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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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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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Hardy-Weinberg Principle01:49

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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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Mutation, Gene Flow, and Genetic Drift01:09

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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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Types of Selection01:46

Types of Selection

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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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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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Fixation probabilities in graph-structured populations under weak selection.

Benjamin Allen1, Christine Sample1, Patricia Steinhagen1

  • 1Department of Mathematics, Emmanuel College, Boston, Massachusetts, United States of America.

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Summary
This summary is machine-generated.

Spatial structure significantly impacts genetic change and natural selection. This study introduces a new method to calculate fixation probabilities on graphs, revealing how structure influences evolution and drift.

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Area of Science:

  • Evolutionary biology
  • Mathematical modeling
  • Population genetics

Background:

  • Spatial structure influences evolutionary dynamics.
  • The Birth-death process on graphs models population structure and selection.
  • Calculating fixation probabilities on arbitrary graphs is computationally challenging.

Purpose of the Study:

  • To derive a computable expression for the fixation probability of weakly-selected mutations on arbitrary weighted graphs.
  • To explore the impact of graph structure on natural selection, genetic drift, and their balance.
  • To identify specific graph structures that amplify or dampen selection.

Main Methods:

  • Derivation of fixation probability using lineage coalescence time.
  • Application of the derived expression to arbitrary weighted graphs.
  • Computational analysis of small graphs and use of a genetic search algorithm.

Main Results:

  • An efficient method to compute weak-selection fixation probabilities for any graph.
  • Identification of graph families with significant effects on fixation probabilities.
  • Demonstration of how graph structure modulates natural selection and neutral drift.

Conclusions:

  • Graph structure plays a critical role in evolutionary processes.
  • The derived method allows for precise quantification of selection's influence based on spatial organization.
  • Understanding mutation processes is key to interpreting evolutionary outcomes in structured populations.