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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
12/02/2010 |
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Data da última atualização: |
05/04/2011 |
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Autoria: |
NOGUEIRA, A. C.; MEDEIROS, A. C. de S. |
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Título: |
Extração e beneficiamento de sementes florestais nativas. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Colombo: Embrapa Florestas, 2007. |
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Páginas: |
7 p. |
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Descrição Física: |
1 CD-ROM. |
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Série: |
(Embrapa Florestas. Circular técnica, 131). |
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Idioma: |
Português |
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Notas: |
Disponível também on-line. |
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Thesagro: |
Beneficiamento; Espécie Nativa; Extração; Semente. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00591naa a2200205 a 4500
001 1657865
005 2011-04-05
008 2007 bl uuuu u00u1 u #d
100 1 $aNOGUEIRA, A. C.
245 $aExtração e beneficiamento de sementes florestais nativas.
260 $c2007
300 $a7 p.$c1 CD-ROM.
490 $a(Embrapa Florestas. Circular técnica, 131).
500 $aDisponível também on-line.
650 $aBeneficiamento
650 $aEspécie Nativa
650 $aExtração
650 $aSemente
700 1 $aMEDEIROS, A. C. de S.
773 $tColombo: Embrapa Florestas, 2007.
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Embrapa Amazônia Oriental (CPATU) |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados; Embrapa Gado de Corte. |
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Data corrente: |
13/12/2023 |
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Data da última atualização: |
18/12/2023 |
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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: |
MARTINS, F. B.; AONO, A. H.; MORAES, A. da C. L.; FERREIRA, R. C. U.; VILELA, M. de M.; PESSOA FILHO, M. A. C. de P.; RODRIGUES-MOTTA, M.; SIMEÃO, R. M.; SOUZA, A. P. de. |
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Afiliação: |
FELIPE BITENCOURT MARTINS, UNIVERSIDADE DE CAMPINAS; ALEXANDRE HILD AONO, UNIVERSIDADE DE CAMPINAS; ALINE DA COSTA LIMA MORAES, UNIVERSIDADE DE CAMPINAS; REBECCA CAROLINE ULBRICHT FERREIRA, UNIVERSIDADE DE CAMPINAS; MARIANE DE MENDONCA VILELA, CNPGC; MARCO AURÉLIO CALDAS DE PINHO PESSO, CPAC; MARIANA RODRIGUES-MOTTA, UNIVERSIDADE DE CAMPINAS; ROSANGELA MARIA SIMEAO, CNPGC; ANETE PEREIRA DE SOUZA, UNIVERSIDADE DE CAMPINAS. |
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Título: |
Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v. 14, 2023. |
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DOI: |
https://doi.org/10.3389/fpls.2023.1303417 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Marco Pessoa-Filho. |
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Conteúdo: |
Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits. MenosTropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coex... Mostrar Tudo |
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Palavras-Chave: |
Feature selection; Gene coexpression networks; Genomic prediction; Machine learning; Major importance markers; RNA-Seq. |
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Thesaurus NAL: |
Forage grasses. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159688/1/Genome-wide-family-prediction-2023.pdf
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Marc: |
LEADER 03815naa a2200325 a 4500
001 2159688
005 2023-12-18
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fpls.2023.1303417$2DOI
100 1 $aMARTINS, F. B.
245 $aGenome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis.$h[electronic resource]
260 $c2023
500 $aNa publicação: Marco Pessoa-Filho.
520 $aTropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.
650 $aForage grasses
653 $aFeature selection
653 $aGene coexpression networks
653 $aGenomic prediction
653 $aMachine learning
653 $aMajor importance markers
653 $aRNA-Seq
700 1 $aAONO, A. H.
700 1 $aMORAES, A. da C. L.
700 1 $aFERREIRA, R. C. U.
700 1 $aVILELA, M. de M.
700 1 $aPESSOA FILHO, M. A. C. de P.
700 1 $aRODRIGUES-MOTTA, M.
700 1 $aSIMEÃO, R. M.
700 1 $aSOUZA, A. P. de
773 $tFrontiers in Plant Science$gv. 14, 2023.
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