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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
25/10/2024 |
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Actualizado : |
13/06/2025 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
CARDONA-CIFUENTES, D.; NEIRA, J. D. R.; ALBUQUERQUE, L. G.; ESPIGOLAN, R.; GONZALEZ-HERRERA, L. G.; AMORIM, S. T.; LÓPEZ-CORREA, R. D.; AGUILAR, I.; BALDI, F. |
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Afiliación : |
DANIEL CARDONA-CIFUENTES, Dpto. Zootecnia, Fac. Ciências Agrarias e Veterinárias, Univ. Estadual Paulista (UNESP), Jaboticabal, SP, Brazil; Fac. Ciencias Agrarias, Fundación Universitaria Agraria de Colombia-UNIAGRARIA, Bogotá, Colombia; JUAN DIEGO RODRIGUEZ NEIRA, Facultad de Ciencias Agroindustriales, Universidad Del Quindío (UNIQUINDIO), Armenia, Quindío, Colombia; LUCIA G. ALBUQUERQUE, Dpto. Zootecnia, Fac. Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (UNESP), SP, Jaboticabal, Brazil; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brasilia, Brazil; RAFAEL ESPIGOLAN, Departamento de Zootecnia e Ciências Biológicas, Universidade Federal de Santa Maria, RS, Palmeira das Missões, Brazil; LUIS GABRIEL GONZALEZ-HERRERA, Grupo de Investigación Biodiversidad y Genética Molecular (BIOGEM), Universidad Nacional de Colombia Sede Medellín, Medellín, Colombia; SABRINA THAISE AMORIM, Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, United States; RODRIGO D. LÓPEZ-CORREA, Facultad de Agronomía, Universidad de la República (UdelaR), Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BALDI, Dpto. Zootecnia, Fac. Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (UNESP), SP, Jaboticabal, Brazil; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brasilia, Brazil. |
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Título : |
Influence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle. |
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Complemento del título : |
Original article. |
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Fecha de publicación : |
2025 |
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Fuente / Imprenta : |
Journal of Animal Breeding and Genetics, May 2025, Volume 142, Issue 3, Pages 263-276. https://doi.org/10.1111/jbg.12900 |
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ISSN : |
0931-2668 |
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DOI : |
10.1111/jbg.12900 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 29 March 2024, Revised 23 July 2024, Accepted 20 August 2024, First published 18 September 2024 -- Correspondence: Fernando Baldi, Departamento de Zootecnia, Universidade Estadual Paulista (UNESP), Via de Acesso Professor Paulo Donato Castellane s/n, Jaboticabal, SP 14884-900, Brazil.
Email: fernandobaldiuy@gmail.com -- Funding: Ministerio de Ciencia, Tecnología e Innovación de Colombia; Ministerio de Ciencia Tecnología e Innovación (Minciencias) of Colombia; Fundación para el Futuro de Colombia (Colfuturo). -- |
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Contenido : |
ABSTRACT.- This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates. © 2024 Wiley-VCH GmbH. Published by John Wiley & Sons Ltd. MenosABSTRACT.- This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, ... Presentar Todo |
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Palabras claves : |
Accuracy; Beef cattle; Bias; Genetic variance; Genomic selection; SISTEMA GANADERO EXTENSIVO - INIA. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 04334naa a2200325 a 4500
001 1064897
005 2025-06-13
008 2025 bl uuuu u00u1 u #d
022 $a0931-2668
024 7 $a10.1111/jbg.12900$2DOI
100 1 $aCARDONA-CIFUENTES, D.
245 $aInfluence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle.$h[electronic resource]
260 $c2025
500 $aArticle history: Received 29 March 2024, Revised 23 July 2024, Accepted 20 August 2024, First published 18 September 2024 -- Correspondence: Fernando Baldi, Departamento de Zootecnia, Universidade Estadual Paulista (UNESP), Via de Acesso Professor Paulo Donato Castellane s/n, Jaboticabal, SP 14884-900, Brazil. Email: fernandobaldiuy@gmail.com -- Funding: Ministerio de Ciencia, Tecnología e Innovación de Colombia; Ministerio de Ciencia Tecnología e Innovación (Minciencias) of Colombia; Fundación para el Futuro de Colombia (Colfuturo). --
520 $aABSTRACT.- This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates. © 2024 Wiley-VCH GmbH. Published by John Wiley & Sons Ltd.
653 $aAccuracy
653 $aBeef cattle
653 $aBias
653 $aGenetic variance
653 $aGenomic selection
653 $aSISTEMA GANADERO EXTENSIVO - INIA
700 1 $aNEIRA, J. D. R.
700 1 $aALBUQUERQUE, L. G.
700 1 $aESPIGOLAN, R.
700 1 $aGONZALEZ-HERRERA, L. G.
700 1 $aAMORIM, S. T.
700 1 $aLÓPEZ-CORREA, R. D.
700 1 $aAGUILAR, I.
700 1 $aBALDI, F.
773 $tJournal of Animal Breeding and Genetics, May 2025, Volume 142, Issue 3, Pages 263-276. https://doi.org/10.1111/jbg.12900
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