A spatially aware likelihood test to detect sweeps from haplotype distributions.

Area of Science:

• Evolutionary Genomics
• Population Genetics
• Bioinformatics

Background:

• Identifying positive selection is crucial for understanding evolutionary history and organismal biology.
• Adaptive mutations drive evolutionary change, but detecting them requires sophisticated genomic analysis.

Purpose of the Study:

• To develop a novel composite likelihood method for detecting recent or ongoing positive selection.
• To infer key parameters of selection sweeps, including the number and width of affected haplotypes.

Main Methods:

• A composite likelihood approach analyzing spatial distortions in the haplotype frequency spectrum.
• Comparison against established haplotype-based selection statistics.
• Application to human populations (1000 Genomes Project) and brown rat data.

Main Results:

• The developed method demonstrates superior performance compared to leading haplotype-based statistics.
• Identified selection patterns at LCT and MHC loci in human populations.
• Detected genes related to olfactory perception in brown rats.

Conclusions:

• The new method effectively identifies positive selection and characterizes sweep dynamics.
• Low-recombination regions may require careful interpretation of selection signals.
• The method is available as user-friendly open-source software for broader application.