Identifying selected regions from heterozygosity and divergence using a light-coverage genomic dataset from two human...

Area of Science:

• Population Genetics
• Genomics
• Evolutionary Biology

Background:

• Selective sweeps leave distinct genomic footprints, including reduced heterozygosity and altered genetic divergence (F(ST)) between populations.
• Detecting recent directional selection is crucial for understanding adaptation and population history.
• Existing methods often require dense genotyping, limiting their application in smaller studies or diverse species.

Purpose of the Study:

• To develop and validate a novel computational approach for identifying targets of directional selection.
• To scan the human genome for regions exhibiting recent selection in European and African American populations.
• To compare newly identified regions with previously reported selection signals and assess method robustness.

Main Methods:

• Developed and validated a resampling-based computational approach to detect selection.
• Scanned 183,997 single nucleotide polymorphism (SNP) loci across the human genome using 31 moving windows.
• Corrected for recombination rate variation and compared findings with nine independent selection studies.

Main Results:

• Identified 180 regions with strong evidence of recent selection in either or both populations.
• Cross-validation with previous studies confirmed findings, particularly in regions supported by multiple independent studies.
• The developed method demonstrated robustness and was validated using diverse datasets and methodologies.

Conclusions:

• The novel resampling-based method effectively identifies regions of recent directional selection.
• The 180 identified regions represent strong candidates for further investigation into human adaptation.
• This approach is valuable for studies with limited genotyping data, applicable to humans and other species.