GSpace

GSpace simulates genomic data for structured population genetic analyses as a simulation-based alternative to analytical n-coalescent approximations, modeling generation-by-generation coalescence, recombination, small-population dynamics, and isolation by distance.

Key Features:

• Generation-by-Generation Coalescence Algorithm: Employs a detailed coalescent algorithm that operates generation-by-generation to model lineage history and temporal evolutionary processes.
• Small Population Size Consideration: Tailored to handle small population sizes where genetic drift significantly affects genetic diversity.
• Recombination Modeling: Incorporates recombination events into simulations to represent genetic reshuffling and linkage patterns.
• Isolation by Distance: Accounts for isolation by distance to model spatially structured gene flow and population differentiation.
• Simulation-Based Alternative to n-Coalescent Approximations: Bypasses traditional n-coalescent analytical approximations that assume large population sizes.

Scientific Applications:

• Population Genetics: Explore the effects of genetic drift, selection, and gene flow in structured populations.
• Conservation Biology: Assess genetic diversity and structure of endangered or fragmented populations.
• Evolutionary Studies: Investigate evolutionary history and population dynamics over time.

Methodology:

Implements generation-by-generation coalescent simulation that includes recombination and isolation-by-distance modeling, explicitly models small population sizes, and bypasses traditional n-coalescent approximations.