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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
31/03/2021 |
Actualizado : |
31/03/2021 |
Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
Autor : |
MOTA, R.R.; TEMPELMAN, R.J.; FERNANDO F CARDOSO; AGUILAR, I.; LOPES, P.S. |
Afiliación : |
RODRIGO REIS MOTA, Michigan State University; Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil; ROBERT J TEMPELMAN, Michigan State University; CARDOSO, F.F., Embrapa Southern Region Animal Husbandry, Bagé, Rio Grande do Sul, Brazil; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PAULO S LOPES, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. |
Título : |
Genomic wide-selection for tick resistance in Hereford and Braford cattle via reaction norm models. |
Complemento del título : |
Volume Species Breeding: Beef cattle, 235. |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.235. |
Idioma : |
Inglés |
Notas : |
Acknowledgments: The authors thank Delta G Connection by providing the data used in this research; Embrapa Southern Region Animal Husbandry and Michigan State University for theoretical and technical support; CAPES, CNPq and FAPEMIG by granting the scholarship. |
Contenido : |
ABSTRACT.
The objective of this study was to compare a conventional genomic model (GBLUP) and its extension to a linear reaction norm model (GLRNM) specifying genotype by environment interaction (G*E) for tick resistance in Brazilian cattle. Tick counts (TC) from 4,363 Hereford and Braford cattle from 146 contemporary groups (CG) were available of which 3,591 animals had BovineSNP50 Illumina v2 BeadChip genotypes. The reaction norm covariate was based on CG estimates of TC from a first-step model. Analysis was conducted based on adapting the single step GBLUP/REML procedure. Five-fold cross validation based on K-means and random partitioning was used to compare the fit of the two models. Cross validation correlations were strong and not significantly different between models for either partitioning strategy. Nevertheless, it seems apparent that G*E for tick infestation exists and can captured by GLRNM models. |
Palabras claves : |
Cross validation; Single-step; Tick counts. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15448/1/Mota-et-al.-2014.-WCGALP.pdf
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Marc : |
LEADER 01877nam a2200205 a 4500 001 1061924 005 2021-03-31 008 2014 bl uuuu u01u1 u #d 100 1 $aMOTA, R.R. 245 $aGenomic wide-selection for tick resistance in Hereford and Braford cattle via reaction norm models.$h[electronic resource] 260 $aIn: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.235.$c2014 500 $aAcknowledgments: The authors thank Delta G Connection by providing the data used in this research; Embrapa Southern Region Animal Husbandry and Michigan State University for theoretical and technical support; CAPES, CNPq and FAPEMIG by granting the scholarship. 520 $aABSTRACT. The objective of this study was to compare a conventional genomic model (GBLUP) and its extension to a linear reaction norm model (GLRNM) specifying genotype by environment interaction (G*E) for tick resistance in Brazilian cattle. Tick counts (TC) from 4,363 Hereford and Braford cattle from 146 contemporary groups (CG) were available of which 3,591 animals had BovineSNP50 Illumina v2 BeadChip genotypes. The reaction norm covariate was based on CG estimates of TC from a first-step model. Analysis was conducted based on adapting the single step GBLUP/REML procedure. Five-fold cross validation based on K-means and random partitioning was used to compare the fit of the two models. Cross validation correlations were strong and not significantly different between models for either partitioning strategy. Nevertheless, it seems apparent that G*E for tick infestation exists and can captured by GLRNM models. 653 $aCross validation 653 $aSingle-step 653 $aTick counts 700 1 $aTEMPELMAN, R.J. 700 1 $aFERNANDO F CARDOSO 700 1 $aAGUILAR, I. 700 1 $aLOPES, P.S.
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| Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
26/11/2015 |
Actualizado : |
15/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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. |
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. |
Título : |
Genomic prediction for tick resistance in Braford and Hereford cattle. |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
Journal of Animal Science, 2015. v. 95, p. 2693-2705. Published June 25, 2015 |
DOI : |
10.2527/jas2014-8832 |
Idioma : |
Inglés |
Notas : |
Article history: Received December 19, 2014 / Accepted April 6, 2015. |
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 |
Palabras claves : |
BEEF CATLLE; GENOMIC SELECTION; HEALTH; TICK RESISTANCE. |
Thesagro : |
GANADO DE CARNE; MEJORAMIENTO GENETICO ANIMAL; SALUD. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
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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