Simulation of 'hitch-hiking' genealogies

Area of Science:

• Population genetics
• Evolutionary biology
• Computational biology

Background:

• Gene genealogies are fundamental to understanding evolutionary processes.
• Existing models like the coalescent and ancestral selection graphs have limitations in incorporating both selection and recombination simultaneously.
• The hitch-hiking effect describes how selection at one locus can influence genetic variation at linked loci.

Purpose of the Study:

• To develop a generalized ancestral influence graph that integrates selection and recombination into gene genealogies.
• To qualitatively and quantitatively investigate the hitch-hiking effect on genealogies.
• To computationally estimate the time to the most recent common ancestor (TMRCA) under various evolutionary scenarios.

Main Methods:

• Derivation of an ancestral influence graph, combining elements of ancestral graphs and ancestral selection graphs.
• Diagrammatic representation of genealogical processes in two-locus, two-allele haploid models.
• Extension of simulation techniques to estimate TMRCA in haploid and diploid models with selection and recombination.

Main Results:

• The developed ancestral influence graph successfully incorporates selection and recombination.
• Simulations demonstrate that the hitch-hiking effect is subtle and alters conditional expected genealogy depth.
• The impact of hitch-hiking depends on the balance between mutation, selection, and recombination rates.

Conclusions:

• The ancestral influence graph provides a powerful framework for studying gene genealogies under complex evolutionary pressures.
• The hitch-hiking effect's influence on TMRCA is nuanced and context-dependent.
• This work advances computational methods for inferring evolutionary history in the presence of selection and recombination.