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bioRxiv · 10.1101/2021.08.04.455037

cageminer: an R/Bioconductor package to prioritize candidate genes by integrating GWAS and gene coexpression networks

Almeida-Silva, F.·Venancio, T. M.
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Abstract

SummaryAlthough genome-wide association studies (GWAS) identify variants associated with traits of interest, they often fail in identifying causative genes underlying a given phenotype. Integrating GWAS and gene coexpression networks can help prioritize high-confidence candidate genes, as the expression profiles of trait-associated genes can be used to mine novel candidates. Here, we present cageminer, the first R package to prioritize candidate genes through the integration of GWAS and coexpression networks. Genes are considered high-confidence candidates if they pass all three filtering criteria implemented in cageminer, namely physical proximity to SNPs, coexpression with known trait-associated genes, and significant changes in expression levels in conditions of interest. Prioritized candidates can also be scored and ranked to select targets for experimental validation. By applying cageminer to a real data set, we demonstrate that it can effectively prioritize candidates, leading to >99% reductions in candidate gene lists. Availability and implementationThe package is available at Bioconductor (http://bioconductor.org/packages/cageminer).

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Almeida-Silva, F., Venancio, T. M.. 2021-08-05. cageminer: an R/Bioconductor package to prioritize candidate genes by integrating GWAS and gene coexpression networks. https://doi.org/10.1101/2021.08.04.455037

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