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

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

The structure of a gene network reveals 7 biological sub-graphs underlying eQTLs in pig

International audienceIntegrative and system biology is a very promising tool for deciphering the biological and genetic mechanisms underlying complex traits. Transcriptomic analyses, in combination with genomic polymorphism, for instance, can give interesting insight on the genetic control of gene expression (eQTL studies). When hundreds of genes are detected with a link between their expression and some genetic polymorphisms (eQTL), the following question raises: what are the biological underlying functions? One tool is the use of a gene network, displaying interactions between genes with a genetic control (having at least an eQTL). There exist several possibilities for inferring a gene network: literature mining (using softwares such as Ingenuity) or inference from gene expression data. Although the first framework is a useful tool, it has some limitations: there is still a serious problem of lack of annotation in the pig genome, and a bias in information provided by Ingenuity (literature mainly devoted to Human, Mouse and Rat). We will hence explore in this work the inference of gene network from expression data. One simple method of inference was focused on, that has proven useful: Gaussian networks (Schäfer and Strimmer 2005). The following problem to be faced is the interpretation of such a "large" network (more than 100 genes). The aim of this study is to propose an adequate method for deciphering the structure of large gene networks. With the use of a good clustering of graph, the structure of one graph can be highlighted, and can reveal several sub graphs, each corresponding to particular biological functions

L'analyse d'un réseau de co-expression génique met en valeur des groupes fonctionnels homogènes et des gènes importants relatifs a un phénotype d'intérêt

National audienceCet article présente l'analyse d'un réseau de co-expression entre gènes dont la particularité est d'être régulés génétiquement. Cette étude est menée selon deux axes : une classification des gènes impliqués dans le réseau permet de mettre en valeur des groupes fonctionnels homogènes. Par ailleurs, une analyse conjointe du réseau et d'un phénotype d'intérêt permet de mettre en évidence des gènes candidats importants

The structure of a gene network reveals 7 biological functions underlying eQTLs in pig

International audienc

The structure of a gene network reveals 7 biological functions underlying eQTLs in pig

International audienc

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

International audienceWhat 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

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

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

Are gene networks always meaningful?

Post analyses in transcriptomic studies become increasingly popular. Giving a biological meaning to a list of differentially expressed genes requires the use of tools such as gene networks. Several approaches are available in the literature for gene network reconstruction. Using gene expression only, the most famous approach is probably Gaussian networks. They are based on the partial correlation between gene expressions. The Eadgene Post-Analysis Workshop was an opportunity to test this approach on a SABRE/EADGENE data set, but the results were difficult to interpret (Jaffrezic and Tosser-Klopp, 2009, BMC proceedings, in press). Two additional attempts are proposed here for a SABRE data set on folliculogenesis in pigs and on a eQTL design in pigs. Stability of gene network building is discussed according to sample size, simple simulations, and biological validation. It is shown that Gaussian networks are a useful tool for biological interpretation of a transcriptomic study, as far as the design is adapted. Then, the obtained network is analysed by the way of methods designed for clustering the vertices of a graph. We show that this methodology helps to emphazise the main structure of the network and provides simplified representations that are useful for the representation of the relations in the network

Are gene networks always meaningful?

Post analyses in transcriptomic studies become increasingly popular. Giving a biological meaning to a list of differentially expressed genes requires the use of tools such as gene networks. Several approaches are available in the literature for gene network reconstruction. Using gene expression only, the most famous approach is probably Gaussian networks. They are based on the partial correlation between gene expressions. The Eadgene Post-Analysis Workshop was an opportunity to test this approach on a SABRE/EADGENE data set, but the results were difficult to interpret (Jaffrezic and Tosser-Klopp, 2009, BMC proceedings, in press). Two additional attempts are proposed here for a SABRE data set on folliculogenesis in pigs and on a eQTL design in pigs. Stability of gene network building is discussed according to sample size, simple simulations, and biological validation. It is shown that Gaussian networks are a useful tool for biological interpretation of a transcriptomic study, as far as the design is adapted. Then, the obtained network is analysed by the way of methods designed for clustering the vertices of a graph. We show that this methodology helps to emphazise the main structure of the network and provides simplified representations that are useful for the representation of the relations in the network

The co-expression network where genes with high betweenness are highlighted.

<p>The names are also given. The list of genes with high betweenness is available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060045#pone.0060045.s007" target="_blank">Table S1</a>.</p