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Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

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...

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Autores principales: De Maeyer, Dries, Weytjens, Bram, De Raedt, Luc, Marchal, Kathleen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4825419/
https://www.ncbi.nlm.nih.gov/pubmed/26802430
http://dx.doi.org/10.1093/gbe/evw010
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author De Maeyer, Dries
Weytjens, Bram
De Raedt, Luc
Marchal, Kathleen
author_facet De Maeyer, Dries
Weytjens, Bram
De Raedt, Luc
Marchal, Kathleen
author_sort De Maeyer, Dries
collection PubMed
description 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
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spelling pubmed-48254192016-04-11 Network-Based Analysis of eQTL Data to Prioritize Driver Mutations De Maeyer, Dries Weytjens, Bram De Raedt, Luc Marchal, Kathleen Genome Biol Evol Research Article 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 Oxford University Press 2016-01-23 /pmc/articles/PMC4825419/ /pubmed/26802430 http://dx.doi.org/10.1093/gbe/evw010 Text en © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research Article
De Maeyer, Dries
Weytjens, Bram
De Raedt, Luc
Marchal, Kathleen
Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
title Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
title_full Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
title_fullStr Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
title_full_unstemmed Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
title_short Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
title_sort network-based analysis of eqtl data to prioritize driver mutations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4825419/
https://www.ncbi.nlm.nih.gov/pubmed/26802430
http://dx.doi.org/10.1093/gbe/evw010
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