Estimating the strength of selective sweeps from deep population diversity data

Area of Science:

• Population Genetics
• Evolutionary Biology
• Genomics

Background:

• Selective sweeps typically reduce genetic diversity around adaptive sites.
• Neutral mutations can increase in frequency during sweeps via hitchhiking.
• Existing methods for estimating selection coefficients rely on preexisting genetic variation.

Purpose of the Study:

• To develop a novel estimator for the selection coefficient based on mutation dynamics during selective sweeps.
• To overcome limitations of existing methods, particularly in regions lacking recombination and preexisting variation.

Main Methods:

• Developed a theoretical framework analyzing the frequency spectrum of novel haplotypes arising during sweeps.
• Derived an estimator for the selection coefficient based on the ratio of mutation rate and selection strength.
• Validated the estimator analytically and numerically, assessing its accuracy under various evolutionary scenarios including genetic drift, recombination, and demographic changes.

Main Results:

• The frequency spectrum of novel haplotypes during a sweep is determined by the mutation rate and selection coefficient.
• The novel estimator accurately infers selection coefficients using high-depth sequencing data, outperforming traditional methods.
• The estimator is robust to factors like genetic drift and can be applied to non-recombining loci.

Conclusions:

• A new, powerful method for estimating selection coefficients during selective sweeps has been developed.
• This approach leverages novel variation, offering advantages over methods relying on preexisting diversity.
• The method was successfully applied to human immunodeficiency virus population data, demonstrating its practical utility.