WISH-R

WISH-R computes and visualizes genome-wide epistatic interactions between genetic variants to identify SNP and gene modules associated with quantitative traits and case-control disease outcomes.

Key Features:

• Epistasis inference: Uses linear or generalized linear models to compute interactions from genomic data together with phenotype or disease data.
• Pairwise calculations at scale: Performs pairwise epistatic calculations across millions of genetic variants.
• Parallelized computation: Implements a fully parallelized framework optimized for high-performance computing environments.
• Genotype dimensionality reduction: Provides built-in functions to reduce genotype data dimensionality and lower computational demands.
• Weighted SNP interaction networks: Builds scale-free weighted SNP interaction networks.
• Trait and disease association: Relates SNP interaction networks to quantitative traits, phenotypes, and case-control disease outcomes.
• Biological integration: Integrates biological knowledge to identify disease- or trait-relevant SNP or gene modules, hub genes, potential biomarkers, and pathways.
• Visualization: Produces visualizations of epistatic interactions and interaction networks.

Scientific Applications:

• Genome-wide epistasis mapping: Detects pairwise SNP interactions across whole genomes in humans, animals, and plants.
• Complex trait analysis: Relates SNP interaction networks to quantitative traits and phenotypes.
• Disease association studies: Identifies disease-relevant SNP and gene modules, hub genes, potential biomarkers, and pathways in case-control studies.
• Network-based biomarker discovery: Uses scale-free weighted SNP interaction networks to discover modules and hub genes associated with complex traits and diseases.

Methodology:

Applies linear or generalized linear models to genomic and phenotype/disease data; performs fully parallelized pairwise epistatic calculations scalable to millions of variants; reduces genotype dimensionality with built-in functions; constructs scale-free weighted SNP interaction networks; relates networks to quantitative traits and case-control outcomes; integrates biological knowledge and provides visualization of interactions.