Modeling recent positive selection using identity-by-descent segments.

Area of Science:

• Population Genetics
• Genomics
• Evolutionary Biology

Background:

• Recent positive selection can lead to an excess of long identity-by-descent (IBD) haplotype segments around a specific genetic locus.
• Studying selective sweeps is crucial for understanding adaptive evolution, but often requires knowledge of the causal allele or time-series data.

Purpose of the Study:

• To develop and validate statistical methods for studying recent positive selection.
• To enable the scanning of genomic regions for excess IBD rates.
• To identify potential sweeping alleles and estimate selection coefficients without prior knowledge of the causal allele.

Main Methods:

• Implemented a selection scan to detect regions with elevated IBD rates.
• Developed a method to estimate the frequency and location of sweeping alleles by comparing variant abundance in outgroups.
• Proposed a parametric bootstrap approach for estimating selection coefficients and quantifying uncertainty.

Main Results:

• The proposed methods demonstrated higher precision in estimating selection coefficients (s ≥ 0.015) compared to existing state-of-the-art methods in simulations.
• Confidence intervals achieved high accuracy, containing the true selection coefficient in nearly 95% of simulations.
• Applied to European ancestry data (Trans-Omics for Precision Medicine), identified eight loci with significant excess IBD, including LCT.

Conclusions:

• The presented methods offer robust and accurate approaches for studying recent adaptive evolution.
• These methods do not require prior knowledge of the causal allele or the use of time-series data.
• The findings provide valuable tools for population geneticists and evolutionary biologists investigating recent selection events.