A theoretical and numerical assessment of genetic variability

Area of Science:

• Population Genetics
• Evolutionary Biology
• Quantitative Genetics

Background:

• Understanding genetic equilibrium is crucial for evolutionary studies.
• Viability selection models explore how allele frequencies change due to fitness differences.
• Previous research has explored various selection schemes and their impact on genetic diversity.

Purpose of the Study:

• To numerically investigate the equilibrium behavior of one-locus viability selection models.
• To analyze the relationship between polymorphism, mean fitness, and selection model structure.
• To evaluate prediction accuracy of equilibrium states from different starting conditions.

Main Methods:

• Numerical simulations of one-locus viability selection.
• Exploration of diverse viability schemes: random, distance-based, and dominance-based.
• Analysis of models with 3 to 8 alleles.
• Comparison of equilibrium states reached from centroid vs. random starting vectors.

Main Results:

• Highly polymorphic equilibria were not consistently associated with the highest mean fitness.
• Increased structure in viability models correlated with higher levels of equilibrium polymorphism.
• For 3-8 alleles, iteration from the centroid accurately predicted equilibrium states from random starting vectors.
• This predictive accuracy diminished for models with 16 alleles.

Conclusions:

• Mean fitness maximization is not a universal outcome of polymorphic equilibria in viability selection.
• Structured viability models promote greater genetic diversity at equilibrium.
• The centroid iteration method is a reliable predictor of equilibrium states for a moderate number of alleles.