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| 21. |  | RESENDE, M. D. V. de. Matemática. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 7-55.| Biblioteca(s): Embrapa Florestas. |
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| 23. | | RESENDE, M. D. V. de. Modelos BLUP univariados multiefeitos. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 226-256.| Biblioteca(s): Embrapa Florestas. |
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| 24. | | RESENDE, M. D. V. de. Modelos computacionais BLUP. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 206-225.| Biblioteca(s): Embrapa Florestas. |
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| 26. | | RESENDE, M. D. V. de. Modelos lineares. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 130-205.| Biblioteca(s): Embrapa Florestas. |
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| 27. | | RESENDE, M. D. V. de. Modelos multivariados. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 257-311.| Biblioteca(s): Embrapa Florestas. |
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| 36. | | RESENDE, M. D. V. de; VENCOVSKUY, R. Condução e utilização de bancos de conservação genetica de especies de eucalipto. Silvicultura, São Paulo, v. 12 , n. 42, t. 3, p. 434-439, 1992. Edição dos Anais do Congresso Florestal Brasileiro, 6., 1990, Campos do Jordão.| Biblioteca(s): Embrapa Florestas. |
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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. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
09/02/2011 |
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Data da última atualização: |
16/09/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
GRATTAPAGLIA, D.; RESENDE, M. D. V. de. |
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Afiliação: |
DARIO GRATTAPAGLIA, CENARGEN; MARCOS DEON VILELA DE RESENDE, CNPF. |
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Título: |
Genomic selection in forest tree breeding. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Tree Genetics & Genomes, v. 7, n. 2, p. 241-255, Apr. 2011. |
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DOI: |
10.1007/s11295-010-0328-4 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (Ne) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by Ne and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when Ne?30, while up to 20 markers/cM are necessary for larger Ne. Shortening the breeding cycle by 50% with GS provides an increase ?100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation. MenosGenomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (Ne) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by Ne and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when Ne?30, while up to 20 markers/cM are necessary for larger Ne. Shortening the breeding cycle by 50% with GS provides an increase ?100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate ... Mostrar Tudo |
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Palavras-Chave: |
Genome-wide selection; Melhoramento florestal; Seleção genômica. |
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Thesaurus NAL: |
linkage disequilibrium; marker-assisted selection. |
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Categoria do assunto: |
-- |
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Marc: |
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024 7 $a10.1007/s11295-010-0328-4$2DOI
100 1 $aGRATTAPAGLIA, D.
245 $aGenomic selection in forest tree breeding.$h[electronic resource]
260 $c2011
520 $aGenomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (Ne) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by Ne and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when Ne?30, while up to 20 markers/cM are necessary for larger Ne. Shortening the breeding cycle by 50% with GS provides an increase ?100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.
650 $alinkage disequilibrium
650 $amarker-assisted selection
653 $aGenome-wide selection
653 $aMelhoramento florestal
653 $aSeleção genômica
700 1 $aRESENDE, M. D. V. de
773 $tTree Genetics & Genomes$gv. 7, n. 2, p. 241-255, Apr. 2011.
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