Genomic Prediction Ability Using Linear and Threshold Approaches for Different Stayability Definitions in Nellore...

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Reproductive Biology

Background:

• Enhancing female longevity through calving improves herd replacement and reduces costs associated with low reproductive efficiency.
• Stayability (STAY) is a categorical trait, defined as a cow's ability to remain productive, posing challenges for traditional genetic evaluation due to its non-normal distribution.

Purpose of the Study:

• To estimate genomic predictions for different STAY definitions in Nellore females based on calving number at specific ages.
• To compare the predictive ability of linear and threshold models for STAY using the linear regression (LR) method.

Main Methods:

• Phenotypic and genotypic data from 187 Nellore herds were analyzed.
• Four STAY definitions (STAY48-2, STAY48-3, STAY54-2, STAY54-3, STAY72-3) were evaluated.
• Genomic estimated breeding values (GEBV) were obtained using univariate linear and threshold models (BLUPF90), with variance components transformed from liability to observed scale.

Main Results:

• Heritability estimates for STAY ranged from 0.16 to 0.22 (liability scale) and 0.07 to 0.09 (observed scale).
• Threshold models demonstrated superior predictive ability over linear models, showing higher accuracies (0.531-0.698 vs. 0.451-0.532), lower bias (-0.0004-0.008 vs. 0.027-0.096), and better dispersion.
• STAY48 with at least two or three calvings exhibited the most consistent performance among the evaluated definitions.

Conclusions:

• Threshold models are more effective than linear models for the genetic evaluation of stayability in Nellore cattle.
• The STAY48 definition, requiring a minimum number of calvings, represents a promising criterion for improving genetic selection for longevity and productivity.