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| Registros recuperados : 18 | |
| 2. |  | ALKIMIM, E. R.; SOUSA, T. V.; SILVA, F. L. da; SAKIYAMA, N. S.; ZAMBOLIM, L.; CAIXETA, E. T. Identificação e validação de marcadores SNP em Coffea canephora usando sequenciamento de nova geração. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 9., 2017, Foz de Iguaçu. Melhoramento de plantas: projetando o futuro. Maringá, PR: SBMP, 2017. p. 552| Biblioteca(s): Embrapa Café. |
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| 4. | | CAIXETA, E. T.; RESENDE, M. D. V. de; ALKIMIM, E. R.; SOUSA, T. V.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; ALVES, R. S. Aceleração do melhoramento do cafeeiro via seleção genômica: agilidade e eficácia no lançamento de novas cultivares. Brasília, DF: Embrapa Café, 2022. 54 p. PDF. (Embrapa Café. Documentos, 17). Projeto SI04 - Genotipagem e seleção genômica em populações de melhoramento de Coffea arabica.| Biblioteca(s): Embrapa Café. |
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| 5. | | SOUSA, T. V.; CAIXETA, E. T.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; SAKIYAMA, N. S.; RESENDE JÚNIOR, M. F. R. de; ZAMBOLIM, L. Population structure and genetic diversity of coffee progenies derived from Catuaí and Híbrido de Timor revealed by genome-wide SNP marker. Tree Genetics & Genomes, v. 13, n. 6, Dec. 2017.| Biblioteca(s): Embrapa Café. |
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| 6. | | SOUSA, T. V.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; SAKIYAMA, N. S.; ZAMBOLIM, L.; CAIXETA, E. T. Estrutura de populações e diversidade genética de Coffea arabica analisadas por marcadores moleculares SNP. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 9., 2017, Foz de Iguaçu. Melhoramento de plantas: projetando o futuro. Maringá, PR: SBMP, 2017. p. 533| Biblioteca(s): Embrapa Café. |
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| 7. | | SOUSA, T. V.; CAIXETA, E. T.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; ZAMBOLIM, E. M.; ZAMBOLIM, L.; SAKIYAMA, N. S. Caracterização molecular de cultivares de café resistentes à ferrugem. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 8., 2013, Salvador. Sustentabilidade e inclusão Social. Brasília, DF: Embrapa Café, 2013.| Biblioteca(s): Embrapa Café. |
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| 8. | | OLIVEIRA, G. F.; MIRANDA, T. L. R.; NASCIMENTO, A. C. C.; NASCIMENTO, M.; CAIXETA, E. T.; SILVA, L. de F.; ALKIMIM, E. R.; SILVA, F. L. da. Discrimination of varietal groups and hybrids of coffea canephora species using multivariate analysis. Revista Brasileira de Biometria, v. 39, n. 1, p. 194-201, 2021.| Biblioteca(s): Embrapa Café. |
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| 9. | | SILVA, L. de F.; ALKIMIM, E. R.; RODRIGUES, H. S.; LELIS, D. T.; SILVA, F. L. da; ZAMBOLIM, L.; CAIXETA, E. T. Diversidade genética e seleção de genitores de Coffea canephora por meio de marcadores moleculares. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 9., 2017, Foz de Iguaçu. Melhoramento de plantas: projetando o futuro. Maringá, PR: SBMP, 2017. p. 54| Biblioteca(s): Embrapa Café. |
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| 10. | | SOUSA, T. V.; CAIXETA, E. T.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; SAKIYAMA, N. S.; ZAMBOLIM, L.; RESENDE, M. D. V. de. Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding. Frontiers in Plant Science, v. 9, January 2019.| Biblioteca(s): Embrapa Café; Embrapa Florestas. |
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| 11. | | ALKIMIM, E. R.; CAIXETA, E. T.; SOUSA, T. V.; PEREIRA, A. A.; OLIVEIRA, A. C. B. de; ZAMBOLIM, L.; SAKIYAMA, N. S. Marker-assisted selection provides arabica coffee with genes from other Coffea species targeting on multiple resistance to rust and coffee berry disease. Molecular Breeding, v. 37, n. 6, p. 1-10, jan. 2017.| Biblioteca(s): Embrapa Café. |
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| 12. | | SOUSA, T. V.; CAIXETA, E. T.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; ZAMBOLIM, L.; SAKIYAMA, N. S. Molecular markers useful to discriminate Coffea arabica cultivars with high genetic similarity. Euphytica, v. 213, n. 3, March, 2017.| Biblioteca(s): Embrapa Café. |
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| 13. | | RESENDE, M. D. V. de; OLIVEIRA, A. C. B. de; CAIXETA, E. T.; ALKIMIM, E. R.; SOUSA, T. V.; PEREIRA, A. A.; ALVES, R. S.; AZEVEDO, C. F. Tamanho amostral e detecção de genes via GWAS em características quantitativas do cafeeiro. Brasília, DF: Embrapa Café, 2022. 23 p. (Embrapa Café. Circular técnica, 7).| Biblioteca(s): Embrapa Café. |
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| 14. | | ALKIMIM, E. R.; CAIXETA, E. T.; SOUSA, T. V.; RESENDE, M. D. V. de; SILVA, F. L. da; SAKIYAMA, N. S.; ZAMBOLIM, L. Selective efficiency of genome-wide selection in Coffea canephora breeding. Tree Genetics & Genomes, v. 16, n. 3, p. 1-11, 2020.| Biblioteca(s): Embrapa Café. |
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| 15. | | ALKIMIM, E. R.; CAIXETA, E. T.; SOUSA, T. V.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; ZAMBOLIM, E. M.; ZAMBOLIM, L.; SAKIYAMA, N. S. Seleção assistida por marcadores moleculares para resistência à ferrugem do cafeeiro. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015.| Biblioteca(s): Embrapa Café. |
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| 16. | | 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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| 17. | | 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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| 18. | | 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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| Registros recuperados : 18 | |
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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: |
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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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