Genomic evaluation with multibreed and crossbred data.

Area of Science:

• Animal Breeding and Genetics
• Genomics
• Quantitative Genetics

Background:

• Various multibreed genomic evaluation strategies are currently employed.
• These methods include using purebred SNP effects for crossbreds, joint evaluations, and separate trait analyses for each breed group.
• Exploiting quantitative trait nucleotides (QTNs) is also explored for enhanced prediction accuracy.

Purpose of the Study:

• To review and analyze existing multibreed genomic evaluation methods.
• To assess the accuracy of crossbred prediction using different approaches.
• To explore the potential of QTNs in improving genomic selection accuracy.

Main Methods:

• Review of existing literature on multibreed genomic evaluation.
• Analysis of prediction accuracy for crossbreds based on purebred data.
• Evaluation of joint multibreed vs. single-breed evaluations.
• Assessment of the impact of putative QTNs on prediction accuracy.

Main Results:

• Prediction of crossbred performance using purebred data shows limited accuracy.
• Joint evaluations may improve crossbred accuracy but potentially decrease purebred accuracy.
• The inclusion of putative QTNs offers only a marginal increase in prediction accuracy.
• A hypothesis suggests that distinct reference populations are required for different breed types and crossbred generations (e.g., F1 vs. F2).

Conclusions:

• Current multibreed genomic evaluation methods face challenges in accurately predicting crossbred performance.
• The effectiveness of joint evaluations is debated, with potential trade-offs in accuracy for purebreds.
• Further research is needed to optimize genomic selection strategies for multibreed populations, potentially requiring type-specific reference populations.