geneAttribution

geneAttribution identifies candidate causal genes associated with risk variants from genetic association studies by integrating user-supplied functional annotations such as expression quantitative trait loci (eQTL) and Hi-C and by applying a proximity-based fallback when annotations are unavailable.

Key Features:

• Input data types: Accepts user-supplied functional annotation data including expression quantitative trait loci (eQTL) and Hi-C genome conformation data.
• Primary attribution strategy: Attributes genomic regions to genes using empirical functional annotations when available.
• Proximity-based fallback: Infers the likelihood of a gene being causal based on physical distance from the input variant when functional annotations are absent.
• File format support: Supports empirical annotation data provided in the UCSC .BED file format.
• Implementation: Implemented as an R package.

Scientific Applications:

• GWAS interpretation: Attributing GWAS risk variants to candidate causal genes.
• Candidate gene prioritization: Prioritizing genes for follow-up in studies of complex traits and diseases.
• Integration of regulatory data: Refining gene-variant links by combining association signals with eQTL and Hi-C evidence.

Methodology:

Integrates user-supplied functional annotations (eQTL, Hi-C) to link genomic regions to genes and, when such annotations are unavailable, defaults to a proximity-based method that infers causality likelihood from the physical distance between variant and gene; accepts UCSC .BED formatted annotations and is implemented in R.