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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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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Tacuarembó. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
28/06/2018 |
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Tipo de producción científica : |
Documentos |
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Autor : |
GIORELLO, D.; JAURENA, M.; BOGGIANO, P.; PÉREZ GOMAR, E. |
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Afiliación : |
DIEGO GERMAN GIORELLO LEITES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ENRIQUE PEREZ GOMAR CAPURRO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Resultados experimentales de la zafra 2011/2012 publicados en el 2° Seminario Internacional de Riego en Cultivos y Pasturas: Respuesta al riego suplementario en pasturas y forrajes |
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Fecha de publicación : |
2013 |
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Fuente / Imprenta : |
In: INIA Tacuarembó; Programa Nacional Producción Pasturas y Forrajes. Riego en cultivos y pasturas sobre suelos de basalto. Jornada de divulgación. Tacuarembó (UY): INIA, 2013. |
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Páginas : |
p. 12-33 |
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Serie : |
(INIA Serie Actividades de Difusión; 706) |
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ISSN : |
1688-9258 |
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Idioma : |
Español |
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Contenido : |
Los sistemas de producción de basalto (4.000.000 ha aproximadamente) en la actualidad se enfrentan al desafío de emprender un camino de intensificación con el objetivo de aumentar el beneficio económico, sin perder de vista la sustentabilidad de los recursos naturales. Una alternativa tecnológica para aumentar y estabilizar la oferta forrajera es la inclusión del riego en pasturas y forrajes, mediante riegos estratégicos con el objetivo de maximizar productividades
en forrajes especializados en producción de materia seca (MS) estival o con el fin de lograr el aumento de la persistencia de pasturas. |
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Thesagro : |
CULTIVO; PASTURA; RIEGO. |
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Asunto categoría : |
--
F06 Riego |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10595/1/ad-706-p.-12-33.pdf
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Marc : |
LEADER 01525naa a2200229 a 4500
001 1009524
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008 2013 bl uuuu u00u1 u #d
022 $a1688-9258
100 1 $aGIORELLO, D.
245 $aResultados experimentales de la zafra 2011/2012 publicados en el 2° Seminario Internacional de Riego en Cultivos y Pasturas$bRespuesta al riego suplementario en pasturas y forrajes
260 $c2013
300 $ap. 12-33
490 $a(INIA Serie Actividades de Difusión; 706)
520 $aLos sistemas de producción de basalto (4.000.000 ha aproximadamente) en la actualidad se enfrentan al desafío de emprender un camino de intensificación con el objetivo de aumentar el beneficio económico, sin perder de vista la sustentabilidad de los recursos naturales. Una alternativa tecnológica para aumentar y estabilizar la oferta forrajera es la inclusión del riego en pasturas y forrajes, mediante riegos estratégicos con el objetivo de maximizar productividades en forrajes especializados en producción de materia seca (MS) estival o con el fin de lograr el aumento de la persistencia de pasturas.
650 $aCULTIVO
650 $aPASTURA
650 $aRIEGO
700 1 $aJAURENA, M.
700 1 $aBOGGIANO, P.
700 1 $aPÉREZ GOMAR, E.
773 $tIn: INIA Tacuarembó; Programa Nacional Producción Pasturas y Forrajes. Riego en cultivos y pasturas sobre suelos de basalto. Jornada de divulgación. Tacuarembó (UY): INIA, 2013.
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