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Negro A, Cesarani A, Cortellari M, Bionda A, Fresi P, Macciotta NPP, Grande S, Biffani S, Crepaldi P. A comparison of genetic and genomic breeding values in Saanen and Alpine goats. Animal 2024; 18:101118. [PMID: 38508133 DOI: 10.1016/j.animal.2024.101118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 03/22/2024] Open
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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Affiliation(s)
- A Negro
- Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy; Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy
| | - A Cesarani
- Dipartimento di Scienze Agrarie, Università degli Studi di Sassari, 07100 Sassari, Italy; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - M Cortellari
- Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy
| | - A Bionda
- Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy.
| | - P Fresi
- Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy
| | - N P P Macciotta
- Dipartimento di Scienze Agrarie, Università degli Studi di Sassari, 07100 Sassari, Italy
| | - S Grande
- Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy
| | - S Biffani
- Istituto di Biologia e Biotecnologia, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
| | - P Crepaldi
- Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy
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Callister AN, Bermann M, Elms S, Bradshaw BP, Lourenco D, Brawner JT. Accounting for population structure in genomic predictions of Eucalyptus globulus. G3 GENES|GENOMES|GENETICS 2022; 12:6654591. [PMID: 35920792 PMCID: PMC9434241 DOI: 10.1093/g3journal/jkac180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/29/2022] [Indexed: 12/02/2022]
Abstract
Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP (“HBLUP”) analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the “Green Triangle” region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.
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Affiliation(s)
| | - Matias Bermann
- Department of Animal and Dairy Science, University of Georgia , Athens, GA 30602, USA
| | - Stephen Elms
- HVP Plantations , Churchill, VIC 3842, Australia
| | - Ben P Bradshaw
- Australian Bluegum Plantations , Albany, WA 6330, Australia
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia , Athens, GA 30602, USA
| | - Jeremy T Brawner
- Department of Plant Pathology, University of Florida , Gainesville, FL 32611, USA
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