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Registro completo
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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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Registros recuperados : 16 | |
1. | | CASTRO, M.; VÁZQUEZ, D.; VON ZITZEWITZ, J.; LADO, B. Influence of terminal drought stress on wheat agronomic traits, industrial quality and dehydrin proteins. In: INTERNATIONAL WHEAT CONFERENCE, 8., 2010 , St. Petersburg, RU. Abstracts...: wheat genetics and breeding for abiotic stresses; poster presentations. St. Petersburg, RU: VIR, 2010. p. 157.Biblioteca(s): INIA La Estanzuela. |
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4. | | LADO, B.; MATUS, I.; BELZILE, F.; POLAND, J.; QUINCKE, M.; CASTRO, M.; VON ZITZEWITZ, J. Genotipado por secuenciación en trigo (Triticum aestivum). In: JORNADAS DE LA SOCIEDAD DE BIOQUÍMICA Y BIOLOGÍA MOLECULAR , 8., SIMPOSIO BIOLOGÍA Y BIOTECNOLOGÍA VEGETAL, 11., 2013. Resumen. Montevideo: SBBM, SUB, 2013.Tipo: Abstracts/Resúmenes |
Biblioteca(s): INIA La Estanzuela. |
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6. | | SILVA, P.; LADO, B.; BRANDARIZ, S.; PEREYRA, S.; GERMAN, S.; VON ZITZEWITZ, J.; GUTIÉRREZ, L.; QUINCKE, M. Herramientas utilizadas y avances en mejoramiento molecular en el Programa de Mejoramiento Genético de Trigo de INIA 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. 81.Tipo: Presentaciones Orales |
Biblioteca(s): INIA La Estanzuela. |
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7. | | SILVA, P.; LADO, B.; BRANDARIZ, S.; BERRO, I.; GUTIÉRREZ, L.; PEREYRA, S.; GERMAN, S.; VON ZITZEWITZ, J.; QUINCKE, M. Herramientas utilizadas y avances en mejoramiento molecular en el programa de mejoramiento genético de trigo de Inia 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. 277-285. (INIA Serie Técnica; 241).Tipo: Capítulo en Libro Técnico-Científico |
Biblioteca(s): INIA La Estanzuela. |
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8. | | MORA, F.; CASTILLO, D.; LADO, B.; MATUS, I.; POLAND, J.; BELZILE, F.; VON ZITZEWITZ, J.; DEL POZO, A. Genome-wide association mapping of agronomic traits and carbon isotope discrimination in a worldwide germplasm collection of spring wheat using SNP markers. Molecular Breeding, 2015, v,35, no.2, 12 p.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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9. | | LADO, B.; POLAND, J.; BELZILE, F.; DEL POZO, A.; MATUS, I.; RODRÍGUEZ, A.; INOSTROZA, L.; LOBOS, G.A.; CASTRO, M.; QUINCKE, M.; LANDECHEA, L.; VON ZITZEWITZ, J. Genotipado por secuenciación del genoma de 384 genotipos de T. aestivum para selección genómica. BAG. Journal of Basic and Applied Genetics ,Ciudad Autónoma de Buenos Aires, v.23, supl.1, p. 267, 2012.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : B - 3 |
Biblioteca(s): INIA La Estanzuela. |
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10. | | SILVA, P.; CALVO-SALAZAR, V.; CONDON, F.; QUINCKE, M.; PRITSCH, C.; GUTIÉRREZ, L.; CASTRO, A.; HERRERA-FOESSEL, S.; VON ZITZEWITZ, J.; GERMAN, S. Effects and interactions of genes Lr34, Lr68 and Sr2 on wheat leaf rust adult plant resistance in Uruguay Euphytica, 2015, v. 204, p. 599?608.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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11. | | BRANDARIZ, S.P.; GONZÁELZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ, L. Effect of using imputed missing data on QTL detection on a wheat GWAS panel. 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. p. 86.Biblioteca(s): INIA La Estanzuela. |
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12. | | BRANDARIZ, S.P.; GONZÁLEZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ , L. Effect of using imputed missing data on QTL detection on a wheat GWAS panel. [Poster]. 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. 304. (INIA Serie Técnica; 241).Biblioteca(s): INIA La Estanzuela. |
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13. | | LADO, B.; MATUS, I.; RODRIGUEZ, A.; INOSTROZA, L.; POLAND, J.; BELZILE ,F.; DEL POZO, A.; QUINCKE, M.; CASTRO, M.; VON ZITZEWITZ, J. Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. G3: Genes, Genomes, Genetics (Bethesda), v. 3, n,12, p. 2105-2114, 2013.OPEN ACCESS. Article history: Received 2013 Aug 26 // Accepted 2013 Sep 18.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : B - 5 |
Biblioteca(s): INIA La Estanzuela. |
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14. | | DEL POZO, A.; LOBOS, G.A.; MATUS, I.; TAPIA, G.; YÁÑEZ, A.; INOSTROZA, L.; RODRÍGUEZ, A.; CASTRO, M.; QUINCKE, M.; VON ZITZEWITZ, J.; LADO, B.; ROMERO, S.; ESCOBAR, A. Caracterización de genotipos de trigo harineros de primavera para tolerancia a estrés hídrico. In: CONGRESO AGRONÓMICO DE CHILE, 63., Temuco, Chile, 2012. Resumen. Temuco: SACH. 2012. p. 58.Tipo: Abstracts/Resúmenes |
Biblioteca(s): INIA La Estanzuela. |
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15. | | 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.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 1 |
Biblioteca(s): INIA La Estanzuela. |
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16. | | VON ZITZEWITZ, J.; CUESTA-MARCOS, A.; CONDON, F.; CASTRO, A. J.; CHAO, S.; COREY, A.; FILICHKIN, T.; FISK, S.P.; GUTIERREZ, L.; HAGGARD, K.; KARSAI, I.; MUEHLBAUER, G. J.; SMITH, K.P.; VEISZ, O.; HAYES, P.M. The genetics of winterhardiness in barley: perspectives from genome-wide association mapping. The Plant Genome, v. 4., n,1, p. 76-91, 2011.Tipo: Artículos Indexados |
Biblioteca(s): INIA La Estanzuela. |
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Registros recuperados : 16 | |
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