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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
21/02/2014 |
Actualizado : |
18/06/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
MISZTAL, I.; LEGARRA, A.; AGUILAR, I. |
Afiliación : |
I. MISZTAL, Department of Animal and Dairy Science, University of Georgia, United States; A. LEGARRA, Institut National de la Recherche Agronomique (INRA), France; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. |
Fecha de publicación : |
2011 |
Fuente / Imprenta : |
Journal of Dairy Science, 2011, 92 (9): 4648-4655. OPEN ACCESS. |
ISSN : |
0022-0302 |
DOI : |
10.3168/jds.2009-2064 |
Idioma : |
Inglés |
Notas : |
Article history: Received January 26, 2009. // Accepted April 29, 2009. |
Contenido : |
ABSTRACT.
Currently, genomic evaluations use multiple-step procedures, which are prone to biases and errors. A single-step procedure may be applicable when genomic predictions can be obtained by modifying the numerator relationship matrix A to H = A + AΔ, where AΔ includes deviations from expected relationships. However, the traditional mixed model equations require H-1, which is usually difficult to obtain for large pedigrees. The computations with H are feasible when the mixed model equations are expressed in an alternate form that also applies for singular H and when those equations are solved by the conjugate gradient techniques. Then the only computations involving H are in the form of Aq or AΔq, where q is a vector. The alternative equations have a nonsymmetric left-hand side. Computing AΔq is inexpensive when the number of nonzeros in AΔ is small, and the product Aq can be calculated efficiently in linear time using an indirect algorithm. Generalizations to more complicated models are proposed. The data included 10.2 million final scores on 6.2 million Holsteins and were analyzed by a repeatability model. Comparisons involved the regular and the alternative equations. The model for the second case included simulated AΔ. Solutions were obtained by the preconditioned conjugate gradient algorithm, which works only with symmetric matrices, and by the bi-conjugate gradient stabilized algorithm, which also works with nonsymmetric matrices. The convergence rate associated with the nonsymmetric solvers was slightly better than that with the symmetric solver for the original equations, although the time per round was twice as much for the nonsymmetric solvers. The convergence rate associated with the alternative equations ranged from 2 times lower without AΔ to 3 times lower for the largest simulated AΔ. When the information attributable to genomics can be expressed as modifications to the numerator relationship matrix, the proposed methodology may allow the upgrading of an existing evaluation to incorporate the genomic information.
© American Dairy Science Association, 2009. MenosABSTRACT.
Currently, genomic evaluations use multiple-step procedures, which are prone to biases and errors. A single-step procedure may be applicable when genomic predictions can be obtained by modifying the numerator relationship matrix A to H = A + AΔ, where AΔ includes deviations from expected relationships. However, the traditional mixed model equations require H-1, which is usually difficult to obtain for large pedigrees. The computations with H are feasible when the mixed model equations are expressed in an alternate form that also applies for singular H and when those equations are solved by the conjugate gradient techniques. Then the only computations involving H are in the form of Aq or AΔq, where q is a vector. The alternative equations have a nonsymmetric left-hand side. Computing AΔq is inexpensive when the number of nonzeros in AΔ is small, and the product Aq can be calculated efficiently in linear time using an indirect algorithm. Generalizations to more complicated models are proposed. The data included 10.2 million final scores on 6.2 million Holsteins and were analyzed by a repeatability model. Comparisons involved the regular and the alternative equations. The model for the second case included simulated AΔ. Solutions were obtained by the preconditioned conjugate gradient algorithm, which works only with symmetric matrices, and by the bi-conjugate gradient stabilized algorithm, which also works with nonsymmetric matrices. The ... Presentar Todo |
Palabras claves : |
BEST LINEAR UNBIASED PREDICTOR; GENETIC EVALUATION; GENOMIC SELECTION; SINGLE NUCLEOTIDE POLYMORPHISM. |
Thesagro : |
EVALUACIÓN GENÉTICA. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12193/1/Computing-procedures-for-genetic-evaluation-including-phenotypic-full-pedigree-and-genomic-information.pdf
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Marc : |
LEADER 02980naa a2200241 a 4500 001 1012841 005 2019-06-18 008 2011 bl uuuu u00u1 u #d 022 $a0022-0302 024 7 $a10.3168/jds.2009-2064$2DOI 100 1 $aMISZTAL, I. 245 $aComputing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information.$h[electronic resource] 260 $c2011 500 $aArticle history: Received January 26, 2009. // Accepted April 29, 2009. 520 $aABSTRACT. Currently, genomic evaluations use multiple-step procedures, which are prone to biases and errors. A single-step procedure may be applicable when genomic predictions can be obtained by modifying the numerator relationship matrix A to H = A + AΔ, where AΔ includes deviations from expected relationships. However, the traditional mixed model equations require H-1, which is usually difficult to obtain for large pedigrees. The computations with H are feasible when the mixed model equations are expressed in an alternate form that also applies for singular H and when those equations are solved by the conjugate gradient techniques. Then the only computations involving H are in the form of Aq or AΔq, where q is a vector. The alternative equations have a nonsymmetric left-hand side. Computing AΔq is inexpensive when the number of nonzeros in AΔ is small, and the product Aq can be calculated efficiently in linear time using an indirect algorithm. Generalizations to more complicated models are proposed. The data included 10.2 million final scores on 6.2 million Holsteins and were analyzed by a repeatability model. Comparisons involved the regular and the alternative equations. The model for the second case included simulated AΔ. Solutions were obtained by the preconditioned conjugate gradient algorithm, which works only with symmetric matrices, and by the bi-conjugate gradient stabilized algorithm, which also works with nonsymmetric matrices. The convergence rate associated with the nonsymmetric solvers was slightly better than that with the symmetric solver for the original equations, although the time per round was twice as much for the nonsymmetric solvers. The convergence rate associated with the alternative equations ranged from 2 times lower without AΔ to 3 times lower for the largest simulated AΔ. When the information attributable to genomics can be expressed as modifications to the numerator relationship matrix, the proposed methodology may allow the upgrading of an existing evaluation to incorporate the genomic information. © American Dairy Science Association, 2009. 650 $aEVALUACIÓN GENÉTICA 653 $aBEST LINEAR UNBIASED PREDICTOR 653 $aGENETIC EVALUATION 653 $aGENOMIC SELECTION 653 $aSINGLE NUCLEOTIDE POLYMORPHISM 700 1 $aLEGARRA, A. 700 1 $aAGUILAR, I. 773 $tJournal of Dairy Science, 2011, 92 (9): 4648-4655. OPEN ACCESS.
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
31/10/2019 |
Actualizado : |
31/10/2019 |
Tipo de producción científica : |
Hojas de Divulgación |
Autor : |
VICENTE, E.; LADO, J.; MANZZIONI, A.; ARRUABARRENA, A.; GIAMBIASI, M.; VARELA, P.; GIMÉNEZ, G.; SILVERA, E.; MACHÍN, A.; GONZÁLEZ-ARCOS, M. |
Afiliación : |
CARLOS ESTEBAN VICENTE CASTRO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOANNA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WASHINGTON ARIEL MANZZIONI FERNANDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANA ARRUABARRENA PASCOVICH, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIO ALEJANDRO GIAMBIASI RODRIGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO NICOLAS VARELA PESSOLANO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO GIMÉNEZ FRANQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; E. SILVERA, Fitopatología Facultad de Agronomía, UDELAR; A. MACHÍN, Fitopatología Facultad de Agronomía, UDELAR; MATIAS GONZÁLEZ-ARCOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
El cultivar de frutilla "INIA Yrupé" (SGS73.1). |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Montevideo (UY): INIA, 2019. |
Páginas : |
4 p. |
Serie : |
(Hoja de Divulgación; 109) |
Idioma : |
Español |
Contenido : |
RESUMEN.
"INIA YRUPÉ" se recomienda para la producción de otoño, invierno y primavera bajo cultivo protegido. Posee un desempeño superior a ?INIA
Ágata? en productividad semi precoz, arquitectura de planta, forma y sabor de fruta, con la ventaja de tener menor incidencia de botrytis,
arañuelas y oidio. |
Thesagro : |
FRUTILLA; VARIEDADES. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13697/1/HD-109-Frutilla-octubre-2019.pdf
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Marc : |
LEADER 00968nam a2200265 a 4500 001 1060375 005 2019-10-31 008 2019 bl uuuu u0uu1 u #d 100 1 $aVICENTE, E. 245 $aEl cultivar de frutilla "INIA Yrupé" (SGS73.1). 260 $aMontevideo (UY): INIA$c2019 300 $a4 p. 490 $a(Hoja de Divulgación; 109) 520 $aRESUMEN. "INIA YRUPÉ" se recomienda para la producción de otoño, invierno y primavera bajo cultivo protegido. Posee un desempeño superior a ?INIA Ágata? en productividad semi precoz, arquitectura de planta, forma y sabor de fruta, con la ventaja de tener menor incidencia de botrytis, arañuelas y oidio. 650 $aFRUTILLA 650 $aVARIEDADES 700 1 $aLADO, J. 700 1 $aMANZZIONI, A. 700 1 $aARRUABARRENA, A. 700 1 $aGIAMBIASI, M. 700 1 $aVARELA, P. 700 1 $aGIMÉNEZ, G. 700 1 $aSILVERA, E. 700 1 $aMACHÍN, A. 700 1 $aGONZÁLEZ-ARCOS, M.
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