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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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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
09/11/2017 |
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Actualizado : |
25/11/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
MASUDA, Y; MISZTAL, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; AGUILAR, I. |
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Afiliación : |
Y. MASUDA, Department of Animal and Dairy Science, University of Georgia; I. MISZTAL, Department of Animal and Dairy Science, University of Georgia; A. LEGARRA, INRA (Institut National de la Recherche Agronomique); S. TSURUTA, Department of Animal and Dairy Science, University of Georgia; D.A.L. LOURENCO, Department of Animal and Dairy Science, University of Georgia; B.O. FRAGOMENI, Department of Animal and Dairy Science, University of Georgia; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Journal of Animal Science, 2017, v. 95(1): 49-52. |
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DOI : |
10.2527/jas.2016.0699 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017.
This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and Food Research Initiative competitive grant 2015-67015-22936). |
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Contenido : |
ABSTRACT.
This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations.
Copyright © 2016. American Society of Animal Science. MenosABSTRACT.
This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When t... Presentar Todo |
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Palabras claves : |
COMPUTATION; GENOMIC SELECTION; INVERSION; NUMERATOR RELATIONSHIP MATRIX; PRECONDITIONED CONJUGATE GRADIENT; SPARSE MATRIX. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02919naa a2200289 a 4500
001 1057743
005 2019-11-25
008 2017 bl uuuu u00u1 u #d
024 7 $a10.2527/jas.2016.0699$2DOI
100 1 $aMASUDA, Y
245 $aTechnical note$bAvoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient.$h[electronic resource]
260 $c2017
500 $aArticle history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017. This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and Food Research Initiative competitive grant 2015-67015-22936).
520 $aABSTRACT. This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations. Copyright © 2016. American Society of Animal Science.
653 $aCOMPUTATION
653 $aGENOMIC SELECTION
653 $aINVERSION
653 $aNUMERATOR RELATIONSHIP MATRIX
653 $aPRECONDITIONED CONJUGATE GRADIENT
653 $aSPARSE MATRIX
700 1 $aMISZTAL, I.
700 1 $aLEGARRA, A.
700 1 $aTSURUTA, S.
700 1 $aLOURENCO, D.A.L.
700 1 $aFRAGOMENI, B.O.
700 1 $aAGUILAR, I.
773 $tJournal of Animal Science, 2017$gv. 95(1): 49-52.
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
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Fecha actual : |
01/07/2020 |
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Actualizado : |
31/07/2020 |
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Tipo de producción científica : |
Revista INIA |
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Autor : |
INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA) |
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Título : |
Revista INIA Uruguay. (N° 61, Junio 2020). |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Montevideo (UY): INIA, 2020. |
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Páginas : |
129 p. |
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Serie : |
(Revista INIA; 61). |
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ISSN : |
1510-9011 |
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Idioma : |
Español |
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Contenido : |
Informe especial: Desafíos de la citricultura en Uruguay y el aporte de INIA a su competitividad. |
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Thesagro : |
ARROZ; BIOTECNOLOGIA; BOVINOS DE CARNE; CAMBIO CLIMÁTICO; CIENCIA; CITRUS; CLIMATOLOGIA; COMUNICACIÓN; CONTROL DE ENFERMEDADES; CULTIVO; CULTIVOS DE GRANO; CULTIVOS DE SECANO; ENTOMOLOGIA; ESPECIES FORRAJERAS; EUCALYPTUS; EXPLOTACION AGRICOLA FAMILIAR; FITOPATOLOGÍA; FORESTALES; FORRAJERAS; FORRAJES; FRUTALES; FRUTICULTURA; GANADO BOVINO; GRANOS; GRAS; HORTALIZAS; HORTICULTURA; IMAGEN CORPORATIVA & COMUNICACIÓN INSTITUCIONAL; INIA; INNOVACION; INVESTIGACIÓN; LECHERÍA; LEGUMINOSAS; MANEJO DE CULTIVOS; MEJORAMIENTO ANIMAL; METEOROLOGIA; MICROBIOLOGÍA; OVINOS; PASTURAS; PRODUCCIÓN ANIMAL; PRODUCCION DE LANA; PRODUCCION DE LECHE; REVISTA INIA 2020; SEMILLAS; SUELOS; SUSTENTABILIDAD AMBIENTAL; TECNOLOGÍA; TRANSFERENCIA DE TECNOLOGIA; VARIEDADES; VITICULTURA. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14482/1/Revista-INIA-61-Junio-2020.pdf
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Marc : |
LEADER 02045nam a2200745 a 4500
001 1061179
005 2020-07-31
008 2020 bl uuuu u00u1 u #d
022 $a1510-9011
100 1 $aINIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA)
245 $aRevista INIA Uruguay. (N° 61, Junio 2020).$h[electronic resource]
260 $aMontevideo (UY): INIA$c2020
300 $a129 p.
490 $a(Revista INIA; 61).
520 $aInforme especial: Desafíos de la citricultura en Uruguay y el aporte de INIA a su competitividad.
650 $aARROZ
650 $aBIOTECNOLOGIA
650 $aBOVINOS DE CARNE
650 $aCAMBIO CLIMÁTICO
650 $aCIENCIA
650 $aCITRUS
650 $aCLIMATOLOGIA
650 $aCOMUNICACIÓN
650 $aCONTROL DE ENFERMEDADES
650 $aCULTIVO
650 $aCULTIVOS DE GRANO
650 $aCULTIVOS DE SECANO
650 $aENTOMOLOGIA
650 $aESPECIES FORRAJERAS
650 $aEUCALYPTUS
650 $aEXPLOTACION AGRICOLA FAMILIAR
650 $aFITOPATOLOGÍA
650 $aFORESTALES
650 $aFORRAJERAS
650 $aFORRAJES
650 $aFRUTALES
650 $aFRUTICULTURA
650 $aGANADO BOVINO
650 $aGRANOS
650 $aGRAS
650 $aHORTALIZAS
650 $aHORTICULTURA
650 $aIMAGEN CORPORATIVA & COMUNICACIÓN INSTITUCIONAL
650 $aINIA
650 $aINNOVACION
650 $aINVESTIGACIÓN
650 $aLECHERÍA
650 $aLEGUMINOSAS
650 $aMANEJO DE CULTIVOS
650 $aMEJORAMIENTO ANIMAL
650 $aMETEOROLOGIA
650 $aMICROBIOLOGÍA
650 $aOVINOS
650 $aPASTURAS
650 $aPRODUCCIÓN ANIMAL
650 $aPRODUCCION DE LANA
650 $aPRODUCCION DE LECHE
650 $aREVISTA INIA 2020
650 $aSEMILLAS
650 $aSUELOS
650 $aSUSTENTABILIDAD AMBIENTAL
650 $aTECNOLOGÍA
650 $aTRANSFERENCIA DE TECNOLOGIA
650 $aVARIEDADES
650 $aVITICULTURA
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