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| Registros recuperados : 46 | |
| Registros recuperados : 46 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sudeste; Embrapa Pecuária Sul. |
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Data corrente: |
13/06/2013 |
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Data da última atualização: |
27/01/2014 |
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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: |
MOKRY, F. B.; HIGA, R. H.; MUDADU, M. de A.; LIMA, A. O. de; MEIRELLES, S. L. C.; SILVA, M. V. G. B. da; CARDOSO, F. F.; OLIVEIRA, M. M. de; URBINATI, I.; NICIURA, S. C. M.; TULLIO, R. R.; ALENCAR, M. M. de; REGITANO, L. C. de A. |
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Afiliação: |
FABIANA BARICHELLO MOKRY, UFSCar; ROBERTO HIROSHI HIGA, CNPTIA; MAURÍCIO DE ALVARENGA MUDADU, CPPSE; ANDRESSA OLIVEIRA DE LIMA, UFSCar; SARAH LAGUNA CONCEIÇÃO MEIRELLES, UFV; MARCOS VINICIUS GUALBERTO BARBOSA DA SILVA, CNPGL; FERNANDO FLORES CARDOSO, CPPSUL; MAURÍCIO MORGADO DE OLIVEIRA, CPPSUL; ISMAEL URBINATI, Unesp; SIMONE CRISTINA MÉO NICIURA, CPPSE; RYMER RAMIZ TULLIO, CPPSE; MAURÍCIO MELLO DE ALENCAR, CPPSE; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE. |
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Título: |
Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
BMC Genetics, London v. 14, n. 47, 2013. |
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Páginas: |
11 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Background: Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal?s life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality. Results: The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism. Conclusions: These results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle. |
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Palavras-Chave: |
Aprendizado de máquina; Inteligência artificial; Machine learning; Metabolismo lipídico; Polimorfismo de nucleotídeo único; Tecido adiposo subcutâneo; Tropical composition cattle. |
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Thesagro: |
Bovino; Gado de Corte. |
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Thesaurus NAL: |
Artificial intelligence; Beef cattle; lipid metabolism; Single nucleotide polymorphism; subcutaneous fat. |
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Categoria do assunto: |
--
G Melhoramento Genético
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/95880/1/Mokry-et-al.-2013-BMC-Genetics-14-47.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/95361/1/PROCI-2013.00026.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/84457/1/Mokry-et-al.-2013-BMC-Genetics-14-47.pdf
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Marc: |
LEADER 02602naa a2200445 a 4500
001 1977539
005 2014-01-27
008 2013 bl uuuu u00u1 u #d
100 1 $aMOKRY, F. B.
245 $aGenome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach.$h[electronic resource]
260 $c2013
300 $a11 p.
520 $aBackground: Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal?s life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality. Results: The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism. Conclusions: These results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle.
650 $aArtificial intelligence
650 $aBeef cattle
650 $alipid metabolism
650 $aSingle nucleotide polymorphism
650 $asubcutaneous fat
650 $aBovino
650 $aGado de Corte
653 $aAprendizado de máquina
653 $aInteligência artificial
653 $aMachine learning
653 $aMetabolismo lipídico
653 $aPolimorfismo de nucleotídeo único
653 $aTecido adiposo subcutâneo
653 $aTropical composition cattle
700 1 $aHIGA, R. H.
700 1 $aMUDADU, M. de A.
700 1 $aLIMA, A. O. de
700 1 $aMEIRELLES, S. L. C.
700 1 $aSILVA, M. V. G. B. da
700 1 $aCARDOSO, F. F.
700 1 $aOLIVEIRA, M. M. de
700 1 $aURBINATI, I.
700 1 $aNICIURA, S. C. M.
700 1 $aTULLIO, R. R.
700 1 $aALENCAR, M. M. de
700 1 $aREGITANO, L. C. de A.
773 $tBMC Genetics, London$gv. 14, n. 47, 2013.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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