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META: A suite of sas programs to analyze multienvironment breeding trials 

Vargas Hernández, M.; Combs, E.; Alvarado, G.; Atlin, G.N.; Mathews, K.L.; Crossa, J. (American Society of Agronomy, 2013)
Multi-environment trials (METs) enable the evaluation of the same genotypes in a variety of environments and management conditions. We present here META (Multi Environment Trial Analysis), a suite of 31 SAS programs that ...
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Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers 

Burgueño, J.; Campos, G. de los; Weigel, K.; Crossa, J. (Crop Science Society of America (CSSA), 2012)
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. ...
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A general Bayesian estimation method of linear-bilinear models applied to plant breeding trials with genotype × environment interaction 

Perez-Elizalde, S.; Jarquin, D.; Crossa, J. (Springer Verlag; American Statistical Association; International Biometrics Society, 2012)
Statistical analyses of two-way tables with interaction arise in many different fields of research. This study proposes the von Mises-Fisher distribution as a prior on the set of orthogonal matrices in a linear-bilinear ...
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Sample size under inverse negative binomial group testing for accuracy in parameter estimation 

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Crossa, J.; Kent Eskridge (Public Library of Science, 2012)
Background. The group testing method has been proposed for the detection and estimation of genetically modified plants (adventitious presence of unwanted transgenic plants, AP). For binary response variables (presence or ...
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Multi-trait and multi-environment QTL analyses for resistance to wheat diseases 

Singh, S.; Hernandez, M.V.; Crossa, J.; Singh, P.K.; Bains, N.S.; Singh, K.; Sharma, I. (Public Library of Science, 2012)
Background: Stripe rust, leaf rust, tan spot, and Karnal bunt are economically significant diseases impacting wheat production. The objectives of this study were to identify quantitative trait loci for resistance to these ...
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QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance 

Dias Almeida, G.; Makumbi, D.; Magorokosho, C.; Nair, S.; Borém, A.; Ribaut, J.M.; Bänziger, M.; Prasanna, B.M.; Crossa, J.; Babu, R. (Springer, 2012)
Despite numerous published reports of quantitative trait loci (QTL) for drought-related traits, practical applications of such QTL in maize improvement are scarce. Identifying QTL of sizeable effects that express more or ...
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Genome-enabled prediction of genetic values using radial basis function neural networks 

Gonzalez-Camacho, J.M.; Campos, G. de los; Perez, P.; Gianola, D.; Cairns, J.E.; Mahuku, G.; Babu, R.; Crossa, J. (Springer, 2012)
The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models ...
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Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments 

Windhausen, V.S.; Atlin, G.N.; Hickey, J.M.; Crossa, J.; Jannink, J.L.; Sorrells, M.E.; Babu, R.; Cairns, J.E.; Amsal Tesfaye Tarekegne; Fentaye Kassa Semagn; Beyene, Y.; Grudloyma, P.; Technow, F.; Riedelsheimer, C.; Melchinger, A.E. (Genetics Society of America, 2012)
Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids ...
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Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R 

Perez, P.; De los Campos, G.; Crossa, J.; Gianola, D. (Crop Science Society of America, 2010)
The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require ...
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1.4 screening experimental designs for quantitative trait loci, association mapping, genotype-by environment interaction, and other investigations 

Federer, W.T.; Crossa, J. (Frontiers, 2012)
Crop breeding programs using conventional approaches, as well as new biotechnological tools, rely heavily on data resulting from the evaluation of genotypes in different environmental conditions (agronomic practices, ...
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Genetic ResourcesInstitutionalIntegrated DevelopmentMaizeSocioeconomicsSustainable IntensificationWheat

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Author
Crossa, J. (14)
Pérez-Rodríguez, Paulino (4)Babu, R. (3)De Los Campos, Gustavo (3)Dreisigacker, S. (3)Gianola, D. (3)Atlin, Gary (2)Burgueño, J. (2)Cairns, J. (2)Gonzalez Camacho, J.M. (2)... View More
Date Issued
2014 (1)2013 (1)2012 (11)2010 (1)
Type
Article (13)Book (1)
Agrovoc
BAYESIAN THEORY (5)WHEAT (5)STATISTICAL METHODS (4)DATA ANALYSIS (3)GENETIC MARKERS (3)QUANTITATIVE TRAIT LOCI (3)COMPUTER APPLICATIONS (2)DISEASE RESISTANCE (2)MAIZE (2)MARKER-ASSISTED SELECTION (2)... View More
Keywords
Bayesian Inference (1)Bilinear Interaction Terms (1)Development Strategy (1)GenPred (1)Multi-Environment Trials (1)Shared Data Resources (1)von Mises–Fisher (1)... View More
Region
Global (14)


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