PheNetic eQTL

PheNetic eQTL infers subnetworks from an organism-specific genome-wide interaction network to connect mutated genes with differentially expressed genes, prioritizing candidate driver genes from coupled genotype–expression phenotype (eQTL) data to interpret adaptive phenotypes.

Key Features:

• Network-Based Approach: Leverages a network-based framework to interpret mutated genes within molecular pathways and detect pathway-level parallelism in clonal systems.
• Input Requirements: Requires coupled genotype–expression phenotype (eQTL) data from independently evolved lines and an organism-specific genome-wide interaction network.
• Subnetwork Inference Problem: Frames mutational consistency at the pathway level as a subnetwork inference problem by inferring subnetworks that optimally connect mutated genes and differentially expressed genes.
• Driver Gene Prioritization: Prioritizes candidate driver genes by evaluating connectivity between mutated genes and differentially expressed genes within inferred subnetworks.
• Validation and Application: Demonstrated using semisynthetic data and two publicly available datasets to prioritize driver genes and provide insights into molecular mechanisms underlying adaptive phenotypes.

Scientific Applications:

• Evolutionary Biology and Genetics: Interprets the genetic basis of adaptation by linking mutations to expression changes in evolutionary biology and genetics studies.
• Clonal Systems and Parallelism Detection: Detects pathway-level parallelism and elucidates adaptive mechanisms using data from independently evolved clonal lines.
• eQTL Driver Discovery: Prioritizes candidate driver genes from eQTL datasets to connect genotype changes with expression phenotypes.

Methodology:

Infers subnetworks from an organism-specific genome-wide interaction network that optimally connect mutated genes with differentially expressed genes and evaluates connectivity within these subnetworks using coupled genotype–expression phenotype (eQTL) data from independently evolved lines.