Wagyu Feeder Check: A genomic-based tool to identify performance differences of Australian Wagyu and Wagyu crossed cattle

Antonio Reverter; Yutao Li; Pâmela A. Alexandre; Sonja Dominik; Carel Teseling; Aaron van den Heuvel; Karen Schutt; Matt McDonagh; Laercio Porto-Neto

doi:10.1071/AN23246

RESEARCH ARTICLE (Open Access)

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Wagyu Feeder Check: A genomic-based tool to identify performance differences of Australian Wagyu and Wagyu crossed cattle

Antonio Reverter

^A ^* , Yutao Li ^A , Pâmela A. Alexandre ^A , Sonja Dominik

^B , Carel Teseling ^C , Aaron van den Heuvel ^C , Karen Schutt ^D , Matt McDonagh ^C and Laercio Porto-Neto ^A

+ Author Affiliations

- Author Affiliations

^A CSIRO Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, Brisbane, Qld 4067, Australia.

^B CSIRO Agriculture and Food, F.D. McMaster Laboratory, Chiswick, New England Highway, Armidale, NSW 2350, Australia.

^C Australian Wagyu Association, 146 Marsh Street, Armidale, NSW 2350, Australia.

^D Neogen Corporation, 14 Hume Drive, Bundamba, Ipswich, Qld 4304, Australia.

^* Correspondence to: toni.reverter-gomez@csiro.au

Handling Editor: Forbes Brien

Animal Production Science 64, AN23246 https://doi.org/10.1071/AN23246

Submitted: 12 July 2023 Accepted: 30 October 2023 Published: 24 November 2023

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Wagyu Feeder Check is a genomic-based tool designed to provide genomic estimated breeding values (GEBV) for five feedlot growth and carcase traits. At present, Wagyu Feeder Check is based on a reference population of 8316 genotyped and phenotyped Australian fullblood (FB; N = 2120) Wagyu and Wagyu-crossed (XB; N = 6196) cattle, principally Wagyu × Angus F1 animals.

Aims

We provide technical details behind the development of the Wagyu Feeder Check and validate the ability of its GEBV to predict differences in performance of Wagyu cattle in daily weight gain at feedlot, carcase weight, carcase eye muscle area, carcase marbling score and carcase rump fat at the P8 site.

Methods

Data supplied from eight commercial supply chains across Australia was used to generate GEBV using mixed-model equations that incorporated a genomic relationship matrix build with 82 504 autosomal markers. The bias, dispersion, and accuracy of the GEBV were evaluated using a four-way cross-validation scheme where, in each turn, the phenotypes from a random 1549 (or 25%) XB cattle were set as missing.

Key results

The genomic estimate of the Wagyu content in the FB and XB population averaged 99.12% and 59.55%, respectively, and with most of the non-Wagyu content associated with Angus. The estimates of heritability (± s.e.) were 0.497 ± 0.016, 0.474 ± 0.004, 0.347 ± 0.014, 0.429 ± 0.003 and 0.422 ± 0.003 for daily weight gain at feedlot, carcase weight, eye muscle area, marbling and rump fat, respectively. Averaged across the four XB validation populations, the accuracy of GEBV was 0.624, 0.634, 0.385, 0.620, and 0.526 for the same set of traits.

Conclusions

Genomic predictions generated by Wagyu Feeder Check can predict differences in feedlot and carcase performance of Australian Wagyu cattle. Given the large content of Angus in the XB population, further research is required to determine the predictive ability of GEBV in Wagyu × Bos indicus and Wagyu × dairy animals.

Implications

Commercial feedlot operators finishing animals with a strong Wagyu breed component will benefit from using Wagyu Feeder Check for decision making.

Keywords: accuracy, beef cattle, bias, carcase, feedlot, genomic predictions, heritability, marbling.

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