scholarly journals Overdispersed gene expression characterizes schizophrenic brains

2018 ◽  
Author(s):  
Guangzao Huang ◽  
Daniel Osorio ◽  
Jinting Guan ◽  
Guoli Ji ◽  
James J. Cai
Keyword(s):  
Gene Expression ◽  
Brain Development ◽  
Genetic Disorders ◽  
Functional Gene ◽  
Functional Changes ◽  
Expression Variability ◽  
Gene Sets ◽  
Common Genetic Variants ◽  
Causal Variants ◽  
Endothelial Growth Factor

AbstractSchizophrenia (SCZ) is a severe, highly heterogeneous psychiatric disorder with varied clinical presentations. The polygenic genetic architecture of SCZ makes identification of causal variants daunting. Gene expression analyses have shown that SCZ may result in part from transcriptional dysregulation of a number of genes. However, most of these studies took the commonly used approach—differential gene expression analysis, assuming people with SCZ are a homogenous group, all with similar expression levels for any given gene. Here we show that the overall gene expression variability in SCZ is higher than that in an unaffected control (CTL) group. Specifically, we applied the test for equality of variances to the normalized expression data generated by the CommonMind Consortium (CMC) and identified 87 genes with significantly higher expression variances in the SCZ group than the CTL group. One of the genes with differential variability, VEGFA, encodes a vascular endothelial growth factor, supporting a vascular-ischemic etiology of SCZ. We also applied a Mahalanobis distance-based test for multivariate homogeneity of group dispersions to gene sets and identified 19 functional gene sets with higher expression variability in the SCZ group than the CTL group. Several of these gene sets are involved in brain development (e.g., development of cerebellar cortex, cerebellar Purkinje cell layer and neuromuscular junction), supporting that structural and functional changes in the cortex cause SCZ. Finally, using expression variability QTL (evQTL) analysis, we show that common genetic variants contribute to the increased expression variability in SCZ. Our results reveal that SCZ brains are characterized by overdispersed gene expression, resulting from dysregulated expression of functional gene sets pertaining to brain development, necrotic cell death, folic acid metabolism, and several other biological processes. Using SCZ as a model of complex genetic disorders with a heterogeneous etiology, our study provides a new conceptual framework for variability-centric analyses. Such a framework is likely to be important in the era of personalized medicine. (313 words)

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

scholarly journals Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

2021 ◽  
Author(s):  
Marios Arvanitis ◽  
Karl Tayeb ◽  
Benjamin J Strober ◽  
Alexis Battle
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Tissue Specificity ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Genetic Effects ◽  
Allelic Heterogeneity ◽  
Common Genetic Variants ◽  
Causal Variants ◽  
Disease Loci

Understanding the mechanisms that underlie genetic regulation of gene expression is crucial to explaining the diversity that governs complex traits. Large scale expression quantitative trait locus (eQTL) studies have been instrumental in identifying genetic variants that influence the expression of target genes. However, a large fraction of disease-associated genetic variants have not been clearly explained by current eQTL data, frustrating attempts to use these data to comprehensively characterize disease loci. One notable observation from recent studies is that cis-eQTL effects are often shared across different cell types and tissues. This would suggest that common genetic variants impacting steady-state, adult gene expression are largely tolerated, shared across tissues, and less relevant to disease. However, allelic heterogeneity and complex patterns of linkage disequilibrium (LD) within each locus may skew the quantification of sharing of genetic effects between tissues, impede our ability to identify causal variants, and hinder the identification of regulatory effects for disease-associated genetic variants. Indeed, recent research suggests that multiple causal variants are often present in many eQTL and complex trait associated loci. Here, we re-analyze tissue-specificity of genetic effects in the presence of LD and allelic heterogeneity, proposing a novel method, CAFEH, that improves the identification of causal regulatory variants across tissues and their relationship to disease loci.

scholarly journals Integrative analysis of transcriptome-wide association study and gene expression profiling identifies candidate genes associated with stroke

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7435
Author(s):  
Jian Yang ◽  
Bin Yan ◽  
Yajuan Fan ◽  
Lihong Yang ◽  
Binbin Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Candidate Genes ◽  
Expression Profiling ◽  
Enrichment Analysis ◽  
Integrative Analysis ◽  
Functional Gene ◽  
Gene Sets ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Stroke is a major public health burden worldwide. Although genetic variation is known to play a role in the pathogenesis of stroke, the specific pathogenic mechanisms are still unclear. Transcriptome-wide association studies (TWAS) is a powerful approach to prioritize candidate risk genes underlying complex traits. However, this approach has not been applied in stroke. Methods We conducted an integrative analysis of TWAS using data from the MEGASTROKE Consortium and gene expression profiling to identify candidate genes for the pathogenesis of stroke. Gene ontology (GO) enrichment analysis was also conducted to detect functional gene sets. Results The TWAS identified 515 transcriptome-wide significant tissue-specific genes, among which SLC25A44 (P = 5.46E−10) and LRCH1 (P = 1.54E−6) were significant by Bonferroni test for stroke. After validation with gene expression profiling, 19 unique genes were recognized. GO enrichment analysis identified eight significant GO functional gene sets, including regulation of cell shape (P = 0.0059), face morphogenesis (P = 0.0247), and positive regulation of ATPase activity (P = 0.0256). Conclusions Our study identified multiple stroke-associated genes and gene sets, and this analysis provided novel insights into the genetic mechanisms underlying stroke.

scholarly journals NGSEA: network-based gene set enrichment analysis for interpreting gene expression phenotypes with functional gene sets

2019 ◽  
Author(s):  
Heonjong Han ◽  
Sangyoung Lee ◽  
Insuk Lee
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Clinical Samples ◽  
Functional Genes ◽  
Biological Processes ◽  
Gene Set Enrichment ◽  
Gene Sets ◽  
Anti Cancer

ABSTRACTGene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets, however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

scholarly journals Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

2015 ◽  
Author(s):  
Jinting Guan ◽  
Ence Yang ◽  
Jizhou Yang ◽  
Yong Zeng ◽  
Guoli Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Genetic Heterogeneity ◽  
Gene Expression Analysis ◽  
Autism Spectrum ◽  
Data Set ◽  
Expression Variability ◽  
Aberrant Gene Expression ◽  
Gene Sets ◽  
Aberrant Gene

AbstractAutism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70% classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.

scholarly journals pathVar: a new method for pathway-based interpretation of gene expression variability

2016 ◽  
Author(s):  
Laurence de Torrente ◽  
Samuel Zimmerman ◽  
Deanne Taylor ◽  
Yu Hasegawa ◽  
Christine A Wells ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Genomics ◽  
Statistical Tests ◽  
Mechanistic Model ◽  
New Method ◽  
Regulatory Control ◽  
Functional Interpretation ◽  
Expression Variability ◽  
Gene Sets ◽  
Phenotypic Group

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression via GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation.

scholarly journals pathVar: a new method for pathway-based interpretation of gene expression variability

2016 ◽  
Author(s):  
Laurence de Torrente ◽  
Samuel Zimmerman ◽  
Deanne Taylor ◽  
Yu Hasegawa ◽  
Christine A Wells ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Genomics ◽  
Statistical Tests ◽  
Mechanistic Model ◽  
New Method ◽  
Regulatory Control ◽  
Functional Interpretation ◽  
Expression Variability ◽  
Gene Sets ◽  
Phenotypic Group

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression via GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation.

scholarly journals pathVar:a new method for pathway-based interpretation of gene expression variability

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3334 ◽  
Author(s):  
Laurence de Torrente ◽  
Samuel Zimmerman ◽  
Deanne Taylor ◽  
Yu Hasegawa ◽  
Christine A. Wells ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Genomics ◽  
Statistical Tests ◽  
Mechanistic Model ◽  
New Method ◽  
Regulatory Control ◽  
Functional Interpretation ◽  
Expression Variability ◽  
Gene Sets ◽  
Phenotypic Group

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we presentpathVar,a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets.pathVaris based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results frompathVarare benchmarked against analyses based on average expression and two methods of GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation. We also provide recommendations for the choice of variability statistic that have been informed through analyses on simulations and real data. Based on the datasets selected, we show howpathVarcan be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.

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