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 grey matter volumes in healthy controls and schizophrenia. We used parallel independent component analysis (parallel-ICA) to examine the relationship between grey 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.

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

“…To overcome this problem and to identify aggregate effects, there has been a recent shift toward using multivariate techniques (12,(24)(25)(26) such as parallel-independent component analysis (para-ICA), an approach previously validated in psychiatric disorders including SZ and Alzheimer's disease, yielding robust results with practical sample sizes (12,24,25,27). Given the substantial overlap in the clinical, neurophysiologic, genetic, and molecular characteristics of SZ and BP, we first sought to clarify similarities and differences between SZ and PBP using DMN connectivity as a quantitative disease marker.…”

DCDC2Polymorphism Is Associated with Left Temporoparietal Gray and White Matter Structures during Development

“…Dyslexia has also been associated with structural deviations of gray and white matter in corresponding regions (Klingberg et al, 2000;Deutsch et al, 2005;Silani et al, 2005;Vinckenbosch et al, 2005;Niogi and McCandliss, 2006;Kronbichler et al, 2008;Altarelli et al, 2013). These differences could rather be seen as the end distribution of a continuum in the general population, without any diagnosis of dyslexia (Klingberg et al, 2000;Nagy et al, 2004;Beaulieu et al, 2005;Deutsch et al, 2005;Niogi and McCandliss, 2006;.…”

“…A small number of candidate genes, such as DYX1C1, DCDC2, and KIAA0319, have been associated with increased risk for reading impairment (Taipale et al, 2003;Schumacher et al, 2006;Eicher et al, 2014) as well as with neuronal migration during cortical development Gabel et al, 2010;Peschansky et al, 2010;Szalkowski et al, 2012). At the cellular level, DYX1C1 and DCDC2 have been implicated in regulating ciliary growth and function (Massinen et al, 2011;Chandrasekar et al, 2013).…”

“…At the cellular level, DYX1C1 and DCDC2 have been implicated in regulating ciliary growth and function (Massinen et al, 2011;Chandrasekar et al, 2013). Impaired ciliary function may lead to misplacement of neurons in the cerebral cortex and may hinder the axonal outgrowth (Higginbotham et al, 2012).…”

A Comparison of Joint ICA and Parallel ICA Multimodal Fusion Methods in Schizophrenia

“…Parallel ICA finds the hidden factors from both modalities and the connections between them by finding the independent components of both the modalities and their connections [29]. Parallel ICA had been used in fusing multiple modalities such as fMRI-EEG [30,31], fMRI-Gene (SNP) [32,33], GM-Gene (SNP) [34,35].…”