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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
18/05/2023 |
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Actualizado : |
18/05/2023 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
ARRUABARRENA, A.; MOLTINI, A.; LUQUE, E.; LAXAGUE, J.; BONJOUR, F.; IBÁÑEZ, F.; GONZÁLEZ-ARCOS, M.; VIDAL, S.; LADO, J. |
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Afiliación : |
ANA ARRUABARRENA PASCOVICH, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANA INÉS MOLTINI PALADINO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MAYZA ELEANA LUQUE NUÑEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE IGNACIO LAXAGUE BARNECHE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FLORENCIA BONJOUR ALANIZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FACUNDO IBÁÑEZ SILVA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MATIAS GONZÁLEZ-ARCOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SABINA VIDAL, Laboratorio de Biología Molecular Vegetal, Instituto de Química Biológica, Facultad de Ciencias, Universidad de la República, Uruguay; JOANNA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Superando barreras genéticas con CRISPR/Cas9: plantas de tomate indeterminadas con más licopeno. 343. (resúmen). |
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Complemento del título : |
Conferencia 10. Mesa Edición genómica de los cultivos. Organiza SBBM (Sociedad de Bioquímica & Biología Molecular). |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
In: Physiological Mini Reviews, 2022, volume 15, Special Issue: III (3er) Congreso Nacional de Biociencias Octubre 2022, Montevideo, Uruguay. p.54. |
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ISSN : |
1669-5410 |
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Idioma : |
Español |
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Notas : |
Resumen publicado en las jornadas de BIOCIENCIAS: II Jornadas Binacionales Argentina-Uruguay; III Congreso Nacional 2022 "Ciencia para el desarrollo sustentable". |
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Contenido : |
En este trabajo, partimos de una línea elite de tomate de crecimiento indeterminado y generamos mutaciones no funcionales dirigidas al gen CYCB mediante edición génica, utilizando el sistema CRISPR/Cas9. |
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Palabras claves : |
Mejoramiento genético vegetal; Solanum lycopersicum. |
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Thesagro : |
BIOTECNOLOGIA. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17141/1/ArruabarrenaA.-et.al-p54-3er-Congreso-Nacional-Biociencias-2022.pdf
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Marc : |
LEADER 01248nam a2200265 a 4500
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008 2022 bl uuuu u01u1 u #d
022 $a1669-5410
100 1 $aARRUABARRENA, A.
245 $aSuperando barreras genéticas con CRISPR/Cas9$bplantas de tomate indeterminadas con más licopeno. 343. (resúmen).$h[electronic resource]
260 $aIn: Physiological Mini Reviews, 2022, volume 15, Special Issue: III (3er) Congreso Nacional de Biociencias Octubre 2022, Montevideo, Uruguay. p.54.$c2022
500 $aResumen publicado en las jornadas de BIOCIENCIAS: II Jornadas Binacionales Argentina-Uruguay; III Congreso Nacional 2022 "Ciencia para el desarrollo sustentable".
520 $aEn este trabajo, partimos de una línea elite de tomate de crecimiento indeterminado y generamos mutaciones no funcionales dirigidas al gen CYCB mediante edición génica, utilizando el sistema CRISPR/Cas9.
650 $aBIOTECNOLOGIA
653 $aMejoramiento genético vegetal
653 $aSolanum lycopersicum
700 1 $aMOLTINI, A.
700 1 $aLUQUE, E.
700 1 $aLAXAGUE, J.
700 1 $aBONJOUR, F.
700 1 $aIBÁÑEZ, F.
700 1 $aGONZÁLEZ-ARCOS, M.
700 1 $aVIDAL, S.
700 1 $aLADO, J.
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Registro original : |
INIA Las Brujas (LB) |
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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
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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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