Detecting Polygenic Adaptation in Admixture Graphs.

Area of Science:

• Evolutionary biology
• Population genetics
• Genomics

Background:

• Polygenic adaptation, involving changes in many genes, is crucial for understanding human evolution.
• Existing methods using genome-wide association studies (GWAS) can identify polygenic adaptation but lack historical context.
• Interpreting the timing and location of polygenic adaptation across diverse populations remains a challenge.

Purpose of the Study:

• To develop a novel method for detecting polygenic adaptation within the historical framework of admixture graphs.
• To pinpoint the specific branches in population history where polygenic adaptation has occurred.
• To enhance the interpretability of polygenic adaptation signals in human populations.

Main Methods:

• Developed a method (PolyGraph) to detect polygenic adaptation using admixture graphs.
• Implemented a Markov chain Monte Carlo (MCMC) algorithm to infer selection strength on graph branches.
• Created fast-to-compute summary statistics to identify branches with polygenic adaptation.

Main Results:

• PolyGraph demonstrates strong power in simulations to detect polygenic adaptation.
• Applied the method to global human population genomic data.
• Identified evidence of polygenic adaptation influencing traits like height and educational attainment.

Conclusions:

• PolyGraph provides a powerful new tool for understanding the historical dynamics of polygenic adaptation in humans.
• The method successfully applied to real-world data, offering insights into trait evolution.
• Polygenic adaptation has shaped diverse human traits across different populations throughout history.