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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
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Fecha : |
21/02/2014 |
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Actualizado : |
12/05/2023 |
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Autor : |
MILLOT, J.C.; DURÁN, H.; FERNÁNDEZ, S.; VIOLA, C. |
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Título : |
Consumo y ganancia de peso de cuatro henos diferentes |
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Fecha de publicación : |
1979 |
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Fuente / Imprenta : |
ln: Reunión Técnica, 2 : 1979 Nov 27-29 : Montevideo Resúmenes. Montevideo (Uruguay): Facultad de Agronomía, 1979. |
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Páginas : |
pBC4 |
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Idioma : |
Español |
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Thesagro : |
BOVINAE; CONSUMO DE PIENSOS; EFICIENCIA DE CONVERSION DEL PIENSO; GANANCIA DE PESO; NUTRICION ANIMAL. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 00659naa a2200217 a 4500
001 1011377
005 2023-05-12
008 1979 bl uuuu u00u1 u #d
100 1 $aMILLOT, J.C.
245 $aConsumo y ganancia de peso de cuatro henos diferentes
260 $c1979
300 $apBC4
650 $aBOVINAE
650 $aCONSUMO DE PIENSOS
650 $aEFICIENCIA DE CONVERSION DEL PIENSO
650 $aGANANCIA DE PESO
650 $aNUTRICION ANIMAL
700 1 $aDURÁN, H.
700 1 $aFERNÁNDEZ, S.
700 1 $aVIOLA, C.
773 $tln: Reunión Técnica, 2 : 1979 Nov 27-29 : Montevideo Resúmenes. Montevideo (Uruguay): Facultad de Agronomía, 1979.
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Registro original : |
INIA Las Brujas (LB) |
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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 : |
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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