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Autor: Jose Crossa
Multi-trait multi-environment genomic prediction of durum wheat
Osval Antonio Montesinos-Lopez ROBERTO TUBEROSA MARCO MACCAFERRI GIUSEPPE SCIARA Karim Ammar Jose Crossa (2019)
In this paper we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (location-year combinations) in Bologna, Italy. The results of the multi-trait deep learning method also were compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method. All models were implemented with and without the genotype×environment interaction term. We found that the best predictions were observed without the genotype×environment interaction term in the univariate and multivariate deep learning methods, but under the GBLUP method, the best predictions were observed taking into account the interaction term. We also found that in general the best predictions were observed under the GBLUP model but the predictions of the multi-trait deep learning model were very similar to those of the GBLUP model.
Dataset
Osval Antonio Montesinos-Lopez Francisco Javier Martin Vallejo Jose Crossa Philomin Juliana Ravi Singh (2018)
The seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.
Dataset
Mary-Francis LaPorte Pattama Hannok Peter Bradbury Jose Crossa natalia palacios rojas Christine Diepenbrock (2024)
Vitamin A deficiency continues to cause challenges around the world including in areas where maize is an important component of human diets. Biofortification offers one solution for alleviating this deficiency. A Carotenoid Association Mapping panel, developed by the International Maize and Wheat Improvement Center (CIMMYT) contains 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. The data in this study were used to assess the accuracy of several genomic prediction (GP) strategies for maize grain carotenoid traits within and between four environments in Mexico. Results are provided for these strategies including Ridge Regression-Best Linear Unbiased Prediction (including all markers versus subsets of markers), Elastic Net, Reproducing Kernel Hilbert Spaces, and Least Absolute Shrinkage and Selection Operator. The findings described in the accompanying journal article indicate the utility of genomic prediction methods for grain carotenoid traits and could inform their resource-efficient implementation in biofortification breeding programs.
Dataset
Replication Data for: Sparse multi-trait genomic prediction under incomplete block designs
Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González JOSAFHAT SALINAS RUIZ Abelardo Montesinos Jose Crossa (2022)
The efficiency of genomic selection methodologies can be increased by sparse testing where a subset of materials are evaluated in different environments. Seven different multi-environment plant breeding datasets were used to evaluate four different methods for allocating lines to environments in a multi-trait genomic prediction problem. The results of the analysis are presented in the accompanying article.
Dataset
Marco Lopez-Cruz Yoseph Beyene Manje Gowda Jose Crossa Paulino Pérez-Rodríguez Gustavo de los Campos (2021)
Genomic prediction models may be used in plant breeding pipelines. They are often calibrated using multi-generation data and there is an open question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Therefore, a study was undertaken to determine whether combining sparse selection indexes (SSIs) and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. This dataset contains the genotypic and phenotypic data from CIMMYT maize doubled haploid lines that were used to perform the analyses. The results of the analyses are presented in the accompanying article.
Dataset
Terence Molnar Marcela Carvalho Juan Burgueño Jose Crossa Samuel Trachsel Monica Mezzalama Denise Costich Sarah Hearne (2018)
These data describe the evaluation of landraces and landrace-derived pre-breeding materials for biotic and abiotic stress resistance as well as for general yield potential in 2016. Populations and accessions of interest for terminal drought and Tar Spot tolerance were evaluated for yield potential and response to both stresses under the MasAgro Biodiversidad project. Populations and accessions of interest for terminal heat and MCMV tolerance were evaluated for response to both stresses under the MAIZE CRP project.
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
Susanne Dreisigacker Jose Crossa Leonardo Abdiel Crespo Herrera Maria Itria Ibba (2024)
This dataset is derived from a study focused on developing an efficient method for arabinoxylan quantification, called PentoQuant. It includes phenotypic and molecular characterization data from 606 bread wheat samples developed through the spring bread wheat breeding program. The dataset comprises total and water-extractable arabinoxylan content values measured using the PentoQuant protocol. Furthermore, it incorporates results obtained from analyzing the same 606 lines with four molecular markers associated with two major QTLs for arabinoxylan content variation in wheat, located on chromosomes 1B and 6B.
Dataset