Identifying loci under positive selection in complex population histories.

Area of Science:

• Evolutionary biology
• Population genetics
• Genomics

Background:

• Understanding natural selection requires detailed modeling of species' evolutionary history.
• Existing methods for detecting positive selection often use oversimplified models of genetic drift.
• Complex population histories involving divergence and admixture are crucial for accurate evolutionary inference.

Purpose of the Study:

• To develop a novel method for detecting strong local adaptation across the genome.
• To enable the analysis of selection in populations with complex demographic histories, including admixture.
• To identify specific genomic regions and evolutionary branches affected by selective sweeps.

Main Methods:

• Introduction of graph-aware retrieval of selective sweeps (GRoSS), a new computational method.
• GRoSS utilizes complex admixture graphs to model population histories.
• The method is applied to genomic data from bovine, codfish, and human populations.

Main Results:

• GRoSS demonstrates high power in detecting past selective sweeps.
• The method successfully identifies the specific population branches most impacted by selection.
• New candidate genes associated with adaptation in immunity, metabolism, muscle mass, milk production, and tameness were identified.
• Large regions of differentiation due to inversions in Atlantic codfish history were pinpointed.

Conclusions:

• GRoSS provides a powerful new tool for detecting local adaptation in complex demographic scenarios.
• The method enhances our ability to understand the genetic basis of adaptation across diverse species.
• This approach can uncover novel genes and evolutionary mechanisms driving adaptation in various populations.