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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; 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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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: |
LEADER 02283naa a2200205 a 4500
001 1876468
005 2015-09-16
008 2011 bl uuuu u00u1 u #d
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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Registro original: |
Embrapa Florestas (CNPF) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
17/04/2008 |
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Data da última atualização: |
17/04/2008 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
CAMPOS, M. A.; SILVA, M. S.; MAGALHÃES, C. P.; RIBEIRO, S. G.; DEL SARTO, R. P.; CRUZ, C. C. M.; MONTE, D. C.; GROSSI DE SÁ, M. F. |
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Afiliação: |
M. A. Campos, UFLA; Marília Santos Silva, CPAC; C. P. Magalhães, Cenargen; S. G. Ribeiro, Cenargen; R. P. Del Sarto, UnB; C. C. M. Cruz, Cenargen; D. C. Monte, UnB; M. F. Grossi de Sá, Cenargen. |
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Título: |
The PR-5 antifungal gene isolated from Solanum nigrum, expressed in Escherichia coli, purified and refolded: a potent version against oomycete and ascomycetes. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE BIOQUÍMICA E BIOLOGIA MOLECULAR, 35., 2006, Águas de Lindóia, SP. Programas e resumos... São Paulo: Sociedade Brasileira de Bioquímica e Biologia Molecular, 2006. |
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Idioma: |
Inglês |
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Thesagro: |
Fungo; Gene; Isolamento. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00873naa a2200229 a 4500
001 1570405
005 2008-04-17
008 2006 bl uuuu u00u1 u #d
100 1 $aCAMPOS, M. A.
245 $aThe PR-5 antifungal gene isolated from Solanum nigrum, expressed in Escherichia coli, purified and refolded$ba potent version against oomycete and ascomycetes.
260 $c2006
650 $aFungo
650 $aGene
650 $aIsolamento
700 1 $aSILVA, M. S.
700 1 $aMAGALHÃES, C. P.
700 1 $aRIBEIRO, S. G.
700 1 $aDEL SARTO, R. P.
700 1 $aCRUZ, C. C. M.
700 1 $aMONTE, D. C.
700 1 $aGROSSI DE SÁ, M. F.
773 $tIn: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE BIOQUÍMICA E BIOLOGIA MOLECULAR, 35., 2006, Águas de Lindóia, SP. Programas e resumos... São Paulo: Sociedade Brasileira de Bioquímica e Biologia Molecular, 2006.
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