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Related Experiment Videos

Predicting lean growth while accounting for correlated traits.

G L Bennett1

  • 1Roman L. Hruska U.S. Meat Animal Research Center, ARS, U.S. Department of Agriculture, Clay Center, NE 68933-0166.

Journal of Animal Science
|January 1, 1992
PubMed
Summary

A new method improves lean tissue growth rate prediction by linking fat and lean growth regressions. This ensures selection for lean tissue accurately reflects correlated fat changes, unlike conventional methods.

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Area of Science:

  • Animal Science
  • Quantitative Genetics

Background:

  • Estimating lean tissue growth rate typically relies on indirect measurements.
  • Accurate estimation is crucial for livestock breeding and management.

Purpose of the Study:

  • To propose a novel procedure for developing prediction equations for lean tissue growth rate.
  • To ensure that selection based on predicted lean growth rate accurately reflects correlated changes in fat deposition.

Main Methods:

  • A restriction is imposed on the regression of fat growth rate on predicted lean growth rate, making it equal to the regression on actual lean growth rate.
  • This restriction can be applied phenotypically or genetically.

Main Results:

  • Phenotypic application of the procedure ensures selection differentials for fat and lean growth rates are proportional to direct selection for lean growth.

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  • This method accounts for correlated changes in fat, which are often ignored by conventional procedures.
  • Conventional methods obscure the biological selection intent by not adequately addressing correlated trait changes.

    • Conclusions:

    • The proposed procedure provides a more biologically relevant method for predicting lean tissue growth rate.
    • It enhances the accuracy of selection criteria by incorporating correlated fat deposition changes.
    • This approach clarifies the original intent of using lean tissue growth rate as a selection criterion in breeding programs.