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Genomic Selection for Economically Important Traits in Dual-Purpose Simmental Cattle
Xiaoxue Zhang1, Dan Wang1, Menghua Zhang1
1College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Abstract:
Genomic selection (GS) is a new landmark method in modern animal breeding programs, and it has become a tool for routine genetic evaluation regarding dual-purpose cattle breeding. In this study, we employed data on milk-production, reproduction, and growth measurements of dual-purpose Simmental cows during the period 1987-2022 from two large-scale farms in Northwest China. For this purpose, we used a single-trait model based on the A-array PBLUP and H-array ssGBLUP to perform genetic evaluation of milk-production, reproduction, and growth traits by applying the restricted maximum likelihood (REML) methods. The results revealed that the heritability based on the additive genetic correlation matrix was approximately 0.09-0.31 for milk-production traits, 0.03-0.43 for reproduction traits, and 0.13-0.43 for growth traits. In addition, the heritability based on the genome-pedigree association matrix was similarly 0.09-0.32 for milk-production traits, 0.04-0.44 for reproductive traits, and 0.14-0.43 for growth traits. In the entire population, the reliability of genomic estimated breeding values (GEBVs) increased by 0.6-3.2%, 0.2-2.4%, and 0.5-1.5% for milk-production, reproductive traits, and growth traits, respectively. In the genotyped population, the reliability of GEBV for milk-production and reproduction traits increased by 1.6-4.0% and 0.4-3.6%, respectively, whereas the reliability of GEBV for growth traits decreased by 12.0-17.0%. These results suggest that the construction of an H-matrix with ssGBLUP could improve the heritability and reliability of breeding values for milk-production and reproduction traits. However, the advantage was not evident for growth traits in smaller populations. The present results thus provide a basis for future application of genomic genetic evaluation of dual-purpose Simmental cattle, providing data support for the selection and marketing of excellent breeding bulls, thereby helping to establish a basis for their independently bred breeding bull.
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