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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 : |
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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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| Registros recuperados : 214 | |
| 201. |  | DIAZ, R.; CORSI, W.; RIOS, A.; ZERBINO, M.S.; PEREA, C.; DÍAZ, M.; VERGES, R.; GERMAN, S.; ABADIE, T.; TROCHE, L. Reseña de la producción e investigación de trigo en el Uruguay. Colonia (Uruguay): CIAAB, 1982. 13 p. (CIAAB Miscelánea ; 44) Informe presentado en la Reunión de Coordinadores Nacionales del Proyecto Trigo del Convenio IIC Cono Sur/BID en La Estanzuela, del 11 al 13 de Agosto de 1981.| Biblioteca(s): INIA La Estanzuela. |
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| 202. | | SEMINARIO INTERNACIONAL DE TRIGO, 2014, LA ESTANZUELA, COLONIA, UY; GERMAN, S.; QUINCKE, M.; VÁZQUEZ, D.; CASTRO, M.; GARCIA-LAMOTHE, A.; PEREYRA, S.; SILVA, P.; RESTAINO, E.; SILVERA, A. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. 89 p. Encuadernado con espiral doble. Habrá edición posterior de los trabajos completos.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA La Estanzuela. |
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| 203. | | CALVO-SALAZAR, V.; SINGH, R. P.; HUERTA-ESPINO, J.; CRUZ-IZ QUIERDO, S.; LOBATO-ORTIZ, R.; SANDOVAL-ISLAS, S.; VARGAS-HERNÁNDEZ, M.; GERMAN, S.; SILVA, P.; BASNET, B. R.; LAN, C. X.; HERRERA-FOESSEL, S. A. Genetic Analysis of Resistance to Leaf Rust and Yellow Rust in Spring Wheat Cultivar Kenya Kongoni. Plant Disease, v. 99, no.1153-1160, 2015. Article history: Accepted for publication 9 February 2015/Published Online:23 Jun 2015.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA La Estanzuela. |
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| 204. | | GUTIÉRREZ, L.; BERBERIAN, N.; CAPETTINI, F.; FALCIONI, E.; FROS, D.; GERMAN, S.; HAYES, P. M.; HUERTA-ESPINO, J.; HERRERA, S.; PEREYRA, S.; PEREZ, C.; SANDOVAL-ISLAS, S.; SINGH, R.; CASTRO, A. Genome-wide association mapping identifies disease resistance QTLs in barley germplasm from Latin America: chapter 18. In: INTERNATIONAL BARLEY GENETICS SYMPOSIUM, 11., 2012, Hangzhou, CN. Advance in barley sciences: proceedings. Dordrecht: Zhejiang University Press/Springer, 2013. p. 209-215| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA La Estanzuela. |
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| 205. | | HICKEY, L.T.; LAWSON, W.; PLATZ, G.J.; FOWLER, R.A; ARIEF, V.N.; DIETERS, M.; GERMAN, S.; FLETCHER, S.; PARK, R.F.; PEREYRA, S.; SINGH, D.; FRANCKOWIAK, J. Mapping quantitative trait loci for partial resistance to powdery mildew in an Australian barley population. Crop Science, v. 52, n. 3, p. 1021-1032, 2012.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 1 |
| Biblioteca(s): INIA La Estanzuela. |
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| 206. | | GUTIÉRREZ, L.; BERBERIAN, N.; CAPETTINI. F.; FALCIONI, E.; FROS, D.; GERMAN, S.; HAYES, P.; HUERTA-ESPINO, J.; HERRERA, S.; PEREYRA, S.; PEREZ, C.; SANDOVAL-ISLAS, S.; SINGH, R.; CASTRO, A. Identifying disease resistance QTLs in barley germplasm from Latin America using genome-wide association mapping. In: NORTH AMERICAN BARLEY RESEARCHERS WORKSHOP, 20., 2011, Corvallis, OR. Abstracts. Corvallis: OSU, 2011.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA La Estanzuela. |
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| 207. | | GUTIÉRREZ, L.; BERBERIAN, N.M.; GERMÁN, S.; PEREYRA, S.; CAPETTINI, F.; FALCONI, E.; HAYES, P.M.; PÉREZ, C.; SANDOVAL-ISLAS, S.; ORJEDA, G.; NEYRA, E.; GONZA, V.A.; CASTRO, A. Multi-environment multi-QTL association mapping in barley In: INTERNATIONAL ANIMAL AND PLANT GENOME CONFERENCE, 21., 2013, San Diego, CA, US. Genome mapping, tagging and characterization: wheat, barley, oat, and related. P0285. [s.l.: INTLPAG], 2013.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA La Estanzuela. |
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| 208. | | ROSAS, J.E.; MARTÍNEZ, S.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; MOSQUERA, G.; CRUZ, M.; GARAYCOCHEA, S.; MONTEVERDE, E.; MOSQUERA, G.; CRUZ, M.; GARAYCOCHEA, S.; MONTEVERDE, E.; MCCOUCH, S.; GERMAN, S.; JANNINK, J.; GUTIÉRREZ, L. Resistencia múltiple a enfermedades tropicales y templadas del tallo y la vaina del arroz. In: UNIVERSIDAD DE LA REPÚBLICA (UDELAR). FACULTAD DE AGRONOMÍA. Resúmenes. Jornadas de Investigación, 8-9 nov., 2018, Montevideo, Uruguay. Montevideo; FAGRO, 2019. p. 19 Trabajo originalmente publicado en: Rosas, J.E.; Martínez, S.; Blanco, P.; Pérez de Vida, F.; Bonnecarrère, V.; Mosquera, G.; Cruz, M. Garaycochea, S.; Monteverde, E.; McCouch, S.; Germán, S.; Jannink, J.L.; Gutiérrez, L. 2018....| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 209. | | ROSAS, J.E.; MARTÍNEZ, S.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; MOSQUERA, G.; CRUZ, M.; GARAYCOCHEA, S.; MONTEVERDE, E.; GERMAN, S.; MCCOUCH, S.; JANNINK, J.; GUTIÉRREZ, L. Resistance to multiple temperate and tropical stem and sheath diseases of rice. The Plant Genome, 2018, v. 11, no. 1. art. 170029. OPEN ACCESS. Doi: https://doi.org/10.3835/plantgenome2017.03.0029 p. 1-13. History paper: Received 29 Mar. 2017, Accepted 19 Sep. 2017. Publihed online December 14, 2017.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : -- - -- |
| Biblioteca(s): INIA Treinta y Tres. |
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| 210. | | ROCHA, C.M.L.; VELLICCE, G.R.; GARCÍA, M.G.; PARDO, E.M.; RACEDO, J.; PERERA, M.F.; DE LUCÍA, A.; BONNECARRERE, M.; GERMAN, S.; MARCELINO, F.; LEDESMA, F.; REZNIKOV, S.; PLOPER, D.L.; WELIN, B.; CASTAGNARO, A.P. Use of AFLP markers to estimate molecular diversity of Phakopsora pachyrhizi. Electronic Journal of Biotechnology, 2015, v. 18, no. 6, p. 439-444. OPEN ACCESS. Article history: Received 24 April 2015. Accepted 23 September 2015. Available online 28 October 2015.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 211. | | PEREYRA, S.; CASTRO, M.; VERO, S.; SILVA, P.; CAL, A.; TISCORNIA, G.; GONZALEZ, N.; BENTOS, D.; ALVAREZ, W.; RABAZA, S.; SEVILLANO, L.; BRANCATTI, G.; FRANCIA, C.; RAFFO, M.A.; GERMAN, S.; PÉREZ, C.; GARMENDIA, G.; PAREJA, L.; RODRÍGUEZ, A.; PENDAS, C.; QUINCKE, M.; VÁZQUEZ, D.; RODRIGUEZ, M. Fusariosis de la espiga en trigo y micotoxinas asociadas: contribuyendo a reducir su riesgo. Cultivos. Revista INIA Uruguay, Diciembre 2023, no.75 p.54-58. (Revista INIA; 75).| Tipo: Artículos en Revistas Agropecuarias |
| Biblioteca(s): INIA Las Brujas. |
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| 212. | | GUTIÉRREZ, L.; GERMAN, S.; PEREYRA, S.; HAYES, P.M.; PÉREZ, C.A.; CAPETTINI, F.; LOCATELLI, A.; BERBERIAN, N.M.; FALCONI, E.E.; ESTRADA, R.; DARIO FROS; GONZA, V.; ALTAMIRANO, H.; HUERTA-ESPINO, J.; NEYRA, E.; ORJEDA, G.; SANDOVAL-ISLAS, S.; SINGH, R.; TURKINGTON, K.; CASTRO, A.J. Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America. Theoretical Applied Genetics, 2015, v.128, n.3 p.501-516. Article history: Received 15 October 2014 / Accepted 17 December 2014 / First Online 30 December 2014.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA La Estanzuela. |
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| 213. | | LEWIS ,C.M.; PERSOONS, A.; BEBBER, D.P.; KIGATHI, R.N.; MAINTZ, J.; FINDLAY, K.; BUENO-SANCHO, V.; CORREDOR-MORENO, P.; HARRINGTON, S.A.; KANGARA, N.; BERLIN, A.; GARCIA, R.; GERMAN, S.; HANZALOVÁ, A.; HODSON, D.P.; HOVMØLLER, M.S.; HUERTA-ESPINO, J.; IMTIAZ, M.; MIRZA, J.I.; JUSTESEN, A.F.; NIKS, R.E.; OMRANI, A.; PATPOUR, M.; PRETORIUS ,Z.A.; ROOHPARVAR, R.; SELA, H.; SINGH, R.P.; STEFFENSON ,B.; VISSER, B.; FENWICK, P.M.; THOMAS, J.; WULFF, B.B.H.; SAUNDERS, D.G.O. Potential for re-emergence of wheat stem rust in the United Kingdom. Veterinary Pathology [Vet Pathol], 2018 Sep 24, p. 300985818798117.OPEN ACCESS. Article history: Date Created: 20181002 //Latest Revision: 20181003.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA La Estanzuela. |
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| 214. | | LANGRIDGE, P.; ALAUX, M.; ALMEIDA, N.F.; AMMAR, K.; BAUM, M.; BEKKAOUI, F.; BENTLEY, A.R.; BERES, B.L.; BERGER, B.; BRAUN, H.-J.; BROWN-GUEDIRA, G.; BURT, C.J.; CACCAMO, M.J.; CATTIVELLI, L.; CHARMET, G.; CIVÁN, P.; CLOUTIER, S.; COHAN, J-P.; DEVAUX, P.; DOOHAN, F.M.; DRECCER, M.F.; FERRAHI, M.; GERMAN, S.; GOODWIN, S.B.; GRIFFITHS, S.; GUZMÁN, C.; HANDA, H.; HAWKESFORD, M.J.; HE, Z.; HUTTNER, E.; IKEDA, T.M.; KILIAN, B.; KING, I.P.; KING, J.; KIRKEGAARD, J.A.; LAGE, J.; LE GOUIS, J.; MONDAL, S.; MULLINS, E.; ORDON, F.; ORTIZ-MONASTERIO, J.I.; ÖZKAN, H.; ÖZTÜRK, I.; PEREYRA, S.; POZNIAK, C.J.; QUESNEVILLE, H.; QUINCKE, M.; REBETZKE, G.J.; CHRISTOPH REIF, J.; SAAVEDRA-BRAVO, T.; SCHURR, U.; SHARMA, S.; SINGH, S.K.; SINGH, R.P.; SNAPE, J.W.; TADESSE, W.; TSUJIMOTO, H.; TUBEROSA, R.; WILLIS, T.G.; ZHANG, X. Meeting the challenges facing wheat production: the strategic research agenda of the Global Wheat Initiative. Agronomy, 2022, volume 12, issue 11, 2767. OPEN ACCESS. doi: https://doi.org/10.3390/agronomy12112767 Article history: Received 26 September 2022; Revised 28 October 2022; Accepted 29 October 2022; Published 7 November 2022. -- Academic Editor: Andreas Katsiotis. -- Corresponding author: Langridge, P.; School of Agriculture, Food and...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 214 | |
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