| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
|
Data corrente: |
09/10/2023 |
|
Data da última atualização: |
24/10/2023 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso |
|
Autoria: |
AFONSO, J.; FORTES, M. R. S.; SHIM, W. J.; CARDOSO, T. F.; BRUSCADIN, J. J.; LIMA, A. O. de; DINIZ, W. J. S.; SILVA-VIGNATO, B.; TAN, W. L. A.; CESAR, A. S. M.; BODEN, M.; MOURÃO, G. B.; ZERLOTINI NETO, A.; COUTINHO, L. L.; REGITANO, L. C. de A. |
|
Afiliação: |
MARINA RUFINO SALINAS FORTES, THE UNIVERSITY OF QUEENSLAND; JENNIFER JESSICA BRUSCADIN, UNIVERSIDADE FEDERAL DE SÃO CARLOS; ANDRESSA OLIVEIRA DE LIMA, UNIVERSITY OF WASHINGTON; AUBURN UNIVERSITY; UNIVERSIDADE DE SÃO PAULO; THE UNIVERSITY OF QUEENSLAND; ALINE SILVA MELLO CESAR, UNIVERSIDADE DE SÃO PAULO; MIKAEL BODEN, THE UNIVERSITY OF QUEENSLAND; GERSON BARRETO MOURÃO, UNIVERSIDADE DE SÃO PAULO; ADHEMAR ZERLOTINI NETO, CNPTIA; UNIVERSIDADE DE SÃO PAULO; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE. |
|
Título: |
Epigenetic repression of genes associated with ribeye area of nelore cattle. |
|
Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
In: CONFERENCE OF THE ASSOCIATION FOR THE ADVANCEMENT OF ANIMAL BREEDING AND GENETICS, 25., 2023, Perth. Animal breeding at the crossroads: proceedings. Armidale: Association for the Advancement of Animal Breeding and Genetics, 2023. p. 206-209. |
|
ISSN: |
1328-3227 |
|
Idioma: |
Inglês |
|
Notas: |
AAABG 2023. Na publicação: A. Zerlotini. |
|
Conteúdo: |
SUMMARY. Understanding the epigenetic repression role in regulating genes involved with the ribeye area (REA) of bovine muscle can help us to predict this trait in the future. Here, we identified genes putatively regulating REA in Nelore cattle and divergently epigenetically repressed between contrasting sample groups. For that, we applied the TRIAGE method with a Rank Product analysis using bovine muscle expression data on high versus low REA groups. Further, we identified overrepresented pathways and biological processes linked to candidate genes, searching for their regulatory direction. This result advances the knowledge about how epigenetic regulation may impact production traits in Nelore cattle. |
|
Palavras-Chave: |
Área de olho de lombo; Candidate regulatory genes; Epigenetic repression; Genes candidatos a reguladores; Longissimus thoracis muscle´s expression data; Repressão epigenética; Ribeye area. |
|
Thesagro: |
Gado Nelore. |
|
Categoria do assunto: |
--
G Melhoramento Genético |
|
Marc: |
LEADER 02118nam a2200397 a 4500
001 2157146
005 2023-10-24
008 2023 bl uuuu u00u1 u #d
022 $a1328-3227
100 1 $aAFONSO, J.
245 $aEpigenetic repression of genes associated with ribeye area of nelore cattle.$h[electronic resource]
260 $aIn: CONFERENCE OF THE ASSOCIATION FOR THE ADVANCEMENT OF ANIMAL BREEDING AND GENETICS, 25., 2023, Perth. Animal breeding at the crossroads: proceedings. Armidale: Association for the Advancement of Animal Breeding and Genetics, 2023. p. 206-209.$c2023
500 $aAAABG 2023. Na publicação: A. Zerlotini.
520 $aSUMMARY. Understanding the epigenetic repression role in regulating genes involved with the ribeye area (REA) of bovine muscle can help us to predict this trait in the future. Here, we identified genes putatively regulating REA in Nelore cattle and divergently epigenetically repressed between contrasting sample groups. For that, we applied the TRIAGE method with a Rank Product analysis using bovine muscle expression data on high versus low REA groups. Further, we identified overrepresented pathways and biological processes linked to candidate genes, searching for their regulatory direction. This result advances the knowledge about how epigenetic regulation may impact production traits in Nelore cattle.
650 $aGado Nelore
653 $aÁrea de olho de lombo
653 $aCandidate regulatory genes
653 $aEpigenetic repression
653 $aGenes candidatos a reguladores
653 $aLongissimus thoracis muscle´s expression data
653 $aRepressão epigenética
653 $aRibeye area
700 1 $aFORTES, M. R. S.
700 1 $aSHIM, W. J.
700 1 $aCARDOSO, T. F.
700 1 $aBRUSCADIN, J. J.
700 1 $aLIMA, A. O. de
700 1 $aDINIZ, W. J. S.
700 1 $aSILVA-VIGNATO, B.
700 1 $aTAN, W. L. A.
700 1 $aCESAR, A. S. M.
700 1 $aBODEN, M.
700 1 $aMOURÃO, G. B.
700 1 $aZERLOTINI NETO, A.
700 1 $aCOUTINHO, L. L.
700 1 $aREGITANO, L. C. de A.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Cerrados. |
|
Data corrente: |
05/05/2021 |
|
Data da última atualização: |
14/05/2021 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
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. |
|
Afiliação: |
CLAUDIO DE ULHOA MAGNABOSCO, CPAC; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF; EDUARDO DA COSTA EIFERT, CPAC; MARCELO GONCALVES NARCISO, CNPAF. |
|
Título: |
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 5, 2020. |
|
Páginas: |
p. 438-448 |
|
Idioma: |
Inglês |
|
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 |
|
Palavras-Chave: |
Bayesian regression models; Carne macia; Deep learning; Genomic selection; Maciez da carne. |
|
Thesagro: |
Carne; Gado de Corte; Genética Animal; Seleção Genética. |
|
Thesaurus NAL: |
Animal breeding; Zebu. |
|
Categoria do assunto: |
--
L Ciência Animal e Produtos de Origem Animal |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223045/1/Magnabosco-Improving-genomic-prediction-accuracy-for-meat-tenderness-in.pdf
|
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Cerrados (CPAC) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
