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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha : |
21/02/2014 |
Actualizado : |
05/11/2020 |
Autor : |
LETELIER A., E. |
Afiliación : |
ELÍAS LETELIER A. |
Título : |
Labranza cero en las terrazas marinas de la VI región y comuna de Santo Domingo : Chile. |
Fecha de publicación : |
1986 |
Fuente / Imprenta : |
ln: Reunión sobre Sistemas de Labranza y Conservación de Suelos : 1985 nov. 5-7 : Marcos Juárez, Córdoba, Argentina Trabajos presentados. Montevideo (Uruguay): IICA/BID/PROCISUR, 1986. |
Páginas : |
p. 105-114 |
Serie : |
(Dialogo / IICA-PROCISUR; 15) |
ISBN : |
92-9039-133-8 |
Idioma : |
Español |
Thesagro : |
CERO-LABRANZA; CHILE; CULTIVO EN TERRAZAS; FACTORES DE RENDIMIENTO; MANEJO DEL SUELO; TERRAZAS; TRITICUM AESTIVUM. |
Asunto categoría : |
A50 Investigación agraria |
Marc : |
LEADER 00808naa a2200229 a 4500 001 1024574 005 2020-11-05 008 1986 bl uuuu u00u1 u #d 020 $a92-9039-133-8 100 1 $aLETELIER A., E. 245 $aLabranza cero en las terrazas marinas de la VI región y comuna de Santo Domingo$bChile. 260 $c1986 300 $ap. 105-114 490 $a(Dialogo / IICA-PROCISUR; 15) 650 $aCERO-LABRANZA 650 $aCHILE 650 $aCULTIVO EN TERRAZAS 650 $aFACTORES DE RENDIMIENTO 650 $aMANEJO DEL SUELO 650 $aTERRAZAS 650 $aTRITICUM AESTIVUM 773 $tln: Reunión sobre Sistemas de Labranza y Conservación de Suelos : 1985 nov. 5-7 : Marcos Juárez, Córdoba, Argentina Trabajos presentados. Montevideo (Uruguay): IICA/BID/PROCISUR, 1986.
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INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
06/12/2019 |
Actualizado : |
05/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
BERRO, I.; LADO, B.; NALIN, R.S.; QUINCKE, M.; GUTIÉRREZ, L. |
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. |
Título : |
Training population optimization for genomic selection. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Plant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028 |
DOI : |
10.3835/plantgenome2019.04.0028 |
Idioma : |
Inglés |
Notas : |
Article histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019. |
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 |
Palabras claves : |
GENOMIC SELECTION; SELECCIÓN GENÓMICA. |
Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
Asunto categoría : |
F01 Cultivo |
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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