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
