A FORTRAN program to simulate the evolution of genetic variability in a small population

Area of Science:

• Quantitative Genetics
• Population Genetics
• Computational Biology

Background:

• Understanding genetic variability is crucial for effective population management and conservation.
• Selection programs in small populations risk depleting genetic diversity, leading to response plateaus.
• Predictive modeling can aid in anticipating and mitigating these genetic issues.

Purpose of the Study:

• To develop and present a FORTRAN-77 program for Monte Carlo simulation of genetic structure evolution in selected populations.
• To investigate the potential for response plateaus in theoretical populations based on size and management strategies.
• To apply the simulation to real-world small populations to predict genetic variability exhaustion.

Main Methods:

• Monte Carlo simulation of genetic structure evolution.
• Detailed simulation of a selection cycle including birth, phenotypic expression, genetic evaluation, selection, reproduction, and death.
• Generation and transmission of exact genotypes through simulated meiosis and gamete pairing.
• User-configurable subroutines for flexible simulation parameterization.

Main Results:

• The program outputs genetic mean and variance for each selection cycle.
• Simulation allows for the prediction of response plateaus and potential exhaustion of genetic variability.
• The model provides a framework for applying theoretical predictions to practical population management.

Conclusions:

• The developed simulation program is a valuable tool for studying genetic variability dynamics in small populations under selection.
• It enables the prediction of genetic resource limitations, informing sustainable breeding and conservation strategies.
• The program's flexibility allows for diverse applications in population genetics research and management.