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

14.4K
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...
14.4K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.2K
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...
6.2K
Gene Conversion02:08

Gene Conversion

10.0K
Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Genomic prediction of wild-derived powdery mildew resistance for strawberry (Fragaria × ananassa) pre-breeding.

Heredity·2026
Same author

A computationally efficient algorithm to leverage average information REML for (co)variance component estimation in the genomic era.

Genetics, selection, evolution : GSE·2024
Same author

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations.

Journal of dairy science·2024
Same author

A computationally feasible multi-trait single-step genomic prediction model with trait-specific marker weights.

Genetics, selection, evolution : GSE·2024
Same author

Efficient large-scale single-step evaluations and indirect genomic prediction of genotyped selection candidates.

Genetics, selection, evolution : GSE·2023
Same author

Improving Genomic Prediction Accuracy in the Chinese Holstein Population by Combining with the Nordic Holstein Reference Population.

Animals : an open access journal from MDPI·2023

Related Experiment Video

Updated: Sep 21, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.2K

Single-step genomic BLUP with genetic groups and automatic adjustment for allele coding.

Ismo Strandén1, Gert P Aamand2, Esa A Mäntysaari3

  • 1Natural Resources Institute Finland (Luke), Jokioinen, Finland. Ismo.Stranden@Luke.Fi.

Genetics, Selection, Evolution : GSE
|June 2, 2022
PubMed
Summary

Genomic estimated breeding values (GEBV) are now independent of marker centering using J factors. This method simplifies calculations and speeds up computing time for genetic analyses.

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.4K
Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.3K

Related Experiment Videos

Last Updated: Sep 21, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.4K
Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.3K

Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Genomic selection

Background:

  • Genomic estimated breeding values (GEBV) derived from single-step genomic BLUP (ssGBLUP) are sensitive to marker data centering.
  • A fixed effect, the J factor, can render GEBV independent of centering methods.
  • This study expands the application of J factors from a single factor to a group of factors.

Purpose of the Study:

  • To investigate the extension of J factors for allele coding independence in GEBV calculations.
  • To develop a computationally efficient method for incorporating J factors into mixed model equations (MME).
  • To evaluate the impact of J factors on GEBV accuracy and computational speed in a real-world dataset.

Main Methods:

  • Extended the use of J factors to a group of factors within the ssGBLUP framework.
  • Developed a transformation for MME to simplify calculations and reduce sparsity.
  • Applied the J factor methodology to a Red dairy cattle fertility dataset.

Main Results:

  • GEBV derived using J factors demonstrated allele coding independence, confirming theoretical predictions.
  • Transformed MME resulted in sparser equations and eliminated the need for direct J factor computation.
  • The analysis of Red dairy cattle fertility data showcased the practical application and benefits of the J factor method.

Conclusions:

  • The implementation of J factors ensures allele coding independence for GEBV, enhancing the robustness of genomic selection.
  • Transformed MME offer a significant computational advantage, leading to faster analysis times compared to traditional methods.
  • This approach provides a more reliable and efficient method for calculating GEBV in livestock breeding programs.