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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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Registro original : |
INIA La Estanzuela (LE) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
08/03/2022 |
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Actualizado : |
02/12/2022 |
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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 : |
RODRÍGUEZ NEIRA, J.D.; PERIPOLLI, E.; DE NEGREIROS M.P.M.; ESPIGOLAN, R.; LÓPEZ-CORREA R.; AGUILAR, I.; LOBO R.B.; BALDI, F. |
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Afiliación : |
JUAN DIEGO RODRIGUEZ NEIRA, Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil; ELISA PERIPOLLI, Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil; MARIA PAULA MARINHO DE NEGREIROS, Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo (Usp), Pirassununga, 13535-900, Brazil; RAFAEL ESPIGOLAN, Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo (Usp), Pirassununga, 13535-900, Brazil; RODRIGO LÓPEZ-CORREA, Departamento de Genética y Mejoramiento Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RAYSILDO B. LOBO, Associação Nacional de Criadores e Pesquisadores (ANCP), Ribeirão Preto, Brazil; FERNANDO BALDI, Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil. |
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Título : |
Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Journal of Applied Genetics, 2022, Volume 63, Issue 2, pages 389-400. doi: https://doi.org/10.1007/s13353-022-00685-0 |
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ISSN : |
1234-1983 |
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DOI : |
10.1007/s13353-022-00685-0 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 26 September 2021; Revised 25 January 2022; Accepted 2 February 2022.
Corresponding author: Rodriguez Neira, J.D.; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, Brazil; email:juan.diego@unesp.br -- This study was supported in conjunction by Programa Estudantes Convênio de Pós-Graduação da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (PECPG-CAPES, call no. 32/2017); the National Association of Breeders and Researchers (ANCP), the Programa Escala de Estudiantes de Pós-Graduação of Asociación de Universidades GRUPO MONTEVIDEO (PEEPg/AUGM-2019); the Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias (FCAV/Unesp); the Universidad de la Republica, Facultad de Veterinaria (UdelaR), Departamento de Genética y Mejoramiento Animal; and the Instituto Nacional de Investigación Agropecuaria of Uruguay (INIA). |
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Contenido : |
ABSTRACT. - This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection.
© 2022, The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences. MenosABSTRACT. - This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10... Presentar Todo |
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Palabras claves : |
Accuracy; Beef cattle; Genomic selection; Inflation; Minor allele frequency; SNP arrays. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 03798naa a2200313 a 4500
001 1062807
005 2022-12-02
008 2022 bl uuuu u00u1 u #d
022 $a1234-1983
024 7 $a10.1007/s13353-022-00685-0$2DOI
100 1 $aRODRÍGUEZ NEIRA, J.D.
245 $aPrediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP.$h[electronic resource]
260 $c2022
500 $aArticle history: Received 26 September 2021; Revised 25 January 2022; Accepted 2 February 2022. Corresponding author: Rodriguez Neira, J.D.; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, Brazil; email:juan.diego@unesp.br -- This study was supported in conjunction by Programa Estudantes Convênio de Pós-Graduação da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (PECPG-CAPES, call no. 32/2017); the National Association of Breeders and Researchers (ANCP), the Programa Escala de Estudiantes de Pós-Graduação of Asociación de Universidades GRUPO MONTEVIDEO (PEEPg/AUGM-2019); the Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias (FCAV/Unesp); the Universidad de la Republica, Facultad de Veterinaria (UdelaR), Departamento de Genética y Mejoramiento Animal; and the Instituto Nacional de Investigación Agropecuaria of Uruguay (INIA).
520 $aABSTRACT. - This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection. © 2022, The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.
653 $aAccuracy
653 $aBeef cattle
653 $aGenomic selection
653 $aInflation
653 $aMinor allele frequency
653 $aSNP arrays
700 1 $aPERIPOLLI, E.
700 1 $aDE NEGREIROS M.P.M.
700 1 $aESPIGOLAN, R.
700 1 $aLÓPEZ-CORREA R.
700 1 $aAGUILAR, I.
700 1 $aLOBO R.B.
700 1 $aBALDI, F.
773 $tJournal of Applied Genetics, 2022, Volume 63, Issue 2, pages 389-400. doi: https://doi.org/10.1007/s13353-022-00685-0
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