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Related Experiment Videos

Calving ease (Co)variance components for a sire-maternal grandsire threshold model.

G R Wiggans1, I Misztal, C P Van Tassell

  • 1Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350. wiggans@aipl.arsusda.gov

Journal of Dairy Science
|June 5, 2003
PubMed
Summary

Estimating variance components for calving ease (CE) using a sire-maternal grandsire (MGS) model revealed significant heritability for direct and maternal effects. These findings support the implementation of advanced genetic models for improving cattle breeding.

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Reproductive Biology

Background:

  • Calving ease (CE) is a critical trait in cattle breeding, impacting calf survival and economic returns.
  • Existing genetic models may not fully capture the complex inheritance patterns of CE.
  • A large-scale US calving records database provides an opportunity to refine genetic evaluations.

Purpose of the Study:

  • To estimate variance components for a sire-maternal grandsire (MGS) threshold model for calving ease (CE).
  • To assess the heritability of direct and maternal effects on CE.
  • To inform the implementation of an improved genetic model for CE.

Main Methods:

  • Utilized a US calving ease database with over 10 million records.
  • Employed a sire-MGS threshold model including random herd-year, sire, MGS, and residual effects.

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  • Created five distinct datasets of approximately 200,000 records each for robust estimation.

    • Main Results:

    • Variance component estimates were consistent across five replicate datasets.
    • Selected estimates yielded high heritabilities: 0.086 for direct and 0.048 for maternal effects.
    • The correlation between direct and maternal effects was estimated at -0.12.

    Conclusions:

    • The sire-MGS model provides reliable estimates for calving ease genetic parameters.
    • Significant direct and maternal heritabilities suggest additive genetic variation for CE.
    • The findings support the integration of the sire-MGS model for enhanced genetic selection in cattle.