Single-step genomic predictions for crossbred Holstein and Jersey cattle using metafounders

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Genomic Evaluation

Background:

• Accurate genetic evaluation is crucial for livestock improvement.
• Pedigree gaps can limit the accuracy of genomic predictions in multibreed populations.
• Metafounders (MF) offer a potential solution to address incomplete pedigree information.

Purpose of the Study:

• To investigate the impact of incorporating metafounders (MF) in single-step GBLUP (ssGBLUP) models for genetic evaluation.
• To assess the effects of different MF scenarios on prediction metrics across various traits and breeds (Holstein, Jersey, and their crossbreds).
• To evaluate the trade-offs between model complexity and prediction accuracy.

Main Methods:

• Utilized a large dataset of Holstein, Jersey, and crossbred cattle with extensive phenotypic and genotypic records.
• Applied a 5-trait repeatability model using ssGBLUP with three distinct MF scenarios (4, 24, and 32 MF) and a conventional model (NO_MF).
• Conducted forward-in-time validation to assess predictability, inflation, and stability of the models.

Main Results:

• Inclusion of MF influenced prediction metrics variably based on trait, breed, and MF configuration.
• Certain MF scenarios (e.g., 4 MF) improved predictability and reduced bias in crossbreds for specific traits.
• MF scenarios enhanced predictive ability for low heritability traits (SCS, DPR) in crossbreds, but decreased stability for high heritability traits (MY, PY, FY) in some cases, indicating potential overfitting.

Conclusions:

• Metafounders present a valuable strategy for managing pedigree gaps in multibreed genetic evaluations.
• The effectiveness of MF depends on careful consideration of trait heritability and population structure.
• Optimizing MF application is essential to prevent overfitting and ensure reliable genetic predictions in cattle populations.