Improved genetic evaluation in Karan Fries cattle using multitrait single-step genomic best linear unbiased...

Area of Science:

• Animal Genetics and Breeding
• Genomic Prediction
• Livestock Improvement

Background:

• Karan Fries (KF) cattle selection has historically relied on pedigree and phenotype data.
• Genomic data integration offers potential for enhanced accuracy in genetic parameter estimation and breeding value prediction.
• Advancements in single-step genomic best linear unbiased prediction (ssGBLUP) methodology are crucial for modern livestock breeding.

Purpose of the Study:

• To evaluate the accuracy of genetic parameter estimation for milk production traits in KF cattle using pedigree and limited genomic data.
• To assess the impact of incorporating genomic information on breeding value prediction accuracy.
• To compare the performance of single-trait and multitrait ssGBLUP models.

Main Methods:

• Analysis of 8,454 phenotypic records and 355 genotyped animals from KF cattle.
• Estimation of variance-covariance components and genetic parameters using Restricted Maximum Likelihood (REML) and ssGREML.
• Application of single-step genomic best linear unbiased prediction (ssGBLUP) for breeding value estimation.

Main Results:

• Heritability estimates for total milk yield (TMY), 305-day milk yield (305-DMY), lactation peak yield (PY), dry period (DP), and age at first calving (AFC) were determined.
• High positive genetic correlations were observed among milk production traits (TMY, 305-DMY, PY).
• Multitrait ssGBLUP improved breeding value prediction accuracy for AFC by 13% and other traits significantly (up to 33.33% for AFC).

Conclusions:

• Genomic data integration effectively enhances the accuracy of genetic parameters for production and reproduction traits in KF cattle.
• Multitrait ssGBLUP enables early selection, leading to more effective breeding programs, even with limited genomic data.
• The findings have global relevance for tropical cattle breeding programs utilizing genomic selection.