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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
29/11/2022 |
Actualizado : |
29/11/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
LANGRIDGE, P.; ALAUX, M.; ALMEIDA, N.F.; AMMAR, K.; BAUM, M.; BEKKAOUI, F.; BENTLEY, A.R.; BERES, B.L.; BERGER, B.; BRAUN, H.-J.; BROWN-GUEDIRA, G.; BURT, C.J.; CACCAMO, M.J.; CATTIVELLI, L.; CHARMET, G.; CIVÁN, P.; CLOUTIER, S.; COHAN, J-P.; DEVAUX, P.; DOOHAN, F.M.; DRECCER, M.F.; FERRAHI, M.; GERMAN, S.; GOODWIN, S.B.; GRIFFITHS, S.; GUZMÁN, C.; HANDA, H.; HAWKESFORD, M.J.; HE, Z.; HUTTNER, E.; IKEDA, T.M.; KILIAN, B.; KING, I.P.; KING, J.; KIRKEGAARD, J.A.; LAGE, J.; LE GOUIS, J.; MONDAL, S.; MULLINS, E.; ORDON, F.; ORTIZ-MONASTERIO, J.I.; ÖZKAN, H.; ÖZTÜRK, I.; PEREYRA, S.; POZNIAK, C.J.; QUESNEVILLE, H.; QUINCKE, M.; REBETZKE, G.J.; CHRISTOPH REIF, J.; SAAVEDRA-BRAVO, T.; SCHURR, U.; SHARMA, S.; SINGH, S.K.; SINGH, R.P.; SNAPE, J.W.; TADESSE, W.; TSUJIMOTO, H.; TUBEROSA, R.; WILLIS, T.G.; ZHANG, X. |
Afiliación : |
PETER LANGRIDGE, School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond, 5064, SA, Australia Wheat Initiative, JKI (Julius Kühn Institute), Federal Research Centre for Cultivated Plants, Berlin, 14195, Germany; MICHAEL ALAUX, INRAE, URGI, Université Paris-Saclay, Versailles, 78026, France; NUNO FELIPE ALMEIDA, ASUR Plant Breeding, Estrées-Saint-Denis, 60190, France; KARIM AMMAR, CIMMYT (International Maize and Wheat Improvement Center), Texcoco, 56237, Mexico; MICHAEL BAUM, ICARDA (International Center for Agricultural Research in the Dry Areas), Rabat, 10106, Morocco; FAOUZI BEKKAOUI, INRA (National Institute for Agricultural Research), Rabat, 10090, Morocco; ALISON R. BENTLEY, CIMMYT (International Maize and Wheat Improvement Center), Texcoco, 56237, Mexico; BRIAN L. BERES, AAFC (Agriculture Agri-Food Canada), Lethbridge Research and Development Centre, Lethbridge, T1J 4B1, AB, Canada; BETTINA BERGER, Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, 5064, SA, Australia; HANS-JOACHIM BRAUN, CIMMYT (International Maize and Wheat Improvement Center), Texcoco, 56237, Mexico; GINA BROWN-GUEDIRA, USDA-ARS (United States Department of Agriculture-Agricultural Research Service), Plant Science Research, Raleigh, 27695, NC, United States; CHRISTOPHER JAMES BURT, RAGT2n, Place du Bourg, Druelle Balsac, 12510, France; MARIO JOSE CACCAMO, NIAB (National Institute of Agricultural Botany), Cambridge, CB3 0LE, United Kingdom; LUIGI CATTIVELLI, CREA (Council for Agricultural Research and Economics), Research Centre for Genomics and Bioinformatics, Fiorenzuola d'Arda, 29017, Italy; GILLES CHARMET, INRAE (National Research Institute for Agriculture, Food and the Environment), University of Clermont-Auvergne, UMR 1095 GDEC, Clermont-Ferrand, 63000, France; PETER CIVÁN, INRAE (National Research Institute for Agriculture, Food and the Environment), University of Clermont-Auvergne, UMR 1095 GDEC, Clermont-Ferrand, 63000, France; SYLVIE CLOUTIER, AAFC (Agriculture and Agri-Food Canada), Ottawa Research and Development Centre, Ottawa, K1A 0C6, ON, Canada; JEAN-PIERRE COHAN, ARVALIS-Institut du Végétal, Loireauxence, 44370, France; PIERRE J. DEVAUX, Florimond Desprez, Research Innovation, Cappelle-en-Pévèle, 59242, France; FIONA M. DOOHAN, School of Biology and Environmental Science and UCD Earth Institute, University College Dublin, Belfield, Dublin 4, Ireland; M. FERNANDA DRECCER, CSIRO (Commonwealth Scientific and Industrial Research Organisation), Agriculture and Food, Queensland Biosciences Precinct, Saint Lucia, 4067, QLD, Australia; MOHA FERRAHI, INRA (National Institute for Agricultural Research), Rabat, 10090, Morocco; SILVIA ELISA GERMAN FAEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; STEPHEN B. GOODWIN, USDA-ARS (United States Department of Agriculture-Agricultural Research Service), West Lafayette, 47907, IN, United States; SIMON GRIFFITHS, John Innes Centre, Norwich, NR4 7UH, United Kingdom; CARLOS GUZMÁN, Departamento de Genética, Escuela Técnica Superior de Ingeniería Agronómica y de Montes, CeiA3, Universidad de Córdoba, Campus de Rabanales, Córdoba, ES-14071, Spain; HIROKAZU HANDA, Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, 606-8502, Japan; MALCOLM JOHN HAWKESFORD, Rothamsted Research, Harpenden, AL5 2JQ, United Kingdom; ZHONGHU HE, Institute of Crop Sciences, CAAS (Chinese Academy of Agricultural Sciences), Beijing, 100081, China; ERIC HUTTNER, ACIAR (Australian Centre for International Agricultural Research), Bruce, 2617, ACT, Australia; TATSUYA M. IKEDA, NARO (National Agriculture and Food Research Organization), Western Region Agricultural Research Center, Fukuyama, 721-8514, Japan; BENJAMIN KILIAN, Global Crop Diversity Trust, Bonn, 53113, Germany; IAN PHILIP KING, School of Biosciences, The University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, United Kingdom; JULIE KING, School of Biosciences, The University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, United Kingdom; JOHN A. KIRKEGAARD, CSIRO (Commonwealth Scientific and Industrial Research Organisation), Agriculture and Food, Canberra, 2601, ACT, Australia; JACOB LAGE, KWS UK, Thriplow, SG8 7RE, United Kingdom; JACQUES LE GOUIS, INRAE (National Research Institute for Agriculture, Food and the Environment), University of Clermont-Auvergne, UMR 1095 GDEC, Clermont-Ferrand, 63000, France; SUCHISMITA MONDAL, Plant Sciences and Plant Pathology Department, Montana State University, Bozeman, 59717, MT, United States; EWEN MULLINS, Teagasc, Carlow, R93 XE12, Ireland; FRANK ORDON, JKI (Julius Kühn Institute), Federal Research Centre for Cultivated Plants, Quedlinburg, 06484, Germany; JOSE IVAN ORTIZ-MONASTERIO, CIMMYT (International Maize and Wheat Improvement Center), Texcoco, 56237, Mexico; HAKAN ÖZKAN, Faculty of Agriculture, Department of Field Crops, University of Çukurova, Adana, 01330, Turkey; IRFAN ÖZTÜRK, Trakya Agricultural Reseach Institute, Edirne, 22100, Turkey; SILVIA ANTONIA PEREYRA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CURTIS J. POZNIAK, Crop Development Centre, University of Saskatchewan, Saskatoon, S7N5A8, SK, Canada; HADI QUESNEVILLE, INRAE, URGI, Université Paris-Saclay, Versailles, 78026, France; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GREG JOHN REBETZKE, CSIRO (Commonwealth Scientific and Industrial Research Organisation), Agriculture and Food, Canberra, 2601, ACT, Australia; JOCHEN CHRISTOPH REIF, IPK (Leibniz Institute of Plant Genetics and Crop Plant Research), OT Gatersleben, Seeland, 06466, Germany; TERESA SAAVEDRA-BRAVO, Wheat Initiative, JKI (Julius Kühn Institute), Federal Research Centre for Cultivated Plants, Berlin, 14195, Germany; ULRICH SCHURR, Forchungszentrum Jülich GmbH, IBG-2: Plant Sciences, Jülich, 52428, Germany; SHIVALI SHARMA, Global Crop Diversity Trust, Bonn, 53113, Germany; SANJAY KUMAR SINGH, ICAR-Indian Agricultural Research Institute, Genetics Division, Pusa, New Delhi, 110012, India; RAVI P. SINGH, CIMMYT (International Maize and Wheat Improvement Center), Texcoco, 56237, Mexico; JOHN W. SNAPE, John Innes Centre, Norwich, NR4 7UH, United Kingdom; WULETAW TADESSE, ICARDA (International Center for Agricultural Research in the Dry Areas), Beirut, 1108-2010, Lebanon; HISASHI TSUJIMOTO, Arid Land Research Centre, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan; ROBERTO TUBEROSA, Department of Agricultural and Food Sciences, University of Bologna, Bologna, 40127, Italy; TIM G. WILLIS, UKRI-BBSRC (UK Research and Innovation-Biotechnology and Biological Research Council), Swindon, SN2 1FL, United Kingdom; XUEYONG ZHANG, Institute of Crop Sciences, CAAS (Chinese Academy of Agricultural Sciences), Beijing, 100081, China. |
Título : |
Meeting the challenges facing wheat production: the strategic research agenda of the Global Wheat Initiative. |
Fecha de publicación : |
2022 |
Fuente / Imprenta : |
Agronomy, 2022, volume 12, issue 11, 2767. OPEN ACCESS. doi: https://doi.org/10.3390/agronomy12112767 |
ISSN : |
2073-4395 |
DOI : |
10.3390/agronomy12112767 |
Idioma : |
Inglés |
Notas : |
Article history: Received 26 September 2022; Revised 28 October 2022; Accepted 29 October 2022; Published 7 November 2022. -- Academic Editor: Andreas Katsiotis. -- Corresponding author: Langridge, P.; School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond, SA, Australia; email:peter.langridge@adelaide.edu.au -- Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). -- This article belongs to the Collection A Series of Special Reviews and Topic Analyses That Explore Major Trends and Challenges in Agronomy (https://www.mdpi.com/journal/agronomy/topical_collections/U67MP747QP ) -- |
Contenido : |
ABSTRACT.- Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat Initiative was tasked with supporting the wheat research community by facilitating collaboration, information and resource sharing and helping to build the capacity to address challenges facing production in an increasingly variable environment. Many countries invest in wheat research. Innovations in wheat breeding and agronomy have delivered enormous gains over the past few decades, with the average global yield increasing from just over 1 tonne per hectare in the early 1960s to around 3.5 tonnes in the past decade. These gains are threatened by climate change, the rapidly rising financial and environmental costs of fertilizer, and pesticides, combined with declines in water availability for irrigation in many regions. The international wheat research community has worked to identify major opportunities to help ensure that global wheat production can meet demand. The outcomes of these discussions are presented in this paper. © 2022 by the authors. |
Palabras claves : |
Agronomy; Climate change; Coordination; Germplasm; Strategy; Wheat; Yield. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16889/1/agronomy-12-02767.pdf
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Marc : |
LEADER 04569naa a2200949 a 4500 001 1063799 005 2022-11-29 008 2022 bl uuuu u00u1 u #d 022 $a2073-4395 024 7 $a10.3390/agronomy12112767$2DOI 100 1 $aLANGRIDGE, P. 245 $aMeeting the challenges facing wheat production$bthe strategic research agenda of the Global Wheat Initiative.$h[electronic resource] 260 $c2022 500 $aArticle history: Received 26 September 2022; Revised 28 October 2022; Accepted 29 October 2022; Published 7 November 2022. -- Academic Editor: Andreas Katsiotis. -- Corresponding author: Langridge, P.; School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond, SA, Australia; email:peter.langridge@adelaide.edu.au -- Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). -- This article belongs to the Collection A Series of Special Reviews and Topic Analyses That Explore Major Trends and Challenges in Agronomy (https://www.mdpi.com/journal/agronomy/topical_collections/U67MP747QP ) -- 520 $aABSTRACT.- Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat Initiative was tasked with supporting the wheat research community by facilitating collaboration, information and resource sharing and helping to build the capacity to address challenges facing production in an increasingly variable environment. Many countries invest in wheat research. Innovations in wheat breeding and agronomy have delivered enormous gains over the past few decades, with the average global yield increasing from just over 1 tonne per hectare in the early 1960s to around 3.5 tonnes in the past decade. These gains are threatened by climate change, the rapidly rising financial and environmental costs of fertilizer, and pesticides, combined with declines in water availability for irrigation in many regions. The international wheat research community has worked to identify major opportunities to help ensure that global wheat production can meet demand. The outcomes of these discussions are presented in this paper. © 2022 by the authors. 653 $aAgronomy 653 $aClimate change 653 $aCoordination 653 $aGermplasm 653 $aStrategy 653 $aWheat 653 $aYield 700 1 $aALAUX, M. 700 1 $aALMEIDA, N.F. 700 1 $aAMMAR, K. 700 1 $aBAUM, M. 700 1 $aBEKKAOUI, F. 700 1 $aBENTLEY, A.R. 700 1 $aBERES, B.L. 700 1 $aBERGER, B. 700 1 $aBRAUN, H.-J. 700 1 $aBROWN-GUEDIRA, G. 700 1 $aBURT, C.J. 700 1 $aCACCAMO, M.J. 700 1 $aCATTIVELLI, L. 700 1 $aCHARMET, G. 700 1 $aCIVÁN, P. 700 1 $aCLOUTIER, S. 700 1 $aCOHAN, J-P. 700 1 $aDEVAUX, P. 700 1 $aDOOHAN, F.M. 700 1 $aDRECCER, M.F. 700 1 $aFERRAHI, M. 700 1 $aGERMAN, S. 700 1 $aGOODWIN, S.B. 700 1 $aGRIFFITHS, S. 700 1 $aGUZMÁN, C. 700 1 $aHANDA, H. 700 1 $aHAWKESFORD, M.J. 700 1 $aHE, Z. 700 1 $aHUTTNER, E. 700 1 $aIKEDA, T.M. 700 1 $aKILIAN, B. 700 1 $aKING, I.P. 700 1 $aKING, J. 700 1 $aKIRKEGAARD, J.A. 700 1 $aLAGE, J. 700 1 $aLE GOUIS, J. 700 1 $aMONDAL, S. 700 1 $aMULLINS, E. 700 1 $aORDON, F. 700 1 $aORTIZ-MONASTERIO, J.I. 700 1 $aÖZKAN, H. 700 1 $aÖZTÜRK, I. 700 1 $aPEREYRA, S. 700 1 $aPOZNIAK, C.J. 700 1 $aQUESNEVILLE, H. 700 1 $aQUINCKE, M. 700 1 $aREBETZKE, G.J. 700 1 $aCHRISTOPH REIF, J. 700 1 $aSAAVEDRA-BRAVO, T. 700 1 $aSCHURR, U. 700 1 $aSHARMA, S. 700 1 $aSINGH, S.K. 700 1 $aSINGH, R.P. 700 1 $aSNAPE, J.W. 700 1 $aTADESSE, W. 700 1 $aTSUJIMOTO, H. 700 1 $aTUBEROSA, R. 700 1 $aWILLIS, T.G. 700 1 $aZHANG, X. 773 $tAgronomy, 2022, volume 12, issue 11, 2767. OPEN ACCESS. doi: https://doi.org/10.3390/agronomy12112767
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
29/09/2014 |
Actualizado : |
25/10/2017 |
Tipo de producción científica : |
Poster |
Autor : |
BRANDARIZ, S.P.; GONZÁELZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ, L. |
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. |
Título : |
Effect of using imputed missing data on QTL detection on a wheat GWAS panel. |
Fecha de publicación : |
2014 |
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. |
Páginas : |
p. 86. |
Idioma : |
Inglés |
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 |
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. |
Thesagro : |
DETECCIÓN DE QTLS; MARCADORES MOLECULARES; TRIGO. |
Asunto categoría : |
-- |
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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