| |
|
|
| Registros recuperados : 125 | |
| 101. |  | ALMEIDA FILHO, J. E. de A.; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JÚNIOR, M. F. R. de. Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019.| Biblioteca(s): Embrapa Florestas. |
|    |
| 102. | | TEIXEIRA, F. R. F.; NASCIMENTO, M.; CECON, P. R.; CRUZ, C. D.; SILVA, F. F. e; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; MARQUES, D. B. D.; SILVA, M. V. G. B.; CARNEIRO, A. P. S.; PAIXAO, D. M. Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models. Genetics and Molecular Research, v. 20, n. 1, gmr18691, 2021.| Biblioteca(s): Embrapa Gado de Leite. |
|  |
| 103. | | SOUSA, I. C. de; NASCIMENTO, M.; SILVA, G. N.; NASCIMENTO, A. C. C.; CRUZ, C. D.; SILVA, F. F. e; ALMEIDA, D. P. de; PESTANA, K. N.; AZEVEDO, C. F.; ZAMBOLIM, L.; CAIXETA, E. T. Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. Scientia Agricola, v. 78, n. 4, e20200021, 2021.| Biblioteca(s): Embrapa Café. |
| |
| 104. | | SILVA, F. F. e; ZAMBRANO, M. F. B.; VARONA, L.; GLÓRIA, L. S.; LOPES, P. S.; SILVA, M. V. G. B.; ARBEX, W. A.; LÁZARO, S. F.; RESENDE, M. D. V. de; GUIMARÃES, S. E. F. Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves. Scientia Agricola, v. 74, n. 1, 2017. 7 P.| Biblioteca(s): Embrapa Florestas; Embrapa Gado de Leite. |
| |
| 105. | | SANTANA, T. E. Z.; SILVA, J. C. F.; SILVA, L. O. C. da; ALVARENGA, A. B.; MENEZES, G. R. de O.; TORRES JUNIOR, R. A. de A.; DUARTE, M. de S.; SILVA, F. F. e. Genome-enabled classification of stayability in Nellore cattle under a machine learning framework. Livestock Science, v. 260, article 104935, 2022.| Biblioteca(s): Embrapa Gado de Corte. |
| |
| 106. | | CARRARA, E. R.; PEIXOTO, M. G. C. D.; VERONEZE, R.; SILVA, F. F. e; RAMOS, P. V. B.; BRUNELI, F. A. T.; ZADRA, L. E. F.; VENTURA, H. T.; JOSAHKIAN, L. A.; LOPES, P. S. Genetic study of quantitative traits supports the use of Guzerá as dual-purpose cattle. Animal Bioscience, v. 35, n. 7, p. 955-963, 2022.| Biblioteca(s): Embrapa Gado de Leite. |
| |
| 107. | | MIRANDA, T. L. R.; RESENDE, M. D. V. de; AZEVEDO, C. F.; NUNES, A. C. P.; TAKAHASHI, E. K.; SIMIQUELI, G. F.; SILVA, F. F. e; ALVES, R. S. Evaluation of a new additive-dominance genomic model and implications for quantitative genetics and genomic selection. Scientia Agricola, v. 79, n. 6, p. 1-7, 2022.| Biblioteca(s): Embrapa Café. |
| |
| 108. | | GRANATO, I. S. C.; MARINHO, C. D.; ALMEIDA FILHO, J. E. de; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Seleção de marcadores para os métodos RR-BLUP e BLASSO na seleção genômica ampla. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 285-288.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
| 109. | | GRANATO, I. S. C.; MARINHO, C. D.; ALMEIDA FILHO, J. E. de; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Seleção de marcadores para os métodos RR-BLUP e BLASSO na seleção genômica ampla. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 285-288.| Biblioteca(s): Embrapa Florestas. |
| |
| 110. | | OLIVEIRA, H. R. de; SILVA, F. F. e; SILVA, M. V. G. B.; SIQUEIRA, O. H. G. B. D. de; MACHADO, M. A.; PANETTO, J. C. do C.; GLÓRIA, L. S.; BRITO, L. F. Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle. Livestock Science, v. 201, p. 78-84, 2017.| Biblioteca(s): Embrapa Gado de Leite. |
| |
| 111. | | EVANGELISTA, J. S. P. C.; PEIXOTO, M. A.; COELHO, I. F.; ALVES, R. S.; SILVA, F. F. e; RESENDE, M. D. V. de; SILVA, F. L. da; BHERING, L. L. Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach. Crop Breeding and Applied Biotechnology, v. 21, n. 1, e359721111, 2021.| Biblioteca(s): Embrapa Café. |
| |
| 112. | | RESENDE, R. T.; SOARES, A. A. V.; FORRESTER, D. I.; MARCATTI, G. E.; SANTOS, A. R. dos; TAKAHASHI, E. K.; SILVA, F. F. e; GRATTAPAGLIA, D.; RESENDE, M. D. V. de; LEITE, H. G. Environmental uniformity, site quality and tree competition interact to determine stand productivity of clonal Eucalyptus. Forest Ecology and Management, v. 410, p. 76-83, Feb. 2018.| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
| |
| 113. | | FARIA, G. M. P. de; PEREIRA, C. S.; GRANATO, I. S. C.; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SASALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Controle de qualidade de dados genotípicos para estudos genômicos em clones de eucalipto. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 1068-1071| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
| 114. | | FARIA, G. M. P. de; PEREIRA, C. S.; GRANATO, I. S. C.; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SASALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Controle de qualidade de dados genotípicos para estudos genômicos em clones de eucalipto. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 84-87.| Biblioteca(s): Embrapa Florestas. |
| |
| 115. | | NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A. Regularized quantile regression applied to genome-enabled prediction of quantitative traits. Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. 12 p.| Biblioteca(s): Embrapa Florestas. |
| |
| 116. | | ALVES, R. S.; RESENDE, M. D. V. de; ROCHA, J. R. do A. S. de C.; PEIXOTO, M. A.; TEODORO, P. E.; SILVA, F. F. e; BHERING, L. L.; SANTOS, G. A. dos. Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding. Bragantia, v. 79, n. 4, 2020. p. 360-376.| Biblioteca(s): Embrapa Café. |
| |
| 117. | | PEIXOTO, M. A.; EVANGELISTA, J. S. P. C.; COELHO, I. F; ALVES, R. A.; LAVIOLA, B. G.; SILVA, F. F. e; RESENDE, M. D. V. de; BHERING, L. L. Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy. PLOS ONE , v. 16, n. 3, e0247775, Mar. 2021. 16| Biblioteca(s): Embrapa Agroenergia; Embrapa Café. |
| |
| 118. | | ALMEIDA FILHO, J. E. de; AZEVEDO, C. F.; MARINHO, C. D.; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Parametrizações em marcadores dominantes para seleção genômica ampla em eucalipto. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 13-16.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
| 119. | | ALMEIDA FILHO, J. E. de; AZEVEDO, C. F.; MARINHO, C. D.; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Parametrizações em marcadores dominantes para seleção genômica ampla em eucalipto. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 13-16.| Biblioteca(s): Embrapa Florestas. |
| |
| 120. | | ALVES, R. S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; ROCHA, J. R. A. S. C.; NUNES, A. C. P.; CARNEIRO, A. P. S.; SANTOS, G. A. dos. Optimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients. Tree Genetics & Genomes, v. 16, n. 2, p. 1-8, 2020.| Biblioteca(s): Embrapa Café. |
| |
| Registros recuperados : 125 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Florestas. |
|
Data corrente: |
17/09/2018 |
|
Data da última atualização: |
17/09/2018 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
RESENDE, R. T.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; MELO, L. C.; PEREIRA, H. S.; SOUZA, T. L. P. O. de; VALDISSER, P. A. M. R.; BRONDANI, C.; VIANELLO, R. P. |
|
Afiliação: |
Rafael T. Resende, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Camila F. Azevedo, UFV; Fabyano Fonseca e Silva, UFV; LEONARDO CUNHA MELO, CNPAF; HELTON SANTOS PEREIRA, CNPAF; THIAGO LIVIO PESSOA OLIV DE SOUZA, CNPAF; PAULA ARIELLE M RIBEIRO VALDISSER, CNPAF; CLAUDIO BRONDANI, CNPAF; ROSANA PEREIRA VIANELLO, CNPAF. |
|
Título: |
Genome-wide association and regional heritability mapping of plant architecture, lodging and productivity in Phaseolus vulgaris. |
|
Ano de publicação: |
2018 |
|
Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 8, p. 2841-2854, Aug. 2018. |
|
Idioma: |
Inglês |
|
Conteúdo: |
The availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G · E interaction (c2 int) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to 100%) of trait heritability. At the same probability threshold, three marker?trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (42% to 64%), probably due to LD between markers and genes and/or rare allele variants not sampled. RHM in autogamous species had the potential to identify larger-effect QTL combining allelic variants that could be effectively incorporated into whole-genome prediction models and tracked through breeding generations using marker-assisted selection. MenosThe availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G · E interaction (c2 int) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to 100%) of trait heritability. At the same probability threshold, three marker?trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (42% to 64%), probably due to LD between markers and genes and/or rare alle... Mostrar Tudo |
|
Palavras-Chave: |
DArTseq; GWAS QTL; Herdabilidade; RHM QTL. |
|
Thesagro: |
Feijão; Melhoramento Genético Vegetal; Phaseolus Vulgaris. |
|
Thesaurus NAL: |
Beans; Heritability; Lodging resistance; Plant architecture; Plant breeding. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/183088/1/2018-M.Deon-G3-Genome-wide.pdf
|
|
Marc: |
LEADER 02867naa a2200373 a 4500
001 2095838
005 2018-09-17
008 2018 bl uuuu u00u1 u #d
100 1 $aRESENDE, R. T.
245 $aGenome-wide association and regional heritability mapping of plant architecture, lodging and productivity in Phaseolus vulgaris.$h[electronic resource]
260 $c2018
520 $aThe availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G · E interaction (c2 int) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to 100%) of trait heritability. At the same probability threshold, three marker?trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (42% to 64%), probably due to LD between markers and genes and/or rare allele variants not sampled. RHM in autogamous species had the potential to identify larger-effect QTL combining allelic variants that could be effectively incorporated into whole-genome prediction models and tracked through breeding generations using marker-assisted selection.
650 $aBeans
650 $aHeritability
650 $aLodging resistance
650 $aPlant architecture
650 $aPlant breeding
650 $aFeijão
650 $aMelhoramento Genético Vegetal
650 $aPhaseolus Vulgaris
653 $aDArTseq
653 $aGWAS QTL
653 $aHerdabilidade
653 $aRHM QTL
700 1 $aRESENDE, M. D. V. de
700 1 $aAZEVEDO, C. F.
700 1 $aSILVA, F. F. e
700 1 $aMELO, L. C.
700 1 $aPEREIRA, H. S.
700 1 $aSOUZA, T. L. P. O. de
700 1 $aVALDISSER, P. A. M. R.
700 1 $aBRONDANI, C.
700 1 $aVIANELLO, R. P.
773 $tG3: Genes, Genomes, Genetics$gv. 8, p. 2841-2854, Aug. 2018.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
