Comparison of genomic prediction methods for residual feed intake in broilers

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Livestock Breeding

Background:

• Residual feed intake (RFI) is a key metric for animal feed efficiency.
• Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) linked to RFI.
• Accurate prediction of RFI is crucial for genetic selection programs.

Purpose of the Study:

• To compare the predictive performance of the genomic best linear unbiased prediction (GBLUP) model with a novel genetic architecture BLUP (GA-BLUP) model.
• To evaluate the impact of incorporating previously identified RFI-associated SNPs into prediction models.
• To determine the optimal weighting parameter (ω) for the GA-BLUP model.

Main Methods:

• Utilized ASREML software for model analysis.
• Employed a 5-fold cross-validation strategy on a validation population.
• Constructed a genetic architecture (GA) matrix using nine RFI-associated SNPs from a discovery population.
• Compared GBLUP and GA-BLUP models based on prediction accuracy.

Main Results:

• The GA-BLUP model, incorporating nine RFI-associated SNPs, demonstrated improved prediction accuracy for RFI compared to the standard GBLUP model.
• An optimal weighting parameter (ω) of 0.981 was determined for RFI, falling within the optimal gradient test range (0.9–1.0).
• The GA-BLUP model achieved a 2% increase in prediction accuracy for RFI when using the optimal ω value compared to the GBLUP model.

Conclusions:

• The GA-BLUP model, enhanced with specific RFI-associated SNPs and an optimal weighting parameter, significantly improves prediction accuracy for residual feed intake.
• This approach offers a more precise tool for genetic selection, aiming to enhance feed efficiency in livestock populations.
• The findings support the integration of genomic architecture information into breeding value estimation for complex traits.