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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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.
Genetic Drift03:33

Genetic Drift

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.
Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
Genetics of Speciation02:16

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A random effects branch-site model for detecting episodic diversifying selection.

Sergei L Kosakovsky Pond1, Ben Murrell, Mathieu Fourment

  • 1Department of Medicine, University of California-San Diego, CA, USA. spond@ucsd.edu

Molecular Biology and Evolution
|June 15, 2011
PubMed
Summary
This summary is machine-generated.

New evolutionary models improve the detection of episodic selection by allowing more flexible branch classifications. This addresses limitations in current branch-site models, reducing false positives and negatives in evolutionary burst detection.

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Last Updated: Jun 1, 2026

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Phylogenetics

Background:

  • Adaptive evolution often occurs in bursts at specific gene sites and lineages.
  • Current branch-site models simplify evolutionary processes by rigidly classifying branches as foreground or background.
  • This simplification can lead to inaccurate evolutionary inference when background branch evolution deviates from assumptions.

Purpose of the Study:

  • To develop a more flexible and accurate statistical framework for detecting episodic selection.
  • To overcome the limitations of current branch-site models that assume rigid branch classifications.
  • To improve the reliability of identifying bursts of adaptive evolution.

Main Methods:

  • Extended Felsenstein's pruning algorithm for efficient likelihood computations.
  • Developed a novel model allowing branch-site variation using a random effects likelihood framework.
  • Modeled evolutionary processes as a mixture of three Markov substitution models at each branch-site combination.

Main Results:

  • The new method demonstrated superior or equivalent performance compared to existing branch-site tests in simulations.
  • Achieved reduced rates of false positives and false negatives.
  • Empirical data analysis highlighted the impact of modeling assumptions on evolutionary inference.

Conclusions:

  • The proposed flexible branch-site model offers a more robust approach to detecting episodic selection.
  • This advancement enhances the accuracy of evolutionary burst detection in phylogenetic analyses.
  • The findings underscore the importance of realistic modeling assumptions in evolutionary studies.