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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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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
25/02/2021 |
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Actualizado : |
25/02/2021 |
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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 : |
MILECH, C. G.; DINI, M.; SCARIOTTO, S.; SANTOS, J.; HERTER, F. G.; RASEIRA, M. C. B. |
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Afiliación : |
C. G. MILECH, Postgraduate Program in Agronomy (PPGA), Faculty of Agronomy 'Eliseu Maciel', Federal University of Pelotas (FAEM-UFPel), Pelotas/RS, Brazil; MAXIMILIANO ANTONIO DINI VIÑOLY, Postgraduate Program in Agronomy (PPGA), Faculty of Agronomy 'Eliseu Maciel', Federal University of Pelotas (FAEM-UFPel), Pelotas/RS, Brazil; S. SCARIOTTO, Laboratory of Fruit Breeding, Embrapa Clima Temperado, Pelotas/RS, Brazil; J. SANTOS, Federal University of Maranhao (UFMA), Sao Luís/MA, Brazil; F. G. HERTER, Postgraduate Program in Agronomy (PPGA), Faculty of Agronomy 'Eliseu Maciel', Federal University of Pelotas (FAEM-UFPel), Pelotas/RS, Brazil; M. C. B. RASEIRA, Laboratory of Fruit Breeding, Embrapa Clima Temperado, Pelotas/RS, Brazil. |
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Título : |
Chilling requirement of ten peach cultivars estimated by different models. |
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Complemento del título : |
Original Research Article. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Journal of Experimental Agriculture International, February 2018, Volume 20, Issue 4, p. 1-9. Article no.JEAI.39204. DOI: https://doi.org/10.9734/JEAI/2018/39204 |
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ISSN : |
2457-0591 |
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DOI : |
10.9734/JEAI/2018/39204 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 23 November 2017; Accepted 31 January 2018; Published 8 February 2018.
Authors' contributions: This work was carried out in collaboration between all authors.Authors CGM, FGHand MCBR designed the study, wrote the protocol and wrote the first draft of the manuscript. Authors MD, SS and JS performed the statistical analysis and managed the literature searches. Author MCBR managed the analyses of the study. All authors read and approved the final manuscript. |
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Contenido : |
ABSTRACT.
The adaptation of a temperate climate fruit cultivar to a certain area depends mainly on its chilling requirement and the chilling accumulation in such places. Several attempts have been made to estimate these two conditions, using different models. The great variation among the models to calculate chilling requirement makes it necessary to determine their efficiency in a given location. Aiming to estimate the chilling requirement of ten peach cultivars, including Bonão, Pepita, Maravilha, Precocinho, Turmalina, Diamante, BR-3, Marfim, Coral, and Cambará do Sul, seven models were tested: Utah, Positive Utah, Low Chill, Taiwan, Chilling Hours (≤7.2°C), Chilling Hours (≤11°C), and Dynamic. The results showed that the estimation of chilling accumulation for all the studied cultivars in all the tested models showed a large variability. None of the tested models was perfect for estimating the chilling requirement, especially considering the variable climatic conditions of southern Brazil. Except for the Utah model, any of the others can be used to provide a rough estimate of the chilling requirement of the cultivars; however, the Taiwan and Low Chill models seem to be more suitable. The chilling requirement, which was estimated based on the average over the 11 years of the study, overestimated the real need, when compared to the yields over those years. There are differences among the studied cultivars; however, with the exception of Cambará do Sul, all the others can yield good crops and show good adaptation to the climatic conditions of the southern Rio Grande do Sul.
© Copyright 2010-Till Date, Journal of Experimental Agriculture International. All rights reserved. MenosABSTRACT.
The adaptation of a temperate climate fruit cultivar to a certain area depends mainly on its chilling requirement and the chilling accumulation in such places. Several attempts have been made to estimate these two conditions, using different models. The great variation among the models to calculate chilling requirement makes it necessary to determine their efficiency in a given location. Aiming to estimate the chilling requirement of ten peach cultivars, including Bonão, Pepita, Maravilha, Precocinho, Turmalina, Diamante, BR-3, Marfim, Coral, and Cambará do Sul, seven models were tested: Utah, Positive Utah, Low Chill, Taiwan, Chilling Hours (≤7.2°C), Chilling Hours (≤11°C), and Dynamic. The results showed that the estimation of chilling accumulation for all the studied cultivars in all the tested models showed a large variability. None of the tested models was perfect for estimating the chilling requirement, especially considering the variable climatic conditions of southern Brazil. Except for the Utah model, any of the others can be used to provide a rough estimate of the chilling requirement of the cultivars; however, the Taiwan and Low Chill models seem to be more suitable. The chilling requirement, which was estimated based on the average over the 11 years of the study, overestimated the real need, when compared to the yields over those years. There are differences among the studied cultivars; however, with the exception of Cambará do Sul, all the ... Presentar Todo |
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Palabras claves : |
Adaptation; Chill hours; Chill portions; Chill units; DORMANCY. |
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Thesagro : |
PRUNUS PERSICA. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15101/1/984-Article-Text-1737-1-10-20181009.pdf
https://www.journaljeai.com/index.php/JEAI/article/download/984/1375/
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Marc : |
LEADER 03090naa a2200289 a 4500
001 1061768
005 2021-02-25
008 2018 bl uuuu u00u1 u #d
022 $a2457-0591
024 7 $a10.9734/JEAI/2018/39204$2DOI
100 1 $aMILECH, C. G.
245 $aChilling requirement of ten peach cultivars estimated by different models.$h[electronic resource]
260 $c2018
500 $aArticle history: Received 23 November 2017; Accepted 31 January 2018; Published 8 February 2018. Authors' contributions: This work was carried out in collaboration between all authors.Authors CGM, FGHand MCBR designed the study, wrote the protocol and wrote the first draft of the manuscript. Authors MD, SS and JS performed the statistical analysis and managed the literature searches. Author MCBR managed the analyses of the study. All authors read and approved the final manuscript.
520 $aABSTRACT. The adaptation of a temperate climate fruit cultivar to a certain area depends mainly on its chilling requirement and the chilling accumulation in such places. Several attempts have been made to estimate these two conditions, using different models. The great variation among the models to calculate chilling requirement makes it necessary to determine their efficiency in a given location. Aiming to estimate the chilling requirement of ten peach cultivars, including Bonão, Pepita, Maravilha, Precocinho, Turmalina, Diamante, BR-3, Marfim, Coral, and Cambará do Sul, seven models were tested: Utah, Positive Utah, Low Chill, Taiwan, Chilling Hours (≤7.2°C), Chilling Hours (≤11°C), and Dynamic. The results showed that the estimation of chilling accumulation for all the studied cultivars in all the tested models showed a large variability. None of the tested models was perfect for estimating the chilling requirement, especially considering the variable climatic conditions of southern Brazil. Except for the Utah model, any of the others can be used to provide a rough estimate of the chilling requirement of the cultivars; however, the Taiwan and Low Chill models seem to be more suitable. The chilling requirement, which was estimated based on the average over the 11 years of the study, overestimated the real need, when compared to the yields over those years. There are differences among the studied cultivars; however, with the exception of Cambará do Sul, all the others can yield good crops and show good adaptation to the climatic conditions of the southern Rio Grande do Sul. © Copyright 2010-Till Date, Journal of Experimental Agriculture International. All rights reserved.
650 $aPRUNUS PERSICA
653 $aAdaptation
653 $aChill hours
653 $aChill portions
653 $aChill units
653 $aDORMANCY
700 1 $aDINI, M.
700 1 $aSCARIOTTO, S.
700 1 $aSANTOS, J.
700 1 $aHERTER, F. G.
700 1 $aRASEIRA, M. C. B.
773 $tJournal of Experimental Agriculture International, February 2018, Volume 20, Issue 4, p. 1-9. Article no.JEAI.39204. DOI: https://doi.org/10.9734/JEAI/2018/39204
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