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Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models

Published online by Cambridge University Press:  05 September 2022

José Teodoro Paiva ,
Rodrigo Reis Mota ,
Paulo Sávio Lopes ,
Hedi Hammami ,
Sylvie Vanderick ,
Hinayah Rojas Oliveira ,
Renata Veroneze ,
Fabyano Fonseca e Silva  and
Nicolas Gengler
José Teodoro Paiva*
Affiliation:
Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, MG, Brazil
Rodrigo Reis Mota
Affiliation:
Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
Paulo Sávio Lopes
Affiliation:
Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, MG, Brazil
Hedi Hammami
Affiliation:
Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
Sylvie Vanderick
Affiliation:
Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
Hinayah Rojas Oliveira
Affiliation:
Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
Renata Veroneze
Affiliation:
Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, MG, Brazil
Fabyano Fonseca e Silva
Affiliation:
Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, MG, Brazil
Nicolas Gengler
Affiliation:
Gembloux Agro-Bio Tech, University of Liège, TERRA Teaching and Research Centre, B-5030 Gembloux, Belgium
*
Author for correspondence: José Teodoro Paiva, Email: [email protected]
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Abstract

The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from −0.22 to −0.59), between milk yield and milk FA (rg from −0.22 to −0.62), and between protein yield and milk FA (rg from −0.11 to −0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.

Keywords

Genetic correlationgenomic predictionMIRsingle-step GBLUPtest-day model
Type
Research Article
Information
Journal of Dairy Research , Volume 89 , Issue 3 , August 2022 , pp. 222 - 230
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation

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Footnotes

*

Present address: Council on Dairy Cattle Breeding – CDCB, Bowie, Maryland, USA.

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