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Related Concept Videos

Genetic Drift03:33

Genetic Drift

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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.
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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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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.
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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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.
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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How to teach genetic drift.

Chun Ming Wang1, Chang Jun Lin1, Hu Yuan Feng1

  • 1School of Life Sciences, Lanzhou University, Lanzhou 730000, China.

Yi Chuan = Hereditas
|January 29, 2021
PubMed
Summary
This summary is machine-generated.

Genetic drift, a key factor in population genetics, is often misunderstood due to insufficient textbook explanations. This study clarifies its inherent, universal, random, non-directional, and regular features, emphasizing random sampling error.

Keywords:
binomial distributionbinomial theoremgenetic driftgenetic equilibriumpopulationsampling error

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Quantitative Genetics

Background:

  • Genetic drift is a fundamental evolutionary mechanism alongside mutation, selection, and migration.
  • Current textbook explanations often lack depth, particularly regarding mathematical underpinnings, hindering student comprehension.
  • Insufficient coverage leads to inadequate learning of genetic drift's principles and implications.

Purpose of the Study:

  • To systematically summarize the five core attributes of genetic drift: inherent, universal, random, non-directional, and regular.
  • To elucidate the essential nature of random sampling error as the inherent feature of genetic drift.
  • To enhance the pedagogical approach to teaching genetic drift by incorporating mathematical foundations and visual aids.

Main Methods:

  • Summarization of genetic drift's five basic attributes.
  • Deductive reasoning starting from minimal population sizes (N=1) to illustrate population size effects.
  • Introduction of the binomial theorem and binomial distribution for mathematical modeling.
  • Utilization of computer simulations for visual and intuitive demonstration.

Main Results:

  • Genetic drift's inherent, universal, random, non-directional, and regular features are clearly defined.
  • Random sampling error is identified as the core inherent characteristic of genetic drift.
  • A clear inverse relationship between population size and the effect of genetic drift is demonstrated.
  • Mathematical models and simulations provide visual and intuitive understanding.

Conclusions:

  • A comprehensive understanding of genetic drift requires acknowledging its fundamental attributes and mathematical basis.
  • The inverse relationship between population size and drift's impact is a critical concept for evolutionary studies.
  • Integrating mathematical models like the binomial theorem and simulations significantly improves the teaching and learning of genetic drift.