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Computing approximate standard errors for genetic parameters derived from random regression models fitted by average

Troy M Fischer1, Arthur R Gilmour, Julius H J van der Werf

  • 1School of Rural Science and Agriculture, University of New England, Armidale, NSW, 2351, Australia. tfischer@une.edu.au

Genetics, Selection, Evolution : GSE
|April 27, 2004
PubMed
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Approximate standard errors (ASE) for random regression variance components are calculated using average information. These calculations allow for estimating the additive genetic variance and heritability along a trajectory, with larger errors found near trajectory ends.

Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Animal breeding

Background:

  • Random regression models are used to analyze (co)variance of traits over time or other continuous variables.
  • Estimating variance components is crucial for genetic improvement programs.
  • Standard errors quantify the uncertainty in variance component estimates.

Purpose of the Study:

  • To present a method for calculating approximate standard errors (ASE) of variance components for random regression coefficients.
  • To demonstrate the calculation of ASE for additive genetic variance and heritability at specific points in a trajectory.
  • To evaluate the magnitude of ASE in an example dataset.

Main Methods:

  • Utilizing the average information (AI) matrix derived from a residual maximum likelihood (REML) procedure.

Related Experiment Videos

  • Defining variance components for additive genetic variance as linear combinations of random regression coefficients.
  • Calculating ASE for these linear combinations and derived heritabilities.

    • Main Results:

    • The average information matrix successfully provided a basis for calculating ASE of variance components.
    • ASE for additive genetic variance and heritability could be computed at any point along the trajectory.
    • In the provided example, ASE were observed to be larger towards the extremities of the trajectory.

    Conclusions:

    • The proposed method allows for the estimation of uncertainty in random regression variance components and derived heritabilities.
    • Understanding the variation in ASE across a trajectory is important for accurate genetic evaluations.
    • This approach enhances the reliability of genetic trend estimations in animal breeding.