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| Registros recuperados : 4 | |
| 1. |  | MOTA, R.R.; TEMPELMAN, R.J.; FERNANDO F CARDOSO; AGUILAR, I.; LOPES, P.S. Genomic wide-selection for tick resistance in Hereford and Braford cattle via reaction norm models. Volume Species Breeding: Beef cattle, 235. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.235. 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...| Biblioteca(s): INIA Las Brujas. |
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| 2. | | MOTTA, R. R.; SILVA, F. F.; LOPES, P. S.; TEMPELMAN, R. J.; SOLLERO, B. P.; AGUILAR, I.; CARDOSO, F. F. Analyses of reaction norms reveal new chromosome regions associated with tick resistance in cattle. Animal, 2018, volume 12, Issue 2, pages 205-214. OPEN ACCESS. doi: https://doi.org/10.1017/S1751731117001562 Article history: Received 12 December 2016; Accepted 22 May 2017; Published online: 13 July 2017.
Corresponding author: R.R. Mota, Gembloux Agro-Bio Tech Faculty, TERRA Teaching and Research Centre, University of Liège, B-5030 Gembloux,...| Biblioteca(s): INIA Las Brujas. |
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| 3. | | SILVA, D.A.; COSTA, C.N.; SILVA, A.A.; SILVA, H.T.; LOPES, P.S.; SILVA, F.F.; VERONEZE, R.; THOMPSON, G.; AGUILAR, I.; CARVALHEIRA, J. Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle. Journal of Animal Breeding and Genetics, 1 May 2020, Volume 137, Issue 3, Pages 305-315. Doi: https://doi.org/10.1111/jbg.12459 Article history: Received: 10 July 2019 / Revised: 31 October 2019 / Accepted: 3 November 2019 / First published: 08 December 2019.
Funding information: The authors acknowledge the Brazilian Holstein Cattle Breeders Association (ABCBRH)...| Biblioteca(s): INIA Las Brujas. |
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| 4. | | MOTA, R. R.; LOPES, P. S.; TEMPELMAN, R. J.; SILVA, F. F.; AGUILAR, I.; GOMES, C. C. G.; CARDOSO, F. F. Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models. Journal of Animal Science, May 2016, Volume 94, Issue 5, Pages 1834 - 1843. Article history: Received December 11, 2015. // Accepted March 10, 2016.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 4 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
11/12/2018 |
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Actualizado : |
06/02/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
MOTA, R. R.; LOPES, P. S.; TEMPELMAN, R. J.; SILVA, F. F.; AGUILAR, I.; GOMES, C. C. G.; CARDOSO, F. F. |
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Afiliación : |
R. R. MOTA, Animal Science Department, Federal University of Viçosa, Brazil; P. S. LOPES, Animal Science Department, Federal University of Viçosa, Viçosa, Brazil; R. J. TEMPELMAN, Animal Science Department, Michigan State University, United States; F. F. SILVA, Animal Science Department, Federal University of Viçosa, Brazil; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; C. C. G. GOMES, Embrapa South Livestock, Brazil; F. F. CARDOSO, eAnimal Science Department, Federal University of Pelotas, Brazil. |
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Título : |
Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Journal of Animal Science, May 2016, Volume 94, Issue 5, Pages 1834 - 1843. |
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ISSN : |
0021-8812 |
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DOI : |
10.2527/jas.2015-0194 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received December 11, 2015. // Accepted March 10, 2016. |
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Contenido : |
ABSTRACT.
Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold K-means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02), 0.67 (SE 0.02), and 0.66 (SE 0.02) for ABLUP, HBLUP, HLRNM, and ALRNM, respectively. For 5-fold random partitioning, HLRNM (0.71 ± 0.01) was statistically different from ABLUP (0.67 ± 0.01). However, no statistical significance was reported when considering HBLUP (0.70 ± 0.01) and ALRNM (0.70 ± 0.01). Our results suggest that SNP marker information does not lead to higher prediction accuracies in reaction norm models. Furthermore, these accuracies decreased as the tick infestation level increased and as the relationship between animals in training and validation data sets decreased.
© 2016 American Society of Animal Science. All rights reserved. MenosABSTRACT.
Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold K-means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02)... Presentar Todo |
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Palabras claves : |
ACCURACY; CROSS-VALIDATION; GENETIC CORRELATION; HERITABILITY. |
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Asunto categoría : |
-- |
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URL : |
https://ainfo.inia.uy/digital/bitstream/item/12162/1/mota2016.pdf
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Marc : |
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022 $a0021-8812
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100 1 $aMOTA, R. R.
245 $aGenome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models.$h[electronic resource]
260 $c2016
500 $aArticle history: Received December 11, 2015. // Accepted March 10, 2016.
520 $aABSTRACT. Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold K-means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02), 0.67 (SE 0.02), and 0.66 (SE 0.02) for ABLUP, HBLUP, HLRNM, and ALRNM, respectively. For 5-fold random partitioning, HLRNM (0.71 ± 0.01) was statistically different from ABLUP (0.67 ± 0.01). However, no statistical significance was reported when considering HBLUP (0.70 ± 0.01) and ALRNM (0.70 ± 0.01). Our results suggest that SNP marker information does not lead to higher prediction accuracies in reaction norm models. Furthermore, these accuracies decreased as the tick infestation level increased and as the relationship between animals in training and validation data sets decreased. © 2016 American Society of Animal Science. All rights reserved.
653 $aACCURACY
653 $aCROSS-VALIDATION
653 $aGENETIC CORRELATION
653 $aHERITABILITY
700 1 $aLOPES, P. S.
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700 1 $aSILVA, F. F.
700 1 $aAGUILAR, I.
700 1 $aGOMES, C. C. G.
700 1 $aCARDOSO, F. F.
773 $tJournal of Animal Science, May 2016, Volume 94, Issue 5, Pages 1834 - 1843.
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