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A computational approach for identification of core modules from a co-expression network and GWAS data.

ABSTRACT: This protocol describes the application of the "omnigenic" model of the genetic architecture of complex traits to identify novel "core" genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with "core-like" characteristics. For complete details on the use and execution of this protocol, please refer to Sabik et al. (2020).

SUBMITTER: Sabik OL

PROVIDER: S-EPMC8385446 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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A computational approach for identification of core modules from a co-expression network and GWAS data.

Sabik Olivia L OL Ackert-Bicknell Cheryl L CL Farber Charles R CR

STAR protocols 20210821 3

This protocol describes the application of the "omnigenic" model of the genetic architecture of complex traits to identify novel "core" genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes wi ...[more]

PMID: 34467232

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Project description:BACKGROUND: Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. RESULTS: We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000) of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM) based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. CONCLUSION: This study reveals new insight into the topological properties of biological networks. The preferential hub-hub connections might be necessary for the formation of modular structure in gene co-expression networks. The study also reveals new insight into the organization of gene functional modules.

Project description:Osteoarthritis (OA) is one of the most prevalent causes of joint disease. However, the pathological mechanisms of OA have remained to be completely elucidated, and further investigation into the underlying mechanisms of OA development and the identification of novel therapeutic targets are urgently required. In the present study, the dataset GSE114007 was downloaded from the Gene Expression Omnibus database. Based on weighted gene co-expression network analysis (WGCNA) and the identification of differentially expressed genes (DEGs), the microarray data were further analyzed to identify hub genes, key transcription factors (TFs) and pivotal signaling pathways involved in the pathogenesis of OA. A total of 1,898 genes were identified to be differentially expressed between OA samples and normal samples. Based on WGCNA, the present study identified 5 hub modules closely associated with OA, and the potential key TFs for hub modules were further explored based on CisTargetX. The results demonstrated that B-Cell Lymphoma 6, Myelin Gene Expression Factor 2, Activating Transcription Factor 3, CCAAT Enhancer Binding Protein γ, Nuclear Factor Interleukin-3-Regulated, FOS Like Antigen-2, FOS-Like Antigen-1, Fos Proto-Oncogene, JunD Proto-Oncogene, Transcription Factor CP2 Like 1, RELA proto-oncogene NF-kB subunit, SRY-box transcription factor 3, V-Ets Avian Erythroblastosis Virus E26 Oncogene Homolog 2, Interferon Regulatory Factor 4 and REL proto-oncogene, NF-kB subunit were the potential key TFs. In addition, osteoclast differentiation, FoxO, MAPK and PI3K/Akt signaling pathways were revealed to be imperative for the pathogenesis of OA, as these 4 pivotal signaling pathways were observed to be tightly linked through 4 key TFs Fos Proto-Oncogene, JUN, JunD Proto-Oncogene and MYC, and 4 DEGs Vascular Endothelial Growth Factor A, Growth Arrest and DNA Damage Inducible α, Growth Arrest and DNA Damage Inducible β and Cyclin D1. The present study identified a set of potential key genes and signaling pathways, and provided an important opportunity to advance the current understanding of OA.

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Dataset Information

A computational approach for identification of core modules from a co-expression network and GWAS data.

Publications

A computational approach for identification of core modules from a co-expression network and GWAS data.

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