Approximate genealogies under genetic hitchhiking.

Area of Science:

• Population Genetics
• Evolutionary Biology
• Bioinformatics

Background:

• Selective sweeps, where advantageous alleles rapidly increase in frequency, reduce genetic variation linked to the selected gene.
• Simulating the genealogy of neutral genetic loci during selective sweeps is crucial for understanding evolutionary dynamics.
• Traditional methods often approximate allele frequency trajectories using logistic growth curves.

Purpose of the Study:

• To introduce and evaluate an alternative method for simulating genealogies during selective sweeps using a Yule tree approximation.
• To compare the accuracy and efficiency of the Yule tree method against the logistic growth curve approximation.

Main Methods:

• Developed a simulation approach based on a Yule tree (random binary splitting) to model genealogies without pre-constructing allele frequency paths.
• Compared the Yule tree approximation with the coalescent approach in a logistic background for key population genetic metrics.

Main Results:

• The Yule tree approximation provides a slightly better estimate of identity by descent during the selective phase.
• It offers a significantly improved approximation for the number of lineages originating from the founder of the selective sweep.
• Both methods perform comparably when approximating the distribution of Tajima's D, a common population genetics statistic.

Conclusions:

• The Yule tree approximation is a viable and often superior alternative for simulating genealogies under selection.
• This method is computationally faster for relevant parameter ranges, enhancing efficiency in population genetic analyses.
• The improved lineage tracking accuracy has implications for studies of recent evolutionary events and demographic inference.