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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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| 1. |  | LANGRIDGE, P.; ALAUX, M.; ALMEIDA, N.F.; AMMAR, K.; BAUM, M.; BEKKAOUI, F.; BENTLEY, A.R.; BERES, B.L.; BERGER, B.; BRAUN, H.-J.; BROWN-GUEDIRA, G.; BURT, C.J.; CACCAMO, M.J.; CATTIVELLI, L.; CHARMET, G.; CIVÁN, P.; CLOUTIER, S.; COHAN, J-P.; DEVAUX, P.; DOOHAN, F.M.; DRECCER, M.F.; FERRAHI, M.; GERMAN, S.; GOODWIN, S.B.; GRIFFITHS, S.; GUZMÁN, C.; HANDA, H.; HAWKESFORD, M.J.; HE, Z.; HUTTNER, E.; IKEDA, T.M.; KILIAN, B.; KING, I.P.; KING, J.; KIRKEGAARD, J.A.; LAGE, J.; LE GOUIS, J.; MONDAL, S.; MULLINS, E.; ORDON, F.; ORTIZ-MONASTERIO, J.I.; ÖZKAN, H.; ÖZTÜRK, I.; PEREYRA, S.; POZNIAK, C.J.; QUESNEVILLE, H.; QUINCKE, M.; REBETZKE, G.J.; CHRISTOPH REIF, J.; SAAVEDRA-BRAVO, T.; SCHURR, U.; SHARMA, S.; SINGH, S.K.; SINGH, R.P.; SNAPE, J.W.; TADESSE, W.; TSUJIMOTO, H.; TUBEROSA, R.; WILLIS, T.G.; ZHANG, X. Meeting the challenges facing wheat production: the strategic research agenda of the Global Wheat Initiative. Agronomy, 2022, volume 12, issue 11, 2767. OPEN ACCESS. doi: https://doi.org/10.3390/agronomy12112767 Article history: Received 26 September 2022; Revised 28 October 2022; Accepted 29 October 2022; Published 7 November 2022. -- Academic Editor: Andreas Katsiotis. -- Corresponding author: Langridge, P.; School of Agriculture, Food and...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
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