ArticleScientific reports2015
Increased Variability of Genomic Transcription in Schizophrenia.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Improving data interpretability with new differential sample variance gene set tests.BMC bioinformatics · 2025Article
- Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression.PLoS computational biology · 2023Article
- Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.NAR genomics and bioinformatics · 2022Article
- Variable expression quantitative trait loci analysis of breast cancer risk variants.Scientific reports · 2021Article
- Coordinated analysis of exon and intron data reveals novel differential gene expression changes.Scientific reports · 2020Article
- Predictive features of gene expression variation reveal mechanistic link with differential expression.Molecular systems biology · 2020Article
- MicroRNA-mRNA Co-sequencing Identifies Transcriptional and Post-transcriptional Regulatory Networks Underlying Muscle Wasting in Cancer Cachexia.Frontiers in genetics · 2020Article
- Gene expression variation and parental allele inheritance in a Xiphophorus interspecies hybridization model.PLoS genetics · 2018Article
- Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling.Frontiers in molecular neuroscience · 2018Article
- Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.Nucleic acids research · 2017Article
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Authors and funding
10 authors.
Funding
Abstract
Schizophrenia (SZ) is a severe chronic mental disorder with a high heritability. Current microarray analyses typically focus on identifying differentially expressed genes or enriched pathways relevant to phenotypes. Whether there is a variability change of the genomic transcription in diseases has rarely been explored. In this study, we compared coefficient of variation (CV, the ratio of the standard deviation to the mean) of genome transcription of early-onset SZ (EOS) patients with controls in a blood mRNA microarray dataset and a blood microRNA (miRNA) microarray dataset. Furthermore, we compared CV of the expression levels of 17 genes in blood of the 30 patients before and after the 12-week treatment using real-time quantitative PCR (RT-qPCR) analysis. Our results indicated a significant increase of CV of genome transcription in patients compared with controls in both the mRNA and the miRNA datasets. The 30 after-treatment patients showed a significant decrease of CV of gene expression compared with the before-treatment patients. Our study may implicate the blood gene expression variability in SZ, providing further evidence supporting the abnormality of peripheral blood transcriptome in SZ. Given that peripheral blood can be easily collected from patients and followed longitudinally, our results may indicate a new way to facilitate the identification of the signatures of clinical subtypes, their prognosis and treatment response.
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