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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
10/08/2020 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
BHATTA, M.; GUTIERREZ, L.; CAMMAROTA, L.; CARDOZO, F.; GERMAN, S.; GÓMEZ-GUERRERO, B.; PARDO, M.F.; LANARO, V.; SAYAS, M.; CASTRO, A.J. |
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Afiliación : |
MADHAV BHATTA, Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Dr., WI, 53706, USA.; LUCIA GUTIERREZ, Agronomy, University of Wisconsin-Madison, 1575 Linden Dr., WI, 53706, USA.; LORENA CAMMAROTA, Department of plant production, Facultad de Agronomía, Universidad de la República, Ruta 3, Km363, Paysandú 60000, Uruguay./Maltería Uruguay S.A. Ruta 55, Km26, Ombúes de Lavalle, Uruguay.; FERNANDA CARDOZO, Maltería Uruguay S.A. Ruta 55, Km26, Ombúes de Lavalle, Uruguay.; SILVIA ELISA GERMAN FAEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BLANCA GÓMEZ-GUERRERO, LATU Foundation, Av Italia 6201, Montevideo 11500, Uruguay.; MARÍA FERNANDA PARDO, Maltería Oriental S.A., Camino Abrevadero 5525, Montevideo 12400, Uruguay.; VALERIA LANARO, LATU Foundation, Av Italia 6201, Montevideo 11500, Uruguay.; MERCEDES SAYAS, Maltería Oriental S.A., Camino Abrevadero 5525, Montevideo 12400, Uruguay.; ARIEL J. CASTRO, Ariel J. Castro ?Department of plant production, Facultad de Agronomía, Universidad de la República, Ruta 3, Km363, Paysandú 60000, Uruguay,. |
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Título : |
Multi-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L.). |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
G3: Genes, Genomes, Genetics, March 1, 2020 vol. 10 no. 3 1113-1124. Open Acces. Doi: https://doi.org/10.1534/g3.119.400968 |
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DOI : |
10.1534/g3.119.400968 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received July 26, 2019/Accepted January 22, 2020/Published online March 5, 2020. This work was funded in part by the following grants from ANII (FSA-1-2013-12977), CSIC (CSIC_I+D_ 1131 and CSIC_Movilidad_ 1131). The work was also funded by the Cereals Breeding and Quantitative Genetics group at the University of Wisconsin - Madison. We would like to acknowledge Dr. Juan Diaz at INIA, who developed the double haploid population and also contributed to the planning of the study. Malteria Oriental S.A. (MOSA) contributed with the experiments in their experimental areas and with some of the lab work. Malteria Uruguay S.A. (MUSA) contributed to the experiments in their experimental areas. We would also like to acknowledge: USDA-ARS small grains genotyping lab at Fargo, North Dakota for genotyping service; the Center for High Throughput Computing (CHTC) service at the University of Wisconsin-Madison for providing the high-performance computing resources; and Dr. Bettina Lado for sharing the R scripts. We would like to thank two anonymous reviewers and editors who provided constructive suggestions to this manuscript. |
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Contenido : |
Abstract:
Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles. MenosAbstract:
Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for ... Presentar Todo |
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Palabras claves : |
GENOMIC PREDICTION; GENPRED; GRAIN QUALITY; GRAIN YIELD; MALTING QUALITY; MULTI-ENVIRONMENT; MULTI-TRAIT; SHARED DATA RESOURCES. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16688/1/G3-Bethesda-2020.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056970/pdf/1113.pdf
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Marc : |
LEADER 04092naa a2200349 a 4500
001 1061265
005 2022-09-05
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1534/g3.119.400968$2DOI
100 1 $aBHATTA, M.
245 $aMulti-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L.).$h[electronic resource]
260 $c2020
500 $aArticle history: Received July 26, 2019/Accepted January 22, 2020/Published online March 5, 2020. This work was funded in part by the following grants from ANII (FSA-1-2013-12977), CSIC (CSIC_I+D_ 1131 and CSIC_Movilidad_ 1131). The work was also funded by the Cereals Breeding and Quantitative Genetics group at the University of Wisconsin - Madison. We would like to acknowledge Dr. Juan Diaz at INIA, who developed the double haploid population and also contributed to the planning of the study. Malteria Oriental S.A. (MOSA) contributed with the experiments in their experimental areas and with some of the lab work. Malteria Uruguay S.A. (MUSA) contributed to the experiments in their experimental areas. We would also like to acknowledge: USDA-ARS small grains genotyping lab at Fargo, North Dakota for genotyping service; the Center for High Throughput Computing (CHTC) service at the University of Wisconsin-Madison for providing the high-performance computing resources; and Dr. Bettina Lado for sharing the R scripts. We would like to thank two anonymous reviewers and editors who provided constructive suggestions to this manuscript.
520 $aAbstract: Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.
653 $aGENOMIC PREDICTION
653 $aGENPRED
653 $aGRAIN QUALITY
653 $aGRAIN YIELD
653 $aMALTING QUALITY
653 $aMULTI-ENVIRONMENT
653 $aMULTI-TRAIT
653 $aSHARED DATA RESOURCES
700 1 $aGUTIERREZ, L.
700 1 $aCAMMAROTA, L.
700 1 $aCARDOZO, F.
700 1 $aGERMAN, S.
700 1 $aGÓMEZ-GUERRERO, B.
700 1 $aPARDO, M.F.
700 1 $aLANARO, V.
700 1 $aSAYAS, M.
700 1 $aCASTRO, A.J.
773 $tG3: Genes, Genomes, Genetics, March 1, 2020 vol. 10 no. 3 1113-1124. Open Acces. Doi: https://doi.org/10.1534/g3.119.400968
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Registro original : |
INIA La Estanzuela (LE) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
13/06/2023 |
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Actualizado : |
01/09/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
GIANNITTI, F.; DORSCH, M.; FERNÁNDEZ-CIGANDA, S.; RABAZA, A.; VÁZQUEZ, S.; CÉSAR, C.; HURTADO, J.; GREIF, G.; RABENECK, D.; BHATNAGAR, J.; RITTER, J. |
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Afiliación : |
FEDERICO GIANNITTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MATÍAS ANDRÉS DORSCH, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SOFÍA FERNÁNDEZ-CIGANDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANA VIRGINIA RABAZA MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SEBASTIÁN VÁZQUEZ, Private practice, Colonia del Sacramento, Uruguay; DEBORAH CÉSAR; JOAQUÍN HURTADO, Unidad de Biología Molecular, Institut Pasteur de Montevideo, Montevideo, Uruguay; GONZALO GREIF, Unidad de Biología Molecular, Institut Pasteur de Montevideo, Montevideo, Uruguay; DEMI B. RABENECK, Infectious Diseases Pathology Branch (IDPB), Centers for Disease Control and Prevention (CDC), Atlanta, GA; JULU BHATNAGAR, Infectious Diseases Pathology Branch (IDPB), Centers for Disease Control and Prevention (CDC), Atlanta, GA; JANA M. RITTER, Infectious Diseases Pathology Branch (IDPB), Centers for Disease Control and Prevention (CDC), Atlanta, GA. |
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Título : |
Canine leproid granuloma caused by a member of the Mycobacterium tuberculosis complex. (Brief Report). |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Journal of Veterinary Diagnostic Investigation. 2023, Volume 35, Issue 4, pages 439-443. https://doi.org/10.1177/10406387231176816 |
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ISSN : |
1943-4936 (online). |
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DOI : |
10.1177/10406387231176816 |
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Idioma : |
Inglés |
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Notas : |
Article history: First published online May 19, 2023; Issue published July 2023. -- Corresponding author: Federico Giannitti, Plataforma de Investigación en Salud Animal, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 50, km 11 (70006), La Estanzuela, Colonia, Uruguay. fgiannitti@inia.org.uy -- Funding: Funded by research grant PL_27 N-23398 from the Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Contenido : |
ABSTRACT.- Canine leproid granuloma (CLG) is a chronic form of dermatitis that has been associated with nontuberculous mycobacterial infections in Africa, Oceania, the Americas, and Europe. We report here a case of CLG associated with a member of the Mycobacterium tuberculosis complex (MTBC), which could be of public health concern. An 8-y-old pet dog developed 0.5-1-cm diameter, raised, firm, nonpruritic, alopecic, painless skin nodules on the external aspects of both pinnae. Histologic examination revealed severe pyogranulomatous dermatitis with intracellular Ziehl-Neelsen-positive bacilli that were immunoreactive by immunohistochemistry using a polyclonal primary antibody that recognizes tuberculous and nontuberculous Mycobacterium species. DNA extracted from formalin-fixed, paraffin-embedded skin sections was tested by a Mycobacterium genus-specific nested PCR assay targeting the 16S rRNA gene. BLAST sequence analysis of 214-bp and 178-bp amplicons showed 99.5% identity with members of the MTBC; however, the agent could not be identified at the species level. Although CLG has been associated traditionally with nontuberculous mycobacterial infections, the role of Mycobacterium spp. within the MTBC as a cause of this condition, and the role of dogs with CLG as possible sources of MTBC to other animals and humans, should not be disregarded given its zoonotic potential. © 2023 The Author(s). |
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Palabras claves : |
Canine leproid granuloma; Dermatitis; Dogs; Mycobacterium tuberculosis complex; Pathology; PLATAFORMA DE INVESTIGACIÓN EN SALUD ANIMAL - INIA; Pyogranuloma. |
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Asunto categoría : |
L40 Estructura animal |
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Marc : |
LEADER 02982naa a2200361 a 4500
001 1064188
005 2023-09-01
008 2023 bl uuuu u00u1 u #d
022 $a1943-4936 (online).
024 7 $a10.1177/10406387231176816$2DOI
100 1 $aGIANNITTI, F.
245 $aCanine leproid granuloma caused by a member of the Mycobacterium tuberculosis complex. (Brief Report).$h[electronic resource]
260 $c2023
500 $aArticle history: First published online May 19, 2023; Issue published July 2023. -- Corresponding author: Federico Giannitti, Plataforma de Investigación en Salud Animal, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 50, km 11 (70006), La Estanzuela, Colonia, Uruguay. fgiannitti@inia.org.uy -- Funding: Funded by research grant PL_27 N-23398 from the Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay.
520 $aABSTRACT.- Canine leproid granuloma (CLG) is a chronic form of dermatitis that has been associated with nontuberculous mycobacterial infections in Africa, Oceania, the Americas, and Europe. We report here a case of CLG associated with a member of the Mycobacterium tuberculosis complex (MTBC), which could be of public health concern. An 8-y-old pet dog developed 0.5-1-cm diameter, raised, firm, nonpruritic, alopecic, painless skin nodules on the external aspects of both pinnae. Histologic examination revealed severe pyogranulomatous dermatitis with intracellular Ziehl-Neelsen-positive bacilli that were immunoreactive by immunohistochemistry using a polyclonal primary antibody that recognizes tuberculous and nontuberculous Mycobacterium species. DNA extracted from formalin-fixed, paraffin-embedded skin sections was tested by a Mycobacterium genus-specific nested PCR assay targeting the 16S rRNA gene. BLAST sequence analysis of 214-bp and 178-bp amplicons showed 99.5% identity with members of the MTBC; however, the agent could not be identified at the species level. Although CLG has been associated traditionally with nontuberculous mycobacterial infections, the role of Mycobacterium spp. within the MTBC as a cause of this condition, and the role of dogs with CLG as possible sources of MTBC to other animals and humans, should not be disregarded given its zoonotic potential. © 2023 The Author(s).
653 $aCanine leproid granuloma
653 $aDermatitis
653 $aDogs
653 $aMycobacterium tuberculosis complex
653 $aPathology
653 $aPLATAFORMA DE INVESTIGACIÓN EN SALUD ANIMAL - INIA
653 $aPyogranuloma
700 1 $aDORSCH, M.
700 1 $aFERNÁNDEZ-CIGANDA, S.
700 1 $aRABAZA, A.
700 1 $aVÁZQUEZ, S.
700 1 $aCÉSAR, C.
700 1 $aHURTADO, J.
700 1 $aGREIF, G.
700 1 $aRABENECK, D.
700 1 $aBHATNAGAR, J.
700 1 $aRITTER, J.
773 $tJournal of Veterinary Diagnostic Investigation. 2023, Volume 35, Issue 4, pages 439-443. https://doi.org/10.1177/10406387231176816
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