Cageminer: an R/Bioconductor package to prioritize candidate genes by integrating genome-wide association studies and gene coexpression networks

Abstract

Abstract Although 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, an 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 (or linkage disequilibrium with) single-nucleotide polymorphisms (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 of Capsicum annuum response to Phytophthora infection (RNA-seq and SNPs from an association panel), we demonstrate that it can effectively prioritize candidates, leading to a significant reduction in candidate gene lists. The package is available at Bioconductor (https://bioconductor.org/packages/cageminer).