Combining large broiler populations into a single genomic evaluation: dealing with genetic divergence

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Genomic Selection

Background:

• Combining divergent breeding populations is common in the poultry industry for efficient genetic improvement.
• Genomic evaluations require robust methodologies to integrate data from distinct populations.
• The single-step GBLUP (Genomic Best Linear Unbiased Prediction) framework offers a potential solution for integrating diverse populations.

Purpose of the Study:

• To assess the feasibility of combining two large, genetically distinct broiler populations for genomic evaluation.
• To compare different modeling approaches within the single-step GBLUP framework to account for inter-population genetic differences.
• To evaluate the impact of combining populations on the accuracy, bias, and dispersion of genetic predictions.

Main Methods:

• Utilized pedigree and genotype data from two large broiler lines, with extensive phenotypic data for body weight, carcass yield, mortality, and feet health.
• Employed a 4-trait single-step GBLUP model, comparing a conventional approach (M1) with models incorporating line-of-origin effects (M2, M3).
• Assessed model performance using accuracy, bias, and dispersion metrics via linear regression, with validation performed both within and across lines.

Main Results:

• Combining the two broiler populations significantly increased prediction accuracy compared to single-line evaluations.
• No substantial differences in accuracy or dispersion were observed among the tested models (M1, M2, M3).
• Models M2 and M3, which explicitly accounted for line of origin and utilized unknown parent groups (UPG), demonstrated the least bias in combined evaluations.

Conclusions:

• Combining genetically distinct broiler populations into a single genomic evaluation is feasible and enhances prediction accuracy.
• Accounting for genetic and non-genetic differences between populations is crucial for minimizing bias in genomic predictions.
• The use of models incorporating line-specific effects and unknown parent groups is recommended for accurate and reliable genomic evaluations in combined populations.