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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
26/09/2014 |
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Actualizado : |
06/11/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LADO, B.; MATUS, I.; RODRIGUEZ, A.; INOSTROZA, L.; POLAND, J.; BELZILE ,F.; DEL POZO, A.; QUINCKE, M.; CASTRO, M.; VON ZITZEWITZ, J. |
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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. |
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Título : |
Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. |
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Fecha de publicación : |
2013 |
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Fuente / Imprenta : |
G3: Genes, Genomes, Genetics (Bethesda), v. 3, n,12, p. 2105-2114, 2013.OPEN ACCESS. |
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ISSN : |
2160-1836. |
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DOI : |
10.1534/g3.113.007807 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 2013 Aug 26 // Accepted 2013 Sep 18. |
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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 |
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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. |
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Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13756/1/G3Bethesda-v.-3-n12-p.-2105-2114-2013.pdf
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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.
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