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Registro completo
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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 : |
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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 $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 Las Brujas. |
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Fecha actual : |
18/03/2022 |
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Actualizado : |
18/03/2022 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
PARUELO, J.; GASPARRI, I.; MARTINO, D.; ETCHEBARNE, V.; RODRÍGUEZ, P.; CIGANDA, V.; PANIZZA, A.; GONZÁLEZ, I.; SIMÓN, C.; TISCORNIA, G.; PEREIRA, M.; SOARES DE LIMA, F.; CÁCERES, D.; BUENO, H.; CARRIQUIRY, R.; PERI, P.; BERNARDI, R.; BERAZATEGUI, M.; RODRÍGUEZ-GALLEGO, L.; BLUMETTO, O.; CASTAGNA, A.; BRAZEIRO, A.; FAROPPA, C.; PENENGO, C.; ESCUDERO, P.; DE LEÓN, G. |
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Afiliación : |
JOSÉ PARUELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.; IGNACIO GASPARRI, Instituto de Ecología Regional (IER) - Universidad Nacional de Tucumán-CONICET; DIEGO MARTINO, Proyecto REDD+Uy (MGAP-MA).; VERÓNICA ETCHEBARNE, Proyecto REDD+Uy (MGAP-MA).; PAULA RODRÍGUEZ, Proyecto REDD+Uy (MGAP-MA).; VERONICA SOLANGE CIGANDA BRASCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AMALIA PANIZZA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IBRAHIM MARTÍN GONZÁLEZ LOZANO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CLAUDIA SIMÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCELO PEREIRA, Instituto Plan Agropecuario (IPA).; FACUNDO SOARES DE LIMA, Instituto Plan Agropecuario (IPA).; DIEGO CÁCERES, Mesa de Ganadería de Campo Natural.; HERNÁN BUENO, Instituto Plan Agropecuario (IPA).; RAFAEL CARRIQUIRY, Instituto Plan Agropecuario (IPA).; PABLO PERI, Instituto Nacional de Tecnología Agropecuaria (INTA).; RAFAEL BERNARDI, Centro Universitario Regional del Este - Universidad de la República (CURE).; MAURO BERAZATEGUI, Centro Universitario Regional del Este - Universidad de la República (CURE).; LORENA RODRÍGUEZ-GALLEGO, Centro Universitario Regional del Este - Universidad de la República (CURE).; OSCAR RICARDO BLUMETTO VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES CASTAGNA DU PRE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALEJANDRO BRAZEIRO, Facultad de Ciencias- Universidad de la República.; CARLOS FAROPPA, Ministerio de Ganadería Agricultura y Pesca - Dirección General Forestal (MGAP-DGF).; CECILIA PENENGO, Ministerio de Ambiente-Dirección Nacional de Cambio Climático (MA-DNCC).; PATRICIA ESCUDERO, Ministerio de Ganadería Agricultura y Pesca - Dirección General Forestal (MGAP-DGF).; GASTÓN DE LEÓN, Centro Universitario Regional del Este - Universidad de la República (CURE). |
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Título : |
Resumen de la jornada de intercambio sobre las oportunidades y desafíos del uso de bosque nativo y sus especies nativas, integrados a la producción ganadera de Uruguay. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
In: Paruelo, J.; Ciganda, V.; Gasparri, I.; Paniiza, A. (eds.técnicos). Oportunidades y desafíos del uso de los bosques nativos integrados a la producción ganadera de Uruguay. Montevideo (UY): INIA, 2022. p.93-103. |
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Serie : |
(INIA Serie Técnica; 261). |
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ISBN : |
e-ISBN: 978-9974-38-470-5 |
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ISSN : |
1688-9266 |
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Idioma : |
Español |
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Contenido : |
En este artículo se presenta la recopilación de opiniones de un grupo de personas expertas de diferentes sectores (científico-técnico y de la gestión pública) en relación a las oportunidades y desafíos que presenta la integración de los bosques nativos de Uruguay a la producción ganadera. La actividad fue desarrollada en el marco de un convenio entre el INIA y el proyecto REDD+ Uruguay. |
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Palabras claves : |
BOSQUE NATIVO; GOBERNANZA; INSTITUCIONALIDAD; REDD-Uy. |
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Thesagro : |
GANADERIA. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16327/1/st-261-2022-p93-103.pdf
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Marc : |
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245 $aResumen de la jornada de intercambio sobre las oportunidades y desafíos del uso de bosque nativo y sus especies nativas, integrados a la producción ganadera de Uruguay.$h[electronic resource]
260 $c2022
490 $a(INIA Serie Técnica; 261).
520 $aEn este artículo se presenta la recopilación de opiniones de un grupo de personas expertas de diferentes sectores (científico-técnico y de la gestión pública) en relación a las oportunidades y desafíos que presenta la integración de los bosques nativos de Uruguay a la producción ganadera. La actividad fue desarrollada en el marco de un convenio entre el INIA y el proyecto REDD+ Uruguay.
650 $aGANADERIA
653 $aBOSQUE NATIVO
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700 1 $aMARTINO, D.
700 1 $aETCHEBARNE, V.
700 1 $aRODRÍGUEZ, P.
700 1 $aCIGANDA, V.
700 1 $aPANIZZA, A.
700 1 $aGONZÁLEZ, I.
700 1 $aSIMÓN, C.
700 1 $aTISCORNIA, G.
700 1 $aPEREIRA, M.
700 1 $aSOARES DE LIMA, F.
700 1 $aCÁCERES, D.
700 1 $aBUENO, H.
700 1 $aCARRIQUIRY, R.
700 1 $aPERI, P.
700 1 $aBERNARDI, R.
700 1 $aBERAZATEGUI, M.
700 1 $aRODRÍGUEZ-GALLEGO, L.
700 1 $aBLUMETTO, O.
700 1 $aCASTAGNA, A.
700 1 $aBRAZEIRO, A.
700 1 $aFAROPPA, C.
700 1 $aPENENGO, C.
700 1 $aESCUDERO, P.
700 1 $aDE LEÓN, G.
773 $tIn: Paruelo, J.; Ciganda, V.; Gasparri, I.; Paniiza, A. (eds.técnicos). Oportunidades y desafíos del uso de los bosques nativos integrados a la producción ganadera de Uruguay. Montevideo (UY): INIA, 2022. p.93-103.
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