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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Cerrados. |
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Data corrente: |
05/05/2021 |
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Data da última atualização: |
14/05/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LOPES, F. B.; MAGNABOSCO, C. de U.; PASSAFARO, T. L.; BRUNES, L. C.; COSTA, M. F. O. e; EIFERT, E. da C.; NARCISO, M. G.; ROSA, G. J. M.; LOBO, R. B.; BALDI, F. |
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Afiliação: |
CLAUDIO DE ULHOA MAGNABOSCO, CPAC; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF; EDUARDO DA COSTA EIFERT, CPAC; MARCELO GONCALVES NARCISO, CNPAF. |
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Título: |
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 5, 2020. |
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Páginas: |
p. 438-448 |
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Idioma: |
Inglês |
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Conteúdo: |
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle MenosThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relative... Mostrar Tudo |
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Palavras-Chave: |
Bayesian regression models; Carne macia; Deep learning; Genomic selection; Maciez da carne. |
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Thesagro: |
Carne; Gado de Corte; Genética Animal; Seleção Genética. |
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Thesaurus Nal: |
Animal breeding; Zebu. |
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Categoria do assunto: |
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L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223045/1/Magnabosco-Improving-genomic-prediction-accuracy-for-meat-tenderness-in.pdf
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Marc: |
LEADER 02672naa a2200373 a 4500
001 2131678
005 2021-05-14
008 2020 bl uuuu u00u1 u #d
100 1 $aLOPES, F. B.
245 $aImproving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.$h[electronic resource]
260 $c2020
300 $ap. 438-448
520 $aThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle
650 $aAnimal breeding
650 $aZebu
650 $aCarne
650 $aGado de Corte
650 $aGenética Animal
650 $aSeleção Genética
653 $aBayesian regression models
653 $aCarne macia
653 $aDeep learning
653 $aGenomic selection
653 $aMaciez da carne
700 1 $aMAGNABOSCO, C. de U.
700 1 $aPASSAFARO, T. L.
700 1 $aBRUNES, L. C.
700 1 $aCOSTA, M. F. O. e
700 1 $aEIFERT, E. da C.
700 1 $aNARCISO, M. G.
700 1 $aROSA, G. J. M.
700 1 $aLOBO, R. B.
700 1 $aBALDI, F.
773 $tJournal of Animal Breeding and Genetics$gv. 137, n. 5, 2020.
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Registro original: |
Embrapa Cerrados (CPAC) |
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Origem |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
25/05/2017 |
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Data da última atualização: |
25/05/2017 |
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Tipo da produção científica: |
Comunicado Técnico/Recomendações Técnicas |
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Autoria: |
VIANA, J. H. M.; CLEMENTE, E. de P.; OLIVEIRA, A. P. de. |
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Afiliação: |
JOAO HERBERT MOREIRA VIANA, CNPMS; ELIANE DE PAULA CLEMENTE ALMEIDA, CNPS; ALINE PACOBAHYBA DE OLIVEIRA, CNPS. |
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Título: |
Procedimento operacional padronizado para quantificação e mensuração de areias via análise de imagens. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Rio de Janeiro: Embrapa Solos, 2016. |
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Páginas: |
9 p. |
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Descrição Física: |
il. color. |
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Série: |
(Embrapa Solos. Comunicado técnico, 73). |
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ISSN: |
1517-5685 |
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Idioma: |
Português |
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Conteúdo: |
O objetivo deste trabalho é apresentar os passos e os resultados do teste de um método para a quantificação de imagens digitais de amostras de areias, baseado no uso de imagens digitais microscópicas processadas no programa ImageJ (2015). |
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Palavras-Chave: |
Amostra de areia; Quantificação de imagens. |
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Thesagro: |
Física do solo; Microscopia. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/160223/1/ComTec-73-Quantificacao-Areias.pdf
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Marc: |
LEADER 00936nam a2200217 a 4500
001 2070020
005 2017-05-25
008 2016 bl uuuu u0uu1 u #d
022 $a1517-5685
100 1 $aVIANA, J. H. M.
245 $aProcedimento operacional padronizado para quantificação e mensuração de areias via análise de imagens.$h[electronic resource]
260 $aRio de Janeiro: Embrapa Solos$c2016
300 $a9 p.$cil. color.
490 $a(Embrapa Solos. Comunicado técnico, 73).
520 $aO objetivo deste trabalho é apresentar os passos e os resultados do teste de um método para a quantificação de imagens digitais de amostras de areias, baseado no uso de imagens digitais microscópicas processadas no programa ImageJ (2015).
650 $aFísica do solo
650 $aMicroscopia
653 $aAmostra de areia
653 $aQuantificação de imagens
700 1 $aCLEMENTE, E. de P.
700 1 $aOLIVEIRA, A. P. de
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Registro original: |
Embrapa Solos (CNPS) |
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