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| Registros recuperados : 12 | |
| 2. |  | ABREO, E.; VAZ, P.; NÚÑEZ, L.; STEWART, S.; ALTIER, N. Caracterización molecular y virulencia de aislamientos de Pythium spp. obtenidos en suelos de Uruguay. In: JORNADA NACIONAL DE FITOPATOLOGÍA, 3; JORNADA NACIONAL DE PROTECCIÓN VEGETAL, 1., 3 SETIEMBRE 2015, MONTEVIDEO, URUGUAY. Libro de Resúmenes. Montevideo (Uruguay) : SUFIT, 2015. p. 29| Biblioteca(s): INIA Las Brujas. |
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| 5. | | VAZ, P.; DINI, S.; NÚÑEZ, L.; ABREO, E.; BEYHAUT, E.; PÉREZ, C.; ALTIER, N. Análisis de las comunidades microbianas benéficas del suelo para el diseño de un índice de salud para la siembra de soja. [p46]. Bloque 3: Manejo de insectos-plaga, malezas y 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. 71. Financiamiento: ANII.| Biblioteca(s): INIA Las Brujas. |
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| 6. | | NÚÑEZ, L.; ALTIER, N.; BEYHAUT, E.; PÉREZ, C.; MARTÍNEZ, S.; ZERBINO, M.; STEWART, S.; VAZ, P. Factores que influyen sobre la presencia de supresores de enfermedades de implantación en soja (Glycine Max L. Merr.). In: JORNADA NACIONAL DE FITOPATOLOGÍA, 3; JORNADA NACIONAL DE PROTECCIÓN VEGETAL, 1., 3 SETIEMBRE 2015, MONTEVIDEO, URUGUAY. Libro de Resúmenes. Montevideo (Uruguay) : SUFIT, 2015. p. 36 Financiamiento: Proyecto ANII, Proyecto INNOVAGRO FSA_1_2013_1_12444.| Biblioteca(s): INIA Las Brujas. |
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| 8. | | MONTAÑEZ, A; RIGAMONTI, N.; VICO, S.; SILVA, C.; NUÑEZ, L.; ZERBINO, M.S. Influence of aerobic treated manure application on the chemical and microbiological properties of soil. Spanish Journal of Agricultural Research, 2019, Volume 17, Issue 4, Article number e1104. OPEN ACCESS. Doi: https://doi.org/10.5424/sjar/2019174-14658 Article history: Received: 06 Feb 2019.// Accepted: 16 Dec 2019.The authors would like to thank Andres Peres del Castillo and Christian Decker for their much-appreciated assistance with field work and Sally Bunning for her technical...| Biblioteca(s): INIA La Estanzuela. |
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| 9. | | NÚÑEZ, L.; HIRIGOYEN, A.; DURANTE, M.; ARROYO, J.; CAZZULI, F.; BREMM, C.; JAURENA, M. Qué factores controlan la proteína del forraje del campo natural?. Pasturas. Revista INIA Uruguay, Setiembre 2022, no.70, p.43-46. (Revista INIA; 70).| Biblioteca(s): INIA Las Brujas. |
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| 10. | | JAURENA, M.; DIAZ, S.; NÚÑEZ, L.; CARDOZO, G.; DE BARBIERI, I.; LATTANZI, F. La proporción verde del forraje como predictor del potencial nutricional del forraje campo natural. [Resumen]. In: CONGRESO ASOCIACIÓN URUGUAYA DE PRODUCCIÓN ANIMAL (6º, Marzo, 2018, Tacuarembó, Uruguay). Resúmenes. Tacuarembó: AUPA, 2018. p. 76| Biblioteca(s): INIA Tacuarembó. |
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| 11. | | NÚÑEZ, L.; HIRIGOYEN, A.; DURANTE, M.; ARROYO, J.; CAZZULI, F.; BREMM, C.; JAURENA, M. What factors control the crude protein content variation of a basaltic "Campos" native grassland of South America? Agronomy, 2022, Volume 12, Issue 8, article 1756. OPEN ACCESS. doi: https://doi.org/10.3390/agronomy12081756 Article history: Received 23 June 2022; Revised 14 July 2022; Accepted 19 July 2022; Published 26 July 2022.
Academic Editors: Edward B. Rayburn, Thomas C. Griggs and Deidre D. Harmon. -- This article belongs to the Special Issue...| Biblioteca(s): INIA Las Brujas. |
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| 12. | | NÚÑEZ, L.; ALTIER, N.; PÉREZ, C.; BEYHAUT, E.; ZERBINO, M.S.; STEWART, S.; MARTÍNEZ, S.; VAZ, P. Herramientas para la evaluación de la salud del suelo en la siembre de soja. [Resumen]. In: Libro de resúmenes de las TERCERAS JORNADAS INTERDISCIPLINARIAS EN BIODIVERSIDAD Y ECOLOGIA. "Desafíos socio-ambientales para el Uruguay del futuro" 28 de Noviembre a 2 de Diciembre 2016, Centro Universitario Regional del Este Rocha, Uruguay. p.117.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 12 | |
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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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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 $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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