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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 : |
04/05/2018 |
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Actualizado : |
26/04/2018 |
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Tipo de producción científica : |
Documentos |
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Autor : |
CASTRO, M.; PEREYRA, S.; AZZIMONTI, G.; GERMAN, S.; MORALES, M.; GARCIA, R.; GONZALEZ, N.; CASTRO, B. |
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Afiliación : |
MARINA CASTRO DERENYI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA ANTONIA PEREYRA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO AZZIMONTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA ELISA GERMAN FAEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA XIMENA MORALES LORENZONI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RICHARD ANSELMO GARCIA USUCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NESTOR RICARDO GONZALEZ PEREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BEATRIZ YANET CASTRO ALVARADO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
4. Resultados experimentales: ensayos sin fungicidas. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
In: Resultados experimentales de la evaluación nacional de cultivares de trigo ciclo largo: período 2017. La Estanzuela (UY): INIA; INASE, 2018. |
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Páginas : |
p. 9-17. |
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Idioma : |
Español |
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Notas : |
Editado por el Equipo de Evaluación de Cultivares Impreso por Unidad de Comunicación y Transferencia de Tecnología INIA La Estanzuela. Convenio INASE-INIA. |
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Palabras claves : |
COMPORTAMIENTO SANITARIO; ESTADO FENOLÓGICO; FALLING NUMBER DE CULTIVARES DE CEBADA CERVECERA; FUSARIOSIS DE LA ESPIGA; HOJA BANDERA; MANCHAS FOLIARES; PRINCIPIO DE ESPIGAZÓN; PROTEINA EN EL GRANO; RENDIMIENTO DE GRANO. |
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Thesagro : |
CALIDAD DE GRANO; CEBADA CERVECERA; ENFERMEDADES DE LAS PLANTAS; EVALUACION DE CULTIVARES; TRIGO. |
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Asunto categoría : |
F01 Cultivo
H20 Enfermedades de las plantas |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/9405/1/PubCebadaPeriodo2017.p.13-42-Castro-et-al.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/9445/1/PubTrigoLargoPeriodo2017-FINAL.p.9-17-Castro-et-al.pdf
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Marc : |
LEADER 01352naa a2200385 a 4500
001 1058519
005 2018-04-26
008 2018 bl uuuu u00u1 u #d
100 1 $aCASTRO, M.
245 $a4. Resultados experimentales$bensayos sin fungicidas.$h[electronic resource]
260 $c2018
300 $ap. 9-17.
500 $aEditado por el Equipo de Evaluación de Cultivares Impreso por Unidad de Comunicación y Transferencia de Tecnología INIA La Estanzuela. Convenio INASE-INIA.
650 $aCALIDAD DE GRANO
650 $aCEBADA CERVECERA
650 $aENFERMEDADES DE LAS PLANTAS
650 $aEVALUACION DE CULTIVARES
650 $aTRIGO
653 $aCOMPORTAMIENTO SANITARIO
653 $aESTADO FENOLÓGICO
653 $aFALLING NUMBER DE CULTIVARES DE CEBADA CERVECERA
653 $aFUSARIOSIS DE LA ESPIGA
653 $aHOJA BANDERA
653 $aMANCHAS FOLIARES
653 $aPRINCIPIO DE ESPIGAZÓN
653 $aPROTEINA EN EL GRANO
653 $aRENDIMIENTO DE GRANO
700 1 $aPEREYRA, S.
700 1 $aAZZIMONTI, G.
700 1 $aGERMAN, S.
700 1 $aMORALES, M.
700 1 $aGARCIA, R.
700 1 $aGONZALEZ, N.
700 1 $aCASTRO, B.
773 $tIn: Resultados experimentales de la evaluación nacional de cultivares de trigo ciclo largo: período 2017. La Estanzuela (UY): INIA; INASE, 2018.
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