Population genetics of polymorphism and divergence under fluctuating selection.

Area of Science:

• Evolutionary genetics
• Population genetics

Background:

• Detecting fluctuating selection traditionally requires time-series genotype frequency data.
• An alternative approach using single-time-point DNA polymorphism data is needed.

Purpose of the Study:

• To develop a novel method for detecting fluctuating selection using DNA polymorphism data.
• To model temporal fluctuations in selection coefficients and derive the expected site-frequency spectrum (SFS).

Main Methods:

• Utilized classical diffusion approximations to model fluctuating selection coefficients.
• Derived the SFS for three fluctuating selection models within a Poisson random-field framework.
• Employed Monte Carlo simulations to assess the power of likelihood-ratio tests against neutral models.

Main Results:

• Fluctuating selection generally results in a U-shaped SFS, with an excess of high-frequency derived mutations.
• The method demonstrates sufficient power to reject neutral hypotheses with hundreds of SNPs and ~20 individuals.
• Distinguishing between fluctuating and constant selection is feasible with ~20 individuals.

Conclusions:

• Fluctuating selection increases the fixation probability of selected sites, even with neutral average selection.
• This phenomenon can elevate the divergence-to-polymorphism ratio, mimicking effects of positive directional selection.
• The proposed method offers a powerful tool for inferring fluctuating selection from contemporary polymorphism data.