Jan 24, 2016

Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

Genome Biology and Evolution
Dries De MaeyerKathleen Marchal

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 inter...Continue Reading

  • References58
  • Citations4

References

  • References58
  • Citations4

Mentioned in this Paper

Study
Biochemical Pathway
Quantitative Trait Loci
Genome
Genes
Bio-Informatics
Candidate Gene Identification
Clone
Organism
Genotype Determination

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