A new inference method for detecting an ongoing selective sweep.

Area of Science:

• Population Genetics
• Evolutionary Biology
• Genomics

Background:

• Detecting positive selection is crucial for understanding evolutionary adaptation.
• Traditional methods often rely solely on the site frequency spectrum (SFS).
• Incorporating linkage disequilibrium (LD) can enhance the detection of selection signatures.

Purpose of the Study:

• To develop a novel, simple method for detecting ongoing selective sweeps in SNP data.
• To integrate both SFS and LD information for improved accuracy.
• To identify core sites responsible for positive selection in human populations.

Main Methods:

• Developed a method using hierarchical barcodes to represent SFS and LD information.
• Introduced the Fc statistic to measure sweep signals in hitchhiking regions.
• Utilized coalescent simulations to evaluate the method's false-positive and false-negative rates.
• Applied the method to known positively selected human loci (LCT, OCA2, EDAR, SLC24A5, ASPM).

Main Results:

• The method successfully identified hitchhiking regions surrounding core sites of selection.
• The Fc statistic effectively measured intra-allelic variability and sweep signals.
• Simulations demonstrated reliable performance in detecting selective sweeps.
• The method was validated on several human genes known to have undergone positive selection.

Conclusions:

• The developed method provides a powerful tool for detecting ongoing selective sweeps.
• Integrating SFS and LD offers a robust approach to identifying genomic regions under positive selection.
• This method can accurately pinpoint core sites driving evolutionary adaptation.