geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation

Area of Science:

• Genomics
• Bioinformatics
• Genetic Epidemiology

Background:

• Genetic association studies, such as Genome-Wide Association Studies (GWAS), identify risk variants associated with diseases.
• Pinpointing the specific causal gene(s) responsible for these associations remains a significant challenge in genetic research.

Purpose of the Study:

• To develop an R package, geneAttribution, for assigning candidate causal genes to genetic risk variants.
• To provide a tool that integrates functional genomic data to improve gene prioritization.

Main Methods:

• The geneAttribution package utilizes user-supplied functional annotation data, including expression quantitative trait loci (eQTL) and Hi-C genome conformation data.
• In the absence of specific annotation, the method defaults to using the physical distance between genes and the input risk variant.
• The package reports the most probable candidate causal genes based on the integrated data.

Main Results:

• The developed R package, geneAttribution, successfully assigns candidate causal genes to genetic risk variants.
• The method demonstrates flexibility by incorporating diverse functional genomic data or relying on genomic proximity.

Conclusions:

• geneAttribution offers a valuable computational tool for researchers investigating the genetic basis of diseases.
• The package facilitates the identification of candidate genes, aiding in the interpretation of genetic association study findings.