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 | Acesso ao texto completo restrito à biblioteca da Embrapa Mandioca e Fruticultura. Para informações adicionais entre em contato com cnpmf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
14/09/2012 |
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Data da última atualização: |
25/05/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, E. J. de; RESENDE, M. D. V. de; SANTOS, V. da S.; FERREIRA, C. F.; OLIVEIRA, G. A. F.; SILVA, M. S. da; OLIVEIRA, L. A. de; VILDOSO, C. I. A. |
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Afiliação: |
EDER JORGE DE OLIVEIRA, CNPMF; MARCOS DEON VILELA DE RESENDE, CNPF; VANDERLEI DA SILVA SANTOS, CNPMF; CLAUDIA FORTES FERREIRA, CNPMF; GILMARA ALVARENGA FACHARDO OLIVEIRA, UFRB; MAIANE SUZARTE DA SILVA, UFRB; LUCIANA ALVES DE OLIVEIRA, CNPMF; CARLOS IVAN AGUILAR VILDOSO, CNPQ. |
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Título: |
Genome-wide selection in cassava. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Euphytica, jun. 2012. |
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Idioma: |
Inglês |
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Conteúdo: |
The main objective of this study was to estimate the selection accuracy and to predict the genetic gain in cassava breeding using genomic selection methodologies. We evaluated 358 cassava genotypes for the following traits: shoot weight (SW), fresh root yield (FRY), starch fraction amylose content (AC), dry matter content (DMC), and starch yield (S-Y). Genotyping was performed using 390 single nucleotide polymorphisms (SNPs), which were used as covariates in the random regression-best linear unbiased prediction model for genomic selection. The heritability values detected by markers for the SW, FRY, AC, DMC, and S-Y traits were 0.25, 0.25, 0.03, 0.20, and 0.26, respectively. Because the low heritability detected for AC, this trait was eliminated from further analysis. Using only the most informative SNPs (118, 92, 56, and 97 SNPs for SW, FRY, DMC, and S-Y, respectively) we observed higher selection accuracy which were 0.83, 0.76, 0.67, and 0.77, respectively to SW, FRY, DMC, and S-Y. With these levels of accuracy and considering a selection cycle reduced by half the time, the theoretical gains with genomic selection compared to phenotypic selection for DMC, FRY, and SW would be 39.42 %, 56.90 %, and 73.96 %, espectively. These results indicate that in the cassava, genomic selection can substantially speed up selection cycles, thereby increasing gains per unit time. Although there are high expectations for incorporating this strategy into breeding programs, we still need to validate the model for other traits and evaluate whether the selection accuracy can be improved using more SNPs. MenosThe main objective of this study was to estimate the selection accuracy and to predict the genetic gain in cassava breeding using genomic selection methodologies. We evaluated 358 cassava genotypes for the following traits: shoot weight (SW), fresh root yield (FRY), starch fraction amylose content (AC), dry matter content (DMC), and starch yield (S-Y). Genotyping was performed using 390 single nucleotide polymorphisms (SNPs), which were used as covariates in the random regression-best linear unbiased prediction model for genomic selection. The heritability values detected by markers for the SW, FRY, AC, DMC, and S-Y traits were 0.25, 0.25, 0.03, 0.20, and 0.26, respectively. Because the low heritability detected for AC, this trait was eliminated from further analysis. Using only the most informative SNPs (118, 92, 56, and 97 SNPs for SW, FRY, DMC, and S-Y, respectively) we observed higher selection accuracy which were 0.83, 0.76, 0.67, and 0.77, respectively to SW, FRY, DMC, and S-Y. With these levels of accuracy and considering a selection cycle reduced by half the time, the theoretical gains with genomic selection compared to phenotypic selection for DMC, FRY, and SW would be 39.42 %, 56.90 %, and 73.96 %, espectively. These results indicate that in the cassava, genomic selection can substantially speed up selection cycles, thereby increasing gains per unit time. Although there are high expectations for incorporating this strategy into breeding programs, we still need to v... Mostrar Tudo |
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Palavras-Chave: |
Manihot esculenta Crantz. |
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Thesaurus Nal: |
breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02220naa a2200229 a 4500
001 1933787
005 2023-05-25
008 2012 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, E. J. de
245 $aGenome-wide selection in cassava.$h[electronic resource]
260 $c2012
520 $aThe main objective of this study was to estimate the selection accuracy and to predict the genetic gain in cassava breeding using genomic selection methodologies. We evaluated 358 cassava genotypes for the following traits: shoot weight (SW), fresh root yield (FRY), starch fraction amylose content (AC), dry matter content (DMC), and starch yield (S-Y). Genotyping was performed using 390 single nucleotide polymorphisms (SNPs), which were used as covariates in the random regression-best linear unbiased prediction model for genomic selection. The heritability values detected by markers for the SW, FRY, AC, DMC, and S-Y traits were 0.25, 0.25, 0.03, 0.20, and 0.26, respectively. Because the low heritability detected for AC, this trait was eliminated from further analysis. Using only the most informative SNPs (118, 92, 56, and 97 SNPs for SW, FRY, DMC, and S-Y, respectively) we observed higher selection accuracy which were 0.83, 0.76, 0.67, and 0.77, respectively to SW, FRY, DMC, and S-Y. With these levels of accuracy and considering a selection cycle reduced by half the time, the theoretical gains with genomic selection compared to phenotypic selection for DMC, FRY, and SW would be 39.42 %, 56.90 %, and 73.96 %, espectively. These results indicate that in the cassava, genomic selection can substantially speed up selection cycles, thereby increasing gains per unit time. Although there are high expectations for incorporating this strategy into breeding programs, we still need to validate the model for other traits and evaluate whether the selection accuracy can be improved using more SNPs.
650 $abreeding
653 $aManihot esculenta Crantz
700 1 $aRESENDE, M. D. V. de
700 1 $aSANTOS, V. da S.
700 1 $aFERREIRA, C. F.
700 1 $aOLIVEIRA, G. A. F.
700 1 $aSILVA, M. S. da
700 1 $aOLIVEIRA, L. A. de
700 1 $aVILDOSO, C. I. A.
773 $tEuphytica, jun. 2012.
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Embrapa Mandioca e Fruticultura (CNPMF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agroindústria de Alimentos. |
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Data corrente: |
10/01/2019 |
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Data da última atualização: |
10/01/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 3 |
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Autoria: |
BARBOSA, E. C.; SILVA, C. H. O.; NASCIMENTO, M.; SILVA, F. F. e; MINIM, V. P. R.; DELIZA, R.; SILVA, S. M. Della L. |
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Afiliação: |
Eduardo Campana Barbosa, UFV; Carlos Henrique Osório Silva, UFV; Moysés Nascimento, UFV; Fabyano Fonseca e Silva, UFV; Valéria Paula Rodrigues Minin, UFV; ROSIRES DELIZA, CTAA; Suzana Maria Della Lucia Silva, UFES. |
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Título: |
Choice-based conjoint analysis: um enfoque bayesiano. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Revista Brasileira de Biometria, Lavras, v.36, n.1, 2018. |
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Páginas: |
19 p. |
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DOI: |
doi:10.1590/1983-08232018361102 1 |
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Idioma: |
Português |
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Palavras-Chave: |
Escolha por iogurtes; Fatorial; Modelo Logit Multinomial. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00712naa a2200241 a 4500
001 2103506
005 2019-01-10
008 2018 bl uuuu u00u1 u #d
024 7 $adoi:10.1590/1983-08232018361102 1$2DOI
100 1 $aBARBOSA, E. C.
245 $aChoice-based conjoint analysis$bum enfoque bayesiano.$h[electronic resource]
260 $c2018
300 $a19 p.
653 $aEscolha por iogurtes
653 $aFatorial
653 $aModelo Logit Multinomial
700 1 $aSILVA, C. H. O.
700 1 $aNASCIMENTO, M.
700 1 $aSILVA, F. F. e
700 1 $aMINIM, V. P. R.
700 1 $aDELIZA, R.
700 1 $aSILVA, S. M. Della L.
773 $tRevista Brasileira de Biometria, Lavras$gv.36, n.1, 2018.
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Embrapa Agroindústria de Alimentos (CTAA) |
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