We used parallel independent component analysis (parallel-ICA) to examine the relationship between gray matter volumes extracted using voxel-based morphometry (VBM) and 16 single nucleotide polymorphisms (SNPs) spanning FOXP2 and four RD-related genes, DCDC2, DYX1C1, KIAA0319 and TTRAP. Parallel-ICA identified five sMRI-SNP relationships. Superior and inferior cerebellar networks were related to DYX1C1 and DCDC2/KIAA0319 respectively in both groups. The superior prefrontal, temporal and occipital networks were positively related to DCDC2 in the schizophrenia, but not the control group. The identified networks closely correspond to the known distribution of language processes in the cortex. Thus, reading and language difficulties in schizophrenia may be related to distributed cortical structural abnormalities associated with RD-related genes.

Figure 1

sMRI networks for (A) superior prefrontal, (B) occipital, (C) temporal networks. Scatterplots show relationship between loading coefficients for sMRI and SNP components. These sMRI components were related to a SNP component that loaded most heavily on DCDC2 (rs1087266).

Figure 2

sMRI networks for (A) inferior and (B) superior cerebellar network. Scatterplots show relationship between loading coefficients for sMRI and SNP components. Inferior Cerebellar network was related to a SNP component that showed the strongest loadings for DCDC2 (rs793862) and KIAA0319 (rs4504469). Superior Cerebellar network was related to a SNP component that showed the strongest loadings for DYX1C1 (rs3743205).

Figure 3

Scatterplots of genotype effects for (A) superior prefrontal network and DCDC2 (rs1087266), (B) occipital network and DCDC2 (rs1087266), and (C) temporal network and DCDC2 (rs108266).