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Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome.

ABSTRACT: Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways.We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.

SUBMITTER: Presson AP

PROVIDER: S-EPMC2625353 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome.

Presson Angela P AP Sobel Eric M EM Papp Jeanette C JC Suarez Charlyn J CJ Whistler Toni T Rajeevan Mangalathu S MS Vernon Suzanne D SD Horvath Steve S

BMC systems biology 20081106

<h4>Background</h4>Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.<h4>Results</h4>We combine WGCNA with genetic marker data to identify a disease-related pathway and its ca ...[more]

PMID: 18986552

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Project description:PurposeIntracerebral hemorrhage (ICH) is a serious public health hazard due to its high morbidity, disability, and mortality. Currently, the exact molecular mechanisms of ICH are unknown. We tried to identify the ICH-related candidate blood messenger RNA (mRNA) biomarkers by microarray analysis and weighted gene co-expression network analysis (WGCNA).Materials and methodsWe collected the blood samples from patients with ICH (n = 4) and from vascular risk factor (VRF) controls (n = 4) and analyzed the mRNA expression profiles by competitive endogenous RNA (ceRNA) microarray. Differentially expressed genes (DEGs) were identified and then a weighted gene co-expression network was constructed. Modules with clinical significance were distinguished. Then, we downloaded two Gene Expression Omnibus (GEO) datasets (GSE24265 and GSE125512). Candidate mRNAs were identified by taking the intersection of the DEGs in our microarray, the interesting genes in the key module, and the DEGs in GSE24265. Functional analysis involving Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and construction of a protein-protein interaction (PPI) network were conducted.ResultsA total of 340 DEGs in our microarray were identified between the ICH group and the control group. Among the eight gene modules established by WGCNA, the yellow module containing 191 genes was the most strongly associated with ICH. Four candidate mRNAs (C3AR1, PAWR, ARNTL2, and LDLRAD4) were identified. In the early stage of ICH (within 24 h), C3AR1, PAWR, and ARNTL2 were highly expressed in the perihematomal tissue, but with low expressions in peripheral blood; in the late stage (72 h after the first blood draw), an obvious upward trend of C3AR1 and PAWR in peripheral blood was seen. Functional analysis showed that candidate mRNAs were concerned with multiple pathways, such as the Wnt signaling pathway and calcium signaling pathway. They might affect the process of ICH through neuroinflammation, cell apoptosis, and pyroptosis.ConclusionWe identified four candidate blood mRNAs (C3AR1, PAWR, ARNTL2, and LDLRAD4) related to ICH. They showed different expression patterns in peripheral blood and perihematomal tissues and changed with time. They might play important roles in ICH through neuroinflammation, cell apoptosis, and pyroptosis and might shed new light to novel biomarkers or therapeutic targets in ICH.

Project description:In dairy cattle, endometritis is a severe infectious disease that occurs following parturition. It is clear that genetic factors are involved in the etiology of endometritis, however, the molecular pathogenesis of endometritis is not entirely understood. In this study, a system biology approach was used to better understand the molecular mechanisms underlying the development of endometritis. Forty transcriptomic datasets comprising of 20 RNA-Seq (GSE66825) and 20 miRNA-Seq (GSE66826) were obtained from the GEO database. Next, the co-expressed modules were constructed based on RNA-Seq (Rb-modules) and miRNA-Seq (mb-modules) data, separately, using a weighted gene co-expression network analysis (WGCNA) approach. Preservation analysis was used to find the non-preserved Rb-modules in endometritis samples. Afterward, the non-preserved Rb-modules were assigned to the mb-modules to construct the integrated regulatory networks. Just highly connected genes (hubs) in the networks were considered and functional enrichment analysis was used to identify the biological pathways associated with the development of the disease. Furthermore, additional bioinformatic analysis including protein-protein interactions network and miRNA target prediction were applied to enhance the reliability of the results. Thirty-five Rb-modules and 10 mb-modules were identified and 19 and 10 modules were non-preserved, respectively, which were enriched in biological pathways related to endometritis like inflammation and ciliogenesis. Two non-preserved Rb-modules were significantly assigned to three mb-modules and three and two important sub-networks in the Rb-modules were identified, respectively, including important mRNAs, lncRNAs and miRNAs genes like IRAK1, CASP3, CCDC40, CCDC39, ZMYND10, FOXJ1, TLR4, IL10, STAT3, FN1, AKT1, CD68, ENSBTAG00000049936, ENSBTAG00000050527, ENSBTAG00000051242, ENSBTAG00000049287, bta-miR-449, bta-miR-484, bta-miR-149, bta-miR-30b and bta-miR-423. The potential roles of these genes have been previously demonstrated in endometritis or related pathways, which reinforced putative functions of the suggested integrated regulatory networks in the endometritis pathogenesis. These findings may help further elucidate the underlying mechanisms of bovine endometritis.

Dataset Information

Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome.

Publications

Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome.

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