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Network-Based Analysis of eQTL Data to Prioritize Driver Mutations.

Dries De Maeyer1, Bram Weytjens1, Luc De Raedt2

  • 1Deptartment 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.

Genome Biology and Evolution
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Summary

Identifying driver genes in clonal systems is crucial for understanding adaptive phenotypes. A new network-based eQTL method simultaneously identifies driver genes and interprets their molecular network roles, aiding evolutionary studies.

Keywords:
experimental evolution, biological networks, gene prioritization, coexisting ecotypes, drug resistance

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Area of Science:

  • Evolutionary biology
  • Systems biology
  • Genetics

Background:

  • Understanding adaptive evolution in clonal systems requires identifying driver genes.
  • Driver gene identification is often confounded by the need for network-level interpretation of their effects.
  • Parallelism at the molecular pathway level means similar phenotypes can arise from mutations in different genes within the same pathway.

Purpose of the Study:

  • To present a novel network-based quantitative trait loci (eQTL) method.
  • To simultaneously address driver gene identification and network-based interpretation of adaptive phenotypes.
  • To provide a tool for analyzing genotype-expression data in independently evolved lines.

Main Methods:

  • Utilized coupled genotype-expression phenotype data (eQTL data) from independently evolved lines with similar adaptive phenotypes.
  • Integrated an organism-specific genome-wide interaction network.
  • Framed the search for mutational consistency at the pathway level as a subnetwork inference problem.

Main Results:

  • The network-based eQTL method successfully prioritizes driver genes based on their connectivity to differentially expressed genes.
  • Demonstrated the method's potential using semisynthetic data and two publicly available datasets.
  • Gained insights into the molecular mechanisms underlying adaptive phenotypes.

Conclusions:

  • The developed network-based eQTL method effectively solves the coupled problems of driver gene identification and network interpretation.
  • This approach enhances the understanding of molecular networks driving adaptive evolution.
  • The method offers a valuable tool for evolutionary and systems biology research.