SurvivalGWAS_SV

SurvivalGWAS_SV performs genome-wide association analyses of time-to-event (survival) outcomes using imputed genotype dosages to enable pharmacogenetic identification of genetic markers associated with treatment response.

Key Features:

• Time-to-event models: Supports Cox proportional hazards and Weibull regression for survival analysis.
• Genotype dosage model: Uses imputed genotypes under a dosage model for association testing.
• Covariate modeling: Adjusts for multiple covariates and incorporates SNP–covariate interaction effects.
• Parallel computing: Compatible with high-performance parallel computing clusters to process large GWAS datasets without memory constraints.
• Implementation and platforms: Implemented in C# and runs on Linux, Mac OS X, and Windows.

Scientific Applications:

• Pharmacogenetic GWAS: Association testing of genetic variants with drug response using time-to-event outcomes.
• Biomarker discovery: Identification of genetic biomarkers associated with patient responses to treatments.
• Large-scale survival GWAS: Analysis of extensive genomic datasets for time-to-event associations across diverse diseases.
• Personalized medicine research: Enabling discovery of genetic determinants of treatment outcomes to support individualized therapeutic strategies.

Methodology:

Models time-to-event outcomes using imputed genotype dosages under a dosage model, fitting Cox proportional hazards or Weibull regression with covariate adjustment and SNP–covariate interaction terms; implemented in C# and parallelizable for cluster deployment to avoid memory constraints.