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| Registros recuperados : 11 | |
| 2. |  | MARESCA, S.; LÓPEZ VALIENTE, S.; RODRÍGUEZ, A.M.; LONG, N.M.; PAVAN, E.; QUINTANS, G. Effect of protein restriction of bovine dams during late gestation on offspring postnatal growth, glucose-insulin metabolism and IGF-1 consentration. Livestock Science, 2018, 212: 120-126. Article history: Received 10 October 2017. Received in revised from 27 March 2018, accepted 11 April 2018.| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 3. | | LÓPEZ-VALIENTE, S.; MAREZCA, S.; RODRÍGUEZ, A. M.; LONG, N. M.; QUINTANS, G.; PALLADINO, R. A. Effect of protein restriction during mid-to late gestation of beef cows on female offspring fertility, lactation performance and calves development. EC Veterinary Science, November 2019, v. 4 (10), p. 1-12. Open Access. Doi: 10.31080/ecve.2019.04.00186 Article history: Received: October 24, 2019; Published: November 13, 2019.| Biblioteca(s): INIA Treinta y Tres. |
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| 5. | | MARESCA, S.; LÓPEZ VALIENTE, S.O.; RODRÍGUEZ, A.M.; LONG, N. M.; PAVAN, E.; QUINTANS, G. Efecto de la restricción proteica de vacas durante la gestación tardía sobre el crecimiento posnatal, el metabolismo de glucosa - insulina y la concentración de IGF-1 de la descendencia. In: QUINTANS, G.; IEWDIUKOW, M. (Ed.). Primer Seminario Técnico de Programación Fetal. Montevideo (UY): INIA, 2019. p. 31-43. (INIA Serie Técnica; 252)| Biblioteca(s): INIA Treinta y Tres. |
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| 6. | | MARESCA, S.; VALIENTE, S.L.; RODRÍGUEZ, A.M.; PAVAN, E.; QUINTANS, G.; LONG, N.M. Late-gestation protein restriction negatively impacts muscle growth and glucose regulation in steer progeny. Domestic Animal Endrocrinology, October 2019, v. 69, p.13-18. Article history: Received 1 October 2018. Received in revised form 14 January 2019, accepted 26 January 2019.
https://doi.org/10.1016/j.domaniend.2019.01.009| Biblioteca(s): INIA Treinta y Tres. |
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| 7. | | LÓPEZ VALIENTE, S.; RODRÍGUEZ, A. M.; LONG, N. M.; QUINTANS, G.; MICCOLI, F. E.; LACAU-MENGIDO, I. M.; MARESCA, S. Age at first gestation in beef heifers affects fetal and postnatal growth, glucose metabolism and IGF1 concentration. Animals 2021, volume 11, issue 12, article number 3393. Open Access. Doi: https://doi.org/10.3390/ani11123393 Article history: Received: 21 October 2021 / Revised: 23 November 2021 / Accepted: 24 November 2021 / Published: 27 November 2021| Biblioteca(s): INIA Treinta y Tres. |
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| 9. | | LÓPEZ VALIENTE, S.; MARESCA, S.; RODRÍGUEZ, A.M.; PALLADINO, R.A.; LACAU-MENGIDO, I.M.; LONG, N.M.; QUINTANS, G. Efecto de la restricción proteica de las vacas Angus durante la gestación tardía: rendimiento reproductivo posterior y producción de leche. In: QUINTANS, G.; IEWDIUKOW, M. (Ed.). Primer Seminario Técnico de Programación Fetal. Montevideo (UY): INIA, 2019. p. 23-30. (INIA Serie Técnica; 252)| Biblioteca(s): INIA Treinta y Tres. |
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| 11. | | MARESCA, S.; LÓPEZ VALIENTE, S.; RODRÍGUEZ, A.M.; TESTA, L.M.; LONG, N.M.; QUINTANS, G.; PAVON, E. Influencia de la restricción proteica en el último tercio de gestación sobre el crecimiento, características de carcasa y calidad de carne de la descendencia. In: QUINTANS, G.; IEWDIUKOW, M. (Ed.). Primer Seminario Técnico de Programación Fetal. Montevideo (UY): INIA, 2019. p. 65-76. (INIA Serie Técnica; 252)| Biblioteca(s): INIA Treinta y Tres. |
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| Registros recuperados : 11 | |
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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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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.
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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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