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 Experiment Videos

Bayesian inference for categorical traits with an application to variance component estimation.

M F Luo1, P J Boettcher, L R Schaeffer

  • 1Department of Animal and Poultry Science, University of Guelph, Ontario, Canada.

Journal of Dairy Science
|April 5, 2001
PubMed
Summary

Bayesian inference models using Gibbs sampling were compared for estimating genetic parameters in categorical traits. Threshold sire and maternal grandsire models offer a feasible alternative to multiple-trait models for variance component analysis.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study.

Journal of dairy science·2021
Same author

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics.

Journal of dairy science·2019
Same author

[The relationship between the changes of serum NGF, HO-1, IL-1 beta and cognitive function in patients with severe OSAHS after comprehensive treatment].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2018
Same author

Necessary changes to improve animal models.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2018
Same author

Genome-wide association scan suggests basis for microtia in Awassi sheep.

Animal genetics·2016
Same author

Measuring the precision of genetic parameters by a simulation technique.

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

Area of Science:

  • Quantitative Genetics
  • Statistical Genetics
  • Animal Breeding

Background:

  • Accurate estimation of genetic parameters is crucial for animal breeding programs.
  • Categorical traits present unique challenges for genetic analysis compared to continuous traits.
  • Bayesian inference methods, including Gibbs sampling, offer powerful tools for complex genetic models.

Purpose of the Study:

  • To compare the accuracy and efficiency of different statistical models for estimating genetic parameters of categorical traits.
  • To evaluate the impact of model choices, such as linear vs. threshold and animal vs. sire-maternal grandsire models, on estimation errors.
  • To assess the influence of fixed vs. random effects and single-trait vs. multiple-trait approaches on Bayesian inference for categorical traits.

Main Methods:

Related Experiment Videos

  • Implementation of Bayesian inference statistical models incorporating direct and maternal genetic effects.
  • Utilized Gibbs sampling for parameter estimation in categorical traits.
  • Compared various model configurations: animal vs. sire and maternal grandsire, linear vs. threshold, single-trait vs. multiple-trait, and fixed vs. random herd-year-season effects.

Main Results:

  • Linear models produced biased genetic parameter estimates for categorical traits.
  • Animal models were unsuitable for categorical traits with threshold models and Gibbs sampling.
  • Threshold models with herd-year-season treated as random effects reduced Monte Carlo errors and improved estimate variances.

Conclusions:

  • The threshold single-trait sire and maternal grandsire model is a viable alternative to multiple-trait models for analyzing variance components in categorical traits.
  • This model effectively accounts for direct and maternal genetic factors.
  • Careful consideration of model selection is essential for accurate genetic parameter estimation in categorical traits.