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| Registros recuperados : 987 | |
| 602. |  | PEREYRA, S.; CASTRO, M.; VERO, S.; SILVA, P.; CAL, A.; TISCORNIA, G.; GONZALEZ, N.; BENTOS, D.; ALVAREZ, W.; RABAZA, S.; SEVILLANO, L.; BRANCATTI, G.; FRANCIA, C.; RAFFO, M.A.; GERMAN, S.; PÉREZ, C.; GARMENDIA, G.; PAREJA, L.; RODRÍGUEZ, A.; PENDAS, C.; QUINCKE, M.; VÁZQUEZ, D.; RODRIGUEZ, M. Fusariosis de la espiga en trigo y micotoxinas asociadas: contribuyendo a reducir su riesgo. Cultivos. Revista INIA Uruguay, Diciembre 2023, no.75 p.54-58. (Revista INIA; 75).| Biblioteca(s): INIA Las Brujas. |
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| 603. | | QUINCKE, M.; GERMAN, S.; PEREYRA, S.; VÁZQUEZ, D.; SILVA, P. Hitos y perspectivas del mejoramiento genético de trigo en Uruguay.[Presentación oral]. 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: presentaciones; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014.| Biblioteca(s): INIA La Estanzuela. |
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| 605. | | BERGER, A.; GASO, D.; CALISTRO, R.; MORALES, X. Limitantes ambientales y potencial de rendimiento de trigo en Uruguay. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 112-123. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 606. | | FASSANA , C.N.; HOFFMAN , E.M.; BERGER, A.; ERNST, O. Nitrogen nutrition index at GS 3.3 is an effective tool to adjust nitrogen required to reach attainable wheat yield. [El índice de nutrición nitrogenada en GS 3.3 es una herramienta eficaz para ajustar el nitrógeno necesario para lograr el rendimiento de trigo alcanzable]. [O índice de nutrição de nitrogênio no GS 3.3 é uma ferramenta eficaz para ajustar o nitrogênio necessário para alcançar a produtividade de trigo atingível]. Plant production. Agrociencia Uruguay, 2022, Vol.26, number 2, e924. https://doi.org/10.31285/AGRO.26.924 -- OPEN ACCESS. Article history: Received 8 Jul 2021; Accepted 21 Jun 2022; Published 30 Aug 2022. -- Correspondence: Nicolás Fassana, fassana@fagro.edu.uy -- Editor: José A. Terra,
Instituto Nacional de Investigación Agropecuaria (INIA), Treinta y Tres,...| Biblioteca(s): INIA Las Brujas. |
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| 607. | | QUINCKE, M.; PETERSON, C.J.; ZEMETRA, R.S.; HANSEN, J.L.; CHEN, J.; RIERA-LIZARAZU, O.; MUNDT, C.C. Quantitative trait loci analysis for resistance to cephalosporium stripe, a vascular wilt disease of wheat. Theoretical and Applied Genetics, 2011, v.122, No.7, p.1339-1349. Article history: Received: 20 August 2010 / Accepted: 6 January 2011 / Published online: 23 January 2011.| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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| 609. | | VÁZQUEZ, D. Aptitud industrial de trigo. Montevideo (Uruguay): INIA, 2009. 46 p. (INIA Serie Técnica ; 177)| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó; INIA Treinta y Tres. |
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| 610. | | ERNST, O.; ESCUDERO, J.; VÁZQUEZ, D.; CADENAZZI, M.; CASTRO, M.; GONZÁLEZ, N.; LARRAMENDI, S.; BENTANCUR, O.; SUBURU, G.; GODIÑO, M. Caracterización de la calidad industrial de variedades de trigo en Uruguay Montevideo (UY): INIA, 2012. 40 p. (Serie FPTA-INIA; 37) Proyecto FPTA 231: Caracterización de la calidad industrial de variedades de trigo sembradas para fabricación de harinas en Uruguay. Período de Ejecución: Nov. 2006-Abr. 2009| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
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| 612. | | CASTRO, M.; VÁZQUEZ, D.; MORALES, M.; CASTRO, B. 4. Resultados experimentales. In: Resultados experimentales de la evaluación nacional de cultivares de trigo calidad industrial: período 2017. La Estanzuela (UY): INIA; INASE, 2018. p. 11-36. Editado por el Equipo de Evaluación de Cultivares. Impreso por Unidad de Comunicación y Transferencia de Tecnología, INIA La Estanzuela. Convenio INASE-INIA.| Biblioteca(s): INIA La Estanzuela. |
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| 615. | | GERMAN, S.; PEREYRA, S.; DIAZ DE ACKERMANN, M.; SILVA, P.; QUINCKE, M.; VÁZQUEZ, D. Mejoramiento por resistencia a enfermedades de trigo en Uruguay.[Presentación oral]. 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: presentaciones; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. p. 45.| Biblioteca(s): INIA La Estanzuela. |
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| 619. | | BRANDARIZ , S.; GONZÁLEZ RAYMÚNDEZ, A.; LADO, B.; MALOSETTI, M.; FRANCO GARCIA, A.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS,I.; DEL POZO, A.; CASTRO, A.J.; GUTIÉRREZ, L. Ascertainment bias from imputation methods evaluation in wheat. BMC Genomics, 2016, v. 17, p.773. OPEN ACCESS. Article history: Received 2016 Feb 24 // Accepted 2016 Sep 23.| Biblioteca(s): INIA La Estanzuela. |
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| 620. | | GAO, L.; KOO, D.H.; JULIANA, P.; RIFE, T.; SINGH, D.; CRISTIANO LEMES DA SILVA; LUX, T.; DORN, K.M.; CLINESMITH, M.; SILVA, P.; WANG, X.; SPANNAGL, M.; MONAT, C.; FRIEBE, B.; STEUERNAGEL, B.; MUEHLBAUER, G.J.; WALKOWIAK, S.; POZNIAK, C.; SINGH, R.; STEIN, N.; MASCHER, M.; FRITZ, A.; POLAND, J. The Aegilops ventricosa 2N v S segment in bread wheat: cytology, genomics and breeding. Theoretical and Applied Genetics, volume 134, pag. 529?542, feb 2021. Open Access. Doi: https://doi.org/10.1007/s00122-020-03712-y Article history:Received: 22 June 2020 / Accepted: 17 October 2020/ Published:12 November 2020/ Issue Date:February 2021| Biblioteca(s): INIA La Estanzuela. |
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| Registros recuperados : 987 | |
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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 da... 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 04362nam 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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