Accuracy of genomic predictions for beef eating quality traits

Title
Accuracy of genomic predictions for beef eating quality traits
Publication Date
2025-06
Author(s)
Aliloo, H
( author )
OrcID: https://orcid.org/0000-0002-5587-6929
Email: haliloo@une.edu.au
UNE Id une-id:haliloo
Forutan, M
McGilchrist, P
( #PLACEHOLDER_PARENT_METADATA_VALUE# )
OrcID: https://orcid.org/0000-0003-3265-1134
Email: pmcgilc2@une.edu.au
UNE Id une-id:pmcgilc2
Hayes, B
Clark, S
( #PLACEHOLDER_PARENT_METADATA_VALUE# )
OrcID: https://orcid.org/0000-0001-8605-1738
Email: sclark37@une.edu.au
UNE Id une-id:sclark37
Editor
Editor(s): Sue Hatcher
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Association for the Advancement of Animal Breeding and Genetics363
Place of publication
Armidale, Australia
UNE publication id
une:1959.11/71570
Abstract

Eating quality is the primary factor influencing consumer purchasing decisions for beef products. Consumer-derived sensory eating quality traits capture the actual eating experience and are directly aligned with consumer expectations. Accurate genomic prediction of these traits can enable selective breeding, allowing for continuous genetic improvement and consistent consumer satisfaction. In this study we used an international dataset from Australia, the USA and Ireland to estimate genetic parameters for five meat eating quality traits and to evaluate the accuracy of genomic estimated breeding values under different cross-validation scenarios. Heritability estimates ranged from 0.19 to 0.31 for Australia, 0.07 to 0.20 for the USA, 0.09 to 0.17 for Ireland, and 0.14 to 0.22 for the combined dataset. Prediction accuracies for all traits were moderate when using the Australia-only reference population but increased to high accuracies when international data were included. This highlights the value of incorporating international data into the reference population, creating a larger and more diverse dataset, and ultimately improving prediction accuracies.

Link
Citation
Proceedings of the AAABG 26th Conference, p. 363-366
Start page
363
End page
366

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