Quantifying Selection with Pool-Seq Time Series Data.

Area of Science:

• Evolutionary biology
• Population genetics

Background:

• Allele frequency time series data are crucial for understanding adaptation and evolutionary forces.
• Evolve and Resequence (E&R) experiments, using whole-genome sequencing of evolving populations, are increasingly popular for studying adaptation.
• Previous studies focused on optimizing experimental parameters for target identification, but lacked guidance for selection strength and dominance estimation.

Purpose of the Study:

• To develop a highly accurate and computationally efficient method for estimating selection parameters from replicated time series data.
• To evaluate the impact of experimental parameters on the reliability of selection parameter estimates in E&R studies.

Main Methods:

• Developed a novel, accurate, and genome-scale applicable method for estimating selection parameters from replicated allele frequency time series data.
• Utilized computer simulations to assess the influence of various experimental parameters on estimation accuracy.

Main Results:

• Identified effective population size (Ne) and the number of replicates as having the most significant impact on the reliability of selection parameter estimates.
• Found that the number of time points and sequencing coverage have a minor effect on estimation accuracy.
• Demonstrated that time series analysis is feasible without substantial increases in sequencing costs.

Conclusions:

• The new method enables accurate estimation of selection parameters, crucial for interpreting E&R experiments.
• Optimizing effective population size and the number of replicates is critical for robust evolutionary studies.
• Time series analysis in E&R studies is practical and likely to become a standard approach, offering valuable insights into adaptation mechanisms.