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| Registros recuperados : 70 | |
| 61. |  | PEREIRA, G. S.; PADILHA, L.; VON PINHO, E. V. R.; TEIXEIRA, R. de K. S.; CARVALHO, C. H. S. de; MALUF, M. P.; CARVALHO, B. L. de. Microsatellite markers in analysis of resistance to coffee leaf miner in Arabica coffee. Pesquisa agropecuária Brasileira, Brasília, v.46, n.12, p.1650-1656, dez. 2011| Biblioteca(s): Embrapa Café. |
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| 62. | | SILVA, M. S.; PATRICIO, F. R. A.; BRAGHINI, M. T.; SALOMÃO, D.; MALUF, M. P.; MARTINATI, J.; FAZUOLI, L. C.; GUERREIRO FILHO, O. Selection of coffee plants resistant to brown eye spot: genetic variability and influence of the nutritional condition on the expression of resistance to the pathogen. In: INTERNATIONAL CONFERENCE ON COFFEE SCIENCE, 24., 2012, Costa Rica. Programme & abstracts. [S.l.]: Association for Science and Information on Coffee, 2012. p. 201| Biblioteca(s): Embrapa Café. |
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| 63. | | CARVALHO, H. F.; FERRÃO, L. F. V.; GALLI, G.; NONATO, J. V. A.; PADILHA, L.; MALUF, M. P.; RESENDE JR., M. F. R. de; FRITSCHE-NETO, R.; GUERREIRO-FILHO, O. On the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee. Tree Genetics & Genomes, v. 19, n. 1, 2023. 10 p.| Biblioteca(s): Embrapa Café. |
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| 64. | | CARVALHO, H. F.; GALLI, G.; FERRÃO, L. F. V.; NONATO, J. V. A.; PADILHA, L.; MALUF, M. P.; RESENDE JR, M. F. R. de; GUERREIRO FILHO, O.; FRITSCHE-NETO, R. The effect of bienniality on genomic prediction of yield in arabica coffee. Euphytica, v. 216, n. 101, p. 100-111, 2020.| Biblioteca(s): Embrapa Café. |
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| 65. | | QUINTERO, F. O. C.; PINTO, L. G.; BARSALOBRES-CAVALLARI, C. F.; ARCURI, M. de L. C.; PINO, L. E.; PERES, L. E. P.; MALUF, M. P.; MAIA, I. G. Identification of a seed maturation protein gene from Coffea arabica (CaSMP) and analysis of its promoter activity in tomato. Plant Cell Reports, v. 37, n.9, p.1257–1268, September 2018.| Biblioteca(s): Embrapa Café. |
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| 66. | | SILVESTRINI, M.; MALUF, M. P.; SILVAROLLA, M. B.; GUERREIRO-FILHO, O.; MEDINA-FILHO, H. P.; VANINI, M. M. T.; OLIVEIRA, A. S.; GAPARI-PEZZOPANE, C. de; FAZUOLI, L. C. Genetic diversity of a Coffea germplasm collection assessed by RAPD markers. GENETIC RESOURCE CROP EVOLUTION, v.55, n.6, p.901-910, 2008. 901-910| Biblioteca(s): Embrapa Café; Embrapa Unidades Centrais. |
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| 67. | | PEREIRA, L. F. P.; SCHENK, J. C. M.; MALUF, M. P.; SANT'ANA, G. C.; NOGUEIRA, L.; PEREIRA, L. A.; SILVA, B. S. R. da; GUIMARÃES, P. de S.; GUERREIRO FILHO, O.; PADILHA, L. Marcadores SNP avaliados em cultivares de Café Arábica. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 10., 2019, Vitória. Pesquisa, Inovação e Sustentabilidade dos Cafés do Brasil. Anais... Brasília, DF: Embrapa Café, 2019. Título em inglês: SNP markers assessed in Arabica coffee cultivars.| Biblioteca(s): Embrapa Café. |
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| 68. | | BABA, V. Y.; BRAGHINI, M. T.; SANTOS, T. B. dos; CARVALHO, K. de; SOARES, J. D. M.; IVAMOTO-SUZUKI, S. T.; MALUF, M. P.; PADILHA, L.; PACCOLA-MEIRELLES, L. D.; PEREIRA, L. F. P.; DOMINGUES, D. S. Transcriptional patterns of Coffea arabica L. nitrate reductase, glutamine and asparagine synthetase genes are modulated under nitrogen suppression and coffee leaf rust. PeerJ, v. 8, e8320, 2020. eCollection 2020.| Biblioteca(s): Embrapa Café. |
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| 69. | | PRADO, G. S.; ROCHA, D. C.; SANTOS, L. N. dos; CONTILIANI, D. F.; NOBILE, P. M.; MARTINATI-SCHENK, J. C.; PADILHA, L.; MALUF, M. P.; LUBINI, G.; PEREIRA, T. C.; MONTEIRO-VITORELLO, C. B.; CRESTE, S.; BOSCARIOL-CAMARGO, R. L.; TAKITA, M. A.; CRISTOFANI-YALY, M.; SOUZA, A. A. de. CRISPR technology towards genome editing of the perennial and semi-perennial crops citrus, coffee and sugarcane. Frontiers in Plant Science, v. 14, article 1331258, 2023.| Biblioteca(s): Embrapa Café. |
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| 70. | | VIEIRA, L. G. E.; ANDRADE, A. C.; COLOMBO, C. A.; MORAES, A. H. de A.; METHA, A.; OLIVEIRA, A. C. de; LABATE, C. A.; MARINO, C. L.; MONTEIRO-VITORELLO, C. de B.; MONTE, D. C.; GIGLIOTI, E.; KIMURA, E. T.; ROMANO, E.; KURAMAE, E. E.; LEMOS, E. G. M.; ALMEIDA, E. R. P. de; JORGE, E. C.; ALBUQUERQUE, E. V. S.; SILVA, F. R. da; VINECKY, F.; SAWAZAKI, H. E.; DORRY, H. F. A.; CARRER, H.; ABREU, I. N.; BATISTA, J. A. N.; TEIXEIRA, J. B.; KITAJIMA, J. P.; XAVIER, K. G.; LIMA, L. M. de; CAMARGO, L. E. A. de; PEREIRA, L. F. P.; COUTINHO, L. L.; LEMOS, M. V. F.; ROMANO, M. R.; MACHADO, M. A.; COSTA, M. M. do C.; SÁ, M. F. G. de; GOLDMAN, M. H. S.; FERRO, M. I. T.; TINOCO, M. L. P.; OLIVEIRA, M. C.; VAN SLUYS, M-A.; SHIMIZU, M. M.; MALUF, M. P.; EIRA, M. T. S. da; GUERREIRO FILHO, O.; ARRUDA, P.; MAZZAFERA, P.; MARIANI, P. D. S. C.; OLIVEIRA, R. L. B. C. de; HARAKAVA, R.; BALBAO, S. F.; TSAI, S. M.; MAURO, S. M. Z. di; SANTOS, S. N.; SIQUEIRA, W. J.; COSTA, G. G. L.; FORMIGHIERI, E. F.; CARAZZOLLE, M. F.; PEREIRA, G. A. G. Brazilian coffee genome project: an EST-based genomic resource. Brazilian Journal of Plant Physiology, v. 18, n. 1, p. 95-108, 2006.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 70 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
16/05/2022 |
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Data da última atualização: |
16/05/2022 |
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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: |
SOUSA, I. C. de; NASCIMENTO, M.; SANT’ANNA, I. de C.; CAIXETA, E. T.; AZEVEDO, C. F.; CRUZ, C. D.; SILVA, F. L. da; ALKIMIM, E. R.; NASCIMENTO, A. C. C.; SERÃO, N. V. L. |
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Afiliação: |
ITHALO COELHO DE SOUSA, IOWA STATE UNIVERSITY; MOYSÉS NASCIMENTO, UFV; ISABELA DE CASTRO SANT’ANNA, IAC; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa; CAMILA FERREIRA AZEVEDO, UFV; COSME DAMIÃO CRUZ, UFV; FELIPE LOPES DA SILVA, UFV; EMILLY RUAS ALKIMIM, UFMT; ANA CAROLINA CAMPANA NASCIMENTO, UFV; NICK VERGARA LOPES SERÃO, IOWA STATE UNIVERSITY. |
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Título: |
Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Plos One, v. 17, n.1, e0262055, 2022. |
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DOI: |
https://doi.org/10.1371/journal.pone.0262055 |
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Idioma: |
Inglês |
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Conteúdo: |
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. The predictive ability was greater when using ANN with three hidden layers than using one hidden layer and GBLUP, with 0.72 and 0.88 for yield and coffee leaf rust resistance, respectively. The concordance rate (CR) of the 10% larger markers effects among the methods varied between 10% and 13.8%, for additive effects and between 5.4% and 11.9% for dominance effects. The narrow-sense (h2a ) and dominance-only (h2a ) heritability estimates were 0.25 and 0.06, respectively, for yield, and 0.67 and 0.03, respectively for rust resistance. The ANN was able to estimate the heritabilities from an additive-dominance genomic architectures and the ANN with three hidden layers obtained best predictive ability when compared with those obtained from GBLUP and ANN with one hidden layer. MenosMany methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. T... Mostrar Tudo |
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Palavras-Chave: |
Rede neural artificial. |
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Thesagro: |
Coffea Canephora; Marcador Genético. |
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Thesaurus NAL: |
Dominance (genetics); Neural networks. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143026/1/Marker-effects-and-heritability.pdf
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Marc: |
LEADER 03240naa a2200301 a 4500
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024 7 $ahttps://doi.org/10.1371/journal.pone.0262055$2DOI
100 1 $aSOUSA, I. C. de
245 $aMarker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.$h[electronic resource]
260 $c2022
520 $aMany methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. The predictive ability was greater when using ANN with three hidden layers than using one hidden layer and GBLUP, with 0.72 and 0.88 for yield and coffee leaf rust resistance, respectively. The concordance rate (CR) of the 10% larger markers effects among the methods varied between 10% and 13.8%, for additive effects and between 5.4% and 11.9% for dominance effects. The narrow-sense (h2a ) and dominance-only (h2a ) heritability estimates were 0.25 and 0.06, respectively, for yield, and 0.67 and 0.03, respectively for rust resistance. The ANN was able to estimate the heritabilities from an additive-dominance genomic architectures and the ANN with three hidden layers obtained best predictive ability when compared with those obtained from GBLUP and ANN with one hidden layer.
650 $aDominance (genetics)
650 $aNeural networks
650 $aCoffea Canephora
650 $aMarcador Genético
653 $aRede neural artificial
700 1 $aNASCIMENTO, M.
700 1 $aSANT’ANNA, I. de C.
700 1 $aCAIXETA, E. T.
700 1 $aAZEVEDO, C. F.
700 1 $aCRUZ, C. D.
700 1 $aSILVA, F. L. da
700 1 $aALKIMIM, E. R.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aSERÃO, N. V. L.
773 $tPlos One$gv. 17, n.1, e0262055, 2022.
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