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

How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
The Ratio of X Chromosome to Autosomes02:45

The Ratio of X Chromosome to Autosomes

In most organisms, sex is determined by the ratio of X and Y chromosomes. However, in some organisms, such as Drosophila and C.elegans, sex is determined by the ratio of the number of X chromosomes to the number of sets of autosomes. The Y chromosome in Drosophila is active but does not determine sex. It contains genes responsible for the production of sperms in adult flies.  
Normal male Drosophila has a ratio of one X chromosome to two sets of autosomes. In contrast, normal female Drosophila...
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...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...

You might also read

Related Articles

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

Sort by
Same author

Estimation of heritability and genetic trend in populations at a physiological limit.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

Variance of prediction error with mixed model equations when relationships are ignored.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

Effect of parentage misidentification on estimates of genetic parameters for milk yield in the Mediterranean Italian buffalo population.

Journal of dairy science·2012
Same author

Prediction of genetic values for feed intake from individual body weight gain and total feed intake of the pen.

Journal of animal science·2010
Same author

Effect of pen mates on growth, backfat depth, and longissimus muscle area of swine.

Journal of animal science·2009
Same author

Effects of social interactions on empirical responses to selection for average daily gain of boars.

Journal of animal science·2008

Related Experiment Video

Updated: Jul 9, 2026

Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses
07:59

Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses

Published on: September 19, 2011

Computing numerator relationships between any pair of animals.

L D Van Vleck1

  • 1Roman L. Hruska U.S. Meat Animal Research Center, ARS-USDA, Lincoln, NE, USA. lvanvleck@unlnotes.unl.edu

Genetics and Molecular Research : GMR
|December 1, 2007
PubMed
Summary
This summary is machine-generated.

A new method simplifies calculating animal genetic relationships using the MTDFNRM program. This approach leverages inbreeding coefficients to efficiently determine the numerator relationship matrix for animal breeding and genetics.

More Related Videos

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

Methods of Pairing and Pair Maintenance of New Zealand White Rabbits (Oryctolagus Cuniculus) Via Behavioral Ethogram, Monitoring, and Interventions
10:00

Methods of Pairing and Pair Maintenance of New Zealand White Rabbits (Oryctolagus Cuniculus) Via Behavioral Ethogram, Monitoring, and Interventions

Published on: March 16, 2018

Related Experiment Videos

Last Updated: Jul 9, 2026

Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses
07:59

Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses

Published on: September 19, 2011

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

Methods of Pairing and Pair Maintenance of New Zealand White Rabbits (Oryctolagus Cuniculus) Via Behavioral Ethogram, Monitoring, and Interventions
10:00

Methods of Pairing and Pair Maintenance of New Zealand White Rabbits (Oryctolagus Cuniculus) Via Behavioral Ethogram, Monitoring, and Interventions

Published on: March 16, 2018

Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Computational Biology

Background:

  • The numerator relationship matrix is crucial for genetic evaluations in animal breeding.
  • Calculating pairwise relationships can be computationally intensive, especially for large populations.
  • Existing methods may require complex data manipulation.

Purpose of the Study:

  • To present a straightforward computational method for determining the numerator relationship between any pair of animals.
  • To utilize the output from the MTDFNRM program for efficient relationship calculation.
  • To enhance the usability of the numerator relationship matrix in genetic analyses.

Main Methods:

  • Augmenting the original pedigree file with a dummy animal.
  • Running the MTDFNRM program with specific options to generate inbreeding coefficients.
  • Calculating the numerator relationship by multiplying the dummy animal's inbreeding coefficient by two.

Main Results:

  • Successfully computed the numerator relationship for specified animal pairs.
  • Demonstrated the method's reliance on MTDFNRM program outputs and inbreeding coefficients.
  • Provided a practical approach for obtaining pairwise relationships.

Conclusions:

  • The described method offers a simple and efficient way to compute the numerator relationship matrix.
  • This technique can be valuable for researchers and practitioners in animal breeding and genetics.
  • Further application of this method can streamline genetic evaluation processes.