An approximate bayesian estimator suggests strong, recurrent selective sweeps in Drosophila

Area of Science:

• Population Genetics
• Evolutionary Biology
• Genomics

Background:

• Beneficial mutations reduce genetic diversity within species.
• Existing models struggle to differentiate between frequent weak and rare strong selection.
• Accurate models are crucial for understanding evolutionary dynamics.

Purpose of the Study:

• To develop a novel estimator to distinguish between different models of beneficial mutation.
• To apply this estimator to Drosophila melanogaster data.
• To assess the impact of various assumptions on selection strength inference.

Main Methods:

• Development of an approximate Bayesian (ABC) polymorphism-based estimator.
• Application to multi-locus polymorphism data from Drosophila melanogaster.
• Sensitivity analysis of parameter estimation to demographic history and mutation rates.

Main Results:

• The ABC estimator can differentiate between models of frequent weak and rare strong selection.
• Estimating selection strength is sensitive to assumptions about substitution rates, recombination, and demographic history.
• Fixed selection parameter assumptions lead to biased estimates (overestimated strength, underestimated rate).

Conclusions:

• The proposed ABC method provides a powerful tool for evolutionary inference.
• Accurate demographic modeling is essential for reliable selection strength estimation.
• Larger genomic datasets will significantly enhance the discriminatory power of this approach for diverse taxa.