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Related Experiment Videos

Variance components and breeding values for growth traits from different statistical models.

G B Ferreira1, M D MacNeil, L D Van Vleck

  • 1Department of Animal Science, University of Nebraska, Lincoln 68583-0908, USA. gbbf@ccr.ufsm.br

Journal of Animal Science
|October 16, 1999
PubMed
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Comparing twelve analytical models for cattle growth traits, this study found that models incorporating direct, maternal genetic, and permanent environmental effects (Models 3 and 9) best fit the data. Ignoring inbreeding did not significantly alter heritability estimates or breeding value rankings.

Area of Science:

  • Animal Genetics and Breeding
  • Quantitative Genetics
  • Livestock Production

Background:

  • Accurate estimation of genetic parameters is crucial for effective selection in livestock breeding programs.
  • Previous studies have explored various models, but optimal approaches for simultaneously estimating direct and maternal genetic effects, along with permanent environmental influences, remain a focus.
  • The impact of inbreeding on genetic parameter estimates in cattle growth traits requires careful consideration.

Purpose of the Study:

  • To compare the performance of twelve different analytical models for estimating genetic parameters of birth weight (BWT), 205-day weight (WWT), and 365-day weight (YWT) in Line 1 Hereford cattle.
  • To evaluate the influence of including inbreeding coefficients on the estimates of genetic parameters and the ranking of predicted breeding values.
  • To identify the most suitable model for accurately estimating direct genetic effects, maternal genetic effects, and maternal permanent environmental effects.

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Main Methods:

  • Data from 4,155 birth weight, 3,884 205-day weight, and 3,476 365-day weight records of Line 1 Hereford cattle (1934-1989) were analyzed.
  • Twelve analytical models were compared, varying in their inclusion of fixed effects (year, sex, age of dam), covariates (birth day, inbreeding), and random effects (animal, sire, dam, maternal grandsire, permanent environmental).
  • Models were assessed based on their fit to the data, heritability estimates, and the ranking of predicted breeding values.

Main Results:

  • Rankings of predicted breeding values and heritability estimates remained consistent whether inbreeding coefficients were included or excluded from the models.
  • Models 3 (including covariance between direct and maternal genetic effects, and maternal permanent environmental effects) and 9 (a sire and dam model accounting for relationships and permanent environmental effects) demonstrated the best fit to the data.
  • Sire and sire-maternal grandsire models yielded the smallest estimates for direct heritability.

Conclusions:

  • Models that simultaneously account for direct genetic, maternal genetic, and maternal permanent environmental effects (Models 3 and 9) provide the most accurate estimation of genetic variances and covariances for cattle growth traits.
  • The inclusion of inbreeding coefficients in the models did not substantially alter the heritability estimates or the relative ranking of animals for breeding values in this dataset.
  • The choice of analytical model can influence estimates of direct and maternal heritability, highlighting the importance of selecting appropriate models for genetic evaluation in beef cattle.