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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Review: optimizing genomic selection for crossbred performance by model improvement and data collection.

Pascal Duenk¹, Piter Bijma¹, Yvonne C J Wientjes¹

¹Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands.

Journal of Animal Science

|July 5, 2021

Summary

Genomic prediction for crossbred (CB) performance can improve livestock breeding. Strategies vary in accuracy based on genetic correlations (rpc), population size, and models used, with dominance models often outperforming additive ones.

Keywords:

accuracy crossbred performance crossbreeding genomic prediction genomic selection response to selection

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Area of Science:

Animal breeding and genetics
Genomic selection
Quantitative genetics

Background:

Improving crossbred (CB) performance is crucial for livestock breeding programs.
Genomic prediction offers a potential tool for selecting purebred (PB) candidates for CB performance.

Purpose of the Study:

To review and compare different genomic prediction strategies for CB performance.
To identify factors influencing the accuracy and response to selection of these strategies.

Main Methods:

Systematic review of 27 studies (simulation and real data).
Comparison of strategies based on genomic prediction models and reference population composition.
Analysis of factors like purebred-crossbred genetic correlation (rpc), genetic distance, and population sizes.

Main Results:

Accuracy and response to selection depend on rpc, genetic distance, and reference population characteristics.
Dominance models can yield higher accuracies than additive models in PB reference populations.
Collecting CB data is beneficial when rpc is low (<0.8), especially with larger reference populations.

Conclusions:

Accurate estimation of rpc is vital for optimizing genomic prediction strategies.
Understanding components of rpc (dominance, GxE) informs model choice and data collection.
Future research should focus on predictive tools for selection accuracy.