Cageminer: an R/Bioconductor package to prioritize candidate genes by integrating genome-wide association studies and gene coexpression networks. Issue 2 (24th August 2022)

• Record Type: Journal Article
• Authors: Almeida-Silva, Fabricio; Venancio, Thiago M
• Editors: Marshall-Colon, Amy
• Is Part Of: In silico plants. Volume 4:Issue 2(2022)
• Publication Date: 2022-08-24
• Subjects: Bioinformatics -- gene discovery -- multi-omics data integration -- population genomics -- systems biology; Plant physiology -- Periodicals; Botany -- Periodicals; Botany -- Mathematical models -- Periodicals; Crop science -- Periodicals; 580
• DOI: 10.1093/insilicoplants/diac018
• Languages: English
• ISSNs: 2517-5025

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 ).