Jove
Visualize
Contact Us

Related Concept Videos

Genomics02:02

Genomics

36.0K
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...
36.0K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.8K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

12.5K
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...
12.5K
Incomplete Dominance01:43

Incomplete Dominance

21.6K
Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
21.6K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

3.9K
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...
3.9K

You might also read

Related Articles

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

Sort by
Same author

Estimating the Value of Including Resilience in a Multi-Trait Selection Index Designed for Australian Merino Sheep.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2025
Same author

Effect of using preselected markers from imputed whole-genome sequence for genomic prediction in Angus cattle.

Genetics, selection, evolution : GSE·2025
Same author

Genetic evaluation of longevity in Australian Angus cattle using random regression models.

Journal of animal science·2025
Same author

The Impact of Genetic and Non-Genetic Factors on Lamb Loin Shear Force.

Animals : an open access journal from MDPI·2024
Same author

Segregation GWAS to linearize a non-additive locus with incomplete penetrance: an example of horn status in sheep.

Genetics, selection, evolution : GSE·2024
Same author

Parameter estimation and assessment of bias in genetic evaluation of carcass traits in Hanwoo cattle using real and simulated data.

Journal of animal science and technology·2024
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 Experiment Video

Updated: Jun 7, 2025

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord
11:13

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

Published on: November 1, 2014

14.6K

Genomic Prediction Using Imputed Whole-Genome Sequence Data in Australian Angus Cattle.

Nantapong Kamprasert1, Hassan Aliloo1, Julius H J van der Werf1

  • 1School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie
|November 15, 2024
PubMed
Summary
This summary is machine-generated.

Whole-genome sequence (WGS) data did not significantly improve genomic prediction accuracy for Australian Angus cattle growth and carcass traits compared to 50K or high-density (HD) markers. Marker density had minimal impact on prediction accuracy across different relatedness groups.

Keywords:
genomic estimated breeding valueimputed genotypeprediction accuracyrelatednesswhole‐genome sequence

More Related Videos

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

10.1K
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

2.1K

Related Experiment Videos

Last Updated: Jun 7, 2025

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord
11:13

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

Published on: November 1, 2014

14.6K
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

10.1K
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

2.1K

Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Genomic Prediction

Background:

  • Genomic breeding values are crucial for selecting superior livestock.
  • Estimating genomic breeding values requires accurate and dense genetic marker data.
  • The utility of whole-genome sequence (WGS) data versus lower-density markers for prediction accuracy is an ongoing research question.

Purpose of the Study:

  • To compare the accuracy and bias of genomic predictions for growth and carcass traits in Australian Angus cattle using three marker densities: 50K, high-density (HD), and WGS.
  • To evaluate the impact of marker density on genomic prediction accuracy across varying levels of relatedness between reference and validation animals.

Main Methods:

  • Whole-genome sequence (WGS) data was utilized to estimate genomic breeding values.
  • Genomic Best Linear Unbiased Prediction (GBLUP) was employed for prediction.
  • Cross-validation was used to assess prediction accuracy and bias.
  • Animals were categorized into subgroups based on relatedness to the reference population.

Main Results:

  • Prediction accuracies were similar across 50K, HD, and WGS marker densities.
  • Accuracies ranged from 0.61 to 0.68 for body weight traits and 0.40 to 0.52 for carcass traits.
  • Marginal decreases in accuracy were observed with increased marker density, with no substantial improvement from WGS.

Conclusions:

  • Whole-genome sequence data did not provide a substantial improvement in genomic prediction accuracy for the studied traits in this Australian Angus population.
  • Population structure likely influenced the lack of significant differences in prediction accuracy across marker densities.
  • Lower-density marker panels may be sufficient for genomic prediction in this context.