Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle

Area of Science:

• Animal Genetics and Breeding
• Quantitative Genetics
• Beef Cattle Production

Background:

• Accurate genetic evaluation is crucial for improving beef cattle traits.
• Traditional multi-trait models have limitations in capturing complex growth patterns.
• Random regression models offer a more dynamic approach to estimate breeding values over time.

Purpose of the Study:

• To quantify the increase in accuracy of expected breeding values for Nelore cattle weights using random regression models.
• To compare the performance of Legendre polynomials and B-spline functions within random regression models.
• To evaluate the impact of different data subsets on genetic evaluation accuracy.

Main Methods:

• Utilized a large dataset of 87,712 weight records from 8,144 Nelore cattle.
• Employed multi-trait and random regression models (Legendre polynomials and B-splines) for genetic analysis.
• Modeled growth trends, residual variances, and genetic/environmental effects using various polynomial orders and functions.

Main Results:

• Random regression models yielded different animal rankings compared to multi-trait models.
• Significant accuracy gains were observed with random regression models, particularly at ages with fewer records.
• B-spline functions demonstrated potential as an alternative to Legendre polynomials for modeling covariance functions.

Conclusions:

• Random regression models provide more accurate expected breeding values than traditional multi-trait models for beef cattle growth.
• Implementing random regression models can lead to higher genetic responses in breeding programs.
• B-spline functions are a viable alternative for modeling genetic parameters in beef cattle.