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| Registros recuperados : 6 | |
| 1. |  | LORENZO, D.; PAZ, D.; DAVIES, P.; VILLAMIL, J.; VILA, R.; CAÑIGUERAL, S.; DELLACASSA, E. Application of multidimensional gas chromatography to the enantioselective characterisation of the essential oil of Eupatorium buniifolium Hooker et Arnott. Phytochemical Analysis, January 2005, Volume 16, Issue 1, Pages 39-44. Article history: Received 4 February 2003 / Revised 27 May 2003 / Accepted 30 September 2003.| Biblioteca(s): INIA Las Brujas. |
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| 3. | | LORENZO, D.; PAZ, D.; DAVIES, P.; VILLAMIL, J.; VILA, R.; CAÑIGUERAL, S.; DELLACASSA, E. Characterization and enantiomeric distribution of some terpenes in the essential oil of a Uruguayan biotype of Salvia sclarea L. Flavour and Fragrance Journal, July 2014, Volume 19, Issue 4, Pages 303-307. Article history: Received 15 September 2002 / Revised 30 January 2003 / Accepted 2 February 2003.| Biblioteca(s): INIA Las Brujas. |
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| 4. | | LORENZO, D.; PAZ, D.; DAVIES, P.; VILA, R.; CAÑIGUERAL, S.; DELLACASSA, E. Composition of a new essential oil type of Lippia alba (Mill.) N.E. Brown from Uruguay Flavour and Fragrance Journal, 2001, Volume 16, Issue 5, Pages 356-359. Article history: Received 11 July 2000 / Revised 21 March 2001 / Accepted 24 March 2001.| Biblioteca(s): INIA Las Brujas. |
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| 5. | | VALLVERDÚ, C.; VILA, R.; LORENZO, D.; PAZ, D.; DELLACASSA, E.; DAVIES, P.; VILLAMIL, J.; TOMI, F.; CASANOVA, J.; CAÑIGUERAL, S. Composition of a new essential oil type of Lippia alba (Mill.) N.E. Brown from Uruguay. Flavour and Fragrance Journal, 2001, Volume 16, Issue 5, Pages 356-359. Article history: Received 11 July 2000 / Revised 21 March 2001 / Accepted 24 March 2001.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 6 | |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
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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Circulación / Nivel : |
Internacional - -- |
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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. |
|
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
|
|
Marc : |
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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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INIA La Estanzuela (LE) |
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