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| 1. |  | VEROCAI, M.; CASTRO, A.; HOFFMAN, E.; CASTRO, M. Evaluación Nacional de Cultivares, fuente de información para evaluar el impacto de años desfavorables. [presentación oral] En: Jornada Nacional de Cultivos de Invierno, 3ra. Canal You Tube: CREA Uruguay, 11-12 abril 2023. Organizaron: MNECCUY (Mesa Nacional de Entidades de Cebada Cervecera), CREA, MTO (Mesa Tecnológica de Oleaginosos), Mesa Nacional de Trigo. -- Coorganizaron: Facultad de Agronomía, INIA. -- Declarada de interés Ministerial: Ministerio de...| Biblioteca(s): INIA Las Brujas. |
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| 2. | | VEROCAI, M.; CASTRO, M.; MANASLISKI, S.; MAZZILLI, S.R. Frost risk in canola and carinata as a function of sowing date in the agricultural central region of South America. Agronomy Journal,2022, volume 114, issue 5, pages 2920-2935. doi: https://doi.org/10.1002/agj2.21154 Article history: Received: 23 August 2021/ Accepted: 10 June 2022. -- Corresponding author: Mazzilli, S.R.; Facultad de Agronomía, Estación Experimental Mario Alberto Cassinoni, Univ. de la República, Ruta 3, km 363, Paysandú, Uruguay;...| Biblioteca(s): INIA La Estanzuela. |
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| 3. | | VEROCAI, M.; BARAIBAR, S.; CAMMAROTA, L.; CARDOZO, F.; GERMAN, S.; GUTIÉRREZ, L.; LOCATELLI, A.; CASTRO, F.; CASTRO, A. Genome-wide association mapping in a nested population representative of elite breeding in Uruguay. 160. (resúmen) Áreas temáticas: Genética. In: Physiological Mini Reviews, 2022, volume 15, Special Issue: III (3er) Congreso Nacional de Biociencias Octubre 2022, Montevideo, Uruguay. p.152-153. Resumen publicado en las jornadas de BIOCIENCIAS: II Jornadas Binacionales Argentina-Uruguay; III Congreso Nacional 2022 "Ciencia para el desarrollo sustentable".| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 3 | |
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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
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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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