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| Registros recuperados : 987 | |
| 441. |  | 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).| Biblioteca(s): INIA La Estanzuela. |
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| 442. | | RAFFO, M.A.; PRITSCH, C.; AZZIMONTI, G.; PEREYRA, S.; QUINCKE, M.; BONNECARRERE, V.; SILVA, P.; CASTRO, A.; LADO, B.; BARAIBAR, S.; GARCIA, R.; GERMAN, S. Introgression of the Coupled Sr2-Fhb1for Resistance to Stem Rust and Fusarium Head Blight into Uruguayan Elite Wheat Cultivars.[Poster] In: Proceedings of the Borlaug Global Rust Initiative Technical Workshop ,14-18 April, 2018, Marrakesh, Marruecos.| Biblioteca(s): INIA La Estanzuela. |
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| 443. | | SILVA, P.; QUINCKE, M.; GERMAN, S. Looking for new sources of partial resistance to wheat rusts in historical germplasm from South America. MV 18 - COMUNICACIONES LIBRES - MV. MEJORAMIENTO VEGETAL In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016. p. 289| Biblioteca(s): INIA Las Brujas. |
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| 451. | | SILVA, P.; RIELLA, V.; GARCIA, R.; PEREIRA, F.; PEREZ, N.; CASTRO, M.; GONZÁLEZ, S.N.; GONZALEZ, N.; PEREYRA, S.; GERMAN, S. Roya estriada de trigo: una nueva realidad para el cultivo. Avances en el conocimiento para un manejo adecuado. Cultivos. Revista INIA Uruguay, Junio 2023, no.73, p.31-35. (Revista INIA; 73).| Biblioteca(s): INIA Las Brujas. |
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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 : |
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.
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700 1 $aLADO, B.
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700 1 $aVON ZITZEWITZ, J.
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700 1 $aMATUS,I.
700 1 $aDEL POZO, A.
700 1 $aCASTRO, A.J.
700 1 $aGUTIÉRREZ, L.
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