Constructing covariance functions for random regression models for growth in Gelbvieh beef cattle

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Beef Cattle Breeding

Background:

• Accurate genetic parameters are crucial for effective genetic selection in beef cattle.
• Random regression models (RRMs) are powerful tools for analyzing longitudinal growth data.
• Existing genetic evaluations often lack detailed growth curve information.

Purpose of the Study:

• To construct genetic parameters for a random regression model of growth in Gelbvieh beef cattle.
• To integrate data from routine evaluations and literature estimates for improved accuracy.
• To extrapolate growth parameters up to 730 days of age.

Main Methods:

• Combined existing Gelbvieh multiple-trait evaluation parameters with Nellore cattle RRM estimates.
• Utilized standardized Legendre polynomials for additive genetic and permanent environmental effects.
• Employed linear splines for residual variances across different ages.
• Fit models using least squares, varying polynomial orders from third to sixth.

Main Results:

• Successfully constructed genetic parameters for a RRM in beef cattle.
• Combining data sources yielded reliable estimates with minimal artifacts.
• Cubic polynomial order provided a good balance between model fit and matrix stability.
• Extrapolation to 730 days was achieved.

Conclusions:

• Genetic parameters for beef cattle growth can be reliably constructed by integrating diverse data sources.
• The developed methodology offers a robust framework for RRM implementation.
• Formulas are adaptable for alternative polynomial and spline functions.