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Author: Jaime Cuevas

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

VIANEY COLIN NAVARRO FRANC AVILES NOVA IGNACIO ARTURO DOMINGUEZ VARA JAIME OLIVARES PEREZ RODOLFO SERRATO CUEVAS (2017)

En años recientes la disposición de los desechos orgánicos de origen agrícola ha causado un incremento de problemas ambientales y económicos, por lo que su eliminación y manejo se vuelven urgentes (Xing et al. 2015). En particular, E. fetida es ampliamente utilizada en el proceso de vermicompostaje de desechos orgánicos tales como el estiércol de ganado porque es de fácil manejo (Kim, 2016). Durante este proceso es importante monitorear a la lombriz para determinar su tasa de crecimiento y reproducción con la finalidad de identificar sus necesidades óptimas las cuales están directamente influenciadas por la calidad y la disponibilidad del alimento (Vodounnou et al., 2016). El objetivo del estudio fue evaluar la tasa de crecimiento y la reproducción de Eisenia fetida alimentada con estiércol composteado de equino y ovino en el Sur del Estado de México.

Article

Eisenia fetida estiércol vermicompostaje CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Osval Antonio Montesinos-Lopez Jose Crossa Jaime Cuevas Morten Lillemo Philomin Juliana Ravi Singh (2018)

A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

Dataset

Bayesian Genomic Prediction with Genotype x Environment Interaction Kernel Models

Jaime Cuevas Osval Antonio Montesinos-Lopez Juan Burgueño Paulino Pérez-Rodríguez Gustavo de los Campos (2017)

The phenomenon of genotype · environment (G · E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G · E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G · E interaction are extensions of a single environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u: We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G · E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u.

Article

Bayesian theory Genotype environment interaction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Genomic prediction of genotype x environment interaction kernel regression models

Jaime Cuevas Jose Crossa Sergio Pérez-Elizalde Paulino Pérez-Rodríguez Gustavo de los Campos Osval Antonio Montesinos-Lopez Juan Burgueño (2016)

In genomic selection (gs), genotype × environment interaction (g × e) can be modeled by a marker × environment interaction (m × e). The g × e may be modeled through a linear kernel or a nonlinear (gaussian) kernel. In this study, we propose using two nonlinear gaussian kernels: the reproducing kernel hilbert space with kernel averaging (rkhs ka) and the gaussian kernel with the bandwidth estimated through an empirical bayesian method (rkhs eb). We performed single-environment analyses and extended to account for g × e interaction (gblup-g × e, rkhs ka-g × e and rkhs eb-g × e) in wheat (triticum aestivum l.) and maize (zea mays l.) data sets. For single-environment analyses of wheat and maize data sets, rkhs eb and rkhs ka had higher prediction accuracy than gblup for all environments. For the wheat data, the rkhs ka-g × e and rkhs eb-g × e models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with gaussian kernels had accuracies up to 17% higher than that of gblup-g × e. For the maize data set, the prediction accuracy of rkhs eb-g × e and rkhs ka-g × e was, on average, 5 to 6% higher than that of gblup-g × e. The superiority of the gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects

Article

Genetics Genomics Yields Agricultural Genetics Gaussian CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Deep kernel for genomic and near infrared predictions in multi-environment breeding trials

Jaime Cuevas Osval Antonio Montesinos-Lopez Philomin Juliana Carlos Guzman Paulino Pérez-Rodríguez Juan Burgueño Jose Crossa (2019)

Article

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS GENOTYPE ENVIRONMENT INTERACTION MODELS PLANT BREEDING

Deep kernel of genomic and near infrared predictions in multi-environment breeding trials

Jaime Cuevas Osval Antonio Montesinos-Lopez Philomin Juliana Paulino Pérez-Rodríguez Juan Burgueño Carlos Guzman Jose Crossa (2019)

In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel methods used in genomic predictions comprise the linear genomic best linear unbiased predictor (GBLUP) kernel (GB) and the Gaussian kernel (GK). These kernels have being used with two statistical models, single environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has being used as phenotype method for prediction of unobserved line performance in plant breeding trials. In this study, we used a non-linear Arc-cosine kernel (AK) that emulates deep learning artificial neural network. We compared AK prediction accuracy with GB and GK kernel methods in four genomic data sets one of them including also pedigree (ABLUP) and NIR (NBLUP) information. Results show that for all four data sets AK and GK kernels gave higher prediction accuracy than the linear GB kernel for single environment as well as GE multi-environment models. In addition, AK gave similar or slightly higher prediction accuracy than the GK kernel.

Dataset

Italo Granato Jaime Cuevas Francisco Javier Luna Vázquez Jose Crossa Osval Antonio Montesinos-Lopez Juan Burgueño Roberto Fritsche-Neto (2018)

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.

Article

Genotype environment interaction Bayesian theory Maize Bayesian Genomic Genotype Environment Interaction GenPred Shared Data Resources BAYESIAN THEORY GENOMICS SELECTION REGRESSION ANALYSIS CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA