Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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 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 result in visible changes...

Plant Breeding and Biotechnology

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.

Bioreactor Controls-III

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Discovering putative novel stem rust resistance loci in wheat genetic resources.

The plant genome·2026

Same author

Epistatic interaction of the genes <i>Raw1</i> and <i>Raw7</i> controls barb size and frequency in barley awn roughness.

bioRxiv : the preprint server for biology·2026

Same author

Association mapping of hybrid seed set and heterosis in a central European multi-parental wheat population.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026

Same author

Wheat historical phenotypic data from European genebanks as an important resource for research and breeding.

Scientific data·2026

Same author

Benchmarking Feature Selection Methods and Prediction Models for Flowering Time Prediction in Maize.

International journal of molecular sciences·2026

Same author

Large-scale multi-omics unveils host-microbiome interactions driving root development and nitrogen acquisition.

Nature plants·2026

Search research articles

Related Experiment Video

Updated: May 7, 2026

Breeding by Design for Functional Rice with Genome Editing Technologies

Breeding by Design for Functional Rice with Genome Editing Technologies

Published on: January 3, 2025

Genomic selection in sugar beet breeding populations.

Tobias Würschum¹, Jochen C Reif, Thomas Kraft

¹State Plant Breeding Institute, University of Hohenheim, 70593 Stuttgart, Germany. tobias.wuerschum@uni-hohenheim.de.

|September 20, 2013

Summary

Genomic selection accurately predicts sugar beet breeding values using genome-wide markers. A diverse training population effectively builds robust genomic selection models for improved crop breeding.

More Related Videos

Genetic Engineering of Dictyostelium discoideum Cells Based on Selection and Growth on Bacteria

Genetic Engineering of Dictyostelium discoideum Cells Based on Selection and Growth on Bacteria

Published on: January 25, 2019

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae

Published on: December 2, 2022

Related Experiment Videos

Last Updated: May 7, 2026

Breeding by Design for Functional Rice with Genome Editing Technologies

Breeding by Design for Functional Rice with Genome Editing Technologies

Published on: January 3, 2025

Genetic Engineering of Dictyostelium discoideum Cells Based on Selection and Growth on Bacteria

Genetic Engineering of Dictyostelium discoideum Cells Based on Selection and Growth on Bacteria

Published on: January 25, 2019

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae

Published on: December 2, 2022

Area of Science:

Plant breeding
Genomics
Quantitative genetics

Background:

Genomic selection (GS) leverages genome-wide marker data for predicting breeding values.
A large sugar beet population (924 lines) encompassing diverse germplasm was utilized.
Lines were phenotyped for six key agronomic traits and genotyped with 677 SNP markers.

Purpose of the Study:

To assess the prediction accuracy of genomic selection in sugar beet.
To evaluate the effectiveness of diverse lines as a training population for genomic selection.
To determine if a calibration model from diverse lines can predict performance within families.

Main Methods:

Ridge regression best linear unbiased prediction (RR-BLUP) was employed.
Fivefold cross-validation was used to assess prediction accuracies.
The study compared prediction accuracies within diverse lines versus within families.

Main Results:

High prediction accuracies were achieved for most traits.
Prediction accuracy was lower when using a diverse training set to predict within families compared to within the diverse set.
However, prediction accuracy within families was comparable to cross-validation within those families.

Conclusions:

Intensively phenotyped and genotyped diverse lines can form robust calibration models for genomic selection in sugar beet.
Genomic selection is a valuable tool that complements existing genomics approaches in sugar beet breeding programs.