title: Network-Based Analysis of eQTL Data to Prioritize Driver Mutations.
abstract: In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html.
authors.affiliation: Deptartment of Information Technology (INTEC, iMINDS), UGent, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Ghent, Belgium Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium.
authors.forename: Dries
authors.initials: D
authors.lastname: De Maeyer
authors.affiliation: Deptartment of Information Technology (INTEC, iMINDS), UGent, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Ghent, Belgium Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium.
authors.forename: Bram
authors.initials: B
authors.lastname: Weytjens
authors.affiliation: Department of Computer Science, KU Leuven, Celestijnenlaan 200A, B-3001 Leuven, Belgium.
authors.forename: Luc
authors.initials: L
authors.lastname: De Raedt
authors.affiliation: Deptartment of Information Technology (INTEC, iMINDS), UGent, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Ghent, Belgium Department of Genetics, University of Pretoria, Hatfield Campus, Pretoria 0028, South Africa Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium kathleen.marchal@intec.ugent.be.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Marchal
mesh_terms: D019295:Computational Biology; D005786:Gene Expression Regulation / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D056726:Genetic Association Studies; D016678:Genome; D005838:Genotype; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics*
keywords: experimental evolution, biological networks, gene prioritization, coexisting ecotypes, drug resistance