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Autor: Osval Antonio Montesinos-Lopez
Replication Data for: Approximate kernels for large data sets In genome-based prediction
Osval Antonio Montesinos-Lopez Johannes Martini Paulino Pérez-Rodríguez Jose Crossa (2020)
The rapid development of molecular markers and sequencing technologies has made it possible to use genomic selection (GS) and genomic prediction (GP) in animal and plant breeding. However, computational difficulties arise when the number of observations is large. This five datasets provided here were used to support a comparative analysis of two genomic-enabled prediction models: the full genomic method single environment (FGSE) and the approximate kernel method for a single environment model (APSE). The data were also used to compare the full genomic method with genotype × environment model (FGGE) to the approximate kernel method with genotype × environment interaction (APGE). The results of the analyses are described in the related publication.
Dataset
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
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
Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments
Rodomiro Ortiz Paulino Pérez-Rodríguez Osval Antonio Montesinos-Lopez Jose Crossa (2023)
Artículo
Potato Traits Cross-Validation Breeding Data CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING
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
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
Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2023)
Disease resistance improvement efforts in plant breeding can help to reduce the negative impact of biotic stresses on crop production.Disease resistance can be assessed through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specially trained staff. Remote sensing (RS) tools can also be used to measure traits such as vegetation indices that can also be used to assess plant responses to diseases. This dataset contains phenotypic and genotypic data from a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH). Data from VS and RS methods for assessing common rust resistance were used in genome wide association study (GWAS) as well as genomic prediction (GP) analyses. A report on the comparison of the results of these analyses is provided in the accompanying article.
Dataset
Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years
Maria Itria Ibba Jose Crossa Osval Antonio Montesinos-Lopez Philomin Juliana Carlos Guzman Susanne Dreisigacker Jesse Poland (2020)
The use of genomic prediction could greatly help to increase the efficiency of selecting for wheat quality traits by reducing the cost and time required for this analysis. This study contains data used to evaluate the prediction performances of 13 wheat quality traits under two multi-trait models [Bayesian multi-trait multi-environment (BMTME) and multi-trait ridge regression (MTR)]. Separate files are provided for each year of data. An additional supplemental data file provides R code for running the analyses as well as a table describing the Average Pearson´s correlation (APC) and mean arctangent absolute percentage error (MAAPE) for the testing sets for each dataset and trait.
Dataset
Osval Antonio Montesinos-Lopez Philomin Juliana Ravi Singh Jesse Poland Paulino Pérez-Rodríguez Jose Crossa DIEGO JARQUIN (2019)
In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information in two different validation schemes. All models included main effects, and others also considered interactions between the different types of covariates via Hadamard products of similarity structures. The pedigree models always gave better results predicting new lines in observed environments than the genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, markers and environments were included. When new lines were predicted in unobserved environments in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design of future breeding programs.
Dataset
Efficacy of plant breeding using genomic information
Osval Antonio Montesinos-Lopez Alison Bentley Carolina Saint Pierre Leonardo Abdiel Crespo Herrera Morten Lillemo Jose Crossa (2023)
Artículo
Genomic Selection Genomic Prediction Genomic Best Linear Unbiased Predictor CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PLANT BREEDING GENOMICS MARKER-ASSISTED SELECTION ENVIRONMENT