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A comparison of genetic and genomic breeding values in Saanen and Alpine goats.
Negro, A; Cesarani, A; Cortellari, M; Bionda, A; Fresi, P; Macciotta, N P P; Grande, S; Biffani, S; Crepaldi, P.
Afiliación
  • Negro A; Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy; Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy.
  • Cesarani A; Dipartimento di Scienze Agrarie, Università degli Studi di Sassari, 07100 Sassari, Italy; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA.
  • Cortellari M; Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy.
  • Bionda A; Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy. Electronic address: arianna.bionda@unimi.it.
  • Fresi P; Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy.
  • Macciotta NPP; Dipartimento di Scienze Agrarie, Università degli Studi di Sassari, 07100 Sassari, Italy.
  • Grande S; Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy.
  • Biffani S; Istituto di Biologia e Biotecnologia, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy.
  • Crepaldi P; Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy.
Animal ; 18(4): 101118, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38508133
ABSTRACT
Nowadays, several countries are developing or adopting genomic selection in the dairy goat sector. The most used method to estimate breeding values is Single-Step Genomic Best Linear Unbiased Prediction (ssGBLUP) which offers several advantages in terms of computational process and accuracy of the estimated breeding values (EBVs). Saanen and Alpine are the predominant dairy goat breeds in Italy, and both have similar breeding programs where EBVs for productive traits are currently calculated using BLUP. This work describes the implementation of genomic selection for these two breeds in Italy, aligning with the selection practices already carried out in the international landscape. The available dataset included 3 611 genotyped animals, 11 470 lactation records, five traits (milk, protein and fat yields, and fat and protein percentages), and three-generation pedigrees. EBVs were estimated using BLUP, GBLUP, and ssGBLUP both with single and multiple trait approaches. The methods were compared in terms of correlation between EBVs and genetic trends. Results were also validated with the linear regression method excluding part of the phenotypic data. In both breeds, EBVs and GEBVs were strongly correlated and the trend of each trait was similar comparing the three methods. The average increase in accuracy across traits and methods amounted to +13 and +10% from BLUP to ssGBLUP for Alpine and Saanen breeds, respectively. Results indicated higher prediction accuracy and correlation for GBLUP and ssGBLUP compared to BLUP, implying that the use of genotypes increases the accuracy of EBVs, particularly in the absence of phenotypic data. Therefore, ssGBLUP is likely to be the most effective method to enhance genetic gain in Italian Saanen and Alpine goats.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genoma / Genómica Límite: Animals Idioma: En Revista: Animal Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genoma / Genómica Límite: Animals Idioma: En Revista: Animal Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido