cageminer

cageminer prioritizes candidate genes by integrating GWAS results with gene coexpression networks to identify likely causative genes underlying trait-associated SNPs.

Key Features:

• Implementation: Implemented as an R/Bioconductor package.
• Data integration: Combines GWAS variant loci with gene coexpression networks using expression profiles of trait-associated genes to nominate candidate genes.
• Three-pronged filtering criteria: Filters candidates by physical proximity to SNPs, coexpression with known trait-associated genes, and significant expression changes under relevant conditions.
• Scoring and ranking: Assigns scores and ranks candidate genes by confidence to prioritize targets for downstream validation.

Scientific Applications:

• Candidate gene prioritization: Prioritizes likely causative genes from GWAS loci by integrating genetic and expression network evidence.
• Experimental target selection: Supports selection of targets for functional validation by ranking candidates according to proximity, coexpression, and differential expression.
• Reduction of candidate lists: Has been reported to reduce candidate gene lists by over 99% in applied datasets.

Methodology:

Integrates GWAS data with gene coexpression networks and applies filters based on SNP proximity, coexpression with trait-associated genes, and significant expression changes, followed by scoring and ranking of candidates.