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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 La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
24/04/2018 |
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Actualizado : |
08/10/2019 |
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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 : |
GUAL, I.; GIANNITTI, F.; HECKER ,Y.P.; SHIVERS, J.; ENTROCASSI, A.C.; MORRELL ,E.L.; PARDINI, L.; FIORENTINO ,M.A.; RODRÍGUEZ FERMEPIN ,M.; UNZAGA ,J.M.; CANTÓN, G.J.; VENTURINI, M.C.; MOORE, D.P. |
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Afiliación : |
I GUAL, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.; FEDERICO GIANNITTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CABA, Argentina.; Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, USA.; Departamento de Bioquímica Clínica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires,CABA, Argentina.; Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Balcarce, Buenos Aires, Argentina.; Laboratorio de Inmunoparasitología, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, La Plata, Argentina; e Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Balcarce, Buenos Aires, Argentina.; dDepartamento de Bioquímica Clínica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires , CABA, Argentina.; f Laboratorio de Inmunoparasitología, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, La Plata, Argentina.; Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Balcarce , Buenos Aires, Argentina.; Laboratorio de Inmunoparasitología, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, La Plata, Argentina.; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CABA, Argentina. |
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Título : |
First case report of Toxoplasma gondii-induced abortions and stillbirths in sheep in Argentina. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Veterinary Parasitology: Regional Studies and Reports , v.12, p. 39-42, 2018. |
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Descripción física : |
History article: Received 4 October 2017 // Received in revised form 8 January 2018// Accepted 9 January 2018 //Available online 11 January 2018. |
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DOI : |
10.1016/j.vprsr.2018.01.001 |
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Idioma : |
Inglés |
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Contenido : |
Abstract:
The aim of this study is to report an episode of reproductive losses due to toxoplasmosis in a sheep flock in Argentina. A total of 15 abortions and 9 stillbirths were recorded in a flock of 190 Texel ewes. The affected ewes were more likely to be seropositive for Toxoplasma gondii (15/24) than ewes that delivered normal lambs (5/34, OR = 9.6, 95%CI = 2.7?34.0, p = 0.0004). A pair of aborted twins was recovered for diagnostic investigation. One of these fetuses and its dam were seropositive for T. gondii. Histological examination of the two fetuses revealed non-suppurative myocarditis and epicarditis, portal hepatitis and multifocal necrotizing encephalitis with protozoal cysts in the brain. T. gondii was detected intralesionally by immunohistochemistry in one fetus and by PCR in both. Further investigations are necessary to evaluate the economic losses due to T. gondii in the Argentinean ovine industry. |
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Palabras claves : |
PATHOLOGY; PÉRDIDAS REPRODUCTIVAS EN OVINOS; PROTOZOA; REPRODUCTIVE LOSSES; RUMINANTES; RUMINANTS; SALUD ANIMAL; TOXOPLASMOSIS. |
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Thesagro : |
OVEJA; PATOLOGIA. |
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Asunto categoría : |
E16 Enfermedades de los animales |
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Marc : |
LEADER 02219naa a2200409 a 4500
001 1058473
005 2019-10-08
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1016/j.vprsr.2018.01.001$2DOI
100 1 $aGUAL, I.
245 $aFirst case report of Toxoplasma gondii-induced abortions and stillbirths in sheep in Argentina.$h[electronic resource]
260 $c2018
300 $cHistory article: Received 4 October 2017 // Received in revised form 8 January 2018// Accepted 9 January 2018 //Available online 11 January 2018.
520 $aAbstract: The aim of this study is to report an episode of reproductive losses due to toxoplasmosis in a sheep flock in Argentina. A total of 15 abortions and 9 stillbirths were recorded in a flock of 190 Texel ewes. The affected ewes were more likely to be seropositive for Toxoplasma gondii (15/24) than ewes that delivered normal lambs (5/34, OR = 9.6, 95%CI = 2.7?34.0, p = 0.0004). A pair of aborted twins was recovered for diagnostic investigation. One of these fetuses and its dam were seropositive for T. gondii. Histological examination of the two fetuses revealed non-suppurative myocarditis and epicarditis, portal hepatitis and multifocal necrotizing encephalitis with protozoal cysts in the brain. T. gondii was detected intralesionally by immunohistochemistry in one fetus and by PCR in both. Further investigations are necessary to evaluate the economic losses due to T. gondii in the Argentinean ovine industry.
650 $aOVEJA
650 $aPATOLOGIA
653 $aPATHOLOGY
653 $aPÉRDIDAS REPRODUCTIVAS EN OVINOS
653 $aPROTOZOA
653 $aREPRODUCTIVE LOSSES
653 $aRUMINANTES
653 $aRUMINANTS
653 $aSALUD ANIMAL
653 $aTOXOPLASMOSIS
700 1 $aGIANNITTI, F.
700 1 $aHECKER ,Y.P.
700 1 $aSHIVERS, J.
700 1 $aENTROCASSI, A.C.
700 1 $aMORRELL ,E.L.
700 1 $aPARDINI, L.
700 1 $aFIORENTINO ,M.A.
700 1 $aRODRÍGUEZ FERMEPIN ,M.
700 1 $aUNZAGA ,J.M.
700 1 $aCANTÓN, G.J.
700 1 $aVENTURINI, M.C.
700 1 $aMOORE, D.P.
773 $tVeterinary Parasitology: Regional Studies and Reports$gv.12, p. 39-42, 2018.
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