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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
01/07/2022 |
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Actualizado : |
01/07/2022 |
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Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
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Autor : |
BRANDA, A.; FEDERICI, M.; BRIANO, C.; DUTRA, F.; ARTIGAS, R.; NICOLINI, P.; CAFFARENA, D.; GIANNITTI, F.; DALLA RIZZA, M.; LLAMBÍ, S. |
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Afiliación : |
ANDREA BRANDA SICA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA TERESA FEDERICI RODRIGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA BRIANO, División Laboratorios Veterinarios, Laboratorio Regional Este, Treinta y Tres - MGAP; FERNANDO DUTRA, División Laboratorios Veterinarios, Laboratorio Regional Este, Treinta y Tres - MGAP; RODY ARTIGAS, Instituto de Producción Animal y Salud de Sistemas Productivos, Unidad de Genética y Mejora Animal, Facultad de Veterinaria - Udelar; PAULA NICOLINI, Centro Universitario de Tacuarembó, Instituto Superior de la Carne, Área Biología Molecular - Udelar; RUBEN DARÍO CAFFARENA LEDESMA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO GIANNITTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCO DALLA RIZZA VILARO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA LLAMBÍ, Instituto de Producción Animal y Salud de Sistemas Productivos, Unidad de Genética y Mejora Animal, Facultad de Veterinaria - Udelar. |
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Título : |
Métodos de diagnóstico genético-molecular para identificar enfermedades monogénicas en bovinos Holando. |
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Complemento del título : |
Biotecnología. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Revista INIA Uruguay, Junio 2022, no.69, p.47-50. |
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Serie : |
(Revista INIA; 69). |
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ISSN : |
1510-9011 |
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Idioma : |
Español |
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Contenido : |
La presencia de animales portadores de enfermedades hereditarias en bovinos Holando ocasiona diversos impactos negativos en las poblaciones de esta raza a nivel mundial y en nuestros sistemas de producción en particular, por lo que su identificación es muy importante. Este artículo se focaliza en el diagnóstico genético-molecular, como herramienta que permite identificar con precisión animales portadores de mutaciones asociadas y así mejorar la toma de decisiones en los apareamientos y en la selección de toros para programas de mejora genética y producción de semen. |
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Palabras claves : |
DIAGNÓSTICO GENÉTICO-MOLECULAR; PLATAFORMA EN SALUD ANIMAL. |
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Thesagro : |
ENFERMEDADES DE LOS ANIMALES; ENFERMEDADES HEREDITARIAS. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16538/1/Revista-INIA-69-Junio-2022-12.pdf
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Marc : |
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245 $aMétodos de diagnóstico genético-molecular para identificar enfermedades monogénicas en bovinos Holando.$h[electronic resource]
260 $c2022
490 $a(Revista INIA; 69).
520 $aLa presencia de animales portadores de enfermedades hereditarias en bovinos Holando ocasiona diversos impactos negativos en las poblaciones de esta raza a nivel mundial y en nuestros sistemas de producción en particular, por lo que su identificación es muy importante. Este artículo se focaliza en el diagnóstico genético-molecular, como herramienta que permite identificar con precisión animales portadores de mutaciones asociadas y así mejorar la toma de decisiones en los apareamientos y en la selección de toros para programas de mejora genética y producción de semen.
650 $aENFERMEDADES DE LOS ANIMALES
650 $aENFERMEDADES HEREDITARIAS
653 $aDIAGNÓSTICO GENÉTICO-MOLECULAR
653 $aPLATAFORMA EN SALUD ANIMAL
700 1 $aFEDERICI, M.
700 1 $aBRIANO, C.
700 1 $aDUTRA, F.
700 1 $aARTIGAS, R.
700 1 $aNICOLINI, P.
700 1 $aCAFFARENA, D.
700 1 $aGIANNITTI, F.
700 1 $aDALLA RIZZA, M.
700 1 $aLLAMBÍ, S.
773 $tRevista INIA Uruguay, Junio 2022, no.69, p.47-50.
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Registro original : |
INIA Las Brujas (LB) |
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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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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
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653 $aFALSO POSITIVO
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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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