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Related Concept Videos

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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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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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Quantifying evolutionary dynamics from variant-frequency time series.

Bhavin S Khatri1,2

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Summary
This summary is machine-generated.

Quantifying selection versus neutrality is key in evolutionary biology. This study uses Fisher's angular transformation to determine evolutionary parameters from variant frequency time-series data, aiding selection detection.

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Area of Science:

  • Evolutionary biology
  • Ecological dynamics
  • Population genetics

Background:

  • The balance between neutral drift and selection is a fundamental question in evolutionary biology and ecology.
  • Deep sequencing enables the analysis of variant frequencies over time, posing new challenges for inferring evolutionary forces.
  • Fisher's angular transformation, a century-old mathematical tool, has potential applications in evolutionary studies.

Purpose of the Study:

  • To develop a method for distinguishing between neutral evolution and selection using time-series data of variant frequencies.
  • To adapt Fisher's angular transformation for analyzing short-term evolutionary dynamics, including drift, selection, and mutation.
  • To provide a theoretical foundation for detecting selection from population variant frequency data.

Main Methods:

  • Application of Fisher's angular transformation to the 2-variant case.
  • Integration with a heuristic approach to model transition probability densities.
  • Analysis of simulation data under varying selection strengths and sampling frequencies.

Main Results:

  • Fisher's angular transformation provides a simple solution for transition probability density at short times.
  • Accurate determination of evolutionary parameters (drift, selection, mutation) is possible under strong selection and frequent sampling.
  • The method demonstrates the feasibility of detecting selection from variant frequency time-series.

Conclusions:

  • Fisher's angular transformation is a powerful, underutilized tool for evolutionary inference.
  • The developed method offers a robust theoretical basis for analyzing variant frequency time-series data.
  • This approach can significantly enhance the ability to detect and quantify selection in populations.