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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
21/02/2014 |
Actualizado : |
05/12/2018 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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. |
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. |
Título : |
Ascertainment bias from imputation methods evaluation in wheat. |
Fecha de publicación : |
2016 |
Fuente / Imprenta : |
BMC Genomics, 2016, v. 17, p.773. |
DOI : |
10.1186/s12864-016-3120-5 |
Idioma : |
Inglés |
Notas : |
OPEN ACCESS. Article history: Received 2016 Feb 24 // Accepted 2016 Sep 23. |
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 |
Palabras claves : |
FALSE POSITIVE; FALSO POSITIVO; GBS; GWAS; POWER; QTLs. |
Thesagro : |
MEJORAMIENTO DE TRIGO. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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 : |
LEADER 02972nam a2200349 a 4500 001 1047336 005 2018-12-05 008 2016 bl uuuu u0uu1 u #d 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 653 $aFALSE POSITIVE 653 $aFALSO POSITIVO 653 $aGBS 653 $aGWAS 653 $aPOWER 653 $aQTLs 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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Registro original : |
INIA La Estanzuela (LE) |
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Biblioteca (s) : |
INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
Fecha actual : |
21/02/2014 |
Actualizado : |
03/02/2018 |
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
Autor : |
BERRETTA, E.J. |
Afiliación : |
ELBIO JOAQUIN BERRETTA CARVALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Principales características climáticas y edáficas de la región de Basalto en Uruguay |
Complemento del título : |
PASTURAS |
Fecha de publicación : |
1998 |
Fuente / Imprenta : |
In: BERRETTA, E.J. (Ed.). Seminario de actualización en tecnologías para basalto. INIA Tacuarembó, 3-4 diciembre 1998. Montevideo (Uruguay): INIA, 1998. |
Páginas : |
p. 3-10 |
Serie : |
(INIA Serie Técnica ; 102) |
ISSN : |
1688-9266 |
Idioma : |
Español |
Notas : |
INIA Tacuarembó |
Contenido : |
El Uruguay está ubicado entre los 30° y 35° de latitud Sur, en una zona subtropical templada. La región Basáltica se extiende
por los Departamentos de Artigas, Salto, Paysandú, Tacuarembó, Rivera, y Durazno, abarcando una superficie de 4.100.000 ha
(MAP, 1979), en un paisaje de planicies, ondulaciones y pequeñas colinas que varía entre 20 y 300 m de altura sobre el nivel del
mar. Las pendientes son suaves, pero en algunas partes de colinas pueden superarel 12%. En el presente trabajo se describen las
principales características del ambiente de esta región limitada por el río Cuareim al norte y el río Negro al sur. |
Palabras claves : |
CLIMA. |
Thesagro : |
FACTORES CLIMATICOS; SUELO BASALTICO; URUGUAY. |
Asunto categoría : |
-- A50 Investigación agraria |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/7789/1/ST-102-3-10.pdf
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Marc : |
LEADER 01343naa a2200217 a 4500 001 1012078 005 2018-02-03 008 1998 bl uuuu u00u1 u #d 022 $a1688-9266 100 1 $aBERRETTA, E.J. 245 $aPrincipales características climáticas y edáficas de la región de Basalto en Uruguay 260 $c1998 300 $ap. 3-10 490 $a(INIA Serie Técnica ; 102) 500 $aINIA Tacuarembó 520 $aEl Uruguay está ubicado entre los 30° y 35° de latitud Sur, en una zona subtropical templada. La región Basáltica se extiende por los Departamentos de Artigas, Salto, Paysandú, Tacuarembó, Rivera, y Durazno, abarcando una superficie de 4.100.000 ha (MAP, 1979), en un paisaje de planicies, ondulaciones y pequeñas colinas que varía entre 20 y 300 m de altura sobre el nivel del mar. Las pendientes son suaves, pero en algunas partes de colinas pueden superarel 12%. En el presente trabajo se describen las principales características del ambiente de esta región limitada por el río Cuareim al norte y el río Negro al sur. 650 $aFACTORES CLIMATICOS 650 $aSUELO BASALTICO 650 $aURUGUAY 653 $aCLIMA 773 $tIn: BERRETTA, E.J. (Ed.). Seminario de actualización en tecnologías para basalto. INIA Tacuarembó, 3-4 diciembre 1998. Montevideo (Uruguay): INIA, 1998.
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