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Registro completo
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Biblioteca (s) : |
INIA Salto Grande; INIA Treinta y Tres. |
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Fecha : |
21/02/2014 |
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Actualizado : |
26/05/2022 |
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Autor : |
GLINSKI, J.; LIPIEC, J. |
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Afiliación : |
JAN GLINSKI; JERZY LIPIEC. |
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Título : |
Soil physical conditions and plant roots |
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Fecha de publicación : |
1990 |
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Fuente / Imprenta : |
Boca Ratón, Florida: CRC, 1990. |
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Páginas : |
250p. |
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ISBN : |
0-8493-6498-1 |
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Idioma : |
Inglés |
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Thesagro : |
ABSORCION DE AGUA; ABSORCION DE SUSTANCIAS NUTRITIVAS; ANATOMIA DE LA PLANTA; APLICACION DE ABONOS; CARACTERISTICAS MORFOLOGICAS SUELO; CONSUMO DE OXIGENO; CONTENIDO DE AGUA EN EL SUELO; CRECIMIENTO; ESTRUCTURA DEL SUELO; FACTORES AMBIENTALES; FACTORES EDAFICOS; MANEJO DEL SUELO; NECESIDADES DE LAS PLANTAS; NUTRICION DE LAS PLANTAS; PROPIEDADES FISICO-QUIMICAS SUELO; RAICES; RELACIONES PLANTA SUELO; SISTEMA POROSO DEL SUELO; TEMPERATURA DEL SUELO; TEXTURA DEL SUELO. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 01105nam a2200373 a 4500
001 1013616
005 2022-05-26
008 1990 bl uuuu u00u1 u #d
020 $a0-8493-6498-1
100 1 $aGLINSKI, J.
245 $aSoil physical conditions and plant roots
260 $aBoca Ratón, Florida: CRC$c1990
300 $a250p.
650 $aABSORCION DE AGUA
650 $aABSORCION DE SUSTANCIAS NUTRITIVAS
650 $aANATOMIA DE LA PLANTA
650 $aAPLICACION DE ABONOS
650 $aCARACTERISTICAS MORFOLOGICAS SUELO
650 $aCONSUMO DE OXIGENO
650 $aCONTENIDO DE AGUA EN EL SUELO
650 $aCRECIMIENTO
650 $aESTRUCTURA DEL SUELO
650 $aFACTORES AMBIENTALES
650 $aFACTORES EDAFICOS
650 $aMANEJO DEL SUELO
650 $aNECESIDADES DE LAS PLANTAS
650 $aNUTRICION DE LAS PLANTAS
650 $aPROPIEDADES FISICO-QUIMICAS SUELO
650 $aRAICES
650 $aRELACIONES PLANTA SUELO
650 $aSISTEMA POROSO DEL SUELO
650 $aTEMPERATURA DEL SUELO
650 $aTEXTURA DEL SUELO
700 1 $aLIPIEC, J.
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Registro original : |
INIA Salto Grande (SG) |
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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. |
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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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