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| 1. |  | LIEBIG, M. A.; FRANZLUEBBERS, A. J.; ALVAREZ, C.; CHIESA, T. D.; LEWCZUK, N.; PIÑEIRO, G.; POSSE, G.; YAHDJIAN, L.; GRACE, P.; CABRAL, O. M. R.; MARTIN NETO, L.; RODRIGUES, R. DE A. R.; AMIRO, B.; ANGERS, D.; HAO, X.; OELBERMANN, M.; TENUTA, M.; MUNKHOLM, L. J.; REGINA, K.; CELLIER, P.; EHRHARDT, F.; RICHARD, G.; DECHOW, R.; AGUS, F.; WIDIARTA, N.; SPINK, J.; BERTI, A.; GRIGNANI, C.; MAZZONCINI, M.; ORSINI, R.; ROGGERO, P. P.; SEDDAIU, G.; TEI, F.; VENTRELLA, D.; VITALI, G.; KISHIMOTO-MO, A.; SHIRATO, Y.; SUDO, S.; SHIN, J.; SCHIPPER, L.; SAVÉ, R.; LEIFELD, J.; SPADAVECCHIA, L.; YELURIPATI, J.; DEL GROSSO, S.; RICE, C.; SAWCHIK, J. MAGGnet: an international network to foster mitigation of agricultural greenhouse gases. Carbon Management v. 7 (3-4): 243-248, 2016. OPEN ACCESS. Published online: 31 May 2016.
This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105,...| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 1 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
18/06/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
MISZTAL, I.; LEGARRA, A.; AGUILAR, I. |
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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. |
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Título : |
Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. |
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Fecha de publicación : |
2011 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2011, 92 (9): 4648-4655. OPEN ACCESS. |
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ISSN : |
0022-0302 |
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DOI : |
10.3168/jds.2009-2064 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received January 26, 2009. // Accepted April 29, 2009. |
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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 |
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Palabras claves : |
BEST LINEAR UNBIASED PREDICTOR; GENETIC EVALUATION; GENOMIC SELECTION; SINGLE NUCLEOTIDE POLYMORPHISM. |
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Thesagro : |
EVALUACIÓN GENÉTICA. |
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Asunto categoría : |
-- |
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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
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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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