Method of weighted gene co-expression network analysis and its application in animal and poultry breeding and genetics

Document Type : Scientific-Extensional Article

Authors

1 Ph.D. Student of Animal and Poultry Breeding & Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Alborz, Iran

2 Associate Professor of Animal Breeding and Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Alborz, Iran

Abstract

Among the different types of networks, gene co-expression networks have the most flexibility to study different traits such as functional and reproductive traits, diseases, etc. Gene co-expression generally refers to the correlation between genes at the levels of transcripts; on the other hand, it can also be used at all biological scales (such as proteins, metabolites, or in combination between transcripts, proteins, and metabolites) to study the correlation relationships between genes. Co-expression networks have become popular in part because of the use of technologies such as microarrays, RNA-Seq, and mass spectrometry, as they allow the study of molecular mediators at different biological scales in a simple and relatively large number of samples. In addition, using this method, it is possible to measure the biological co-expression of genes simultaneously in specific cell types. By comparison, most protein-protein interaction (PPI) networks merely indicate general interactions between genes that do not refer to cell type and gene expression time, while gene expression networks can be reconstructed using data obtained from specific cell types from different individuals (upstream and downstream about a phenotypic trait such as individuals with high- and low-fertility) and throughout the developmental stages. One of the most widely used algorithms for constructing gene co-expression networks is weighted gene co-expression network analysis (WGCNA) due to its widespread use in many co-expression studies that will be instructive to explain how it works. As a result of identifying modules, genes, and metabolic-signaling pathways associated with various studied traits using the WGCNA method, they may show new insights into molecular mechanisms. Consequently, the purpose of this study was to provide a brief description of the method of weighted gene co-expression network analysis and its application in animal and poultry breeding and genetics.

Keywords

References

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