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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
29/09/2014 |
Actualizado : |
25/10/2017 |
Tipo de producción científica : |
Poster |
Autor : |
BRANDARIZ, S.P.; GONZÁELZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ, L. |
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. |
Título : |
Effect of using imputed missing data on QTL detection on a wheat GWAS panel. |
Fecha de publicación : |
2014 |
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. |
Páginas : |
p. 86. |
Idioma : |
Inglés |
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 |
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. |
Thesagro : |
DETECCIÓN DE QTLS; MARCADORES MOLECULARES; TRIGO. |
Asunto categoría : |
-- |
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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INIA La Estanzuela (LE) |
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
12/11/2015 |
Actualizado : |
12/11/2015 |
Tipo de producción científica : |
Actividades de Difusión |
Autor : |
JORNADA ANUAL ARROZ-SOJA, 1985, CIAAB-INIA, TREINTA Y TRES, UY.; CHEBATAROFF, N.; DEAMBROSI, E.; BLANCO, P.H.; ÁVILA, S.; LAVECCHIA, A.; BLANCO, F.; ZORRILLA DE SAN MARTÍN, G.; ACEVEDO, A. |
Afiliación : |
ENRIQUE GERMAN DEAMBROSI CHURRUT, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIFLOR STELLA ÁVILA SILVA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES PASCUAL LAVECCHIA GONZALEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO GUILLERMO BLANCO BORDÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GONZALO ROBERTO ZORRILLA DE SAN MARTÍN PEREYRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANTONIO MARIA ACEVEDO VEGA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Arroz-soja: resultados experimentales 1984-85. |
Fecha de publicación : |
1985 |
Fuente / Imprenta : |
Treinta y Tres (Uruguay): CIAAB; EEE, 1985 |
Páginas : |
119 p. |
Idioma : |
Español |
Notas : |
Centro de Investigaciones Agrícolas "Alberto Boerger" (CIAAB), Estación Experimental del Este (EEE).
Jornada anual de arroz y soja, setiembre 1985. |
Contenido : |
ARROZ
Mejoramiento: Introducción. Épocas de siembra. Ensayos regionales varietales. Ensayos regionales preliminares. Respuesta de variedades de arroz a épocas de siembra y nitrógeno. Selección en poblaciones segregantes.
Manejo: Ensayos regionales de fertilización. Control de malezas.
Fitopatología: Camas de infección. Identificación de razas fisiológicas de Pyricularia oryzae. Enfermedades del tallo. Patologìa de la semilla de arroz.
SOJA
Mejoramiento. Ensayos varietales.
Fitopatología: Curasemillas.
RIEGO Y DRENAJE
Evaluación del consumo de agua. Evaluación de maíz con riego en suelos arroceros
SERVICIO DE SEMILLAS
Producción de semillas. Encuesta de semillas de arroz |
Thesagro : |
ARROZ; CLIMA; CONTROL DE MALEZAS; ENFERMEDADES DE LAS PLANTAS; FERTILIZACION; FITOMEJORAMIENTO; MALEZAS; MANEJO DEL CULTIVO; SEMILLA; SOJA; VARIEDADES. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5166/1/Arroz-Soja1984-19852.pdf
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Marc : |
LEADER 01811nam a2200361 a 4500 001 1053850 005 2015-11-12 008 1985 bl uuuu u00u1 u #d 100 1 $aJORNADA ANUAL ARROZ-SOJA, 1985, CIAAB-INIA, TREINTA Y TRES, UY. 245 $aArroz-soja$bresultados experimentales 1984-85.$h[electronic resource] 260 $aTreinta y Tres (Uruguay): CIAAB; EEE$c1985 300 $a119 p. 500 $aCentro de Investigaciones Agrícolas "Alberto Boerger" (CIAAB), Estación Experimental del Este (EEE). Jornada anual de arroz y soja, setiembre 1985. 520 $aARROZ Mejoramiento: Introducción. Épocas de siembra. Ensayos regionales varietales. Ensayos regionales preliminares. Respuesta de variedades de arroz a épocas de siembra y nitrógeno. Selección en poblaciones segregantes. Manejo: Ensayos regionales de fertilización. Control de malezas. Fitopatología: Camas de infección. Identificación de razas fisiológicas de Pyricularia oryzae. Enfermedades del tallo. Patologìa de la semilla de arroz. SOJA Mejoramiento. Ensayos varietales. Fitopatología: Curasemillas. RIEGO Y DRENAJE Evaluación del consumo de agua. Evaluación de maíz con riego en suelos arroceros SERVICIO DE SEMILLAS Producción de semillas. Encuesta de semillas de arroz 650 $aARROZ 650 $aCLIMA 650 $aCONTROL DE MALEZAS 650 $aENFERMEDADES DE LAS PLANTAS 650 $aFERTILIZACION 650 $aFITOMEJORAMIENTO 650 $aMALEZAS 650 $aMANEJO DEL CULTIVO 650 $aSEMILLA 650 $aSOJA 650 $aVARIEDADES 700 1 $aCHEBATAROFF, N. 700 1 $aDEAMBROSI, E. 700 1 $aBLANCO, P.H. 700 1 $aÁVILA, S. 700 1 $aLAVECCHIA, A. 700 1 $aBLANCO, F. 700 1 $aZORRILLA DE SAN MARTÍN, G. 700 1 $aACEVEDO, A.
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