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| 5. |  | MEROTTO, A. JR.; GAZZIERO, D. L. P.; OLIVEIRA, M. C.; SCURSONI, J.; GARCIA, A.; FIGUEROA, R.; TURRAA, G. M. Herbicide use history and perspective in South America. Review article. Advances in Weed Science, 2022, Volume 40, Special Issue 1, Article e020220050. OPEN ACCESS. doi: https://doi.org/10.51694/AdvWeedSci/2022;40:seventy-five010 Article history: Received July 10, 2022; Approved September 15, 2022; Publication in this collection 14 Nov 2022 -- Gold Open Access. -- Correspondence author: Merotto, A.; Crop Science Department, Federal University of Rio Grande do...| Biblioteca(s): INIA Las Brujas. |
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| 8. | | CRUZ, M.; TORRES, E.; BERRÍO, L.; PULVER, E.; JENNINGS, P.; BLANCO, P.H.; GAGGERO, T.; DA CRUZ, R.; ROSSO, A.; OLIVEIRA, M. Metodologías de evaluación y resultados obtenidos en el programa de tolerancia al frío del arroz. Fondo Latinoamericano para Arroz de Riego (FLAR). ln: Congresso Brasileiro de Arroz Irrigado, 3.; Reuniao da Cultura do Arroz Irrigado, 25., 2003, Camboriú, SC, Brasil Anais. Camboriú, SC (Brasil): EPAGRIS. Estaçao Experimental de Itajaí, 2003. p. 795-797.| Biblioteca(s): INIA Treinta y Tres. |
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| 9. | | TORO OSPINA, A.M.; AGUILAR, I.; VARGAS DE OLIVEIRA, M.H.; CRUZ DOS SANTOS CORREIA, L. E.; VERCESI FILHO, A. E.; ALBUQUERQUE, L.G.; JOSINEUDSON AUGUSTO II DE VASCONCELOS SILVA Assessing the accuracy of imputation in the Gyr breed using different SNP panels. Genome, 2021 Oct, Volume 64 , Issue 10, pag 893-899. Open Acces. Doi: https://doi.org/10.1139/gen-2020-0081 Article history: Received 22 May 2020./ Accepted 17 April 2021. Corresponding author: Alejandra Maria Toro Ospina (email: toroospina92@gmail.com)| Biblioteca(s): INIA La Estanzuela. |
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| 10. | | CARDOSO, F. F.; YOKOO, M. J. I.; GULIAS-GOMES, C. C.; OLIVEIRA, M. M. DE; TEIXEIRA, B. B. M.; ROSO, V. M.; BRITO, F. V.; CAETANO, A. R.; AGUILAR, I. Avaliação genômica de touros Hereford e Braford. Bagé: Embrapa Pecuária Sul, 2012. 32 p. (Embrapa Pecuária Sul. Documentos, 127).| Biblioteca(s): INIA Las Brujas. |
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| 11. | | CARDOSO, F. F.; GOMES, C.C.G.; SOLLERO, B. P.; OLIVEIRA, M. M.; ROSO, V. M.; PICCOLI, M. L.; HIGA, R. H.; YOKOO, M. J.; CAETANO, A. R.; AGUILAR, I. Genomic prediction for tick resistance in Braford and Hereford cattle. Journal of Animal Science, 2015. v. 95, p. 2693-2705. Published June 25, 2015 Article history: Received December 19, 2014 / Accepted April 6, 2015.| Biblioteca(s): INIA Las Brujas. |
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| 12. | | DE SOUZA TEIXEIRA, O.; KUCZYNSKI DA ROCHA ,M.; GIL SESSIM, A.; DEZORDI SARTORI. E.; MACHADO DA ROSA, Y.; MUNIZ DE OLIVEIRA, M.C.; ABUD LIMA, J.; CANOZZI, M.E.A.; URDAPILLETA TAROUCO, J.; DE FARIA VALLE, S.; MCMANUS, C.; BARCELLOS, J.O.J. Weaning at 30, 75 and 180 days: Comparison between immune responses of beef calves. Research in Veterinary Science, September 2021, Volume 138, Pages 53-61. Doi: https://doi.org/10.1016/j.rvsc.2021.06.002 Article history: Received 11 January 2021/ Revised 10 May 2021/ Accepted 1 June 2021/ Available online 2 June 2021.| Biblioteca(s): INIA La Estanzuela. |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
26/11/2015 |
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Actualizado : |
15/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
CARDOSO, F. F.; GOMES, C.C.G.; SOLLERO, B. P.; OLIVEIRA, M. M.; ROSO, V. M.; PICCOLI, M. L.; HIGA, R. H.; YOKOO, M. J.; CAETANO, A. R.; AGUILAR, I. |
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Afiliación : |
F. F. CARDOSO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); Universidad Federal de Pelotas; C.C.G. GOMES, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); B. P. SOLLERO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); M.M. OLIVEIRA, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); V.M. ROSO, Gensys Associated Consulants; M.L. PICCOLI, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); R.H. HIGA, Gensys Associated Consulants; Universidad Federal de Rio Grande Do Sul (UFRGS); M.J. YOKKO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); A.R. CAETANO, EMBRAPA (Empresa Brasileira de Pesquisa Agropecuária); IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Genomic prediction for tick resistance in Braford and Hereford cattle. |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Journal of Animal Science, 2015. v. 95, p. 2693-2705. Published June 25, 2015 |
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DOI : |
10.2527/jas2014-8832 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received December 19, 2014 / Accepted April 6, 2015. |
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Contenido : |
ABSTRACT.
One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and
928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV
accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for k-means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breedspecific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance.
© 2015 American Society of Animal Science. All rights reserved. MenosABSTRACT.
One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and
928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 f... Presentar Todo |
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Palabras claves : |
BEEF CATLLE; GENOMIC SELECTION; HEALTH; TICK RESISTANCE. |
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Thesagro : |
GANADO DE CARNE; MEJORAMIENTO GENETICO ANIMAL; SALUD. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 03639naa a2200337 a 4500
001 1054002
005 2019-10-15
008 2015 bl uuuu u00u1 u #d
024 7 $a10.2527/jas2014-8832$2DOI
100 1 $aCARDOSO, F. F.
245 $aGenomic prediction for tick resistance in Braford and Hereford cattle.$h[electronic resource]
260 $c2015
500 $aArticle history: Received December 19, 2014 / Accepted April 6, 2015.
520 $aABSTRACT. One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and 928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for k-means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breedspecific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance. © 2015 American Society of Animal Science. All rights reserved.
650 $aGANADO DE CARNE
650 $aMEJORAMIENTO GENETICO ANIMAL
650 $aSALUD
653 $aBEEF CATLLE
653 $aGENOMIC SELECTION
653 $aHEALTH
653 $aTICK RESISTANCE
700 1 $aGOMES, C.C.G.
700 1 $aSOLLERO, B. P.
700 1 $aOLIVEIRA, M. M.
700 1 $aROSO, V. M.
700 1 $aPICCOLI, M. L.
700 1 $aHIGA, R. H.
700 1 $aYOKOO, M. J.
700 1 $aCAETANO, A. R.
700 1 $aAGUILAR, I.
773 $tJournal of Animal Science, 2015.$gv. 95, p. 2693-2705. Published June 25, 2015
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