Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection

Area of Science:

• Evolutionary genetics
• Population genetics
• Quantitative genetics

Background:

• Modeling the balance between stabilizing selection and migration is crucial for understanding adaptation.
• Previous models often assumed constant genetic variance or normality, limiting their realism.

Purpose of the Study:

• To compare the performance of the infinitesimal model against explicit multilocus models.
• To assess the accuracy of the infinitesimal model in approximating population genetic dynamics under selection and migration.

Main Methods:

• Utilized quantitative genetic models of varying complexity, including the infinitesimal model and explicit multilocus models.
• Incorporated varying numbers of alleles per locus and unequal effect sizes.

Main Results:

• Predictions for population mean deviation from optimum were highly similar across all models, validating the infinitesimal model's approximation.
• The infinitesimal model generally estimated higher genetic variance than multilocus models, with the discrepancy diminishing as the number of loci increased.

Conclusions:

• The non-Gaussian infinitesimal model serves as a robust approximation for population adaptation.
• The effective number of loci, rather than the number of alleles per locus, more significantly influences differences between multilocus models.