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Related Concept Videos

Gene Flow02:39

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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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Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
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Horizontal gene transfer (HGT) is a process where genetic material moves between organisms within the same generation, unlike vertical gene transfer, which occurs from parent to offspring. HGT plays a crucial role in microbial evolution, adaptation, and survival, particularly in shared environments like the human gut.Mobile genetic elements such as plasmids, prophages, integrons, insertion sequences, and transposons facilitate this process. HGT occurs through three primary mechanisms:...
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Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.

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Related Experiment Video

Updated: May 11, 2026

Small-Cage Laboratory Trials of Genetically-Engineered Anopheline Mosquitoes
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A continuous method for gene flow.

Michal Palczewski1, Peter Beerli

  • 1Department of Scientific Computing, Florida State University, Tallahassee, Florida 32306, USA. mp05e@my.fsu.edu

Genetics
|May 14, 2013
PubMed
Summary
This summary is machine-generated.

A new method, transition probability-structured coalescence (TPSC), efficiently estimates population genetics parameters. TPSC improves high migration rate accuracy but may bias low migration rate estimations due to its approximation.

Keywords:
Markov chain Monte Carlo (MCMC)coalescentcontinuous Markov modelgene flowmigrationnonhomogeneous Poisson process

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Modern population genetics inference relies on the coalescence framework.
  • Estimating parameters for structured populations involves inserting migration events into genealogies.
  • High gene flow datasets necessitate extensive computational calculations for traditional methods.

Purpose of the Study:

  • To introduce a novel method, transition probability-structured coalescence (TPSC), for efficient population genetics inference.
  • To address the computational burden of high gene flow scenarios in structured populations.
  • To compare the performance of TPSC with existing methods like MIGRATE.

Main Methods:

  • TPSC replaces discrete migration events with probability statements, enabling faster calculations.
  • The method's speed is independent of the amount of gene flow.
  • An approximation simplifying lineage interactions is used in the current TPSC implementation.

Main Results:

  • TPSC allows for efficient calculation of coalescence densities, especially with high gene flow.
  • Simulations show TPSC provides more precise estimation of high migration rates compared to MIGRATE.
  • The approximation in TPSC leads to biased estimation of low migration rates.

Conclusions:

  • TPSC offers a computationally efficient approach for population genetics inference, particularly for high gene flow.
  • While accurate for high migration rates, TPSC's approximation introduces bias at low rates.
  • The straightforward implementation of TPSC facilitates broader application in phylogenetic and population inference programs.