↓ Skip to main content

Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling

Overview of attention for article published in Frontiers in Molecular Neuroscience, June 2018
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
61 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling
Published in
Frontiers in Molecular Neuroscience, June 2018
DOI 10.3389/fnmol.2018.00192
Pubmed ID
Authors

Anna A. Igolkina, Chris Armoskus, Jeremy R. B. Newman, Oleg V. Evgrafov, Lauren M. McIntyre, Sergey V. Nuzhdin, Maria G. Samsonova

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 16%
Student > Ph. D. Student 9 15%
Student > Bachelor 6 10%
Student > Master 5 8%
Unspecified 4 7%
Other 7 11%
Unknown 20 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 15%
Medicine and Dentistry 7 11%
Neuroscience 5 8%
Unspecified 4 7%
Agricultural and Biological Sciences 4 7%
Other 10 16%
Unknown 22 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 June 2018.
All research outputs
#17,980,413
of 23,090,520 outputs
Outputs from Frontiers in Molecular Neuroscience
#2,087
of 2,930 outputs
Outputs of similar age
#237,121
of 328,268 outputs
Outputs of similar age from Frontiers in Molecular Neuroscience
#74
of 110 outputs
Altmetric has tracked 23,090,520 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,930 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,268 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.