Person: Burgueño, J.
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Burgueño
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J.
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Burgueño, J.
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0000-0002-1468-48675 results
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- QTLs asociados al contenido de carotenos en hojas de maiz (Zea mays L.)(Colegio de Postgraduados, 2012) Silva-Pérez, V.; Gómez-Merino, F.C.; Garcia-Zavala, J.J.; Burgueño, J.; Santacruz-Varela, A.; Palacios-Rojas, N.; Tiessen, A.Los carotenos son pigmentos antioxidantes que captan foto¬nes del espectro de luz visible y protegen a los tejidos de las plantas contra la foto-oxidación, lo cual está relacionado con la tasa fotosintética y rendimiento de los cultivos. La biosín-tesis de los carotenos ocurre dentro de los plástidos, donde se sintetizan a partir de la vía mevalónica independiente, con la participación de más de 10 enzimas y un número de facto¬res aún no definidos. Con el objetivo de identificar regiones cromosómicas de maíz (Zea mays L.) asociadas al conteni¬do de carotenos, se midió el contenido de estos compuestos en hojas de 200 líneas endogámicas recombinantes de maíz, provenientes de la cruza B73×Mo17, sembradas en Irapuato, México, en el ciclo primavera-verano 2009. Los QTLs locali¬zados se asociaron a los siguientes compuestos: luteína, a-ca-roteno, b-caroteno, b-criptoxantina y zeaxantina. El caroteno presente en mayor cantidad en las hojas fue b-caroteno (80 % del total) y el menor b-criptoxantina (0.2 %). En los cro¬mosomas 1, 2, 4, 5, 6 y 8, se detectaron 21 QTLs significati¬vos. En el bin 1.07 se localizó un QTL altamente significativo (p£0.001) con valor aditivo alto para luteína (-89.16 mg g-1 PMS, r2@0.07), a-caroteno (-25.41 mg g-1 PMS, r2@0.07) y b-caroteno (-674.98 mg g-1 PMS, r2@0.09). Un QTL de b-criptoxantina en el cromosoma 8 fue detectado mediante el marcador psy2 que es parálogo de la enzima fitoeno sintasa, y otro QTL coincidió con un QTL evaluado para ácido abscí¬sico, relacionado al gen caroteno dioxigenasa. Se infiere que en el genoma de maíz existen factores aún no identificados relacionados al contenido de carotenos en hojas verdes, y los QTLs identificados en este estudio podrían ayudar a encon trar nuevos genes o factores que determinan el contenido de carotenos en hojas de maíz.
Publication - Genomic prediction of genetic values for resistance to wheat rusts(Crop Science Society of America, 2012) Ornella, L.; Singh, S.; Pérez-Rodríguez, P.; Burgueño, J.; Singh, R.P.; Tapia, E.; Bhavani, S.; Dreisigacker, S.; Braun, H.J.; Mathews, K.L.; Crossa, J.Durable resistance to the rust diseases of wheat (Triticum aestivum L.) can be achieved by developing lines that have race-nonspecific adult plant resistance conferred by multiple minor slow-rusting genes. Genomic selection (GS) is a promising tool for accumulating favorable alleles of slow-rusting genes. In this study, five CIMMYT wheat populations evaluated for resistance were used to predict resistance to stem rust (Puccinia graminis) and yellow rust (Puccinia striiformis) using Bayesian least absolute shrinkage and selection operator (LASSO) (BL), ridge regression (RR), and support vector regression with linear or radial basis function kernel models. All parents and populations were genotyped using 1400 Diversity Arrays Technology markers and different prediction problems were assessed. Results show that prediction ability for yellow rust was lower than for stem rust, probably due to differences in the conditions of infection of both diseases. For within population and environment, the correlation between predicted and observed values (Pearson?s correlation [ñ]) was greater than 0.50 in 90% of the evaluations whereas for yellow rust, ñ ranged from 0.0637 to 0.6253. The BL and RR models have similar prediction ability, with a slight superiority of the BL confirming reports about the additive nature of rust resistance. When making predictions between environments and/or between populations, including information from another environment or environments or another population or populations improved prediction.
Publication - Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers(Crop Science Society of America (CSSA), 2012) Burgueño, J.; De Los Campos, G.; Weigel, K.; Crossa, J.Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. This study presents multienvironment (multitrait) models for GS and compares the predictive accuracy of these models with: (i) multienvironment analysis without pedigree and marker information, and (ii) multienvironment pedigree or/and marker-based models. A statistical framework for incorporating pedigree and molecular marker information in models for multienvironment data is described and applied to data that originate from wheat (Triticum aestivum L.) multienvironment trials. Two prediction problems relevant to plant breeders are considered: (CV1) predicting the performance of untested genotypes (?newly? developed lines), and (CV2) predicting the performance of genotypes that have been evaluated in some environments but not in others. Results confirmed the superiority of models using both marker and pedigree information over those based on pedigree information only. Models with pedigree and/or markers had better predictive accuracy than simple linear mixed models that do not include either of these two sources of information. We concluded that the evaluation of such trials can benefit greatly from using multienvironment GS models.
Publication - Diseños experimentales con testigos repetidos(Colegio de Postgraduados, 2005) Burgueño, J.; Martinez-Garza, A.; Crossa, J.; Mastache-Lagunas, A.In some stages of the breeding of crops, breeders must select the most promising lines from a very large number of sets of new varieties. Selection is conducted via direct comparison of new varieties that are tested within a single experimental unit, with control varieties systematically intercropped among them and replicated over a large number of plots. Researchers often do not realize that, by following some simple design rules, they might be susceptible of a precise and accurate statistical analysis. This paper discusses the subject with precision, and establishes rules of design and a statistical analysis technique appropriate for completely randomized experimental designs or randomized complete blocks with blocks of the same size.
Publication - User's guide for spatial analysis of field variety trials using ASREML(CIMMYT, 2000) Burgueño, J.; Cadena, A.; Crossa, J.; Banziger, M.; Gilmour, A.; Cullis, B.This manual describes an approach to the spatial analysis of field experiments based on the software package AS residual maximum likelihood (ASREML; Gilmour et al. 1999). It describes common sources of spatial variation and explains how these can be identified and accounted for in an analysis.
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