SIMLD

SIMLD simulates realistic linkage disequilibrium (LD) patterns using empirical SNP marker information to support population genetics and disease-association study design.

Key Features:

• High Initial LD Levels: Initiates simulations with populations exhibiting potentially high LD levels to enable controlled modeling of LD decay.
• LD Decay Simulation: Systematically reduces LD through processes that mimic natural mating and recombination to achieve desired LD levels.
• Empirical Data Integration: Utilizes SNP marker information from three distinct HapMap populations to ground simulations in empirical data.
• Case-Control Sample Generation: Produces case-control samples tailored to various specified disease models for association analyses.

Scientific Applications:

• Population genetics: Generate simulated datasets to study LD structure and its dynamics across generations.
• Evolutionary biology: Explore effects of mating patterns, recombination rates, and selection pressures on LD dynamics.
• Disease association mapping: Create case-control datasets to design and validate studies for identifying genetic markers associated with diseases.
• Hypothesis testing: Test hypotheses related to genetic linkage and recombination using realistic simulated genetic structures.

Methodology:

SIMLD initializes populations with potentially high LD, employs an algorithmic approach to overcome instability from random allele assignments, reduces LD via simulated mating and recombination, uses SNP markers from three HapMap populations, and generates case-control samples according to specified disease models.