The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs

Abstract

What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

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“…We performed a gene co-expression network analysis to investigate the phenotypic change of genes associated with AMI (Kumari et al, 2012; Villa-Vialaneix et al, 2013). Twelve genes from the AMI and control groups were chosen as key regulatory genes (| Dif _ Kcore | > 30), and it was shown that there was a significant change in the expression pattern of genes in the AMI group (Supplement Table 5), namely CNN2 (calponin 2), CRYZ (quinone oxidoreductase), SULT1A1 (sulfotransferase 1A1), SULT1A2 (sulfotransferase 1A2), PRMT2 (protein arginine methyltransferase 2), ATP1B1 (ATPase, Na + /K + transporting, beta 1 polypeptide), CX3CR1 (CX3C chemokine receptor 1), GCH1 (GTP cyclohydrolase 1), INSIG1 (insulin-induced gene protein), CXCL5 (C-X-C motif chemokine 5), GBP3 (guanylate-binding protein 3) and HEG1 (heart development protein with EGF-like domains 1).We hypothesized that the 12 key regulatory genes are likely to be closely related to the occurrence and development of AMI (Figure 3).…”

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“…To better dissect the underlying biology behind eQTL regulation, a gene network approach was carried out to explore the 272 genes regulated by eQTL to identify communities with possible links with a pHu level (Villa-Vialaneix et al, 2013). Thereby 20 genes and especially one community of genes were identified to be correlated to pHu.…”