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    PhenoGeneRanker, a network propagation tool, effectively prioritizes genes and strains related to hypertension using multiplex biological networks. This approach improves upon aggregated networks for better disease gene discovery and understanding genotype-phenotype relationships.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Systems Biology

    Background:

    • Uncovering genotype-phenotype relationships is crucial in genomics.
    • Gene prioritization from high-throughput studies is essential for managing large gene lists.
    • Network propagation methods are state-of-the-art for gene prioritization, assuming functional gene proximity in biological networks.

    Purpose of the Study:

    • Introduce the PhenoGeneRanker Bioconductor package.
    • Apply PhenoGeneRanker to multi-omics rat genome datasets for hypertension research.
    • Evaluate PhenoGeneRanker's performance in ranking disease-related genes and strains.

    Main Methods:

    • Utilized PhenoGeneRanker, a network-propagation algorithm for multiplex heterogeneous networks.
    • Calculated empirical p-values for gene and phenotype ranks via random stratified sampling.
    • Applied the package to multi-omics rat genome data for hypertension-related gene and strain ranking.

    Main Results:

    • PhenoGeneRanker demonstrated superior performance in ranking hypertension-related genes using multiplex gene networks compared to aggregated networks.
    • Ranking of hypertension-related strains was more effective with multiplex phenotype networks than with single or aggregated networks.
    • Gene Ontology (GO) enrichment of top-ranked genes identified significant hypertension-related GO terms.

    Conclusions:

    • PhenoGeneRanker, implemented as a Bioconductor package, enhances gene and strain prioritization for complex diseases like hypertension.
    • Multiplex network analysis provides a more accurate approach for gene prioritization and disease-strain association than aggregated networks.
    • The tool facilitates the discovery of genotype-phenotype relationships and relevant biological pathways through statistically validated rankings.