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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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Types of Selection01:46

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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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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.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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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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What is Population Genetics?01:25

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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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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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Detecting and measuring selection from gene frequency data.

Renaud Vitalis1, Mathieu Gautier, Kevin J Dawson

  • 1Institut National de la Recherche Agronomique, Unité Mixte de Recherche CBGP, (Inra, Ird, Cirad, Montpellier-SupAgro) 34988 Montferrier-sur-Lez Cedex, France.

Genetics
|December 24, 2013
PubMed
Summary
This summary is machine-generated.

We developed SelEstim, a new method to detect genetic selection and estimate its intensity in populations. This tool accurately identifies selected loci, like the LCT gene related to lactase persistence.

Keywords:
adaptationgenome scanhierarchical Bayesian modelnatural selectionpopulation differentiation

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Area of Science:

  • Population Genetics
  • Genomics
  • Evolutionary Biology

Background:

  • High-throughput sequencing generates extensive population genotype data.
  • Identifying genes under natural selection is crucial but challenging.
  • Distinguishing selected loci from neutral variation requires robust methods.

Purpose of the Study:

  • Introduce SelEstim, a novel model-based method for detecting selection.
  • Estimate the intensity of selection at identified loci.
  • Validate SelEstim's performance using simulations and real genomic data.

Main Methods:

  • Developed a diffusion approximation model for allele frequency distribution in structured populations.
  • Employed Markov chain Monte Carlo (MCMC) for Bayesian inference.
  • Applied SelEstim to SNP data from the Human Genome Diversity Panel.

Main Results:

  • SelEstim effectively identifies loci under selection and estimates selection intensity.
  • Simulations confirm SelEstim's power in detecting selection.
  • Analysis revealed strong positive selection upstream of the LCT gene, linked to lactase persistence.

Conclusions:

  • SelEstim is a powerful tool for identifying genetic selection.
  • Selection intensity estimates correlate with lactase persistence phenotypes geographically.
  • The LCT gene shows significant evidence of recent positive selection in specific human populations.