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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
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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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 : |
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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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
10/07/2017 |
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Actualizado : |
17/07/2017 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Nacionales |
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Circulación / Nivel : |
Nacional - -- |
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Autor : |
BERETTA, A.; BASSAHUN, D.; TORRES, D.; MUSSELLI, R.; GARCIA, L. |
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Afiliación : |
ANDRES NICOLAS BERETTA BLANCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DANIEL FERNANDO BASSAHUN RODRIGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DEBORAH LUCIANA TORRES GUERRERO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BEATRIZ RAQUEL MUSSELLI NEGRIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LETICIA IRENE GARCIA BARRETO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Acidez titulable a pH = 7 estimada a partir del pH de una mezcla suelo:buffer. (Titratable Acidity at pH = 7 Estimated from the pH of a Soil:Buffer Mixture). |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Agrociencia Uruguay, v. 21, n.1, p. 105-108, 2017. |
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Idioma : |
Español |
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Notas : |
Article history: Recibido: 2015-11-19 Aceptado: 2016-11-23 |
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Contenido : |
Resumen:
El valor de acidez titulable a pH = 7 en suelo (AT_pH7) se utiliza con fines taxonómicos y para estimar la necesidad de
encalado. Es posible predecir AT_pH7 por el valor de pH de la mezcla suelo:solución buffer (pH_equilibrio). A cada muestra
se le agregó acetato de calcio buffereado a pH = 7, se midió el pH_equilibrio y el álcali necesario para llegar a pH=7. Con
pH_equilibrio se estimó AT_pH7 por regresión (AT_eq) o por la cantidad de álcali neutralizado para alcanzar el pH_equilibrio
(AT_OH). Existió un ajuste aceptable entre AT_pH7, AT_eq y AT_OH, pero sus diferencias no tuvieron distribución normal.
Con AT_OH y AT_eq, se subestimó el promedio de AT_pH7. El valor de pH_equilibrio fue más preciso que AT_pH7, en tanto
que las medidas AT_eq y AT_OH tuvieron menor precisión. Al utilizar AT_eq y AT_OH, no se introdujeron errores significativos
al clasificar los suelos por su saturación en bases. Ambas mediciones se realizaron en menor tiempo que AT_pH7. |
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Palabras claves : |
ACIDEZ POTENCIAL; BASE SATURATION; CAPACIDAD INTERCAMBIO; CATION EXCHANGE CAPACITY; CATIÓNICO; DESATURATED SOIL; POTENTIAL ACIDITY; SATURACIÓN EN BASES; SATURATED SOIL; SUELO DESATURADO; SUELO SATURADO. |
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Asunto categoría : |
P30 Ciencia del suelo y manejo del suelo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/7064/1/Agrociencia-21-1p.105-108.pdf
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Marc : |
LEADER 02000naa a2200313 a 4500
001 1057361
005 2017-07-17
008 2017 bl uuuu u00u1 u #d
100 1 $aBERETTA, A.
245 $aAcidez titulable a pH = 7 estimada a partir del pH de una mezcla suelo$bbuffer. (Titratable Acidity at pH = 7 Estimated from the pH of a Soil:Buffer Mixture).$h[electronic resource]
260 $c2017
500 $aArticle history: Recibido: 2015-11-19 Aceptado: 2016-11-23
520 $aResumen: El valor de acidez titulable a pH = 7 en suelo (AT_pH7) se utiliza con fines taxonómicos y para estimar la necesidad de encalado. Es posible predecir AT_pH7 por el valor de pH de la mezcla suelo:solución buffer (pH_equilibrio). A cada muestra se le agregó acetato de calcio buffereado a pH = 7, se midió el pH_equilibrio y el álcali necesario para llegar a pH=7. Con pH_equilibrio se estimó AT_pH7 por regresión (AT_eq) o por la cantidad de álcali neutralizado para alcanzar el pH_equilibrio (AT_OH). Existió un ajuste aceptable entre AT_pH7, AT_eq y AT_OH, pero sus diferencias no tuvieron distribución normal. Con AT_OH y AT_eq, se subestimó el promedio de AT_pH7. El valor de pH_equilibrio fue más preciso que AT_pH7, en tanto que las medidas AT_eq y AT_OH tuvieron menor precisión. Al utilizar AT_eq y AT_OH, no se introdujeron errores significativos al clasificar los suelos por su saturación en bases. Ambas mediciones se realizaron en menor tiempo que AT_pH7.
653 $aACIDEZ POTENCIAL
653 $aBASE SATURATION
653 $aCAPACIDAD INTERCAMBIO
653 $aCATION EXCHANGE CAPACITY
653 $aCATIÓNICO
653 $aDESATURATED SOIL
653 $aPOTENTIAL ACIDITY
653 $aSATURACIÓN EN BASES
653 $aSATURATED SOIL
653 $aSUELO DESATURADO
653 $aSUELO SATURADO
700 1 $aBASSAHUN, D.
700 1 $aTORRES, D.
700 1 $aMUSSELLI, R.
700 1 $aGARCIA, L.
773 $tAgrociencia Uruguay$gv. 21, n.1, p. 105-108, 2017.
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