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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
21/02/2014 |
Actualizado : |
29/10/2018 |
Tipo de producción científica : |
Documentos |
Autor : |
MONDINO, P.; ALANIZ, S.; LEONI, C. |
Afiliación : |
CAROLINA LEONI VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Manejo integrado de las enfermedades del duraznero en Uruguay. |
Fecha de publicación : |
2009 |
Fuente / Imprenta : |
In: INIA Las Brujas; Programa Nacional Producción Frutícola. Manual de manejo de plagas y enfermedades en frutales de carozo. Avances. Seminario de actualización técnica en frutales de carozo. Canelones (UY): INIA Las Brujas, 2009. |
Páginas : |
p. 47-86. |
Serie : |
(INIA Serie Actividades de Difusión; 593) |
Idioma : |
Español |
Thesagro : |
BACTERIOSIS; DURAZNERO; ENFERMEDADES DE LAS PLANTAS; ENFERMEDADES FUNGOSAS; INFECCIONES POR NEMATODOS; LUCHA INTEGRADA. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/11720/1/sad-593-2009-p.47-86.pdf
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Marc : |
LEADER 00850naa a2200229 a 4500 001 1008799 005 2018-10-29 008 2009 bl uuuu u00u1 u #d 100 1 $aMONDINO, P. 245 $aManejo integrado de las enfermedades del duraznero en Uruguay. 260 $c2009 300 $ap. 47-86. 490 $a(INIA Serie Actividades de Difusión; 593) 650 $aBACTERIOSIS 650 $aDURAZNERO 650 $aENFERMEDADES DE LAS PLANTAS 650 $aENFERMEDADES FUNGOSAS 650 $aINFECCIONES POR NEMATODOS 650 $aLUCHA INTEGRADA 700 1 $aALANIZ, S. 700 1 $aLEONI, C. 773 $tIn: INIA Las Brujas; Programa Nacional Producción Frutícola. Manual de manejo de plagas y enfermedades en frutales de carozo. Avances. Seminario de actualización técnica en frutales de carozo. Canelones (UY): INIA Las Brujas, 2009.
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
10/08/2020 |
Actualizado : |
05/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
BHATTA, M.; GUTIERREZ, L.; CAMMAROTA, L.; CARDOZO, F.; GERMAN, S.; GÓMEZ-GUERRERO, B.; PARDO, M.F.; LANARO, V.; SAYAS, M.; CASTRO, A.J. |
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,. |
Título : |
Multi-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L.). |
Fecha de publicación : |
2020 |
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 |
DOI : |
10.1534/g3.119.400968 |
Idioma : |
Inglés |
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. |
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 |
Palabras claves : |
GENOMIC PREDICTION; GENPRED; GRAIN QUALITY; GRAIN YIELD; MALTING QUALITY; MULTI-ENVIRONMENT; MULTI-TRAIT; SHARED DATA RESOURCES. |
Asunto categoría : |
-- |
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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