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

Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...

You might also read

Related Articles

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

Sort by
Same author

JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models.

Nucleic acids research·2025
Same author

Artificial intelligence is beginning to create value for selected small animal veterinary applications while remaining immature for others.

Journal of the American Veterinary Medical Association·2025
Same author

An early prediction model for canine chronic kidney disease based on routine clinical laboratory tests.

Scientific reports·2022
Same author

Development and initial validation of a dog quality of life instrument.

Scientific reports·2022
Same author

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

Journal of veterinary internal medicine·2019
Same author

Core Hunter 3: flexible core subset selection.

BMC bioinformatics·2018

Related Experiment Video

Updated: Jun 22, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Moving Beyond Managing Realized Genomic Relationship in Long-Term Genomic Selection.

Herman De Beukelaer1, Yvonne Badke2, Veerle Fack3

  • 1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Gent, Belgium herman.debeukelaer@ugent.be.

Genetics
|April 7, 2017
PubMed
Summary
This summary is machine-generated.

Long-term genomic selection needs strategies balancing genetic gain and diversity. New methods, IND-HE and IND-RA, outperform existing genomic optimal contributions selection (GOCS) and weighted genomic selection (WGS) by better managing genetic gain and diversity.

Keywords:
diversitygenomic selectionlong-term gainoptimizationset selection

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Related Experiment Videos

Last Updated: Jun 22, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Genomic selection strategies

Background:

  • Long-term genomic selection (GS) requires balancing genetic gain with population diversity to sustain breeding progress.
  • Existing strategies like genomic optimal contributions selection (GOCS) and weighted genomic selection (WGS) have limitations in achieving this balance.

Purpose of the Study:

  • To compare GOCS and WGS head-to-head in a simulation model for recurrent selection.
  • To introduce and evaluate two new set selection methods (IND-HE and IND-RA) for long-term genomic selection.

Main Methods:

  • Simulation model for a recurrent selection scheme.
  • Head-to-head comparison of GOCS and WGS.
  • Development and testing of two new set selection methods (IND-HE and IND-RA) maximizing a weighted index.

Main Results:

  • Both GOCS and WGS increased long-term genetic gain and decreased inbreeding compared to standard GS, but had limitations.
  • GOCS failed to optimally balance genetic gain and inbreeding, while WGS struggled with rare allele frequency management.
  • The new IND-HE and IND-RA methods outperformed GOCS and WGS in balancing genetic gain and diversity.

Conclusions:

  • IND-HE and IND-RA offer a significant advancement for efficient long-term genomic selection.
  • These methods show promise for practical application in breeding programs, improving the balance between genetic gain and diversity.
  • Further testing is recommended, but the inherent benefits suggest broad applicability.