A practical longitudinal model for evaluating growth in Gelbvieh cattle

Area of Science:

• Animal Genetics and Breeding
• Quantitative Genetics
• Livestock Production

Background:

• Genetic evaluation of growth traits in beef cattle is crucial for breeding programs.
• Traditional multiple-trait (MTM) analysis excludes records outside specific age ranges, potentially losing valuable data.
• A significant portion of records for weaning weight (Wwt) and yearling weight (Ywt) fall outside accepted MTM ranges.

Purpose of the Study:

• To compare the effectiveness of random regression (RRM) analysis with multiple-trait (MTM) analysis for genetic evaluation of growth in Gelbvieh beef cattle.
• To assess the impact of including all available records versus only those within MTM age ranges on breeding value predictions.

Main Methods:

• Two RRM evaluations were conducted using cubic Legendre polynomials (RRML) and linear splines (RRMS) with three knots.
• Data Set 1 (d1) included all available records; Data Set 2 (d2) used only records within MTM age ranges.
• Convergence was achieved for RRML models after imposing diagonalization, with longitudinal models converging faster than MTM.

Main Results:

• Correlations between MTM and RRM (RRML-d2, RRMS-d2) were high (>=0.99) for all traits when using data within MTM ranges.
• Including all records (RRML-d1, RRMS-d1) resulted in slightly lower correlations for Wwt and Ywt compared to MTM, indicating the influence of additional data.
• RRM demonstrated the ability to incorporate records from all ages with computing costs comparable to MTM.

Conclusions:

• Random regression analysis is a viable and effective alternative to multiple-trait analysis for genetic evaluation of growth in Gelbvieh cattle.
• RRM allows for the utilization of all available performance records, potentially leading to more accurate breeding value predictions.
• The RRM approach offers computational efficiency similar to MTM while providing greater flexibility in data inclusion.