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Frequency-dependent Selection

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.

What is Natural Selection?

What is Natural Selection?

Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.

Types of Selection

Types of Selection

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...

Limits to Natural Selection

Limits to Natural Selection

Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.

Genetic Drift

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.

Determination of Expected Frequency

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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Related Experiment Video

Updated: May 10, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Learning natural selection from the site frequency spectrum.

Roy Ronen¹, Nitin Udpa, Eran Halperin

¹Bioinformatics and Systems Biology Program, University of California, San Diego, California 92093.

|June 18, 2013

Summary

This summary is machine-generated.

Scientists developed SFselect, a new method to detect genetic adaptation. This tool identifies genes responding to environmental changes, improving our understanding of evolution in fruit flies and humans.

Keywords:

natural selection site frequency spectrum supervised learning

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Area of Science:

Population Genetics
Evolutionary Biology
Genomics

Background:

Identifying genes under selection is crucial for understanding adaptation.
Existing methods quantify the site frequency spectrum (SFS) skew to detect selection.
Selective sweeps leave distinct genomic signatures, but their characteristics vary.

Purpose of the Study:

To develop a novel statistical test for detecting genomic signatures of selection.
To characterize how selective sweeps influence the scaled site frequency spectrum (SFS).
To identify loci under selection in experimental and natural populations.

Main Methods:

Utilized supervised learning on simulated data to identify SFS features indicative of selection.
Developed a new test, SFselect, based on these features.
Applied SFselect to polymorphism data from Drosophila melanogaster and human populations.

Main Results:

SFselect effectively distinguishes various selective sweeps from neutral evolution.
The test outperforms existing methods across a range of selection scenarios.
Identified novel loci involved in hypoxia tolerance in Drosophila and potential adaptive regions in humans.

Conclusions:

SFselect provides a robust and sensitive approach for detecting genetic adaptation.
The study highlights the role of the Notch pathway in Drosophila hypoxia tolerance.
The method identified novel regions of potential adaptation in human populations.