Maximum Likelihood Estimation of Fitness Components in Experimental Evolution.

Area of Science:

• Evolutionary biology
• Population genetics
• Quantitative genetics

Background:

• Estimating fitness differences between allelic variants is crucial for understanding experimental evolution.
• Current methods often assume fixed selection coefficients, overlooking fitness component variations.

Purpose of the Study:

• To develop a flexible maximum likelihood framework to disentangle and estimate individual fitness components (mating success, fecundity, viability) from genotype frequency data.
• To apply this framework to quantify fitness components in males and females separately.

Main Methods:

• Developed a maximum likelihood framework for analyzing allele frequency time series data.
• Applied the method to experimentally evolved *Drosophila melanogaster* populations tracking the *yellow* gene.
• Estimated fitness components including mating preference and viability.

Main Results:

• The *yellow* gene mutation significantly reduces wild-type female mating preference for mutant males.
• A modest reduction in viability was observed for both male and female *yellow* mutants.
• The study successfully disentangled different fitness components, demonstrating the method's utility.

Conclusions:

• The developed framework effectively quantifies fitness components, offering a more nuanced understanding of selection.
• This approach is broadly applicable for analyzing genetic variants and could inform the dynamics of gene drives.
• Fitness is a complex trait influenced by multiple components, which can be individually estimated.