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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
06/12/2019 |
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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 : |
BERRO, I.; LADO, B.; NALIN, R.S.; QUINCKE, M.; GUTIÉRREZ, L. |
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Afiliación : |
Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay.; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA./ Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay. |
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Título : |
Training population optimization for genomic selection. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Plant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028 |
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DOI : |
10.3835/plantgenome2019.04.0028 |
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Idioma : |
Inglés |
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Notas : |
Article histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019. |
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Contenido : |
ABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009). MenosABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individ... Presentar Todo |
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Palabras claves : |
GENOMIC SELECTION; SELECCIÓN GENÓMICA. |
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Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16707/1/The-Plant-Genome-2019-Berro-Training-Population-Optimization-for-Genomic-Selection.pdf
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.3835/plantgenome2019.04.0028
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Marc : |
LEADER 02385naa a2200241 a 4500
001 1060511
005 2022-09-05
008 2019 bl uuuu u00u1 u #d
024 7 $a10.3835/plantgenome2019.04.0028$2DOI
100 1 $aBERRO, I.
245 $aTraining population optimization for genomic selection.$h[electronic resource]
260 $c2019
500 $aArticle histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019.
520 $aABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009).
650 $aTRIGO
650 $aTRITICUM AESTIVUM
653 $aGENOMIC SELECTION
653 $aSELECCIÓN GENÓMICA
700 1 $aLADO, B.
700 1 $aNALIN, R.S.
700 1 $aQUINCKE, M.
700 1 $aGUTIÉRREZ, L.
773 $tPlant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028
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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 : |
16/09/2014 |
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Actualizado : |
18/06/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
VÁZQUEZ, D.; BERGER, A.G.; CUNIBERTI , M.; BAINOTTI , C.; ZAVARIZ DE MIRANDA , M.; SCHEEREN , P.L.; JOBET, C.; ZÚÑIGA, J.; CABRERA, G.; VERGES, R.; PEÑA, R.J |
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Afiliación : |
DANIEL VÁZQUEZ PEYRONEL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES GUSTAVO BERGER RICCA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Influence of cultivar and environment on quality of Latin American wheats. |
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Fecha de publicación : |
2012 |
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Fuente / Imprenta : |
Journal of cereal science , v. 56, n.2, p. 196-203, 2012. |
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DOI : |
10.1016/j.jcs.2012.03.004 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 15 August 2011 / Received in revised form / 20 March 2012 / Accepted 27 March 2012. |
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Contenido : |
ABSTRACT.
Wheat consumption is growing, with processors asking for wheat-based products showing better and more consistent quality. Genotype, environment and their interaction (G #1; E) play an important role in the final expression of quality attributes. An international research consortium was developed in order to evaluate the magnitude of genotype, environment and G #1; E effects on wheat quality of cultivars developed for different agro-ecological zones in Latin America. Genotypes released in Argentina, Brazil, Chile, Mexico, Paraguay and Uruguay, were cultivated in twenty different environments within the participating countries. Each environment was characterized for cultural practices, soil type and climatic conditions. Grain yield and analyses of test weight, protein, ash, gluten, Alveograph, Farinograph, Falling Number, SDS sedimentation and flour color were determined. Allelic variations of puroindolines and
glutenins were determined in all the genotypes evaluated. Both puroindoline and gluten protein alleles corresponded to genotypes possessing medium to very good bread making quality. Large variability for most quality attributes evaluated was observed, with wider ranges in quality parameters across environments than among genotypes; even for parameters known to be largely determined by genotype. The importance of growing environment on grain quality was proved, suggesting that breeders’ quality objectives should be adapted to the targeted environments.
#1; 2012 Elsevier Ltd. All rights reserved MenosABSTRACT.
Wheat consumption is growing, with processors asking for wheat-based products showing better and more consistent quality. Genotype, environment and their interaction (G #1; E) play an important role in the final expression of quality attributes. An international research consortium was developed in order to evaluate the magnitude of genotype, environment and G #1; E effects on wheat quality of cultivars developed for different agro-ecological zones in Latin America. Genotypes released in Argentina, Brazil, Chile, Mexico, Paraguay and Uruguay, were cultivated in twenty different environments within the participating countries. Each environment was characterized for cultural practices, soil type and climatic conditions. Grain yield and analyses of test weight, protein, ash, gluten, Alveograph, Farinograph, Falling Number, SDS sedimentation and flour color were determined. Allelic variations of puroindolines and
glutenins were determined in all the genotypes evaluated. Both puroindoline and gluten protein alleles corresponded to genotypes possessing medium to very good bread making quality. Large variability for most quality attributes evaluated was observed, with wider ranges in quality parameters across environments than among genotypes; even for parameters known to be largely determined by genotype. The importance of growing environment on grain quality was proved, suggesting that breeders’ quality objectives should be adapted to the targeted environments.
#1; 201... Presentar Todo |
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Palabras claves : |
CALIDAD DE TRIGO; GENOTIPO; INTERACCIÓN GXE; MEDIO AMBIENTE. |
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Thesagro : |
MEJORAMIENTO CULTIVOS DE INVIERNO. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02557naa a2200325 a 4500
001 1050322
005 2019-06-18
008 2012 bl uuuu u00u1 u #d
024 7 $a10.1016/j.jcs.2012.03.004$2DOI
100 1 $aVÁZQUEZ, D.
245 $aInfluence of cultivar and environment on quality of Latin American wheats.$h[electronic resource]
260 $c2012
500 $aArticle history: Received 15 August 2011 / Received in revised form / 20 March 2012 / Accepted 27 March 2012.
520 $aABSTRACT. Wheat consumption is growing, with processors asking for wheat-based products showing better and more consistent quality. Genotype, environment and their interaction (G #1; E) play an important role in the final expression of quality attributes. An international research consortium was developed in order to evaluate the magnitude of genotype, environment and G #1; E effects on wheat quality of cultivars developed for different agro-ecological zones in Latin America. Genotypes released in Argentina, Brazil, Chile, Mexico, Paraguay and Uruguay, were cultivated in twenty different environments within the participating countries. Each environment was characterized for cultural practices, soil type and climatic conditions. Grain yield and analyses of test weight, protein, ash, gluten, Alveograph, Farinograph, Falling Number, SDS sedimentation and flour color were determined. Allelic variations of puroindolines and glutenins were determined in all the genotypes evaluated. Both puroindoline and gluten protein alleles corresponded to genotypes possessing medium to very good bread making quality. Large variability for most quality attributes evaluated was observed, with wider ranges in quality parameters across environments than among genotypes; even for parameters known to be largely determined by genotype. The importance of growing environment on grain quality was proved, suggesting that breeders’ quality objectives should be adapted to the targeted environments. #1; 2012 Elsevier Ltd. All rights reserved
650 $aMEJORAMIENTO CULTIVOS DE INVIERNO
653 $aCALIDAD DE TRIGO
653 $aGENOTIPO
653 $aINTERACCIÓN GXE
653 $aMEDIO AMBIENTE
700 1 $aBERGER, A.G.
700 1 $aCUNIBERTI , M.
700 1 $aBAINOTTI , C.
700 1 $aZAVARIZ DE MIRANDA , M.
700 1 $aSCHEEREN , P.L.
700 1 $aJOBET, C.
700 1 $aZÚÑIGA, J.
700 1 $aCABRERA, G.
700 1 $aVERGES, R.
700 1 $aPEÑA, R.J
773 $tJournal of cereal science$gv. 56, n.2, p. 196-203, 2012.
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