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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
28/02/2008 |
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Data da última atualização: |
30/01/2026 |
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Autoria: |
LEGARRA, A.; MISZTAL, I. |
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Título: |
Computing strategies in genome-wide selection. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Journal of Dairy Science, v. 91, n. 1. p 360-366, 2007 |
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Descrição Física: |
Technical note |
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Idioma: |
Inglês |
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Conteúdo: |
Genome-wide genetic evaluation might involve the computation of BLUP-like estimations, potentially including thousands of covariates (i.e., single-nucleotide polymorphism markers) for each record. This implies dense Henderson's mixed-model equations and considerable computing resources in time and storage, even for a few thousand records. Possible computing options include the type of storage and the solving algorithm. This work evaluated several computing options, including half-stored Cholesky decomposition, Gauss-Seidel, and 3 matrix-free strategies: Gauss-Seidel, Gauss-Seidel with residuals update, and preconditioned conjugate gradients. Matrix-free Gauss-Seidel with residuals update adjusts the residuals after computing the solution for each effect. This avoids adjusting the left-hand side of the equations by all other effects at every step of the algorithm and saves considerable computing time. Any Gauss-Seidel algorithm can easily be extended for variance component estimation by Markov chain-Monte Carlo. Let m and n be the number of records and markers, respectively. Computing time for Cholesky decomposition is proportional to n3. Computing times per round are proportional to mn2 in matrix-free Gauss-Seidel, to n2 for half-stored Gauss-Seidel, and to n and m for the rest of the algorithms. Algorithms were tested on a real mouse data set, which included 1,928 records and 10,946 single-nucleotide polymorphism markers. Computing times were in the order of a few minutes for Gauss-Seidel with residuals update and preconditioned conjugate gradients, more than 1 h for half-stored Gauss-Seidel, 2 h for Cholesky decomposition, and 4 d for matrix-free Gauss-Seidel. Preconditioned conjugate gradients was the fastest. Gauss-Seidel with residuals update would be the method of choice for variance component estimation as well as solving. MenosGenome-wide genetic evaluation might involve the computation of BLUP-like estimations, potentially including thousands of covariates (i.e., single-nucleotide polymorphism markers) for each record. This implies dense Henderson's mixed-model equations and considerable computing resources in time and storage, even for a few thousand records. Possible computing options include the type of storage and the solving algorithm. This work evaluated several computing options, including half-stored Cholesky decomposition, Gauss-Seidel, and 3 matrix-free strategies: Gauss-Seidel, Gauss-Seidel with residuals update, and preconditioned conjugate gradients. Matrix-free Gauss-Seidel with residuals update adjusts the residuals after computing the solution for each effect. This avoids adjusting the left-hand side of the equations by all other effects at every step of the algorithm and saves considerable computing time. Any Gauss-Seidel algorithm can easily be extended for variance component estimation by Markov chain-Monte Carlo. Let m and n be the number of records and markers, respectively. Computing time for Cholesky decomposition is proportional to n3. Computing times per round are proportional to mn2 in matrix-free Gauss-Seidel, to n2 for half-stored Gauss-Seidel, and to n and m for the rest of the algorithms. Algorithms were tested on a real mouse data set, which included 1,928 records and 10,946 single-nucleotide polymorphism markers. Computing times were in the order of a few minutes f... Mostrar Tudo |
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Thesagro: |
Genoma; Seleção. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02314naa a2200169 a 4500
001 1313879
005 2026-01-30
008 2007 bl uuuu u00u1 u #d
100 1 $aLEGARRA, A.
245 $aComputing strategies in genome-wide selection.$h[electronic resource]
260 $c2007
300 $cTechnical note
520 $aGenome-wide genetic evaluation might involve the computation of BLUP-like estimations, potentially including thousands of covariates (i.e., single-nucleotide polymorphism markers) for each record. This implies dense Henderson's mixed-model equations and considerable computing resources in time and storage, even for a few thousand records. Possible computing options include the type of storage and the solving algorithm. This work evaluated several computing options, including half-stored Cholesky decomposition, Gauss-Seidel, and 3 matrix-free strategies: Gauss-Seidel, Gauss-Seidel with residuals update, and preconditioned conjugate gradients. Matrix-free Gauss-Seidel with residuals update adjusts the residuals after computing the solution for each effect. This avoids adjusting the left-hand side of the equations by all other effects at every step of the algorithm and saves considerable computing time. Any Gauss-Seidel algorithm can easily be extended for variance component estimation by Markov chain-Monte Carlo. Let m and n be the number of records and markers, respectively. Computing time for Cholesky decomposition is proportional to n3. Computing times per round are proportional to mn2 in matrix-free Gauss-Seidel, to n2 for half-stored Gauss-Seidel, and to n and m for the rest of the algorithms. Algorithms were tested on a real mouse data set, which included 1,928 records and 10,946 single-nucleotide polymorphism markers. Computing times were in the order of a few minutes for Gauss-Seidel with residuals update and preconditioned conjugate gradients, more than 1 h for half-stored Gauss-Seidel, 2 h for Cholesky decomposition, and 4 d for matrix-free Gauss-Seidel. Preconditioned conjugate gradients was the fastest. Gauss-Seidel with residuals update would be the method of choice for variance component estimation as well as solving.
650 $aGenoma
650 $aSeleção
700 1 $aMISZTAL, I.
773 $tJournal of Dairy Science$gv. 91, n. 1. p 360-366, 2007
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Registro original: |
Embrapa Florestas (CNPF) |
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| Registros recuperados : 5 | |
| 2. |  | AGUILAR, I.; LEGARRA, A.; CARDOSO, F. F.; MASUDA, Y.; LOURENCO, D.; MISZTAL, I. Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. Genetics Selection Evolution, v. 51, n. 28, 20 June 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Pecuária Sul. |
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| 3. | | NOBRE, P. R. C.; LOPES, P. S.; TORRES, R. A.; SILVA, L. O. C. da; REGAZZI, A. J.; TORRES JÚNIOR, R. A. A.; MISZTAL, I. Analyses of growth curves of Nellore cattle by Bayesian method via Gibbs sampling. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, Belo Horizonte, v. 55, n. 4, p. 480-490, ago. 2003. Título em português: Análises de curvas de crescimento de gado Nelore pela metodologia Bayesiana via Gibbs sampling.| Tipo: Artigo em Periódico Indexado |
| Biblioteca(s): Embrapa Gado de Corte. |
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| 4. | | CARDOSO, F. F.; GULIAS-GOMES, C. C.; OLIVEIRA, M. M.; ROSO, V. M.; PICCOLI, M. L.; BRITO, F. V.; HIGA, R. H.; PAIVA, S. R.; SILVA, M. V. G. B.; REGITANO, L. C. de A.; YOKOO, M. J.; CAETANO, A. R.; MISZTAL, I.; AGUILAR, I. Genomic selection for tick resistance in Braford and Hereford cattle using single-step methodology. In: CONFERENCE OF INTERNATIONAL SOCIETY FOR ANIMAL GENETICS, 33., 2012, Cairns, AU. Abstracts... Cairns: ISAG, 2012. p. 97-98. ISAG, 2012.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pecuária Sudeste; Embrapa Recursos Genéticos e Biotecnologia. |
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| 5. | | CARDOSO, F. F.; GULIAS-GOMES, C. C.; OLIVEIRA, M. M.; ROSO, V. M.; PICCOLI, M. L.; BRITO, F. V.; HIGA, R. H.; PAIVA, S. R.; SILVA, M. V. G. B.; REGITANO, L. C. de A.; YOKOO, M. J. I.; CAETANO, A. R.; MISZTAL, I.; AGUILAR, I. Genomic select for tick resistance in Bradford and Hereford cattle using single-step methodology. In: CONFERENCE OF THE INTERNATIONAL SOCIETY FOR ANIMAL GENETICS, 33., 2012, Cairns. Programme and abstract book... Cairns: ISAG, 2012.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| Registros recuperados : 5 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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