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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Salto Grande; INIA Tacuarembó; INIA Treinta y Tres. |
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Fecha : |
21/02/2014 |
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Actualizado : |
29/10/2015 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
RIOS, A.; FAGGI, N.; SCREMINI, G. |
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Afiliación : |
AMALIA RIOS GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NICOLÁS FAGGI REAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUILLERMO SCREMINI SANGUINETTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Control de gramilla (Cynodon dactylon) en sistemas pastoriles con aplicaciones de glifosato. |
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Fecha de publicación : |
1997 |
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Fuente / Imprenta : |
ln: CONGRESO NACIONAL DE INGENIERÍA AGRONÓMICA, 7.; JORNADA DE SIEMBRA DIRECTA, 1997, MONTEVIDEO, UY. Compendio de trabajos presentados. Montevideo, UY: AIA, 1997. |
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Páginas : |
p. 137-138. |
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Idioma : |
Español |
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Palabras claves : |
AVENA BYZANTINA; AVENA CV LE 1095; CONTROL INTEGRADO DE MALEZAS; CYNDA; FERTILIZACIÓN NITROGENADA EN CONTROL DE GRAMILLA; INTERFERENCIA MALEZA PASTURA; LOLIUM MULTIFLORUM; PRODUCCIÓN DE VERDEO INVERNAL; RAIGRÁS CV LE 284; RENDIMIENTO DE MOHA; TRÉBOL BLANCO CV ZAPICÁN; TRÉBOL ROJO CV 116. |
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Thesagro : |
CYNODON DACTYLON; ESCARDA; GRAMILLA; LUCHA INTEGRADA; MOHA; SETARIA ITALICA; SIEMBRA DIRECTA; TRIFOLIUM PRATENSE; TRIFOLIUM REPENS; URUGUAY. |
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Asunto categoría : |
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H60 Malezas y escardas |
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Marc : |
LEADER 01308naa a2200409 a 4500
001 1046505
005 2015-10-29
008 1997 bl uuuu u00u1 u #d
100 1 $aRIOS, A.
245 $aControl de gramilla (Cynodon dactylon) en sistemas pastoriles con aplicaciones de glifosato.
260 $c1997
300 $ap. 137-138.
650 $aCYNODON DACTYLON
650 $aESCARDA
650 $aGRAMILLA
650 $aLUCHA INTEGRADA
650 $aMOHA
650 $aSETARIA ITALICA
650 $aSIEMBRA DIRECTA
650 $aTRIFOLIUM PRATENSE
650 $aTRIFOLIUM REPENS
650 $aURUGUAY
653 $aAVENA BYZANTINA
653 $aAVENA CV LE 1095
653 $aCONTROL INTEGRADO DE MALEZAS
653 $aCYNDA
653 $aFERTILIZACIÓN NITROGENADA EN CONTROL DE GRAMILLA
653 $aINTERFERENCIA MALEZA PASTURA
653 $aLOLIUM MULTIFLORUM
653 $aPRODUCCIÓN DE VERDEO INVERNAL
653 $aRAIGRÁS CV LE 284
653 $aRENDIMIENTO DE MOHA
653 $aTRÉBOL BLANCO CV ZAPICÁN
653 $aTRÉBOL ROJO CV 116
700 1 $aFAGGI, N.
700 1 $aSCREMINI, G.
773 $tln: CONGRESO NACIONAL DE INGENIERÍA AGRONÓMICA, 7.; JORNADA DE SIEMBRA DIRECTA, 1997, MONTEVIDEO, UY. Compendio de trabajos presentados. Montevideo, UY: AIA, 1997.
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Registro original : |
INIA La Estanzuela (LE) |
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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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