Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Genetic Screens02:46

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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by
Same author

PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery.

Database : the journal of biological databases and curation·2026
Same author

Toward explainable and generalizable data-driven modeling in real wastewater treatment plants: Utilizing bidimensional interpretable deep learning and cross-scenario transfer learning.

Journal of environmental management·2026
Same author

A genome-wide association study of arabinoxylan content in flour of triticale (×<i>Triticosecale</i> Wittmack).

Frontiers in plant science·2026
Same author

Enhancing zero-shot scene recognition through semantic autoencoders and visual relation transfer.

Scientific reports·2025
Same author

Multi-target prediction in volatolomics with deep neural networks: Modeling volatile organic compounds produced by Brochothrix thermosphacta under modified atmospheres.

Food research international (Ottawa, Ont.)·2025
Same author

A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis.

Journal of imaging·2025

Related Experiment Video

Updated: Jun 13, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Graph-based data selection for the construction of genomic prediction models.

Steven Maenhout1, Bernard De Baets, Geert Haesaert

  • 1Department of Biosciences and Landscape Architecture, University College Ghent, B-9000 Gent, Belgium. steven.maenhout@hogent.be

Genetics
|May 19, 2010
PubMed
Summary

Genomic selection improves crop and animal breeding by optimizing training data. This study presents methods to select individuals and markers for accurate genomic prediction models, especially for hybrid breeding programs.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jun 13, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Agricultural Science
  • Genetics
  • Bioinformatics

Background:

  • Genomic selection (GS) enables accurate prediction of agronomic performance using molecular marker data.
  • Current GS models require dense marker profiles for phenotyped individuals, often limited by genotyping budgets.
  • Phenotypic data expansion necessitates efficient strategies for constructing robust GS models.

Purpose of the Study:

  • To develop optimal selection strategies for individuals and molecular markers in genomic prediction.
  • To address the challenge of constructing GS models for hybrid breeding programs with limited parental marker data.
  • To evaluate the impact of sample size and quality on prediction accuracy in hybrid maize.

Main Methods:

  • Developed methods for optimal individual selection based on phenotypic data quality.
  • Implemented strategies for reducing molecular marker numbers while ensuring genome coverage.
  • Created an approach to identify optimal parental inbred lines for hybrid breeding programs.

Main Results:

  • Demonstrated effective selection of individuals and markers to enhance genomic prediction model accuracy.
  • Showcased the ability to reduce marker density without compromising genome coverage.
  • Validated the methods in a simulation study for hybrid maize, highlighting trade-offs between sample size and quality.

Conclusions:

  • Optimal selection of individuals and markers significantly improves genomic prediction accuracy.
  • The developed methods are crucial for cost-effective and efficient genomic selection in breeding programs.
  • These strategies are particularly valuable for hybrid breeding programs aiming to maximize progeny performance.