Genomic Prediction Ability for Novel Profitability Traits Using Different Models in Nelore Cattle

Area of Science:

• Animal Genetics and Breeding
• Quantitative Genetics
• Genomic Selection

Background:

• Accurate genomic prediction of economically important traits is crucial for efficient livestock breeding programs.
• Nelore cattle breeding programs require robust methods to improve traits such as accumulated profitability (APF) and profit per kilogram of liveweight gain (PFT).

Purpose of the Study:

• To evaluate the accuracy, bias, and dispersion of various genomic prediction models for APF and PFT in Nelore cattle.
• To compare the predictive performance of single-trait, multi-trait, and weighted single-step genomic best linear unbiased prediction (ssGBLUP) models.

Main Methods:

• Utilized a dataset of 3969 phenotypic records for APF and PFT, with pedigree information for 38,930 animals.
• Genotyped 2449 animals using the Clarifide Nelore 3.0 SNP panel.
• Evaluated nine genomic prediction models, including single-trait, two-trait, three-trait, multi-trait ssGBLUP, and weighted single-step GBLUP (WssGBLUP) approaches.

Main Results:

• Multi-trait ssGBLUP (MT_ss) model showed significantly higher prediction accuracy for PFT (0.665) and improved accuracy for APF (0.561).
• Linear WssGBLUP models (ST_sswl1, ST_sswl2) demonstrated high phenotypic prediction ability for both traits, outperforming other models.
• Single-trait ssGBLUP and non-linear weighting models did not consistently enhance prediction accuracy.

Conclusions:

• Multi-trait genomic selection models provide superior predictive ability for novel, economically important traits like PFT and APF in Nelore cattle.
• Implementing multi-trait genomic selection can lead to greater genetic gains compared to other evaluated models in breeding programs.
• The study highlights the effectiveness of advanced genomic prediction strategies for optimizing breeding objectives in beef cattle.