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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 Tacuarembó. |
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Fecha actual : |
07/06/2018 |
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Actualizado : |
11/11/2019 |
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Tipo de producción científica : |
Documentos |
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Autor : |
DEL CAMPO, M.; RODRIGUEZ, A.; SALLES, F.; HERNÁNDEZ, M.; FERRON, M.; MONDRAGÓN-ANCELMO, J; BOTTERO, D.; FREITAS, G.; ALBERNAZ, F.; SOARES DE LIMA, J.M.; ANCHAÑO, A.; GIORELLO, D. |
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Afiliación : |
MARCIA DEL CAMPO GIGENA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANALIA VERONICA RODRIGUEZ PEREYRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MIRNA JUDIT FERRON ERRAMOUSPE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JAIME MONDRAGÓN-ANCELMO; SERGIO DANIEL BOTTERO REGGI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO CELIAR FREITAS VAZQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FRANCO DANIEL ALBERNAZ SEJAS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN MANUEL SOARES DE LIMA LAPETINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALVARO MARTIN ANCHAÑO VIDAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DIEGO GERMAN GIORELLO LEITES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Castración en bovinos. |
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Fecha de publicación : |
2011 |
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Fuente / Imprenta : |
ln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, setiembre 2011, Paysandú, Uruguay. Propuestas tecnológicas para el incremento de la productividad, la valorización y el ingreso económico para sistemas ganaderos de basalto. Tacuarembó (Uruguay): INIA, 2011. |
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Páginas : |
p. 60-63 |
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Serie : |
(INIA Serie Actividades de Difusión ; 657) |
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Idioma : |
Español |
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Contenido : |
Se realizaron experimentos con animales de diferente edad (1 semana, 1 mes, 6/7 meses) y utilizando diferentes métodos de castración. |
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Thesagro : |
BIENESTAR ANIMAL. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10079/1/SAD657p60-63.pdf
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Marc : |
LEADER 01112naa a2200289 a 4500
001 1058676
005 2019-11-11
008 2011 bl uuuu u00u1 u #d
100 1 $aDEL CAMPO, M.
245 $aCastración en bovinos.
260 $c2011
300 $ap. 60-63
490 $a(INIA Serie Actividades de Difusión ; 657)
520 $aSe realizaron experimentos con animales de diferente edad (1 semana, 1 mes, 6/7 meses) y utilizando diferentes métodos de castración.
650 $aBIENESTAR ANIMAL
700 1 $aRODRIGUEZ, A.
700 1 $aSALLES, F.
700 1 $aHERNÁNDEZ, M.
700 1 $aFERRON, M.
700 1 $aMONDRAGÓN-ANCELMO, J
700 1 $aBOTTERO, D.
700 1 $aFREITAS, G.
700 1 $aALBERNAZ, F.
700 1 $aSOARES DE LIMA, J.M.
700 1 $aANCHAÑO, A.
700 1 $aGIORELLO, D.
773 $tln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, setiembre 2011, Paysandú, Uruguay. Propuestas tecnológicas para el incremento de la productividad, la valorización y el ingreso económico para sistemas ganaderos de basalto. Tacuarembó (Uruguay): INIA, 2011.
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INIA Tacuarembó (TBO) |
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