Person: Pérez-Rodríguez, P.
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Pérez-Rodríguez
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P.
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Pérez-Rodríguez, P.
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0000-0002-3202-178411 results
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- Genomic prediction of the performance of tropical doubled haploid maize lines under artificial Striga hermonthica (Del.) Benth. infestation(Oxford University Press, 2024) Kimutai, J.J.C.; Makumbi, D.; Burgueño, J.; Perez-Rodriguez, P.; Crossa, J.; Gowda, M.; Menkir, A.; Pacheco Gil, R.A.; Ifie, B.E.; Tongoona, P.B.; Danquah, E.; Prasanna, B.M.
Publication - Optimizing genomic parental selection for categorical and continuous-categorical multi-trait mixtures(MDPI, 2024) Villar-Hernández, B.d.J.; Perez-Rodriguez, P.; Vitale, P.; Gerard, G.S.; Montesinos-Lopez, O.A.; Saint Pierre, C.; Crossa, J.; Dreisigacker, S.
Publication - Deep learning methods improve genomic prediction of wheat breeding(Frontiers Media S.A., 2024) Montesinos-Lopez, A.; Crespo Herrera, L.A.; Dreisigacker, S.; Gerard, G.S.; Vitale, P.; Saint Pierre, C.; Velu, G.; Tarekegn, Z.T.; Chavira-Flores, M.; Pérez-Rodríguez, P.; Ramos-Pulido, S.; Lillemo, M.; Huihui Li; Montesinos-Lopez, O.A.; Crossa, J.
Publication - Results from rapid-cycle recurrent genomic selection in spring bread wheat(Genetics Society of America, 2023) Dreisigacker, S.; Pérez-Rodríguez, P.; Crespo Herrera, L.A.; Bentley, A.R.; Crossa, J.
Publication - Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials(Genetics Society of America, 2021) Montesinos-López, A.; Runcie, D.E.; Ibba, M.I.; Pérez-Rodríguez, P.; Montesinos-Lopez, O.A.; Crespo Herrera, L.A.; Bentley, A.R.; Crossa, J.
Publication - lme4GS: an R-Package for Genomic Selection(Frontiers, 2021) Caamal-Pat D.; Pérez-Rodríguez, P.; Crossa, J.; Velasco Cruz, C.; Pérez-Elizalde, S.; Vázquez-Peña, M.
Publication - Application of multi-trait Bayesian decision theory for parental genomic selection(Genetics Society of America, 2021) Villar-Hernández, B.d.J.; Pérez-Elizalde, S.; Martini, J.W.R.; Toledo, F.H.; Pérez-Rodríguez, P.; Krause, M.; Value; Covarrubias, E.; Crossa, J.
Publication - 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.
Publication - 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.
Publication - Genomic prediction models for count data(Springer Verlag, 2015) Montesinos-Lopez, O.A.; Montesinos-López, A.; Pérez-Rodríguez, P.; Eskridge, K.; Xinyao He; Juliana, P.; Singh, P.; Crossa, J.Whole genome prediction models are useful tools for breeders when selecting candidate individuals early in life for rapid genetic gains. However, most prediction models developed so far assume that the response variable is continuous and that its empirical distribution can be approximated by a Gaussian model. A few models have been developed for ordered categorical phenotypes, but there is a lack of genomic prediction models for count data. There are well-established regression models for count data that cannot be used for genomic-enabled prediction because they were developed for a large sample size (n) and a small number of parameters (p); however, the rule in genomic-enabled prediction is that p is much larger than the sample size n. Here we propose a Bayesian mixed negative binomial (BMNB) regression model for counts, and we present the conditional distributions necessary to efficiently implement a Gibbs sampler. The proposed Bayesian inference can be implemented routinely. We evaluated the proposed BMNB model together with a Poisson model, a Normal model with untransformed response, and a Normal model with transformed response using a logarithm, and applied them to two real wheat datasets from the International Maize and Wheat Improvement Center. Based on the criteria used for assessing genomic prediction accuracy, results indicated that the BMNB model is a viable alternative for analyzing count data.
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