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Biblioteca (s) : |
INIA Tacuarembó; INIA Treinta y Tres. |
Fecha : |
08/04/2015 |
Actualizado : |
03/02/2018 |
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
Autor : |
DEL CAMPO, M.; BRITO, G.; DE OLIVEIRA COSTA, F.; VERGARA, E.; ANCHAÑO, E.; FRUGONI, J.; BOTTERO, S.; LEVRATTO, J.; RODRIGUEZ, H.; HERNANDEZ, S.; ESCAYOLA, A.; OLIVERA, P. |
Afiliación : |
MARCIA DEL CAMPO GIGENA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO WALTER BRITO DIAZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; F. DE OLIVERA COSTA; E. VERGARA; E. ANCHAÑO; JULIO CESAR FRUGONI SILVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SERGIO DANIEL BOTTERO REGGI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN CARLOS LEVRATTO CORTES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; H. RODRIGUEZ; SANTIAGO RAFAEL HERNANDEZ VILA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALVARO GONZALO ESCAYOLA FERREIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; P. OLIVERA. |
Título : |
II. Efecto del manejo previo a la faena sobre el bienestar animal y la calidad de producto. Año 2. |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
In: BERRETTA, E.; MONTOSSI, F.; BRITO, G. (Ed.). Alternativas tecnológicas para los sistemas ganaderos del basalto. Montevideo, UY: INIA, 2014. |
Páginas : |
p. 539-554 |
Serie : |
(Serie Técnica; 217) |
ISSN : |
1688-9266 |
Idioma : |
Español |
Palabras claves : |
CORTISOL; TRANSPORTE Y FAENA. |
Thesagro : |
BIENESTAR ANIMAL. |
Asunto categoría : |
-- L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/4260/1/ST-217P539-554.pdf
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Marc : |
LEADER 00957naa a2200313 a 4500 001 1052452 005 2018-02-03 008 2014 bl uuuu u00u1 u #d 022 $a1688-9266 100 1 $aDEL CAMPO, M. 245 $aII. Efecto del manejo previo a la faena sobre el bienestar animal y la calidad de producto. Año 2. 260 $c2014 300 $ap. 539-554 490 $a(Serie Técnica; 217) 650 $aBIENESTAR ANIMAL 653 $aCORTISOL 653 $aTRANSPORTE Y FAENA 700 1 $aBRITO, G. 700 1 $aDE OLIVEIRA COSTA, F. 700 1 $aVERGARA, E. 700 1 $aANCHAÑO, E. 700 1 $aFRUGONI, J. 700 1 $aBOTTERO, S. 700 1 $aLEVRATTO, J. 700 1 $aRODRIGUEZ, H. 700 1 $aHERNANDEZ, S. 700 1 $aESCAYOLA, A. 700 1 $aOLIVERA, P. 773 $tIn: BERRETTA, E.; MONTOSSI, F.; BRITO, G. (Ed.). Alternativas tecnológicas para los sistemas ganaderos del basalto. Montevideo, UY: INIA, 2014.
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Registro original : |
INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
11/12/2018 |
Actualizado : |
06/02/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
A - 1 |
Autor : |
MOTA, R. R.; LOPES, P. S.; TEMPELMAN, R. J.; SILVA, F. F.; AGUILAR, I.; GOMES, C. C. G.; CARDOSO, F. F. |
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. |
Título : |
Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models. |
Fecha de publicación : |
2016 |
Fuente / Imprenta : |
Journal of Animal Science, May 2016, Volume 94, Issue 5, Pages 1834 - 1843. |
ISSN : |
0021-8812 |
DOI : |
10.2527/jas.2015-0194 |
Idioma : |
Inglés |
Notas : |
Article history: Received December 11, 2015. // Accepted March 10, 2016. |
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 |
Palabras claves : |
ACCURACY; CROSS-VALIDATION; GENETIC CORRELATION; HERITABILITY. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12162/1/mota2016.pdf
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Marc : |
LEADER 03053naa a2200277 a 4500 001 1059370 005 2019-02-06 008 2016 bl uuuu u00u1 u #d 022 $a0021-8812 024 7 $a10.2527/jas.2015-0194$2DOI 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. 700 1 $aTEMPELMAN, R. J. 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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