| |
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA La Estanzuela. |
|
Fecha : |
06/12/2019 |
|
Actualizado : |
05/09/2022 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
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
|
|
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
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA La Estanzuela (LE) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Tacuarembó. |
|
Fecha actual : |
25/06/2018 |
|
Actualizado : |
20/02/2019 |
|
Tipo de producción científica : |
Documentos |
|
Autor : |
JAURENA, M.; DE BARBIERI, I.; LAGOMARSINO, X.; LIMA, G.; PIÑEIRO, A.; LIMA, D.; SUÁREZ, M.; MEROLA, R.; GUTIERREZ, D.; ROVIRA, F. |
|
Afiliación : |
MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; XIMENA MARIA LAGOMARSINO LARRIERA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GERONIMO LIMA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALVARO MARTIN PIÑEIRO RODRIGUEZ MACEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DAVID WILLIAMS LIMA GONZALEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RUBEN EDI MEROLA BRITOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DANIEL ANTONIO GUTIERREZ RESTANO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO ROVIRA GALARRAGA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
|
Título : |
Impacto de la asignación de pastura y nivel de suplementación en la sustentabilidad de pasturas naturales sobre suelos superficiales de basalto. |
|
Fecha de publicación : |
2006 |
|
Fuente / Imprenta : |
ln: INIA TACUAREMBÓ. ESTACIÓN EXPERIMENTAL GLENCOE. Día de campo. Producción animal, pasturas. Estación Experimental Glencoe, Paysandú, 14 noviembre, 2006. Tacuarembó (Uruguay): INIA, 2006. |
|
Páginas : |
p. 5-6 |
|
Serie : |
(INIA Serie Actividades de Difusión ; 473) |
|
Idioma : |
Español |
|
Contenido : |
Objetivo general: Determinar el impacto de las variables carga animal y asignación de forraje combinadas con niveles de suplementación en la evolución de la vegetación de una pastura natural sobre suelos superficiales de Basalto. Objetivos específicos: • Estudiar los cambios de los atributos de la vegetación en respuesta a las variables carga animal y asignación de forraje en pastoreo ovino. • Seleccionar atributos y grupos funcionales indicadores que relacionen el impacto de la presión de pastoreo
con la degradación por sobrepastoreo. |
|
Palabras claves : |
ANIMAL PRODUCTION. |
|
Thesagro : |
PASTURAS NATURALES. |
|
Asunto categoría : |
L01 Ganadería |
|
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10477/1/SAD-473p5-6.pdf
|
|
Marc : |
LEADER 01525naa a2200277 a 4500
001 1058730
005 2019-02-20
008 2006 bl uuuu u00u1 u #d
100 1 $aJAURENA, M.
245 $aImpacto de la asignación de pastura y nivel de suplementación en la sustentabilidad de pasturas naturales sobre suelos superficiales de basalto.
260 $c2006
300 $ap. 5-6
490 $a(INIA Serie Actividades de Difusión ; 473)
520 $aObjetivo general: Determinar el impacto de las variables carga animal y asignación de forraje combinadas con niveles de suplementación en la evolución de la vegetación de una pastura natural sobre suelos superficiales de Basalto. Objetivos específicos: • Estudiar los cambios de los atributos de la vegetación en respuesta a las variables carga animal y asignación de forraje en pastoreo ovino. • Seleccionar atributos y grupos funcionales indicadores que relacionen el impacto de la presión de pastoreo con la degradación por sobrepastoreo.
650 $aPASTURAS NATURALES
653 $aANIMAL PRODUCTION
700 1 $aDE BARBIERI, I.
700 1 $aLAGOMARSINO, X.
700 1 $aLIMA, G.
700 1 $aPIÑEIRO, A.
700 1 $aLIMA, D.
700 1 $aSUÁREZ, M.
700 1 $aMEROLA, R.
700 1 $aGUTIERREZ, D.
700 1 $aROVIRA, F.
773 $tln: INIA TACUAREMBÓ. ESTACIÓN EXPERIMENTAL GLENCOE. Día de campo. Producción animal, pasturas. Estación Experimental Glencoe, Paysandú, 14 noviembre, 2006. Tacuarembó (Uruguay): INIA, 2006.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Tacuarembó (TBO) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
| Expresión de búsqueda válido. Check! |
|
|
