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| Registros recuperados : 175 | |
| 161. |  | CAPUCHO, A. S.; SOUZA, A. F. de; ZAMBOLIM, E. M.; CAIXETA, E. T.; RUFINO, R. J. N.; BARBOSA, J. C.; ALVARENGA, S. M.; ZAMBOLIM, L. Viabilidade de uredosporos da ferrugem do cafeeiro (Hemileia vastatrix Berk. et Br.) sob diferentes métodos de preservação in vitro. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 4., 2005, Londrina. Anais... Brasília, DF: Embrapa Café, 2005.| Biblioteca(s): Embrapa Café. |
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| 163. | | SILVA, G. N.; NASCIMENTO, M.; SANT'ANNA, I. de C.; CRUZ, C. D.; CAIXETA, E. T.; CARNEIRO, P. C. S.; ROSADO, R. D. S.; PESTANA, K. N.; ALMEIDA, D. P. de; OLIVEIRA, M. da S. Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 3, p. 186-193, mar. 2017. Título em português: Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábica.| Biblioteca(s): Embrapa Café; Embrapa Unidades Centrais. |
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| 164. | | SILVA, L. de F.; ALKIMIM, E. R.; BARREIRO, P. R. R. M.; LEICHTWEIS, B. G.; SILVA, A. C. A.; SILVA, R. A. da; SOUSA, T. V.; NASCIMENTO, M.; CAIXETA, E. T. Genome-wide association study of plant architecture and diseases resistance in Coffea canephora. Euphytica, v. 218, jun. 2022. 13 p.| Biblioteca(s): Embrapa Café. |
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| 165. | | PESTANA, K. N.; CAPUCHO, A. S.; CAIXETA, E. T.; ALMEIDA, D. P. de; ZAMBOLIM, E. M.; CRUZ, C. D.; ZAMBOLIM, L.; PEREIRA, A. A.; OLIVEIRA, A. C. B. de; SAKIYAMA, N. S. Inheritance study and linkage mapping of resistance loci to Hemileia vastatrix in Híbrido de Timor UFV 443-03. The Genetic & Genomes, v. 11, n. 72, 2015.| Biblioteca(s): Embrapa Café. |
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| 166. | | ALKIMIM, E. R.; CAIXETA, E. T.; SOUSA, T. V.; GOIS, I. B.; SILVA, F. L. da; SAKIYAMA, N. S.; ZAMBOLIM, L.; ALVES, R. S.; RESENDE, M. D. V. de. Designing the best breeding strategy for Coffea canephora: Genetic evaluation of pure and hybrid individuals aiming to select for productivity and disease resistance traits. Plos One, v. 16, n. 12, e0260997, 2021.| Biblioteca(s): Embrapa Café. |
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| 167. | | ANGELO, P. C. da S.; YAMAGISHI, M. E. B.; SILVA, A. G.; PONCE, T. P.; ARIYOSHI, C.; SHIGUEOKA, L. H.; ARANTES, T.; GASPARINI, A. K.; SERA, G. H.; PEREIRA, L. F. P.; CAIXETA, E. T. Could genes allocated to SH3 loci contribute to other resistance factors? In: ASIC CONFERENCE ON COFFEE SCIENCE, 29., 2023, Hanoi, Vietnam. Books of abstracts... Montpellier: ASIC, 2023. p. 167. ASIC 2023.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Café. |
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| 168. | | 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. |
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| 169. | | CASTRO, I. S. L.; BARREIROS, P. R. R. M.; MENDES, T. A. de O.; FLOREZ, J. C.; SILVA, E. M. de A.; PORTO, B. N.; ZAMBOLIM, L.; CAIXETA, E. T. Gene expression and interactome analysis of candidate effectors associated with pre- and post-haustorial hemileia vastatrix-coffee interaction. Journal of Biotechnology and Biomedicine, v. 5, n. 4, p. 198-213, 2022.| Biblioteca(s): Embrapa Café. |
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| 170. | | SOUZA, R. de L.; DIAS, L. A. dos S.; CORRÊA, T. R.; CAIXETA, E. T.; FERNANDES, E. da C.; MUNIZ, D. R.; ROSMANINHO, L. B. de C.; CARDOSO, P. M. R. Genetic variability revealed by microsatellite markers in a germplasm collection of Jatropha curcas L. in Brazil: an important plant for biofuels. Crop Breeding and Applied Biotechnology, v. 19, n. 3, p. 337-346, 2019.| Biblioteca(s): Embrapa Café. |
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| 171. | | ALMEIDA, D. P. de; CAIXETA, E. T.; MOREIRA, K. F.; OLIVEIRA, A. C. B. de; FREITAS, K. N. P. de; PEREIRA, A. A.; ROSADO, R. D. S.; ZAMBOLIM, L.; CRUZ, C. D. Marker-assisted pyramiding of multiple disease resistance genes in coffee genotypes (coffea arabica). Agronomy, v. 11, n. 9, 1763, 2021.| Biblioteca(s): Embrapa Café. |
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| 172. | | 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. Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. Plos One, v. 17, n.1, e0262055, 2022.| Biblioteca(s): Embrapa Café. |
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| 173. | | ALMEIDA, D. P. de; CASTRO, I. S. L.; MENDES, T. A. de O.; ALVES, D. R.; BARKA, G. D.; BARREIROS, P. R. R. M.; ZAMBOLIM, L.; SAKIYAMA, N. S.; CAIXETA, E. T. Receptor-Like Kinase (RLK) as a candidate gene conferring resistance to Hemileia vastatrix in coffee. Scientia Agricola, v.78, n.6, e20200023, 2021.| Biblioteca(s): Embrapa Café. |
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| 174. | | MACIEL-ZAMBOLIM, E.; ZAMBOLIM, L.; SOUZA, A. F. de; PICANÇO, M. C.; LOPES, U. P.; SOUZA NETO, P. N. de; RIOS, J. A.; COSTA, R. D.; FONTES, L. F. P.; MANTOVANI, E. C.; CAIXETA, E. T.; QUEIROZ, M. E. Produção integrada de café. In: ZAMBOLIM, L.; NASSER, L. C. B.; ANDRIGUETO, J. R.; TEIXEIRA, J. M. A.; KOSOSKI, A. R.; FACHINELLO, J. C. (Org.). Produção integrada no Brasil: agropecuária sustentável alimentos seguros. Brasília, DF: Ministério da Agricultura, Pecuária e Abastecimento, 2009.| Biblioteca(s): Embrapa Café; Embrapa Unidades Centrais. |
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| 175. | | PORTO, B. N.; CAIXETA, E. T.; MATHIONI, S. M.; VIDIGAL, P. M. P.; ZAMBOLIM, L.; ZAMBOLIM, E. M.; DONOFRIO, N.; POLSON, S. W.; MAIA, T. A.; CHEN, C.; ADETUNJI, M.; KINGHAM, B.; DALIO, R. J. D.; RESENDE, M. L. V. de. Genome sequencing and transcript analysis of Hemileia vastatrix reveal expression dynamics of candidate effectors dependent on host compatibility. PLOSONE, April 18, 2019.| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 175 | |
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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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008 2022 bl uuuu u00u1 u #d
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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