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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
21/12/2023 |
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Actualizado : |
21/12/2023 |
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Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
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Autor : |
CAZZULI, F.; DURANTE, M.; HIRIGOYEN, A.; SÁNCHEZ, J.; ROVIRA, P.J.; BERETTA, V.; SIMEONE, A.; JAURENA, M.; SAVIAN, J.V.; POPPI, D.; MONTOSSI, F.; LAGOMARSINO, X.; LUZARDO, S.; BRITO, G.; VELAZCO, J.I.; LATTANZI, F.; BREMM, C. |
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Afiliación : |
FIORELLA CARLA CAZZULI ALBA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTÍN DURANTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; INTA, Concepción del Uruguay, Argentina; ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JAVIER SÁNCHEZ, University of Prince Edward Island, Canadá; PABLO JUAN ROVIRA SANZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VIRGINIA BERETTA, UPIC/Fagro/Udelar, Uruguay; ÁLVARO SIMEONE, UPIC/Fagro/Udelar, Uruguay; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JEAN VICTOR SAVIAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DENNIS POPPI, University of Queensland, Australia; FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; XIMENA MARIA LAGOMARSINO LARRIERA, FCA-UDE, Uruguay; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO WALTER BRITO DIAZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ IGNACIO VELAZCO DE LOS REYES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO A. LATTANZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA BREMM, URFGS, Brasil. |
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Título : |
Dinámica de la respuesta a la suplementación invernal de bovinos en crecimiento sobre campo natural. |
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Complemento del título : |
Producción animal. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Revista INIA Uruguay, Diciembre 2023, no.75 p.12-16. |
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Serie : |
(Revista INIA; 75). |
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ISSN : |
1510-9011 |
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Idioma : |
Español |
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Notas : |
*Grados.día: es una forma de estimar la temperatura acumulada, como la sumatoria de la diferencia entre la temperatura promedio de cada día y una
temperatura llamada "base", que en el caso de este estudio fue 0 °C. El día inicial de la suma corresponde al inicio de la suplementación, a inicios de
invierno, que en nuestro estudio era variable según el año evaluado. |
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Contenido : |
El análisis de la dinámica de la respuesta en desempeño animal a la suplementación invernal sobre campo natural, en una amplia serie de
experimentos, reveló marcadas diferencias en su fase inicial. Específicamente, la presencia o no de un período de entre 400 y 800 grados.día* durante el cual la suplementación no mejora el desempeño animal y que, por ende, impacta negativamente en la eficiencia. En parte, estas diferencias están asociadas a la disponibilidad de forraje, la tasa de sustitución y el clima. |
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Palabras claves : |
PASTOREO DE CAMPO NATURAL; SISTEMA GANADERO EXTENSIVO - INIA. |
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Thesagro : |
PASTOREO; SUPLEMENTACION INVERNAL. |
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Asunto categoría : |
L02 Alimentación animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17447/1/Revista-INIA-75-dic-2023-4.pdf
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Marc : |
LEADER 01984naa a2200397 a 4500
001 1064409
005 2023-12-21
008 2023 bl uuuu u00u1 u #d
022 $a1510-9011
100 1 $aCAZZULI, F.
245 $aDinámica de la respuesta a la suplementación invernal de bovinos en crecimiento sobre campo natural.$h[electronic resource]
260 $c2023
490 $a(Revista INIA; 75).
500 $a*Grados.día: es una forma de estimar la temperatura acumulada, como la sumatoria de la diferencia entre la temperatura promedio de cada día y una temperatura llamada "base", que en el caso de este estudio fue 0 °C. El día inicial de la suma corresponde al inicio de la suplementación, a inicios de invierno, que en nuestro estudio era variable según el año evaluado.
520 $aEl análisis de la dinámica de la respuesta en desempeño animal a la suplementación invernal sobre campo natural, en una amplia serie de experimentos, reveló marcadas diferencias en su fase inicial. Específicamente, la presencia o no de un período de entre 400 y 800 grados.día* durante el cual la suplementación no mejora el desempeño animal y que, por ende, impacta negativamente en la eficiencia. En parte, estas diferencias están asociadas a la disponibilidad de forraje, la tasa de sustitución y el clima.
650 $aPASTOREO
650 $aSUPLEMENTACION INVERNAL
653 $aPASTOREO DE CAMPO NATURAL
653 $aSISTEMA GANADERO EXTENSIVO - INIA
700 1 $aDURANTE, M.
700 1 $aHIRIGOYEN, A.
700 1 $aSÁNCHEZ, J.
700 1 $aROVIRA, P.J.
700 1 $aBERETTA, V.
700 1 $aSIMEONE, A.
700 1 $aJAURENA, M.
700 1 $aSAVIAN, J.V.
700 1 $aPOPPI, D.
700 1 $aMONTOSSI, F.
700 1 $aLAGOMARSINO, X.
700 1 $aLUZARDO, S.
700 1 $aBRITO, G.
700 1 $aVELAZCO, J.I.
700 1 $aLATTANZI, F.
700 1 $aBREMM, C.
773 $tRevista INIA Uruguay, Diciembre 2023, no.75 p.12-16.
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
29/09/2014 |
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Actualizado : |
25/10/2017 |
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Tipo de producción científica : |
Poster |
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Autor : |
BRANDARIZ, S.P.; GONZÁELZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO LINDNER, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; MARTIN CONRADO QUINCKE WALDEN, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; JARISLAV RAMON VON ZITZEWITZ VON SALVIATI, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; MARINA CASTRO DERENYI, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Effect of using imputed missing data on QTL detection on a wheat GWAS panel. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: posters; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. |
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Páginas : |
p. 86. |
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Idioma : |
Inglés |
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Contenido : |
Molecular markers are an essential component of plant and animal breeding programs. One inexpensive way of obtaining molecular markers is through Next-Generation Sequencing (NGS). Genotyping-by-sequencing (GBS) is one of the NGS techniques which have been successfully used for complex genomes like wheat. A particularity of GBS is that it generates a lot of missing information which is generally imputed. Imputation is required for Genomic Prediction studies and several studies demonstrate its value. However, the effectiveness of missing data imputation for Genome-wide association (GWAS) studies has not been demonstrated. Data imputation for GWAS where one marker at a time is being studied could potentially create biased estimates. The aim of this study was to compare the effects of using either missing or imputed data for Quantitative Trait Loci (QTL) detection in a wheat GWAS pannel. A set of 384 advanced lines of wheat was included in this study consisting of 186 genotypes from INIA (Instituto Nacional de Investigación Agropecuaria) in Uruguay, 55 genotypes from INIA in Chile and 143 genotypes from CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo). SNPs were obtained using the Tassel-GBS Pipeline. We excluded SNPs with more than 50 % missing data and SNPs with a minor allele frequency (MAF) more extreme than 10%. Sequence database available from the SyntheticxOpata map (synop) was used to construct the maps, obtaining a final data set with more than 18K SNPs. Missing data was handled in three different ways to create the SNP datasets used for QTL detection: 1) data not-imputed, 2) data imputed by the realized relationship matrix method multivariate normal expectation maximization (MVN-EM), and 3) data imputed by the mean. A number of QTL (either 25 or 50) with different heritability (0.2 and 0.7) were simulated on top of each dataset. The following mixed model was used to recover QTL: , where : phenotypic vector, : SNPs matrix, : unknown vector of allele effects to be estimated, : matrix that relates each measurement to population origin, : populations vector, : kinship matrix, : vector of random background polygenic effects, and : residual error. We used a liberal 0.01 significance level. The power to detect QTL was estimated for each dataset and differences among medians of QTL detection power among imputed datasets were studied using the Friedman test and non-parametric contrasts. For this purpose, methods of imputations were defined as treatments and simulation scenarios as blocks. The QTL detection power with the MVN-EM matrix was lower than with the mean imputed matrix or the no imputed matrix. No differences in QTL detection power were found between the mean imputed matrix or the no imputed matrix. Based on our results, imputing does not seem to improve QTL detection power. MenosMolecular markers are an essential component of plant and animal breeding programs. One inexpensive way of obtaining molecular markers is through Next-Generation Sequencing (NGS). Genotyping-by-sequencing (GBS) is one of the NGS techniques which have been successfully used for complex genomes like wheat. A particularity of GBS is that it generates a lot of missing information which is generally imputed. Imputation is required for Genomic Prediction studies and several studies demonstrate its value. However, the effectiveness of missing data imputation for Genome-wide association (GWAS) studies has not been demonstrated. Data imputation for GWAS where one marker at a time is being studied could potentially create biased estimates. The aim of this study was to compare the effects of using either missing or imputed data for Quantitative Trait Loci (QTL) detection in a wheat GWAS pannel. A set of 384 advanced lines of wheat was included in this study consisting of 186 genotypes from INIA (Instituto Nacional de Investigación Agropecuaria) in Uruguay, 55 genotypes from INIA in Chile and 143 genotypes from CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo). SNPs were obtained using the Tassel-GBS Pipeline. We excluded SNPs with more than 50 % missing data and SNPs with a minor allele frequency (MAF) more extreme than 10%. Sequence database available from the SyntheticxOpata map (synop) was used to construct the maps, obtaining a final data set with more than 18K SNPs. Mi... Presentar Todo |
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Palabras claves : |
GBS; GENOMIC PREDICTION; GENOMIC WIDE ASSOCIATION; GENOTYPING BY SEQUENCING; GWAS; MARCADORES MOLECULARES; MULTIVARIATE NORMAL EXPECTATION MAXIMIZATION; MVN-EM; NEXT GENERATION SEQUENCING; NGS; QTL; QUANTITATIVE TRAIT LOCI DETECTION; SINGLE NUCLEOTIDE POLYMORPHISMS; SNPs; TRITICUM. |
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Thesagro : |
DETECCIÓN DE QTLS; MARCADORES MOLECULARES; TRIGO. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 04260nam a2200433 a 4500
001 1050639
005 2017-10-25
008 2014 bl uuuu u00u1 u #d
100 1 $aBRANDARIZ, S.P.
245 $aEffect of using imputed missing data on QTL detection on a wheat GWAS panel.
260 $aIn: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: posters; resúmenes. La Estanzuela, Colonia, UY: INIA$c2014
300 $ap. 86.
520 $aMolecular markers are an essential component of plant and animal breeding programs. One inexpensive way of obtaining molecular markers is through Next-Generation Sequencing (NGS). Genotyping-by-sequencing (GBS) is one of the NGS techniques which have been successfully used for complex genomes like wheat. A particularity of GBS is that it generates a lot of missing information which is generally imputed. Imputation is required for Genomic Prediction studies and several studies demonstrate its value. However, the effectiveness of missing data imputation for Genome-wide association (GWAS) studies has not been demonstrated. Data imputation for GWAS where one marker at a time is being studied could potentially create biased estimates. The aim of this study was to compare the effects of using either missing or imputed data for Quantitative Trait Loci (QTL) detection in a wheat GWAS pannel. A set of 384 advanced lines of wheat was included in this study consisting of 186 genotypes from INIA (Instituto Nacional de Investigación Agropecuaria) in Uruguay, 55 genotypes from INIA in Chile and 143 genotypes from CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo). SNPs were obtained using the Tassel-GBS Pipeline. We excluded SNPs with more than 50 % missing data and SNPs with a minor allele frequency (MAF) more extreme than 10%. Sequence database available from the SyntheticxOpata map (synop) was used to construct the maps, obtaining a final data set with more than 18K SNPs. Missing data was handled in three different ways to create the SNP datasets used for QTL detection: 1) data not-imputed, 2) data imputed by the realized relationship matrix method multivariate normal expectation maximization (MVN-EM), and 3) data imputed by the mean. A number of QTL (either 25 or 50) with different heritability (0.2 and 0.7) were simulated on top of each dataset. The following mixed model was used to recover QTL: , where : phenotypic vector, : SNPs matrix, : unknown vector of allele effects to be estimated, : matrix that relates each measurement to population origin, : populations vector, : kinship matrix, : vector of random background polygenic effects, and : residual error. We used a liberal 0.01 significance level. The power to detect QTL was estimated for each dataset and differences among medians of QTL detection power among imputed datasets were studied using the Friedman test and non-parametric contrasts. For this purpose, methods of imputations were defined as treatments and simulation scenarios as blocks. The QTL detection power with the MVN-EM matrix was lower than with the mean imputed matrix or the no imputed matrix. No differences in QTL detection power were found between the mean imputed matrix or the no imputed matrix. Based on our results, imputing does not seem to improve QTL detection power.
650 $aDETECCIÓN DE QTLS
650 $aMARCADORES MOLECULARES
650 $aTRIGO
653 $aGBS
653 $aGENOMIC PREDICTION
653 $aGENOMIC WIDE ASSOCIATION
653 $aGENOTYPING BY SEQUENCING
653 $aGWAS
653 $aMARCADORES MOLECULARES
653 $aMULTIVARIATE NORMAL EXPECTATION MAXIMIZATION
653 $aMVN-EM
653 $aNEXT GENERATION SEQUENCING
653 $aNGS
653 $aQTL
653 $aQUANTITATIVE TRAIT LOCI DETECTION
653 $aSINGLE NUCLEOTIDE POLYMORPHISMS
653 $aSNPs
653 $aTRITICUM
700 1 $aGONZÁELZ-REYMÚNDEZ, A.
700 1 $aLADO, B.
700 1 $aQUINCKE, M.
700 1 $aVON ZITZEWITZ, J.
700 1 $aCASTRO, M.
700 1 $aMATUS, I.
700 1 $aDEL POZO, A.
700 1 $aGUTIÉRREZ, L.
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