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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.

Mutation, Gene Flow, and Genetic Drift

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).

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.

Hardy-Weinberg Principle

Hardy-Weinberg Principle

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.

Multi-species Conserved Sequences

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...

Genetic Screens

Genetic Screens

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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Related Experiment Video

Updated: Jul 24, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models.

Vivak Soni¹, Parul Johri^1,2, Jeffrey D Jensen¹

¹School of Life Sciences, Arizona State University, Tempe, AZ, USA.

Biorxiv : the Preprint Server for Biology

|July 3, 2023

Summary

This summary is machine-generated.

Detecting positive selection in population genomics is challenging. Realistic models show that recurrent selective sweeps are often undetectable, with high false positive rates, necessitating caution in interpreting genomic scans.

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Published on: June 23, 2012

Area of Science:

Population genetics
Evolutionary biology
Genomics

Background:

Selective sweeps, indicating recent positive selection, are commonly detected using genomic scans.
Existing methods often assume simple models of isolated, recent sweeps on neutral backgrounds.
However, biological reality involves recurrent sweeps with varying ages and fitness effects.

Approach:

This study employed forward-in-time simulations to assess the performance of common sweep detection statistics.
Realistic evolutionary baseline models were incorporated, including background selection, population size changes, and mutation rate heterogeneity.
The power to detect recurrent selective sweeps under these complex models was evaluated.

Key Points:

False positive rates often exceed true positive rates across evaluated parameters.
Recurrent selective sweeps are frequently undetectable unless selection is exceptionally strong.
Realistic evolutionary baselines are crucial for reducing false positives but do not guarantee detection power.

Conclusions:

Interpreting genomic scans for selection requires caution due to complex evolutionary processes.
Current methods have limited power to detect recurrent selective sweeps under realistic conditions.
Future research should focus on developing more sensitive methods for detecting selection in complex scenarios.