Person:
Crossa, J.

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Crossa
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Crossa, J.
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Keyword: GenPred ×

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Now showing 1 - 10 of 22
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    Bayesian multitrait kernel methods improve multienvironment genome-based prediction
    (Oxford University Press, 2022) Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Montesinos-López, A.; Ramirez-Alcaraz, J.M.; Poland, J.; Singh, R.P.; Dreisigacker, S.; Crespo Herrera, L.A.; Mondal, S.; Velu, G.; Juliana, P.; Huerta-Espino, J.; Shrestha, S.; Varshney, R.K.; Crossa, J.
    Publication
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    A zero altered Poisson random forest model for genomic-enabled prediction
    (Genetics Society of America, 2021) Montesinos-Lopez, O.A.; Montesinos-López, A.; Mosqueda-Gonzalez, B.A.; Montesinos-Lopez, J.C.; Crossa, J.; Lozano, N.; Singh, P.K.; Valladares-Anguiano, F.A.
    Publication
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    Genomic prediction enhanced sparse testing for multi-environment trials
    (Genetics Society of America, 2020) Jarquin, D.; Howard, R.; Crossa, J.; Beyene, Y.; Gowda, M.; Martini, J.W.R.; Covarrubias, E.; Burgueño, J.; Pacheco Gil, Rosa Angela; Grondona, M.; Wimmer, V.; Prasanna, B.M.
    Publication
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    Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm
    (Genetics Society of America, 2020) Mageto, E.; Crossa, J.; Pérez-Rodríguez, P.; Dhliwayo, T.; Palacios-Rojas, N.; Lee, M.; Rui Guo; San Vicente Garcia, F.M.; Xuecai Zhang; Hindu, V.
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    A multivariate Poisson deep learning model for genomic prediction of count data
    (Genetics Society of America, 2020) Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Singh, P.K.; Lozano, N.; Barrón-López, A.; Montesinos-López, A.; Crossa, J.
    Publication
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    Maximum a posteriori Threshold Genomic Prediction model for ordinal traits
    (Genetics Society of America, 2020) Montesinos-López, A.; Gutierrez-Pulido, H.; Montesinos-Lopez, O.A.; Crossa, J.
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    New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes
    (Genetics Society of America, 2019) Montesinos-Lopez, O.A.; Martin Vallejo, F.J.; Crossa, J.; Gianola, D.; Hernandez-Suarez, C.M.; Montesinos-López, A.; Juliana, P.; Singh, R.P.
    Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multiple-trait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype x environment (GxE) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson's correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS.
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    An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction
    (Genetics Society of America, 2019) Montesinos-Lopez, O.A.; Montesinos-López, A.; Luna Vázquez, F.J.; Toledo, F.H.; Pérez-Rodríguez, P.; Lillemo, M.; Crossa, J.
    Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by GS to perform predictions of candidate genotypes that were not phenotyped. Because there is no universally better model for prediction and models for each type of response variable are needed (continuous, binary, ordinal, count, etc.), an active area of research aims to develop statistical models for the prediction of univariate and multivariate traits in GS. However, most of the models developed so far are for univariate and continuous (Gaussian) traits. Therefore, to overcome the lack of multivariate statistical models for genome-based prediction by improving the original version of the BMTME, we propose an improved Bayesian multi-trait and multi-environment (BMTME) R package for analyzing breeding data with multiple traits and multiple environments. We also introduce Bayesian multi-output regressor stacking (BMORS) functions that are considerably efficient in terms of computational resources. The package allows parameter estimation and evaluates the prediction performance of multi-trait and multi-environment data in a reliable, efficient and user-friendly way. We illustrate the use of the BMTME with real toy datasets to show all the facilities that the software offers the user. However, for large datasets, the BME() and BMTME() functions of the BMTME R package are very intense in terms of computing time; on the other hand, less intensive computing is required with BMORS functions BMORS() and BMORS_Env() that are also included in the BMTME package.
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    A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding
    (Genetics Society of America, 2019) Montesinos-Lopez, O.A.; Martin Vallejo, F.J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-López, A.; Juliana, P.; Singh, R.P.
    Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.
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    BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models
    (Genetics Society of America, 2018) Granato, I.; Cuevas, J.; Luna Vázquez, F.J.; Crossa, J.; Montesinos-Lopez, O.A.; Burgueño, J.; Fritsche-Neto, R.
    One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is 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 genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices 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 for creating genomic GE kernels and making genomic predictions.
    Publication