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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
03/07/2021 |
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Actualizado : |
03/07/2021 |
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Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
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Autor : |
MORALES-PIÑEYRUA, J.; DOTTI, N.; FERNÁNDEZ, S.; CARTAYA, A.; RUPRECHTER SCHÖLDERLE, G. |
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Afiliación : |
JESSICA TATIANA MORALES PIÑEYRUA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NATALIA DOTTI, Médica/o Veterinaria/o de libre ejercicio.; SEBASTIÁN FERNÁNDEZ, Médica/o Veterinaria/o de libre ejercicio.; ANDREA MARIA DEL CARMEN CARTAYA DE LEON, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GRETEL RUPRECHTER SCHÖLDERLE, Laboratorio de Endocrinología yMetabolismo Animal, Facultad de Veterinaria - Udelar. |
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Título : |
Eficiencia de tres herramientas diagnósticas de cetosis subclínica. |
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Complemento del título : |
Producción Animal. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Revista INIA Uruguay, Junio 2021, no.65, p.18-22. |
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Serie : |
(Revista INIA; 65). |
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ISSN : |
1510-9011 |
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Idioma : |
Español |
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Contenido : |
La importancia económica y el impacto que tiene la cetosis subclínica sobre la productividad y el bienestar de las vacas lecheras hace imprescindible el estudio sobre su diagnóstico. Herramientas precisas y prácticas son requeridas por los productores para monitorear esta enfermedad. La utilización de la medición de BHB en leche por el método FTIR (Laboratorio de calidad de leche de INIA La Estanzuela), es una buena herramienta de diagnóstico de cetosis subclínica para ser utilizada de forma rutinaria en el tambo. |
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Palabras claves : |
CETOSIS; CETOSIS SUBCLÍNICA. |
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Thesagro : |
DIAGNOSTICO DE LABORATORIO; ENFERMEDADES DE LOS ANIMALES; GANADO DE LECHE. |
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Asunto categoría : |
L20 Ecología animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15756/1/Revista-INIA-65-Junio-2021-05.pdf
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Marc : |
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100 1 $aMORALES-PIÑEYRUA, J.
245 $aEficiencia de tres herramientas diagnósticas de cetosis subclínica.$h[electronic resource]
260 $c2021
490 $a(Revista INIA; 65).
520 $aLa importancia económica y el impacto que tiene la cetosis subclínica sobre la productividad y el bienestar de las vacas lecheras hace imprescindible el estudio sobre su diagnóstico. Herramientas precisas y prácticas son requeridas por los productores para monitorear esta enfermedad. La utilización de la medición de BHB en leche por el método FTIR (Laboratorio de calidad de leche de INIA La Estanzuela), es una buena herramienta de diagnóstico de cetosis subclínica para ser utilizada de forma rutinaria en el tambo.
650 $aDIAGNOSTICO DE LABORATORIO
650 $aENFERMEDADES DE LOS ANIMALES
650 $aGANADO DE LECHE
653 $aCETOSIS
653 $aCETOSIS SUBCLÍNICA
700 1 $aDOTTI, N.
700 1 $aFERNÁNDEZ, S.
700 1 $aCARTAYA, A.
700 1 $aRUPRECHTER SCHÖLDERLE, G.
773 $tRevista INIA Uruguay, Junio 2021, no.65, p.18-22.
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/05/2016 |
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Actualizado : |
11/12/2018 |
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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 : |
MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; AGUILAR, I.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; LAWLOR, T.J. |
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Afiliación : |
Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; INRA (Institut National de la Recherche Agronomique); IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; Holstein Association USA Inc. |
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Título : |
Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2016, v.99, no.3, p.1968-1974. OPEN ACCESS |
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DOI : |
10.3168/jds.2015-10540 |
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Idioma : |
Inglés |
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Notas : |
OPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016 |
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Contenido : |
ABSTRACT.
The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up View the MathML source for 569,404 genotyped animals with 10,000 core animals took 1.3 h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals.
© 2016, THE AUTHORS. Published by FASS and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). MenosABSTRACT.
The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 rand... Presentar Todo |
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Palabras claves : |
FINAL SCORE; GENOMIC EVALUATION; GENOMIC RELATIONSHIP MATRIX. |
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Thesagro : |
SsGBLUP; TORO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12160/1/1-s2.0-S0022030216000825-main.pdf
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Marc : |
LEADER 03610naa a2200289 a 4500
001 1054839
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008 2016 bl uuuu u00u1 u #d
024 7 $a10.3168/jds.2015-10540$2DOI
100 1 $aMASUDA, Y.
245 $aImplementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.$h[electronic resource]
260 $c2016
500 $aOPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016
520 $aABSTRACT. The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up View the MathML source for 569,404 genotyped animals with 10,000 core animals took 1.3 h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals. © 2016, THE AUTHORS. Published by FASS and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
650 $aSsGBLUP
650 $aTORO
653 $aFINAL SCORE
653 $aGENOMIC EVALUATION
653 $aGENOMIC RELATIONSHIP MATRIX
700 1 $aMISZTAL, I.
700 1 $aTSURUTA, S.
700 1 $aLEGARRA, A.
700 1 $aAGUILAR, I.
700 1 $aLOURENCO, D.A.L.
700 1 $aFRAGOMENI, B.O.
700 1 $aLAWLOR, T.J.
773 $tJournal of Dairy Science, 2016$gv.99, no.3, p.1968-1974. OPEN ACCESS
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