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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
29/09/2014 |
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Actualizado : |
25/10/2017 |
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Tipo de producción científica : |
Poster |
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Autor : |
BRANDARIZ, S.P.; GONZÁELZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO LINDNER, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; MARTIN CONRADO QUINCKE WALDEN, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; JARISLAV RAMON VON ZITZEWITZ VON SALVIATI, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; MARINA CASTRO DERENYI, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Effect of using imputed missing data on QTL detection on a wheat GWAS panel. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: posters; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. |
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Páginas : |
p. 86. |
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Idioma : |
Inglés |
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Contenido : |
Molecular markers are an essential component of plant and animal breeding programs. One inexpensive way of obtaining molecular markers is through Next-Generation Sequencing (NGS). Genotyping-by-sequencing (GBS) is one of the NGS techniques which have been successfully used for complex genomes like wheat. A particularity of GBS is that it generates a lot of missing information which is generally imputed. Imputation is required for Genomic Prediction studies and several studies demonstrate its value. However, the effectiveness of missing data imputation for Genome-wide association (GWAS) studies has not been demonstrated. Data imputation for GWAS where one marker at a time is being studied could potentially create biased estimates. The aim of this study was to compare the effects of using either missing or imputed data for Quantitative Trait Loci (QTL) detection in a wheat GWAS pannel. A set of 384 advanced lines of wheat was included in this study consisting of 186 genotypes from INIA (Instituto Nacional de Investigación Agropecuaria) in Uruguay, 55 genotypes from INIA in Chile and 143 genotypes from CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo). SNPs were obtained using the Tassel-GBS Pipeline. We excluded SNPs with more than 50 % missing data and SNPs with a minor allele frequency (MAF) more extreme than 10%. Sequence database available from the SyntheticxOpata map (synop) was used to construct the maps, obtaining a final data set with more than 18K SNPs. Missing data was handled in three different ways to create the SNP datasets used for QTL detection: 1) data not-imputed, 2) data imputed by the realized relationship matrix method multivariate normal expectation maximization (MVN-EM), and 3) data imputed by the mean. A number of QTL (either 25 or 50) with different heritability (0.2 and 0.7) were simulated on top of each dataset. The following mixed model was used to recover QTL: , where : phenotypic vector, : SNPs matrix, : unknown vector of allele effects to be estimated, : matrix that relates each measurement to population origin, : populations vector, : kinship matrix, : vector of random background polygenic effects, and : residual error. We used a liberal 0.01 significance level. The power to detect QTL was estimated for each dataset and differences among medians of QTL detection power among imputed datasets were studied using the Friedman test and non-parametric contrasts. For this purpose, methods of imputations were defined as treatments and simulation scenarios as blocks. The QTL detection power with the MVN-EM matrix was lower than with the mean imputed matrix or the no imputed matrix. No differences in QTL detection power were found between the mean imputed matrix or the no imputed matrix. Based on our results, imputing does not seem to improve QTL detection power. MenosMolecular markers are an essential component of plant and animal breeding programs. One inexpensive way of obtaining molecular markers is through Next-Generation Sequencing (NGS). Genotyping-by-sequencing (GBS) is one of the NGS techniques which have been successfully used for complex genomes like wheat. A particularity of GBS is that it generates a lot of missing information which is generally imputed. Imputation is required for Genomic Prediction studies and several studies demonstrate its value. However, the effectiveness of missing data imputation for Genome-wide association (GWAS) studies has not been demonstrated. Data imputation for GWAS where one marker at a time is being studied could potentially create biased estimates. The aim of this study was to compare the effects of using either missing or imputed data for Quantitative Trait Loci (QTL) detection in a wheat GWAS pannel. A set of 384 advanced lines of wheat was included in this study consisting of 186 genotypes from INIA (Instituto Nacional de Investigación Agropecuaria) in Uruguay, 55 genotypes from INIA in Chile and 143 genotypes from CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo). SNPs were obtained using the Tassel-GBS Pipeline. We excluded SNPs with more than 50 % missing data and SNPs with a minor allele frequency (MAF) more extreme than 10%. Sequence database available from the SyntheticxOpata map (synop) was used to construct the maps, obtaining a final data set with more than 18K SNPs. Mi... Presentar Todo |
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Palabras claves : |
GBS; GENOMIC PREDICTION; GENOMIC WIDE ASSOCIATION; GENOTYPING BY SEQUENCING; GWAS; MARCADORES MOLECULARES; MULTIVARIATE NORMAL EXPECTATION MAXIMIZATION; MVN-EM; NEXT GENERATION SEQUENCING; NGS; QTL; QUANTITATIVE TRAIT LOCI DETECTION; SINGLE NUCLEOTIDE POLYMORPHISMS; SNPs; TRITICUM. |
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Thesagro : |
DETECCIÓN DE QTLS; MARCADORES MOLECULARES; TRIGO. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 04260nam a2200433 a 4500
001 1050639
005 2017-10-25
008 2014 bl uuuu u00u1 u #d
100 1 $aBRANDARIZ, S.P.
245 $aEffect of using imputed missing data on QTL detection on a wheat GWAS panel.
260 $aIn: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: posters; resúmenes. La Estanzuela, Colonia, UY: INIA$c2014
300 $ap. 86.
520 $aMolecular markers are an essential component of plant and animal breeding programs. One inexpensive way of obtaining molecular markers is through Next-Generation Sequencing (NGS). Genotyping-by-sequencing (GBS) is one of the NGS techniques which have been successfully used for complex genomes like wheat. A particularity of GBS is that it generates a lot of missing information which is generally imputed. Imputation is required for Genomic Prediction studies and several studies demonstrate its value. However, the effectiveness of missing data imputation for Genome-wide association (GWAS) studies has not been demonstrated. Data imputation for GWAS where one marker at a time is being studied could potentially create biased estimates. The aim of this study was to compare the effects of using either missing or imputed data for Quantitative Trait Loci (QTL) detection in a wheat GWAS pannel. A set of 384 advanced lines of wheat was included in this study consisting of 186 genotypes from INIA (Instituto Nacional de Investigación Agropecuaria) in Uruguay, 55 genotypes from INIA in Chile and 143 genotypes from CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo). SNPs were obtained using the Tassel-GBS Pipeline. We excluded SNPs with more than 50 % missing data and SNPs with a minor allele frequency (MAF) more extreme than 10%. Sequence database available from the SyntheticxOpata map (synop) was used to construct the maps, obtaining a final data set with more than 18K SNPs. Missing data was handled in three different ways to create the SNP datasets used for QTL detection: 1) data not-imputed, 2) data imputed by the realized relationship matrix method multivariate normal expectation maximization (MVN-EM), and 3) data imputed by the mean. A number of QTL (either 25 or 50) with different heritability (0.2 and 0.7) were simulated on top of each dataset. The following mixed model was used to recover QTL: , where : phenotypic vector, : SNPs matrix, : unknown vector of allele effects to be estimated, : matrix that relates each measurement to population origin, : populations vector, : kinship matrix, : vector of random background polygenic effects, and : residual error. We used a liberal 0.01 significance level. The power to detect QTL was estimated for each dataset and differences among medians of QTL detection power among imputed datasets were studied using the Friedman test and non-parametric contrasts. For this purpose, methods of imputations were defined as treatments and simulation scenarios as blocks. The QTL detection power with the MVN-EM matrix was lower than with the mean imputed matrix or the no imputed matrix. No differences in QTL detection power were found between the mean imputed matrix or the no imputed matrix. Based on our results, imputing does not seem to improve QTL detection power.
650 $aDETECCIÓN DE QTLS
650 $aMARCADORES MOLECULARES
650 $aTRIGO
653 $aGBS
653 $aGENOMIC PREDICTION
653 $aGENOMIC WIDE ASSOCIATION
653 $aGENOTYPING BY SEQUENCING
653 $aGWAS
653 $aMARCADORES MOLECULARES
653 $aMULTIVARIATE NORMAL EXPECTATION MAXIMIZATION
653 $aMVN-EM
653 $aNEXT GENERATION SEQUENCING
653 $aNGS
653 $aQTL
653 $aQUANTITATIVE TRAIT LOCI DETECTION
653 $aSINGLE NUCLEOTIDE POLYMORPHISMS
653 $aSNPs
653 $aTRITICUM
700 1 $aGONZÁELZ-REYMÚNDEZ, A.
700 1 $aLADO, B.
700 1 $aQUINCKE, M.
700 1 $aVON ZITZEWITZ, J.
700 1 $aCASTRO, M.
700 1 $aMATUS, I.
700 1 $aDEL POZO, A.
700 1 $aGUTIÉRREZ, L.
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
19/09/2014 |
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Actualizado : |
23/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
B - 3 |
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Autor : |
ALEM, D.; NARANCIO, R.; DIAZ, P.; REBUFFO, M.; ZARZA, R.; DALLA RIZZA, M. |
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Afiliación : |
DIEGO MARTIN ALEM GLISON, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; RAFAEL NARANCIO FERES, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; PAOLA ALEXANDRA DIAZ DELLAVALLE, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; MONICA IRENE REBUFFO GFELLER, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; RODRIGO TABARE ZARZA FUENTES, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; MARCO DALLA RIZZA VILARO, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Molecular characterization of Lotus corniculatus cultivars using transferable microsatellite markers. |
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Fecha de publicación : |
2011 |
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Fuente / Imprenta : |
Ciencia e Investigación Agraria, Santiago de Chile, v. 38, n. 3, p. 463-471, 2011. |
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ISSN : |
0304-5609 |
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DOI : |
10.4067/S0718-16202011000300015 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received September 7 2010. /Accepted June 21, 2011. |
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Contenido : |
Abstract :Lotus corniculatus L. is the most important agricultural species in the genus Lotus and is the most widely distributed Lotus species worldwide. L. corniculatus genotypes form complex groups that are difficult to recognize both morphologically and biochemically. Given the extensive and expensive process of isolating Simple Sequence Repeats (SSR, also called microsatellites), the possibility of using microsatellites already identified in related species is highly attractive. The aim of this work was the identification and validation of transferable microsatellite markers in L. corniculatus, and using those markers to study the genetic variability among four cultivars. Each cultivar of L. corniculatus was represented by 15 genotypes. Ten microsatellite markers were evaluated, and from those, four were selected based on their discriminative values observed among cultivars. We detected 29 alleles for the four markers, and there was an average of 7.25 alleles per locus. The marker TM0197 had the fewest number of alleles (5) and TM0083 had the highest number of alleles (10). The polymorphic information content (PIC) for the selected markers varied from 0.19 to 0.35, and the markers were therefore classified as highly informative. Based on the markers, we found high variability between individuals of the same cultivar. The use of transferable microsatellite markers could be useful to differentiate individuals at a relatively low cost, showing a great potential for use in breeding programs. MenosAbstract :Lotus corniculatus L. is the most important agricultural species in the genus Lotus and is the most widely distributed Lotus species worldwide. L. corniculatus genotypes form complex groups that are difficult to recognize both morphologically and biochemically. Given the extensive and expensive process of isolating Simple Sequence Repeats (SSR, also called microsatellites), the possibility of using microsatellites already identified in related species is highly attractive. The aim of this work was the identification and validation of transferable microsatellite markers in L. corniculatus, and using those markers to study the genetic variability among four cultivars. Each cultivar of L. corniculatus was represented by 15 genotypes. Ten microsatellite markers were evaluated, and from those, four were selected based on their discriminative values observed among cultivars. We detected 29 alleles for the four markers, and there was an average of 7.25 alleles per locus. The marker TM0197 had the fewest number of alleles (5) and TM0083 had the highest number of alleles (10). The polymorphic information content (PIC) for the selected markers varied from 0.19 to 0.35, and the markers were therefore classified as highly informative. Based on the markers, we found high variability between individuals of the same cultivar. The use of transferable microsatellite markers could be useful to differentiate individuals at a relatively low cost, showing a great potential for use in b... Presentar Todo |
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Palabras claves : |
GENETIC VARIABILITY; LOTUS JAPONICUS; MICROSATELLITES; SIMPLE SEQUENCE REPEATS; SSR; VARIABILIDAD GENÉTICA. |
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Thesagro : |
CULTIVOS. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.scielo.cl/pdf/ciagr/v38n3/art15.pdf
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Marc : |
LEADER 02470naa a2200301 a 4500
001 1050403
005 2019-10-23
008 2011 bl uuuu u00u1 u #d
022 $a0304-5609
024 7 $a10.4067/S0718-16202011000300015$2DOI
100 1 $aALEM, D.
245 $aMolecular characterization of Lotus corniculatus cultivars using transferable microsatellite markers.$h[electronic resource]
260 $c2011
500 $aArticle history: Received September 7 2010. /Accepted June 21, 2011.
520 $aAbstract :Lotus corniculatus L. is the most important agricultural species in the genus Lotus and is the most widely distributed Lotus species worldwide. L. corniculatus genotypes form complex groups that are difficult to recognize both morphologically and biochemically. Given the extensive and expensive process of isolating Simple Sequence Repeats (SSR, also called microsatellites), the possibility of using microsatellites already identified in related species is highly attractive. The aim of this work was the identification and validation of transferable microsatellite markers in L. corniculatus, and using those markers to study the genetic variability among four cultivars. Each cultivar of L. corniculatus was represented by 15 genotypes. Ten microsatellite markers were evaluated, and from those, four were selected based on their discriminative values observed among cultivars. We detected 29 alleles for the four markers, and there was an average of 7.25 alleles per locus. The marker TM0197 had the fewest number of alleles (5) and TM0083 had the highest number of alleles (10). The polymorphic information content (PIC) for the selected markers varied from 0.19 to 0.35, and the markers were therefore classified as highly informative. Based on the markers, we found high variability between individuals of the same cultivar. The use of transferable microsatellite markers could be useful to differentiate individuals at a relatively low cost, showing a great potential for use in breeding programs.
650 $aCULTIVOS
653 $aGENETIC VARIABILITY
653 $aLOTUS JAPONICUS
653 $aMICROSATELLITES
653 $aSIMPLE SEQUENCE REPEATS
653 $aSSR
653 $aVARIABILIDAD GENÉTICA
700 1 $aNARANCIO, R.
700 1 $aDIAZ, P.
700 1 $aREBUFFO, M.
700 1 $aZARZA, R.
700 1 $aDALLA RIZZA, M.
773 $tCiencia e Investigación Agraria, Santiago de Chile$gv. 38, n. 3, p. 463-471, 2011.
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