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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
21/02/2014 |
Actualizado : |
05/12/2018 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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. |
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. |
Título : |
Ascertainment bias from imputation methods evaluation in wheat. |
Fecha de publicación : |
2016 |
Fuente / Imprenta : |
BMC Genomics, 2016, v. 17, p.773. |
DOI : |
10.1186/s12864-016-3120-5 |
Idioma : |
Inglés |
Notas : |
OPEN ACCESS. Article history: Received 2016 Feb 24 // Accepted 2016 Sep 23. |
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 |
Palabras claves : |
FALSE POSITIVE; FALSO POSITIVO; GBS; GWAS; POWER; QTLs. |
Thesagro : |
MEJORAMIENTO DE TRIGO. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
31/07/2018 |
Actualizado : |
11/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
DOSTER, E.; ROVIRA, P.J.; NOYES, N.R.; BURGESS, B. A.; YANG, X.; WEINROTH, M.D.; LAKIN, S.M.; DEAN, C.J.; LINKE, L.; MAGNUSON, R.; JONES, K.I.; BOUCHER, C.; RUIZ, J.; BELK, K.E.; MORLEY, P.S. |
Afiliación : |
ENRIQUE DOSTER, Microbial Ecology Group, Colorado State University, USA. Department of Microbiology, Inmunology and Pathology, Colorado State University. USA.; PABLO JUAN ROVIRA SANZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. Microbial Ecology Group, Colorado State University. Department of Microbiology, Imnunology and Pathology, Colorado State University, USA.; NOELLE R. NOYES., Microbial Ecology Group, Colorado State University. Department of Veterinary Population Medicine, University of Minnesota.; BRANDY A. BURGESS, Department of Population Health, University of Georgia, USA.; XIANG YANG, Microbial Ecology Group, Colorado State University. Department of Animal Sciences, Colorado State University, USA.; MARGARET D. WEINROTH, Microbial Ecology Group, Colorado State University. Department of Animal Sciences, Colorado State University, USA.; STEVEN M. LAKIN, Microbial Ecology Group, Colorado State University. Department of Microbiology, Immunology and Pathology, Colorado State University, USA.; CHRISTOPHER J. DEAN, Microbial Ecology Group, Colorado State University. Department of Microbiology, Immunology and Pathology, Colorado State University, USA.; LYNDSEY LINKE, Department of Clinical Sciences, Colorado State University, USA.; ROBERTA MAGNUSON, Department of Clinical Sciences, Colorado State University, USA.; KENNETH I. JONES, Department of Biochemistry and Molecular Genetics, University of Colorado, USA.; CHRISTINA BOUCHER, Department of Computer and Information Science and Engineering, University of Florida, USA.; JAMIE RUIZ, Department of Computer and Information Science and Engineering, University of Florida, USA.; KEITH E. BELK, Microbial Ecology Group, Colorado State University. Department of Microbiology, Imnunology and Pathology, Colorado State University, USA.; PAUL S. MORLEY, Microbial Ecology Group, Colorado State University, USA. Department of Microbiology, Inmunology and Pathology, Colorado State University. USA. |
Título : |
Investigating effects of tulathromycin metaphylaxis on the fecal resistome and microbiome of commercial feedlot cattle early in the feeding period. |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
Frontier in Microbiology, 2018, 9:1715. |
Páginas : |
14 p. |
DOI : |
10.3389/fmicb.2018.01715 |
Idioma : |
Inglés |
Notas : |
Article history: Received: 14 April 2018; Accepted: 09 July 2018; Published: 30 July 2018.
Open Access journal.
https://doi.org/10.3389/fmicb.2018.01715 |
Contenido : |
The objective was to examine effects of treating commercial beef feedlot cattle with therapeutic doses of tulathromycin, a macrolide antimicrobial drug, on changes in the fecal resistome and microbiome using shotgun metagenomic sequencing. Two pens of cattle were used, with all cattle in one pen receiving metaphylaxis treatment (800 mg subcutaneous tulathromycin) at arrival to the feedlot, and all cattle in the other pen remaining unexposed to parenteral antibiotics throughout the study period. Fecal samples were collected from 15 selected cattle in each group just prior to treatment (Day 1), and again 11 days later (Day 11). Shotgun sequencing was performed on isolated metagenomic DNA, and reads were aligned to a resistance and a taxonomic database to identify alignments to antimicrobial resistance (AMR) gene accessions and microbiome content. Overall, we identified AMR genes accessions encompassing 9 classes of AMR drugs and encoding 24 unique AMR mechanisms. Statistical analysis was used to identify differences in the resistome and microbiome between the untreated and treated groups at both timepoints, as well as over time. Based on composition and ordination analyses, the resistome and microbiome were not significantly different between the two groups on Day 1 or on Day 11. However, both the resistome and microbiome changed significantly between these two sampling dates. These results indicate that the transition into the feedlot?and associated changes in diet, geography, conspecific exposure, and environment?may exert a greater influence over the fecal resistome and microbiome of feedlot cattle than common metaphylactic antimicrobial drug treatment. MenosThe objective was to examine effects of treating commercial beef feedlot cattle with therapeutic doses of tulathromycin, a macrolide antimicrobial drug, on changes in the fecal resistome and microbiome using shotgun metagenomic sequencing. Two pens of cattle were used, with all cattle in one pen receiving metaphylaxis treatment (800 mg subcutaneous tulathromycin) at arrival to the feedlot, and all cattle in the other pen remaining unexposed to parenteral antibiotics throughout the study period. Fecal samples were collected from 15 selected cattle in each group just prior to treatment (Day 1), and again 11 days later (Day 11). Shotgun sequencing was performed on isolated metagenomic DNA, and reads were aligned to a resistance and a taxonomic database to identify alignments to antimicrobial resistance (AMR) gene accessions and microbiome content. Overall, we identified AMR genes accessions encompassing 9 classes of AMR drugs and encoding 24 unique AMR mechanisms. Statistical analysis was used to identify differences in the resistome and microbiome between the untreated and treated groups at both timepoints, as well as over time. Based on composition and ordination analyses, the resistome and microbiome were not significantly different between the two groups on Day 1 or on Day 11. However, both the resistome and microbiome changed significantly between these two sampling dates. These results indicate that the transition into the feedlot?and associated changes in diet, geography... Presentar Todo |
Palabras claves : |
METAGENOMICS; METAPHYLAXIS; MICROBIOME; RESISTOME; TULATHROMYCIN. |
Thesagro : |
BOVINOS; FEEDLOT. |
Asunto categoría : |
L73 Enfermedades de los animales |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10933/1/fmicb-09-01715.pdf
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Marc : |
LEADER 02931naa a2200409 a 4500 001 1058855 005 2019-10-11 008 2018 bl uuuu u00u1 u #d 024 7 $a10.3389/fmicb.2018.01715$2DOI 100 1 $aDOSTER, E. 245 $aInvestigating effects of tulathromycin metaphylaxis on the fecal resistome and microbiome of commercial feedlot cattle early in the feeding period.$h[electronic resource] 260 $c2018 300 $a14 p. 500 $aArticle history: Received: 14 April 2018; Accepted: 09 July 2018; Published: 30 July 2018. Open Access journal. https://doi.org/10.3389/fmicb.2018.01715 520 $aThe objective was to examine effects of treating commercial beef feedlot cattle with therapeutic doses of tulathromycin, a macrolide antimicrobial drug, on changes in the fecal resistome and microbiome using shotgun metagenomic sequencing. Two pens of cattle were used, with all cattle in one pen receiving metaphylaxis treatment (800 mg subcutaneous tulathromycin) at arrival to the feedlot, and all cattle in the other pen remaining unexposed to parenteral antibiotics throughout the study period. Fecal samples were collected from 15 selected cattle in each group just prior to treatment (Day 1), and again 11 days later (Day 11). Shotgun sequencing was performed on isolated metagenomic DNA, and reads were aligned to a resistance and a taxonomic database to identify alignments to antimicrobial resistance (AMR) gene accessions and microbiome content. Overall, we identified AMR genes accessions encompassing 9 classes of AMR drugs and encoding 24 unique AMR mechanisms. Statistical analysis was used to identify differences in the resistome and microbiome between the untreated and treated groups at both timepoints, as well as over time. Based on composition and ordination analyses, the resistome and microbiome were not significantly different between the two groups on Day 1 or on Day 11. However, both the resistome and microbiome changed significantly between these two sampling dates. These results indicate that the transition into the feedlot?and associated changes in diet, geography, conspecific exposure, and environment?may exert a greater influence over the fecal resistome and microbiome of feedlot cattle than common metaphylactic antimicrobial drug treatment. 650 $aBOVINOS 650 $aFEEDLOT 653 $aMETAGENOMICS 653 $aMETAPHYLAXIS 653 $aMICROBIOME 653 $aRESISTOME 653 $aTULATHROMYCIN 700 1 $aROVIRA, P.J. 700 1 $aNOYES, N.R. 700 1 $aBURGESS, B. A. 700 1 $aYANG, X. 700 1 $aWEINROTH, M.D. 700 1 $aLAKIN, S.M. 700 1 $aDEAN, C.J. 700 1 $aLINKE, L. 700 1 $aMAGNUSON, R. 700 1 $aJONES, K.I. 700 1 $aBOUCHER, C. 700 1 $aRUIZ, J. 700 1 $aBELK, K.E. 700 1 $aMORLEY, P.S. 773 $tFrontier in Microbiology, 2018, 9:1715.
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