Identifying and Classifying Shared Selective Sweeps from Multilocus Data.

Area of Science:

• Population Genetics
• Evolutionary Biology
• Genomics

Background:

• Positive selection drives beneficial alleles to high frequencies, causing selective sweeps that reduce genetic diversity around selected sites.
• Identifying shared selective sweeps in ancestral populations is crucial for understanding the timing and history of adaptation, but existing methods are limited.

Purpose of the Study:

• To introduce a novel statistic, SS-H12, for identifying genomic regions under shared positive selection across multiple populations.
• To differentiate between true ancestral sweeps and independent convergent sweeps using SS-H12 and associated statistics.

Main Methods:

• Developed the SS-H12 statistic based on the theory of haplotype homozygosity (H12) to detect shared sweeps.
• Utilized SS-H12 in conjunction with a ratio statistic ([Formula: see text]/[Formula: see text]) to classify sweeps as hard or soft.
• Applied the method to human whole-genome sequences to identify shared sweep candidates.

Main Results:

• SS-H12 effectively identifies shared positive selection across populations, requiring a minimum of two populations.
• The method accurately distinguishes between ancestral and convergent sweeps with high power across various demographic models.
• Identified known ancestral sweeps (e.g., LCT, SLC24A5, GPHN) and novel candidates, including an ancestral sweep at RGS18 and a convergent sweep at C2CD5.

Conclusions:

• SS-H12 provides a powerful and robust tool for detecting shared positive selection and classifying sweep types.
• The findings offer insights into the evolutionary history of human populations, including adaptations related to platelet response and insulin regulation.