Random regression analyses using B-spline functions to model growth of Nellore cattle

Area of Science:

• Animal Genetics and Breeding
• Quantitative Genetics
• Statistical Genomics

Background:

• Accurate estimation of (co)variance components is crucial for genetic evaluations in livestock.
• Traditional methods may not fully capture the complex genetic and environmental influences on growth traits over time.
• Nellore cattle breeding programs require robust models to optimize selection for weight and productivity.

Purpose of the Study:

• To estimate (co)variance components for weight in Nellore cattle from birth to adulthood.
• To evaluate the effectiveness of B-spline functions in random regression models for describing growth curves.
• To identify the most parsimonious and accurate model for genetic parameter estimation.

Main Methods:

• Utilized 82,064 weight records from 8145 Nellore females.
• Employed random regression models with B-spline functions for direct additive, maternal genetic, animal, and maternal permanent environmental effects.
• Compared B-spline models against multi-trait analyses and Legendre polynomial models.

Main Results:

• A model using quadratic B-splines with specific knot numbers for different effects provided the best fit.
• This model effectively described the covariance structure of weight records across the growth trajectory.
• The optimal model was more parsimonious than alternative approaches.

Conclusions:

• B-spline functions offer a flexible and efficient method for modeling (co)variance components in animal breeding.
• Selection for early-age weight in Nellore cattle must consider its impact on mature cow weight.
• Current selection strategies may have limited ability to modify the growth curve for rapid early growth while maintaining adult weight.