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Related Experiment Videos

Multiple-trait Gibbs sampler for animal models: flexible programs for Bayesian and likelihood-based (co)variance

C P Van Tassell1, L D Van Vleck

  • 1Roman L. Hruska U.S. Meat Animal Research Center, USDA-ARS, University of Nebraska, Lincoln 68583-0908, USA. curtvt@aipl.arsusda.gov

Journal of Animal Science
|November 1, 1996
PubMed
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A new software tool, multiple-trait Gibbs sampling (MTGSAM), accurately estimates genetic variance components in animal models. This method, along with others, proved empirically unbiased for genetic analysis.

Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Accurate estimation of genetic (co)variance components is crucial for animal breeding and genetic improvement.
  • Existing methods may have limitations in handling complex genetic models and multiple traits simultaneously.

Purpose of the Study:

  • To develop and evaluate a novel software package, multiple-trait Gibbs sampling in animal models (MTGSAM), for inferring (co)variance components.
  • To assess the performance of MTGSAM compared to established methods like multiple-trait derivative-free restricted maximum likelihood (MTDFREML).

Main Methods:

  • Development of FORTRAN programs implementing a Gibbs sampling algorithm for multiple-trait (co)variance component inference.
  • Simulation of datasets with correlated genetic effects, covariates, fixed effects, and random effects for two traits across four generations.

Related Experiment Videos

  • Application of MTGSAM with both informative and flat prior distributions, and comparison with MTDFREML.

    • Main Results:

    • MTGSAM successfully estimated variance components for simulated data, demonstrating empirical unbiasedness.
    • Correlations between estimates from MTGSAM (using flat priors) and MTDFREML were consistently high, exceeding 0.99.
    • Both informative and flat prior distributions in MTGSAM yielded unbiased estimates.

    Conclusions:

    • The MTGSAM software provides a robust and publicly available tool for (co)variance component inference in animal models.
    • MTGSAM is a viable alternative to MTDFREML, offering comparable accuracy in estimating genetic parameters.
    • The developed algorithm effectively handles complex models, including missing data and correlated genetic effects.