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Related Concept Videos

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).
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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.
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Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

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Related Experiment Video

Updated: May 18, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

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Published on: February 3, 2023

Inference on population histories by approximating infinite alleles diffusion.

Jukka Sirén1, William P Hanage, Jukka Corander

  • 1Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. jukka.p.siren@helsinki.fi

Molecular Biology and Evolution
|September 21, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method for reconstructing evolutionary population histories using large genetic datasets. The approach enables deeper insights into the population genetics of species like Streptococcus pneumoniae.

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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Computational biology

Background:

  • Reconstructing past evolutionary events is crucial in biology.
  • Statistical inference of population histories is challenging but advancing with new computational tools.

Purpose of the Study:

  • To develop a novel likelihood-based computational framework for inferring population histories.
  • To generalize existing models for allele frequency fluctuations.

Main Methods:

  • Developed a new model approximating the infinite alleles Wright-Fisher model.
  • Implemented the model using an adaptive Markov chain Monte Carlo algorithm.
  • The method is optimized for large population samples and few loci.

Main Results:

  • Successfully reconstructed the global population history of Streptococcus pneumoniae.
  • Demonstrated the model's capability to handle large population datasets.

Conclusions:

  • The new population genetics approach provides significant biological insights.
  • The method is suitable for analyzing large population samples in evolutionary studies.