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

Incomplete Dominance01:43

Incomplete Dominance

30.1K
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.
30.1K
Correlations02:20

Correlations

36.5K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.5K
Correlation and Causation01:27

Correlation and Causation

42.9K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.9K
Conjugate Addition (1,4-Addition) vs Direct Addition (1,2-Addition)01:27

Conjugate Addition (1,4-Addition) vs Direct Addition (1,2-Addition)

4.4K
α,β-Unsaturated carbonyl compounds with two electrophilic sites, the carbonyl carbon, and the β carbon, are susceptible to nucleophilic attack via two modes: conjugate or 1,4-addition and direct or 1,2-addition.
Conjugate addition results in a thermodynamically stable product. The reaction retains the stronger C=O bond at the expense of the weaker C=C π bond. The process is slow as the β carbon is less electrophilic than the carbonyl carbon.
Direct addition products are...
4.4K
Genomics02:02

Genomics

40.9K
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...
40.9K
Correlation01:09

Correlation

15.2K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.2K

You might also read

Related Articles

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

Sort by
Same author

Bound states in doped charge transfer insulators.

Nature communications·2026
Same author

A molybdenum-promoted nickel-aluminum alloy catalyst for high-efficient hydrogenation reduction of nitrate to ammonia and nitrogen.

RSC advances·2026
Same author

Molecular Glues Recruiting RNF213 As an E3 Ligase for Targeted Protein Degradation: A Minimal Dibromoacetamide Warhead As a Recruitment Ligand.

Journal of the American Chemical Society·2026
Same author

Solving the Hubbard model with neural quantum states.

Nature communications·2026
Same author

Impact of Estimating Genetic Variance in the Target Group on Reliability Metrics of the Linear Regression Validation Method Under Selection.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2026
Same author

Pyroptosis-immunity-microbiome axis in acute upper gastrointestinal bleeding: mechanisms, risk prediction, and individualized strategies.

Frontiers in medicine·2026

Related Experiment Video

Updated: Feb 10, 2026

Assessing Social Dominance in Mouse Models Using the Tube Test
03:34

Assessing Social Dominance in Mouse Models Using the Tube Test

Published on: June 6, 2025

1.5K

Genomic Model with Correlation Between Additive and Dominance Effects.

Tao Xiang1,2,3, Ole Fredslund Christensen3, Zulma Gladis Vitezica4

  • 1Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, P. R. China Tao.Xiang@mail.hzau.edu.cn.

Genetics
|May 11, 2018
PubMed
Summary
This summary is machine-generated.

Genomic evaluation can now estimate dominance effects, revealing a correlation between additive and dominance effects influenced by selection. Accounting for this correlation slightly improves genetic prediction accuracy in genomic best linear unbiased prediction (GBLUP).

Keywords:
GenPredGenomic Selectionadditive genetic effectscorrelationdominance genetic effectsgenomic modelshared data resource

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.4K
Somatic Genome-Engineered Mouse Models Using In Vivo Microinjection and Electroporation
08:06

Somatic Genome-Engineered Mouse Models Using In Vivo Microinjection and Electroporation

Published on: May 5, 2023

2.5K

Related Experiment Videos

Last Updated: Feb 10, 2026

Assessing Social Dominance in Mouse Models Using the Tube Test
03:34

Assessing Social Dominance in Mouse Models Using the Tube Test

Published on: June 6, 2025

1.5K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.4K
Somatic Genome-Engineered Mouse Models Using In Vivo Microinjection and Electroporation
08:06

Somatic Genome-Engineered Mouse Models Using In Vivo Microinjection and Electroporation

Published on: May 5, 2023

2.5K

Area of Science:

  • Quantitative Genetics
  • Genomic Selection
  • Animal and Plant Breeding

Background:

  • Dominance genetic effects are often excluded from pedigree-based genetic evaluations.
  • Genomic evaluation methods, particularly genomic best linear unbiased prediction (GBLUP), enable the estimation of dominance effects.
  • Existing GBLUP approaches typically ignore potential correlations between additive and dominance genetic effects.

Purpose of the Study:

  • To investigate the impact of selection on the relationship between additive and dominance genetic effects.
  • To develop a GBLUP-compatible method for estimating correlated additive and dominance effects.
  • To evaluate the effect of incorporating these correlations on the accuracy of genetic value prediction.

Main Methods:

  • Utilized single nucleotide polymorphism (SNP) markers to construct combined additive and dominance relationship matrices.
  • Employed standard restricted maximum likelihood (REML) algorithms with an equivalent model for parameter estimation.
  • Conducted a simulation study to assess the estimation of correlations and prediction accuracy.

Main Results:

  • Selection orients the correlation between functional additive and dominance effects when the most frequent allele is used as the reference.
  • Selection induces a negative covariance between genotypic additive and dominance genetic values.
  • Incorporating these correlations into GBLUP slightly improved prediction accuracy compared to models assuming uncorrelated effects.

Conclusions:

  • A novel GBLUP approach can estimate correlated additive and dominance effects using marker genotypes and standard REML algorithms.
  • Accounting for selection-induced correlations offers a slight improvement in prediction accuracy for genetic values.
  • Orthogonal models perform similarly to correlated models in terms of prediction accuracy, suggesting robustness.