SNPsyn

SNPsyn identifies synergistic pairs of single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) data to estimate genetic interactions that may contribute to complex disease phenotypes.

Key Features:

• Interaction analysis: Employs an information-theoretic method to detect and estimate synergy between SNP pairs in GWAS datasets.
• Computational implementation: Computationally intensive components are implemented in C++ and can be executed on clusters or grids for scalability and performance.
• Gene set enrichment analysis: Supports gene set enrichment analysis to link identified SNP interactions to biological pathways and functions.
• SNP synergy networks: Constructs networks of synergistic SNP pairs to represent genetic interaction relationships.

Scientific Applications:

• Complex-disease genetics: Identifies SNP-SNP interactions that can elucidate the genetic architecture of complex diseases in GWAS data.
• Biomarker and target discovery: Reveals synergistic SNP pairs that can suggest candidate biomarkers or therapeutic targets.
• Pathway and functional analysis: Integrates SNP interaction results with gene set enrichment to associate interactions with biological pathways and functions.

Methodology:

Uses an information-theoretic interaction analysis to estimate SNP synergy, performs gene set enrichment analysis, implements computation in C++ with scalability on clusters or grids, and constructs SNP synergy networks.