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| Registros recuperados : 987 | |
| 121. |  | LADO, B.; BATTENFIELD, S.; SILVA, P.; QUINCKE, M.; GUZMAN, C.; SINGH, R.P.; DREISIGACKER, S.; PEÑA, J.; FRITZ, A.; POLAND, J.; GUTIERREZ, L. Comparing strategies to select crosses using genomic prediction in two wheat breeding programs. In: International Wheat Genetics Symposium, 12, Tulln, Austria; April 23-28, 2017; BOKU: University of Natural Resources and Life Sciences, Vienna, Austria. p.88-90.| Biblioteca(s): INIA La Estanzuela. |
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| 124. | | SAINT PIERRE, C.; GHANEM, M.E.; BEN M'BAREK, S.; AZZIMONTI, G.; PEREYRA, S.; MARZA, F.; YAHYAOUI, A.; SINGH, P.; BAUM, M.; BRAUN, H. Global network for precision wheat phenotypin.[Resumen]. In: Latin-American Conference on Plant Phenotyping and Phenomics for Plant Breeding, 2, 2017, São Carlos, SP. Proceedings... São Carlos: Embrapa Instrumentação, 2017. p. 77.| Biblioteca(s): INIA La Estanzuela. |
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| 125. | | AZZIMONTI, G.; GARCIA, R.; GONZALEZ, N.; DOMENIGUINI, V.; CAROLINA SAINT-PIERRE, C.; SINGH, P.K.; QUINCKE, M.; PEREYRA, S.; GERMAN, S. Field-based phenotyping for wheat diseases within a new multiple diseases platform in Uruguay: promoting germplasm sharing to increase resistance diversity. P 309-Topic: Future of Wheat Improvement in Different Parts of the World. In: Buerstmayr, H.; Lang-Mladek, C.; Steiner, B.; Michel, S.; Buerstmayr, M.; Lemmens, M.; Vollmann, J.; Grausgruber, H. (Eds.). Proceedings of the 13th International Wheat Genetics Symposium. Tulln, Austria; April 23-28, 2017. p.485.| Biblioteca(s): INIA La Estanzuela. |
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| 126. | | ESTEVES, P.; MASTROPIERRO, M.; CASTILLO, A.; HERNANDEZ, L.; RODRIGUEZ, M.; DE LEON, W.; PEREIRA, F.; QUINCKE, M. Herramientas biotecnológicas para el mejoramiento genético de cultivos. Revista INIA Uruguay, 2017, no.48, p. 62-66. (Revista INIA; 48)| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
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| 127. | | RAFFO, M.A.; PRITSCH, C.; AZZIMONTI, G.; PEREYRA, S.; QUINCKE, M.; BONNECARRERE, V.; SILVA, P.; CASTRO, A.; LADO, B.; BARAIBAR, S.; PEREYRA, S.; GERMAN, S. Introducción de Fhb1 en germoplasma elite de trigo para incrementar la resistencia a fusariosis de espiga en Uruguay. [p24]. Bloque 2: Mejoramiento por resistencia a enfermedades. In: Sociedad Uruguaya de Fitopatología Jornada Uruguaya de Fitopatología, 4., Jornada Uruguaya de Protección Vegetal, 2., 1° setiembre, 2017, Montevideo, Uruguay. Libro de resúmenes. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2017. p. 44. Financiamiento: Agencia Nacional de Investigación e Innovación (POS_NAC_2016_1_129877).| Biblioteca(s): INIA Las Brujas. |
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| 129. | | ELICHALT, M.; RUSSO, M.; VÁZQUEZ, D.; SUBURÚ, G.; TIHISTA, H.; GODIÑO, M. Lípidos, sodio y fibra dietética en harina de trigo y pan artesanal en Uruguay: aporte nutricional según recomendaciones para distintos grupos de población. [Lipids, sodium and dietary fiber in wheat flour and artisan bread in Uruguay: nutritional intake according to the recommendations for different population groups.] Revista Chilena de Nutrición, 44(1), p. 71-78., 2017. Este trabajo fue recibido el 25 de Mayo de 2016 y aceptado para ser publicado el 16 de Diciembre de 2016.| Biblioteca(s): INIA Las Brujas. |
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| 132. | | BUERSTMAYR, H.; LANG-MLADEK, C.; STEINER, B.; MICHEL, S.; BUERSTMAYR, M.; LEMMENS, M.; VOLLMANN, J.; GRAUSGRUBER, H. (Ed.). Proceedings of the 13th International Wheat Genetics Symposium. Tulln, Austria; April 23-28, 2017. BOKU: University of Natural Resources and Life Sciences, Vienna, Austria. 512 p. International Organizing Committee: Yasunari Ogihara (Japan-chair), Ahmed Amri (ICARDA), Cristobal Uauy (UK), Elena Salina (Russia), Hélène Lucas (Wheat Initiative), Mark E. Sorrells (USA), Parveen Chhuneja (India), Peter Langridge...| Biblioteca(s): INIA La Estanzuela. |
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| 134. | | LADO, B.; BATTENFIELD, S. D.; GUZMÁN, C.; QUINCKE, M.; SINGH, R. P.; DREISIGACKER, S.; PEÑA, R. J.; FRITZ, AL.; SILVA, P.; POLAND, J.; GUTIÉRREZ, L. Strategies for selecting crosses using genomic prediction in two wheat breeding programs. The Plant Genome, 2017, v.10, Issue 2, 12p. OPEN ACCESS Article history: Received: Dec 14, 2016 // Accepted: Mar 18, 2017 // Published: July 6, 2017.
B. Lado and S. Battenfield contributed equally.Assigned to Associate Editor Nicholas Tinker.
This is an open access article distributed under...| Biblioteca(s): INIA Las Brujas. |
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| 140. | | BARAIBAR, S.; SILVA, P.; PRITSCH, C.; GERMAN, S. Transfiriendo combinaciones de genes de resistencia a roya de tallo a background de trigo adaptado al Uruguay. [p18*]. Bloque 2: Mejoramiento por resistencia a enfermedades. In: Sociedad Uruguaya de Fitopatología Jornada Uruguaya de Fitopatología, 4., Jornada Uruguaya de Protección Vegetal, 2., 1° setiembre, 2017, Montevideo, Uruguay. Libro de resúmenes. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2017. p. 38. [p18*]: *Trabajo ya presentado en el XVI Congreso Latinoamericano de Genética, Uruguay.
Financiamiento: Proyecto INNOVAGRO FSA_1_2013_1_12980, ANII.| Biblioteca(s): INIA Las Brujas. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
05/12/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
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. |
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Afiliación : |
SOFÍA P. BRANDARIZ, Universidad de la República (UdelaR); Facultad de Agronomía, Uruguay.; AGUSTÍN GONZÁLEZ REYMÚNDEZ; BETTINA LADO; MARCOS MALOSETTI; ANTONIO AUGUSTO FRANCO GARCIA; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JARISLAV RAMON VON ZITZEWITZ VON SALVIATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARINA CASTRO DERENYI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IVÁN MATUS; ALEJANDRO DEL POZO; ARIEL J. CASTRO; LUCÍA GUTIÉRREZ. |
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Título : |
Ascertainment bias from imputation methods evaluation in wheat. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
BMC Genomics, 2016, v. 17, p.773. |
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DOI : |
10.1186/s12864-016-3120-5 |
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Idioma : |
Inglés |
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Notas : |
OPEN ACCESS. Article history: Received 2016 Feb 24 // Accepted 2016 Sep 23. |
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Contenido : |
Abstract
BACKGROUND:
Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.
RESULTS:
In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available.
CONCLUSIONS:
Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel. MenosAbstract
BACKGROUND:
Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.
RESULTS:
In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between ... Presentar Todo |
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Palabras claves : |
FALSE POSITIVE; FALSO POSITIVO; GBS; GWAS; POWER; QTLs. |
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Thesagro : |
MEJORAMIENTO DE TRIGO. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12122/1/s12864-016-3120-5.pdf
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-3120-5
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Marc : |
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024 7 $a10.1186/s12864-016-3120-5$2DOI
100 1 $aBRANDARIZ , S.
245 $aAscertainment bias from imputation methods evaluation in wheat.$h[electronic resource]
260 $aBMC Genomics, 2016, v. 17, p.773.$c2016
500 $aOPEN ACCESS. Article history: Received 2016 Feb 24 // Accepted 2016 Sep 23.
520 $aAbstract BACKGROUND: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. RESULTS: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. CONCLUSIONS: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.
650 $aMEJORAMIENTO DE TRIGO
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700 1 $aGONZÁLEZ RAYMÚNDEZ, A.
700 1 $aLADO, B.
700 1 $aMALOSETTI, M.
700 1 $aFRANCO GARCIA, A.
700 1 $aQUINCKE, M.
700 1 $aVON ZITZEWITZ, J.
700 1 $aCASTRO, M.
700 1 $aMATUS,I.
700 1 $aDEL POZO, A.
700 1 $aCASTRO, A.J.
700 1 $aGUTIÉRREZ, L.
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