Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling

Abstract

Schizophrenia (SCZ) is a psychiatric disorder of unknown etiology. There is evidence suggesting that aberrations in neurodevelopment are a significant attribute of schizophrenia pathogenesis and progression. To identify biologically relevant molecular abnormalities affecting neurodevelopment in SCZ we used cultured neural progenitor cells derived from olfactory neuroepithelium (CNON cells). Here, we tested the hypothesis that variance in gene expression differs between individuals from SCZ and control groups. In CNON cells, variance in gene expression was significantly higher in SCZ samples in comparison with control samples. Variance in gene expression was enriched in five molecular pathways: serine biosynthesis, PI3K-Akt, MAPK, neurotrophin and focal adhesion. More than 14% of variance in disease status was explained within the logistic regression model (C-value = 0.70) by predictors accounting for gene expression in 69 genes from these five pathways. Structural equation modeling (SEM) was applied to explore how the structure of these five pathways was altered between SCZ patients and controls. Four out of five pathways showed differences in the estimated relationships among genes: between KRAS and NF1, and KRAS and SOS1 in the MAPK pathway; between PSPH and SHMT2 in serine biosynthesis; between AKT3 and TSC2 in the PI3K-Akt signaling pathway; and between CRK and RAPGEF1 in the focal adhesion pathway. Our analysis provides evidence that variance in gene expression is an important characteristic of SCZ, and SEM is a promising method for uncovering altered relationships between specific genes thus suggesting affected gene regulation associated with the disease. We identified altered gene-gene interactions in pathways enriched for genes with increased variance in expression in SCZ. These pathways and loci were previously implicated in SCZ, providing further support for the hypothesis that gene expression variance plays important role in the etiology of SCZ.

Keywords: gene network modeling; neurodevelopmental disorders; schizophrenia; signaling pathways; structural equation models.

Figure 1

Structural equation modeling (SEM) fits for four gene networks representing serine biosynthesis, PI3K-Akt, MAPK and focal adhesion signaling. Each arrow contains three-line text information: the first line is the estimation of a path coefficient on control set of samples (and the standard error); the second line is the estimation of a path coefficient on Schizophrenia (SCZ) set; the third line shows the significance of difference between the estimates. p-values higher than 0.05 are marked by “ns” and blue color (non-significant), less than 0.05–by (*) and yellow color less than 0.01–by (**) and red color less than 0.001–by (***) and dark red color.