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Autor: Jose Crossa
Genomic-enabled prediction in maize using kernel models with genotype × environment interaction
Massaine e Sousa Jaime Cuevas Evellyn Couto Paulino Pérez-Rodríguez DIEGO JARQUIN Roberto Fritsche-Neto Juan Burgueño Jose Crossa (2017)
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Dataset
Replication Data for: A Bayesian Linear Phenotypic Selection Index to Predict the Net Genetic Merit
J. Jesús Cerón Rojas Sergio Pérez-Elizalde Jose Crossa Johannes Martini (2021)
In breeding, the plant net genetic merit may be predicted through the linear phenotypic selection index (LPSI). This paper associated with this dataset proposes a Bayesian LPSI (BLPSI). The supplemental files provided in this dataset include data that were used to compare the two indices as well as figures showing the results from these comparisons. The analysis revealed that the BLPSI is a good option when carrying out phenotypic selections in breeding programs.
Dataset
BGGE: A new package for genomic prediction incorporating genotype by environments models
Italo Granato Jaime Cuevas Francisco Javier Luna Vázquez Jose Crossa Juan Burgueño Roberto Fritsche-Neto (2018)
One of the major issues in plant breeding is the occurrence of genotype by environment (GE) interaction. Several models have been created to understand this phenomenon and explore it by selecting the most stable genotypes. In the genomic era, several models were employed to simultaneously improve selection by using markers and account for GE interaction. Some of these models use special genetic covariance matrices. In addition, multi-environment trials scales are getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genome GE models. Here we propose a function to create the genomic kernels needed to fit these models. This function makes genome predictions through a Bayesian linear mixed model approach. A particular treatment is given for structured dispersed covariance matrices; in particular, those structured as a block diagonal that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option to create genome GE kernels and make genomic predictions.
Dataset
Results from rapid-cycle recurrent genomic selection in spring bread wheat
Susanne Dreisigacker Paulino Pérez-Rodríguez Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2023)
Artículo
Genomic-Assisted Breeding Molecular Markers Pedigree Information Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS GENETIC MARKERS WHEAT BREEDING PROGRAMMES
A variational Bayes genomic-enabled prediction model with genotype × environment interaction
Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Luna Vázquez JOSAFHAT SALINAS RUIZ (2017)
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Dataset
Marco Lopez-Cruz Susanne Dreisigacker Leonardo Abdiel Crespo Herrera Alison Bentley Ravi Singh Suchismita Mondal Paulino Pérez-Rodríguez Jose Crossa (2021)
When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.
Dataset
Rapid cycling genomic selection in a multi-parental tropical maize population
XUECAI ZHANG Paulino Pérez-Rodríguez Juan Burgueño Jean-Luc Jannink Edward Buckler Prasanna Boddupalli Mateo Vargas Hernández Jose Crossa (2017)
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Dataset
Daniel Runcie Maria Itria Ibba Osval Antonio Montesinos-Lopez Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2021)
Several different genome-based prediction models are available for the analysis of multi-trait data in genomic selection. The supplemental files included in this dataset provide six extensive multi-trait wheat datasets (quality and grain yield) that enable the comparison of performance of genomic-enabled-prediction when calculating the prediction accuracy using different methods. The related article describes the results of the analysis and reports that trait grain yield prediction performance is better under a multi-trait model as compared with the single-trait model.
Dataset
Jesse Poland Susanne Dreisigacker Sandesh Kumar Shrestha Ravi Singh Suchismita Mondal Philomin Juliana Jose Crossa Jessica Rutkoski (2016)
Genetic profiling of wheat breeding lines from the CIMMYT bread wheat breeding program was carried out over several years.
Dataset
Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize
Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2024)
Artículo
Common Rust Rp1 Locus CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS REMOTE SENSING VEGETATION INDEX MAIZE CHROMOSOME MAPPING