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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 : |
09/09/2014 |
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Actualizado : |
23/10/2019 |
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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 : |
LOURENCO, D.A.L.; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; EZRA, E.; RON, M.; SHIRAK, A.; WELLER, J.I. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2014, v.97, no.3, p.1742-1752. OPEN ACCESS. |
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ISSN : |
0022-0302 |
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DOI : |
10.3168/jds.2013-6916 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received September 10, 2013. / Accepted December 6, 2013. |
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Contenido : |
ABSTRACTS.
Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R2) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R2 and regression coefficients was observed from parities 1 through 3. Fat and protein yields had the lowest R2 for all the methods. On average, R2 was lowest for parent averages, followed by GBLUP, BayesC, ssGBLUP, and WssGBLUP. For some traits, R2 for direct genomic values from BayesC and GBLUP were lower than those for parent averages. Genomic estimated breeding values using ssGBLUP were the least biased, and this method appears to be a suitable tool for genomic evaluation of a small genotyped population, as it automatically accounts for parental index, allows for inclusion of female genomic information without preadjustments in evaluations, and uses the same model as in traditional evaluations. Weighted ssGBLUP has the potential for higher evaluation accuracy.
© 2014 American Dairy Science Association. MenosABSTRACTS.
Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R2) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R2 and regression coe... Presentar Todo |
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Thesagro : |
MEJORAMIENTO GENÉTICO ANIMAL; MODELOS MATEMÁTICOS; SELECCIÓN DE GENOTIPOS; SELECCIÓN GENÓMICA. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3043/1/Aguilar-I.-2014-Jr.Dairy-Sci.-v.973-p.1742-1752.pdf
https://www.journalofdairyscience.org/article/S0022-0302(14)00052-6/pdf
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Marc : |
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100 1 $aLOURENCO, D.A.L.
245 $aMethods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses.$h[electronic resource]
260 $c2014
500 $aArticle history: Received September 10, 2013. / Accepted December 6, 2013.
520 $aABSTRACTS. Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R2) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R2 and regression coefficients was observed from parities 1 through 3. Fat and protein yields had the lowest R2 for all the methods. On average, R2 was lowest for parent averages, followed by GBLUP, BayesC, ssGBLUP, and WssGBLUP. For some traits, R2 for direct genomic values from BayesC and GBLUP were lower than those for parent averages. Genomic estimated breeding values using ssGBLUP were the least biased, and this method appears to be a suitable tool for genomic evaluation of a small genotyped population, as it automatically accounts for parental index, allows for inclusion of female genomic information without preadjustments in evaluations, and uses the same model as in traditional evaluations. Weighted ssGBLUP has the potential for higher evaluation accuracy. © 2014 American Dairy Science Association.
650 $aMEJORAMIENTO GENÉTICO ANIMAL
650 $aMODELOS MATEMÁTICOS
650 $aSELECCIÓN DE GENOTIPOS
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700 1 $aEZRA, E.
700 1 $aRON, M.
700 1 $aSHIRAK, A.
700 1 $aWELLER, J.I.
773 $tJournal of Dairy Science, 2014$gv.97, no.3, p.1742-1752. OPEN ACCESS.
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