|
|
Registro completo
|
Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
09/12/2019 |
Actualizado : |
09/03/2021 |
Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
Autor : |
RODRÍGUEZ, V.; PARODI, P.; SCHANZEMBACH, M.; MATTO, C.; GRILLE, L.; GIANNECCHINI , E.; RIVERO, R. |
Afiliación : |
VICTOR RODRIGUEZ, Dilave; PABLO PARODI TEXEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCOS SCHANZEMBACH, Dilave; CAROLINA MATTO, DILAVE; LUCIA GRILLE, DILAVE; EDGARDO GIANNECCHINI, DILAVE; RODOLFO RIVERO, DILAVE. |
Título : |
Descripción de tres focos de granuloma nasal bovina. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
In: Jornadas Uruguayas de Buiatría, 47., 2019, Paysandú, UY.; Matto, ; Gianeechini, E. (Ed.). Paysandú: Centro Médico Veterinario de Paysandú/Sociedad Uruguaya de Buiatría, 2019. |
Páginas : |
P. 131-133 |
Idioma : |
Español |
Palabras claves : |
BOVINOS LECHEROS; PLATAFORMA DE SALUD ANIMAL; RINITIS ATÓPICA. |
Asunto categoría : |
L73 Enfermedades de los animales |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13896/1/JUB.no.47.2019.p.131-133.pdf
|
Marc : |
LEADER 00756nam a2200217 a 4500 001 1060517 005 2021-03-09 008 2019 bl uuuu u01u1 u #d 100 1 $aRODRÍGUEZ, V. 245 $aDescripción de tres focos de granuloma nasal bovina.$h[electronic resource] 260 $aIn: Jornadas Uruguayas de Buiatría, 47., 2019, Paysandú, UY.; Matto, ; Gianeechini, E. (Ed.). Paysandú: Centro Médico Veterinario de Paysandú/Sociedad Uruguaya de Buiatría$c2019 300 $aP. 131-133 653 $aBOVINOS LECHEROS 653 $aPLATAFORMA DE SALUD ANIMAL 653 $aRINITIS ATÓPICA 700 1 $aPARODI, P. 700 1 $aSCHANZEMBACH, M. 700 1 $aMATTO, C. 700 1 $aGRILLE, L. 700 1 $aGIANNECCHINI , E. 700 1 $aRIVERO, R.
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 La Estanzuela. |
Fecha actual : |
26/09/2014 |
Actualizado : |
06/11/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
B - 5 |
Autor : |
LADO, B.; MATUS, I.; RODRIGUEZ, A.; INOSTROZA, L.; POLAND, J.; BELZILE ,F.; DEL POZO, A.; QUINCKE, M.; CASTRO, M.; VON ZITZEWITZ, J. |
Afiliación : |
BETTINA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARINA CASTRO DERENYI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JARISLAV RAMON VON ZITZEWITZ VON SALVIATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. |
Fecha de publicación : |
2013 |
Fuente / Imprenta : |
G3: Genes, Genomes, Genetics (Bethesda), v. 3, n,12, p. 2105-2114, 2013.OPEN ACCESS. |
ISSN : |
2160-1836. |
DOI : |
10.1534/g3.113.007807 |
Idioma : |
Inglés |
Notas : |
Article history: Received 2013 Aug 26 // Accepted 2013 Sep 18. |
Contenido : |
Abstract:
In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models. MenosAbstract:
In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confir... Presentar Todo |
Palabras claves : |
GBLUP; GENOMIC SELECTION; GENOTIPADO POR SECUENCIACIÓN; GENOTYPING BY SEQUENCING; GENPRED; LOCUS DE UN CARÁCTER CUANTITATIVO; MEJOR PREDICTOR LINEAR INSESGADO; POLIMORFISMO DE NUCLEÓTICO SIMPLE; QTL; QUANTITATIVE TRAIT LOCUS; SELECCIÓN GENÓMICA; SHARED DATA RESOURCES; SINGLE NUCLEOTIDE POLYMORPHISM; SPATIAL CORRECTION; WHEAT. |
Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13756/1/G3Bethesda-v.-3-n12-p.-2105-2114-2013.pdf
|
Marc : |
LEADER 03249naa a2200469 a 4500 001 1050586 005 2019-11-06 008 2013 bl uuuu u00u1 u #d 022 $a2160-1836. 024 7 $a10.1534/g3.113.007807$2DOI 100 1 $aLADO, B. 245 $aIncreased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.$h[electronic resource] 260 $c2013 500 $aArticle history: Received 2013 Aug 26 // Accepted 2013 Sep 18. 520 $aAbstract: In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models. 650 $aTRIGO 650 $aTRITICUM AESTIVUM 653 $aGBLUP 653 $aGENOMIC SELECTION 653 $aGENOTIPADO POR SECUENCIACIÓN 653 $aGENOTYPING BY SEQUENCING 653 $aGENPRED 653 $aLOCUS DE UN CARÁCTER CUANTITATIVO 653 $aMEJOR PREDICTOR LINEAR INSESGADO 653 $aPOLIMORFISMO DE NUCLEÓTICO SIMPLE 653 $aQTL 653 $aQUANTITATIVE TRAIT LOCUS 653 $aSELECCIÓN GENÓMICA 653 $aSHARED DATA RESOURCES 653 $aSINGLE NUCLEOTIDE POLYMORPHISM 653 $aSPATIAL CORRECTION 653 $aWHEAT 700 1 $aMATUS, I. 700 1 $aRODRIGUEZ, A. 700 1 $aINOSTROZA, L. 700 1 $aPOLAND, J. 700 1 $aBELZILE ,F. 700 1 $aDEL POZO, A. 700 1 $aQUINCKE, M. 700 1 $aCASTRO, M. 700 1 $aVON ZITZEWITZ, J. 773 $tG3: Genes, Genomes, Genetics (Bethesda)$gv. 3, n,12, p. 2105-2114, 2013.OPEN ACCESS.
Descargar
Esconder MarcPresentar Marc Completo |
Registro original : |
INIA La Estanzuela (LE) |
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
Expresión de búsqueda válido. Check! |
|
|