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Frequency-dependent Selection01:21

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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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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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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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Detecting and Quantifying Changing Selection Intensities from Time-Sampled Polymorphism Data.

Hyunjin Shim1, Stefan Laurent1, Sebastian Matuszewski1

  • 1School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland.

G3 (Bethesda, Md.)
|February 13, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using approximate Bayesian computation to detect nonrandom changes in selection intensity over time. The approach helps identify shifts in evolutionary pressures and optimize experimental designs for genetic studies.

Keywords:
Wright-Fisher modelapproximate Bayesian computationchange point analysisfluctuating selectiontime-sampled data

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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Bioinformatics

Background:

  • Fluctuating selection models explain allele frequency changes but often assume random fluctuations.
  • Detecting nonrandom shifts in selection intensity from time-series data remains a challenge.

Purpose of the Study:

  • To develop a novel method for detecting and evaluating nonrandom changes in selection intensity using time-sampled genetic data.
  • To jointly estimate the timing and strength of selection coefficient changes.

Main Methods:

  • Utilized Wright-Fisher approximate Bayesian computation (ABC) approaches.
  • Developed a method to jointly estimate change point position and selection coefficients from allele trajectories.
  • Conducted simulation studies to optimize parameter ranges and input values.

Main Results:

  • The novel ABC-based method successfully detects and quantifies nonrandom changes in selection intensity.
  • Simulation results provide guidelines for optimal experimental design.
  • The method was applied to historical data of Panaxia dominula and influenza virus genome data.

Conclusions:

  • The developed method offers a robust approach to analyze nonrandom selection dynamics in evolutionary processes.
  • This technique can reveal insights into historical evolutionary debates and identify adaptive genetic changes in pathogens.