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| Registros recuperados : 158 | |
| Registros recuperados : 158 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/09/2018 |
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Data da última atualização: |
05/02/2019 |
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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: |
OLIVEIRA, A. A. de; PASTINA, M. M.; SOUZA, V. F. de; PARRELLA, R. A. da C.; NODA, R. W.; SIMEONE, M. L. F.; SCHAFFERT, R. E.; MAGALHAES, J. V. de; DAMASCENO, C. M. B.; MARGARIDO, G. R. A. |
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Afiliação: |
Amanda Avelar de Oliveira; MARIA MARTA PASTINA, CNPMS; Vander Filipe de Souza; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; ROBERTO WILLIANS NODA, CNPMS; MARIA LUCIA FERREIRA SIMEONE, CNPMS; ROBERT EUGENE SCHAFFERT, CNPMS; JURANDIR VIEIRA DE MAGALHAES, CNPMS; CYNTHIA MARIA BORGES DAMASCENO, CNPMS; Gabriel Rodrigues Alves Margarido. |
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Título: |
Genomic prediction applied to high-biomass sorghum for bioenergy production. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Molecular Breeding, v. 38, n. 49, p. 1-16, 2018. |
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DOI: |
10.1007/s11032-018-0802-5 |
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Idioma: |
Inglês |
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Conteúdo: |
The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC?, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs. MenosThe increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC?, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction n... Mostrar Tudo |
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Palavras-Chave: |
Genotipagem. |
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Thesagro: |
Bioenergia; Biomassa. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/183412/1/Genomic-prediction.pdf
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Marc: |
LEADER 02655naa a2200277 a 4500
001 2096210
005 2019-02-05
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1007/s11032-018-0802-5$2DOI
100 1 $aOLIVEIRA, A. A. de
245 $aGenomic prediction applied to high-biomass sorghum for bioenergy production.$h[electronic resource]
260 $c2018
520 $aThe increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC?, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.
650 $aBioenergia
650 $aBiomassa
653 $aGenotipagem
700 1 $aPASTINA, M. M.
700 1 $aSOUZA, V. F. de
700 1 $aPARRELLA, R. A. da C.
700 1 $aNODA, R. W.
700 1 $aSIMEONE, M. L. F.
700 1 $aSCHAFFERT, R. E.
700 1 $aMAGALHAES, J. V. de
700 1 $aDAMASCENO, C. M. B.
700 1 $aMARGARIDO, G. R. A.
773 $tMolecular Breeding$gv. 38, n. 49, p. 1-16, 2018.
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Embrapa Milho e Sorgo (CNPMS) |
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