| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Café; Embrapa Recursos Genéticos e Biotecnologia. |
|
Data corrente: |
10/01/2023 |
|
Data da última atualização: |
11/07/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
ESTOPA, R. A.; PALUDETO, J. G. Z.; MÜLLER, B. S. F.; OLIVEIRA, R. A. de; AZEVEDO, C. F.; RESENDE, M. D. V. de; TAMBARUSSI, E. V.; GRATTAPAGLIA, D. |
|
Afiliação: |
REGIANE ABJAUD ESTOPA, FORESTRY R&D - KLABIN S.A; JOÃO GABRIEL ZANON PALUDETO, FORESTRY R&D - KLABIN S.A; BÁRBARA SALOMÃO FARIA MÜLLER, UNIVERSITY OF FLORIDA; RICARDO AUGUSTO DE OLIVEIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCOS DEON VILELA DE RESENDE, CNPCa; EVANDRO VAGNER TAMBARUSSI, UNIVERSIDADE ESTADUAL DO CENTRO-OESTE; DARIO GRATTAPAGLIA, Cenargen. |
|
Título: |
Genomic prediction of growth and wood quality traits in Eucalyptus benthamii using different genomic models and variable SNP genotyping density. |
|
Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
New Forests, 54, 2023. |
|
DOI: |
https://doi.org/10.1007/s11056-022-09924-y |
|
Idioma: |
Inglês |
|
Conteúdo: |
Genomic selection (GS) is poised to revolutionize eucalypt tree improvement by shortening breeding cycles and increasing selection intensities. This could be particularly valuable for alternative, non-mainstream Eucalyptus species that are still in the initial stages of breeding. Eucalyptus benthamii is important for its adaptation to frost-prone subtropical regions. In this work, we compared seven genomic prediction models, six Bayesian and one frequentist GBLUP (Genomic Best Linear Unbiased Prediction) with the conventional pedigree-based ABLUP approach. Models were evaluated for their ability to estimate heritabilities and predict wood quality traits (wood density, extractives, lignin, and carbohydrates content) and volume growth in 77 open-pollinated families of Eucalyptus benthamii. We also evaluated predictive abilities and heritabilities using variable numbers of SNP in the models. Heritabilities ranged from 0.09 (extractives content) using Bayesian Lasso (BL) to 0.55 (wood density) using ABLUP. Predictive abilities (PA) ranged from 0.12 (for volume using ABLUP) to 0.44 (for wood density using three Bayesian models). All seven genomic models performed similarly well and better than the pedigree model for all traits, except extractives content. Subsets of 5000?7000 SNPs yielded heritabilities and PAs nearly as large as using all 15,293 SNPs. However, a low-density SNP panel might not be economically and technically advantageous compared to the current high-density multi-species Eucalyptus EUCHIP60k. Our results support a positive outlook to implement GS to accelerate Eucalyptus benthamii breeding for adaptation to frost-prone regions. MenosGenomic selection (GS) is poised to revolutionize eucalypt tree improvement by shortening breeding cycles and increasing selection intensities. This could be particularly valuable for alternative, non-mainstream Eucalyptus species that are still in the initial stages of breeding. Eucalyptus benthamii is important for its adaptation to frost-prone subtropical regions. In this work, we compared seven genomic prediction models, six Bayesian and one frequentist GBLUP (Genomic Best Linear Unbiased Prediction) with the conventional pedigree-based ABLUP approach. Models were evaluated for their ability to estimate heritabilities and predict wood quality traits (wood density, extractives, lignin, and carbohydrates content) and volume growth in 77 open-pollinated families of Eucalyptus benthamii. We also evaluated predictive abilities and heritabilities using variable numbers of SNP in the models. Heritabilities ranged from 0.09 (extractives content) using Bayesian Lasso (BL) to 0.55 (wood density) using ABLUP. Predictive abilities (PA) ranged from 0.12 (for volume using ABLUP) to 0.44 (for wood density using three Bayesian models). All seven genomic models performed similarly well and better than the pedigree model for all traits, except extractives content. Subsets of 5000?7000 SNPs yielded heritabilities and PAs nearly as large as using all 15,293 SNPs. However, a low-density SNP panel might not be economically and technically advantageous compared to the current high-density mult... Mostrar Tudo |
|
Thesaurus Nal: |
Eucalyptus benthamii; Genomics; Genotyping; Wood quality. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 02502naa a2200265 a 4500
001 2150837
005 2023-07-11
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11056-022-09924-y$2DOI
100 1 $aESTOPA, R. A.
245 $aGenomic prediction of growth and wood quality traits in Eucalyptus benthamii using different genomic models and variable SNP genotyping density.$h[electronic resource]
260 $c2023
520 $aGenomic selection (GS) is poised to revolutionize eucalypt tree improvement by shortening breeding cycles and increasing selection intensities. This could be particularly valuable for alternative, non-mainstream Eucalyptus species that are still in the initial stages of breeding. Eucalyptus benthamii is important for its adaptation to frost-prone subtropical regions. In this work, we compared seven genomic prediction models, six Bayesian and one frequentist GBLUP (Genomic Best Linear Unbiased Prediction) with the conventional pedigree-based ABLUP approach. Models were evaluated for their ability to estimate heritabilities and predict wood quality traits (wood density, extractives, lignin, and carbohydrates content) and volume growth in 77 open-pollinated families of Eucalyptus benthamii. We also evaluated predictive abilities and heritabilities using variable numbers of SNP in the models. Heritabilities ranged from 0.09 (extractives content) using Bayesian Lasso (BL) to 0.55 (wood density) using ABLUP. Predictive abilities (PA) ranged from 0.12 (for volume using ABLUP) to 0.44 (for wood density using three Bayesian models). All seven genomic models performed similarly well and better than the pedigree model for all traits, except extractives content. Subsets of 5000?7000 SNPs yielded heritabilities and PAs nearly as large as using all 15,293 SNPs. However, a low-density SNP panel might not be economically and technically advantageous compared to the current high-density multi-species Eucalyptus EUCHIP60k. Our results support a positive outlook to implement GS to accelerate Eucalyptus benthamii breeding for adaptation to frost-prone regions.
650 $aEucalyptus benthamii
650 $aGenomics
650 $aGenotyping
650 $aWood quality
700 1 $aPALUDETO, J. G. Z.
700 1 $aMÜLLER, B. S. F.
700 1 $aOLIVEIRA, R. A. de
700 1 $aAZEVEDO, C. F.
700 1 $aRESENDE, M. D. V. de
700 1 $aTAMBARUSSI, E. V.
700 1 $aGRATTAPAGLIA, D.
773 $tNew Forests, 54, 2023.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Café (CNPCa) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 625 | |
| 5. |  | RESENDE, M. D. V. de. REML e componentes de variância. 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. 395-448.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Florestas. |
|   |
| 10. | | RESENDE, M. D. V. de. Estatística espacial, séries temporais e competição (interação social). 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. 504-558.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Florestas. |
| |
| 11. | | RESENDE, M. D. V. de. Estatística. 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. 56-129.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Florestas. |
| |
| 19. | | 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.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Florestas. |
| |
| 20. | | 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.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Florestas. |
| |
| Registros recuperados : 625 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
