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Related Concept Videos

Genetics of Speciation02:16

Genetics of Speciation

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Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Updated: Sep 8, 2025

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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The bounded coalescent model: Conditioning a genealogy on a minimum root date.

Jake Carson1, Alice Ledda2, Luca Ferretti3

  • 1Mathematics Institute, University of Warwick, United Kingdom.

Journal of Theoretical Biology
|June 13, 2022
PubMed
Summary

A new algorithm simulates the bounded coalescent model efficiently, improving computational speed for population genetics and phylogenetic analyses. This method aids in understanding the last common ancestor

Keywords:
Coalescent modelHeterochronous samplingMost recent common ancestorPhylodynamicsPhylogenetics

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Area of Science:

  • Population Genetics
  • Phylogenetics
  • Computational Biology

Background:

  • The coalescent model describes how sampled individuals trace back to a common ancestor.
  • The bounded coalescent model conditions this by requiring the common ancestor to exist after a specific time.
  • This model is relevant for speciation, gene transfer, and transmission studies but lacks detailed analysis.

Purpose of the Study:

  • To develop and analyze a direct simulation algorithm for the bounded coalescent model.
  • To provide methods for calculating probabilities required for model realizations.
  • To investigate the impact of time-bounded common ancestors on phylogenetic properties.

Main Methods:

  • Developed a novel algorithm for direct simulation from the bounded coalescent model, avoiding rejection sampling.
  • Derived methods to compute the probability of the last common ancestor occurring after a specified date.
  • Implemented the simulation algorithm and probability calculations in an R package (BoundedCoalescent).

Main Results:

  • The direct simulation algorithm is computationally more efficient than rejection sampling.
  • The probability calculations are essential for density estimation under the bounded coalescent model.
  • The time bound significantly influences key properties of resulting phylogenies.

Conclusions:

  • The new algorithm provides an efficient tool for simulating the bounded coalescent model.
  • The methods are applicable to both isochronous and heterochronous sampling scenarios.
  • The study enhances the analytical and computational toolkit for evolutionary and population genetics research.