Detecting and Quantifying Changing Selection Intensities from Time-Sampled Polymorphism Data.

Area of Science:

• Evolutionary biology
• Population genetics
• Bioinformatics

Background:

• Fluctuating selection models explain allele frequency changes but often assume random fluctuations.
• Detecting nonrandom shifts in selection intensity from time-series data remains a challenge.

Purpose of the Study:

• To develop a novel method for detecting and evaluating nonrandom changes in selection intensity using time-sampled genetic data.
• To jointly estimate the timing and strength of selection coefficient changes.

Main Methods:

• Utilized Wright-Fisher approximate Bayesian computation (ABC) approaches.
• Developed a method to jointly estimate change point position and selection coefficients from allele trajectories.
• Conducted simulation studies to optimize parameter ranges and input values.

Main Results:

• The novel ABC-based method successfully detects and quantifies nonrandom changes in selection intensity.
• Simulation results provide guidelines for optimal experimental design.
• The method was applied to historical data of Panaxia dominula and influenza virus genome data.

Conclusions:

• The developed method offers a robust approach to analyze nonrandom selection dynamics in evolutionary processes.
• This technique can reveal insights into historical evolutionary debates and identify adaptive genetic changes in pathogens.