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Registro completo
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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 : |
02/10/2019 |
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Actualizado : |
21/01/2020 |
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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 : |
CANOZZI, M.E.A.; MARQUES, P.M.; TEIXEIRA, O. DE S.; PIMENTEL, C.M.M.; DILL, M.D.; BARCELLOS, J.O.J. |
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Afiliación : |
MARÍA EUGENIA ANDRIGHETTO CANOZZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO ROCHA MARQUES, Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul (UFRGS), 91540-000, Porto Alegre, RS, Brasil.; ODILENE DE SOUZA TEIXEIRA, Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul (UFRGS), 91540-000, Porto Alegre, RS, Brasil.; CONCEPTA MARGARET MCMANUS PIMENTEL, Instituto de Ciências Biológicas, Universidade Federal de Brasília (UnB), Brasília, DF, Brasil.; MATHEUS DHEIN DILL, Unidade Acadêmica de Garanhuns (UAG), Universidade Federal Rural de Pernambuco (UFRPE), Garanhuns, PE, Brasil.; JÚLIO OTÁVIO JARDIM BARCELLOS, Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul (UFRGS), 91540-000, Porto Alegre, RS, Brasil. |
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Título : |
Typology of beef production systems according to bioeconomic efficiency in the south of Brazil. [Tipologia de sistemas de produção de carne bovina de acordo com a eficiência bioeconômica no sul do Brasil]¨. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Ciencia Rural, Santa Marìa, september 2019, vol.49, no.10,e20190030. OPEN ACCESS. Doi: 10.1590/0103-8478cr20190030 |
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ISSN : |
0103-8478 On-line |
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DOI : |
10.1590/0103-8478cr20190030 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 01.14.19 / Approved 07.26.19/ Returned by the author 08.21.19. |
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Contenido : |
ABSTRACT: The objective of this study was to evaluate the bioeconomic efficiency of beef cattle production systems in the south of Brazil. A
survey was conducted with 33 beef cattle farmers operating with complete cycle production systems in areas larger or equal to 900 ha. Answers
were classified in two drivers, technology (TEC) and management (MAN), which were separated into subfactors with their respective components.
Multiple correspondence analysis, Tukey test, cluster analysis, and Pearson correlation were the statistics procedures. The TEC components were
above normal for Brazilian farmers, but the gross margin is still lower than that needed to generate financial resources for a proper remuneration
of cattle farmer. Farmers were classified into three clusters according to bioeconomic efficiency: low (LEL), intermediate (IEL), or high-efficiency
level (HEL). The TEC driver differentiated the LEL x HEL clusters and the MAN, mainly expressed in costs, the IEL x HEL. Positive correlation
between age at first mating and age at slaughter in the HEL cluster explains the higher costs when compared to IEL because of the use of
differentiated feed resources. Investments in technologies related to herd feeding in HEL cluster improved the productivity by only 20% and the
cost per hectare by 95 USD ha-1 in comparison to IEL cluster. The main differences between farmers are because of the use of technologies related
to feeding and cost management. Therefore, before implementing a new technology, an economic evaluation is necessary.
RESUMO: O objetivo deste estudo foi avaliar a eficiência bioeconômica em sistemas de produção de bovinos de corte no sul do Brasil. A pesquisa foi realizada com 33 pecuaristas que operam com sistemas de produção baseado em ciclo completo, em áreas maiores ou iguais a 900 hectares. As respostas foram classificadas em dois fatores: tecnologia (TEC) e gestão (GES), os quais foram separados em subfatores com seus respectivos componentes. A análise de correspondência múltipla, teste de Tukey, análise de cluster e correlação de Pearson foram os procedimentos estatísticos. Os componentes do TEC estavam acima do normal para os pecuaristas brasileiros, mas a margem bruta ainda é menor do que a necessária para gerar recursos financeiros para uma remuneração adequada ao pecuarista. Os pecuaristas foram classificados em três grupos de acordo com a eficiência bioeconômica: baixo (BNE), intermediário (INE) ou alto nível de eficiência (ANE). O driver TEC diferenciou os clusters BNE x ANE e o GES, expresso principalmente em custos, o INE x ANE. A correlação positiva entre a idade no primeiro acasalamento e a idade ao abate no agrupamento ANE explica os custos mais elevados quando comparados aos INE, devido ao uso de recursos alimentares diferenciados. Os investimentos em tecnologias relacionadas à alimentação de rebanho no agrupamento ANE melhoraram a produtividade em apenas 20% e o custo por hectare em 95 USD ha-1 em comparação ao cluster INE. As principais diferenças entre os pecuaristas se devem ao uso de tecnologias relacionadas à alimentação e ao gerenciamento de custos. Portanto, antes de implementar uma nova tecnologia, uma avaliação econômica é necessária. MenosABSTRACT: The objective of this study was to evaluate the bioeconomic efficiency of beef cattle production systems in the south of Brazil. A
survey was conducted with 33 beef cattle farmers operating with complete cycle production systems in areas larger or equal to 900 ha. Answers
were classified in two drivers, technology (TEC) and management (MAN), which were separated into subfactors with their respective components.
Multiple correspondence analysis, Tukey test, cluster analysis, and Pearson correlation were the statistics procedures. The TEC components were
above normal for Brazilian farmers, but the gross margin is still lower than that needed to generate financial resources for a proper remuneration
of cattle farmer. Farmers were classified into three clusters according to bioeconomic efficiency: low (LEL), intermediate (IEL), or high-efficiency
level (HEL). The TEC driver differentiated the LEL x HEL clusters and the MAN, mainly expressed in costs, the IEL x HEL. Positive correlation
between age at first mating and age at slaughter in the HEL cluster explains the higher costs when compared to IEL because of the use of
differentiated feed resources. Investments in technologies related to herd feeding in HEL cluster improved the productivity by only 20% and the
cost per hectare by 95 USD ha-1 in comparison to IEL cluster. The main differences between farmers are because of the use of technologies related
to feeding and cost management. Therefore, before implementing a ... Presentar Todo |
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Palabras claves : |
ANÁLISE DE CLUSTER; ANIMAL PRODUCTION; BOVINOS; CATTLE; CLUSTER ANALYSIS; CUSTO DE PRODUÇÃO; PRODUÇÃO ANIMAL; PRODUCTION COST. |
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Thesagro : |
PRODUCCION ANIMAL. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13391/1/CR-Canozzi-et-al.-2019.pdf
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Marc : |
LEADER 04478naa a2200325 a 4500
001 1060258
005 2020-01-21
008 2019 bl uuuu u00u1 u #d
022 $a0103-8478 On-line
024 7 $a10.1590/0103-8478cr20190030$2DOI
100 1 $aCANOZZI, M.E.A.
245 $aTypology of beef production systems according to bioeconomic efficiency in the south of Brazil. [Tipologia de sistemas de produção de carne bovina de acordo com a eficiência bioeconômica no sul do Brasil]¨.$h[electronic resource]
260 $c2019
500 $aArticle history: Received 01.14.19 / Approved 07.26.19/ Returned by the author 08.21.19.
520 $aABSTRACT: The objective of this study was to evaluate the bioeconomic efficiency of beef cattle production systems in the south of Brazil. A survey was conducted with 33 beef cattle farmers operating with complete cycle production systems in areas larger or equal to 900 ha. Answers were classified in two drivers, technology (TEC) and management (MAN), which were separated into subfactors with their respective components. Multiple correspondence analysis, Tukey test, cluster analysis, and Pearson correlation were the statistics procedures. The TEC components were above normal for Brazilian farmers, but the gross margin is still lower than that needed to generate financial resources for a proper remuneration of cattle farmer. Farmers were classified into three clusters according to bioeconomic efficiency: low (LEL), intermediate (IEL), or high-efficiency level (HEL). The TEC driver differentiated the LEL x HEL clusters and the MAN, mainly expressed in costs, the IEL x HEL. Positive correlation between age at first mating and age at slaughter in the HEL cluster explains the higher costs when compared to IEL because of the use of differentiated feed resources. Investments in technologies related to herd feeding in HEL cluster improved the productivity by only 20% and the cost per hectare by 95 USD ha-1 in comparison to IEL cluster. The main differences between farmers are because of the use of technologies related to feeding and cost management. Therefore, before implementing a new technology, an economic evaluation is necessary. RESUMO: O objetivo deste estudo foi avaliar a eficiência bioeconômica em sistemas de produção de bovinos de corte no sul do Brasil. A pesquisa foi realizada com 33 pecuaristas que operam com sistemas de produção baseado em ciclo completo, em áreas maiores ou iguais a 900 hectares. As respostas foram classificadas em dois fatores: tecnologia (TEC) e gestão (GES), os quais foram separados em subfatores com seus respectivos componentes. A análise de correspondência múltipla, teste de Tukey, análise de cluster e correlação de Pearson foram os procedimentos estatísticos. Os componentes do TEC estavam acima do normal para os pecuaristas brasileiros, mas a margem bruta ainda é menor do que a necessária para gerar recursos financeiros para uma remuneração adequada ao pecuarista. Os pecuaristas foram classificados em três grupos de acordo com a eficiência bioeconômica: baixo (BNE), intermediário (INE) ou alto nível de eficiência (ANE). O driver TEC diferenciou os clusters BNE x ANE e o GES, expresso principalmente em custos, o INE x ANE. A correlação positiva entre a idade no primeiro acasalamento e a idade ao abate no agrupamento ANE explica os custos mais elevados quando comparados aos INE, devido ao uso de recursos alimentares diferenciados. Os investimentos em tecnologias relacionadas à alimentação de rebanho no agrupamento ANE melhoraram a produtividade em apenas 20% e o custo por hectare em 95 USD ha-1 em comparação ao cluster INE. As principais diferenças entre os pecuaristas se devem ao uso de tecnologias relacionadas à alimentação e ao gerenciamento de custos. Portanto, antes de implementar uma nova tecnologia, uma avaliação econômica é necessária.
650 $aPRODUCCION ANIMAL
653 $aANÁLISE DE CLUSTER
653 $aANIMAL PRODUCTION
653 $aBOVINOS
653 $aCATTLE
653 $aCLUSTER ANALYSIS
653 $aCUSTO DE PRODUÇÃO
653 $aPRODUÇÃO ANIMAL
653 $aPRODUCTION COST
700 1 $aMARQUES, P.M.
700 1 $aTEIXEIRA, O. DE S.
700 1 $aPIMENTEL, C.M.M.
700 1 $aDILL, M.D.
700 1 $aBARCELLOS, J.O.J.
773 $tCiencia Rural, Santa Marìa, september 2019, vol.49, no.10,e20190030. OPEN ACCESS. Doi: 10.1590/0103-8478cr20190030
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