Pérez-Rodríguez, P.

Person:
Pérez-Rodríguez, Paulino

Last Name

Pérez-Rodríguez

First Name

Paulino

Name

Pérez-Rodríguez, P.

ORCID ID

0000-0002-3202-1784

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Now showing 1 - 10 of 21

Enhancing wheat genomic prediction by a hybrid kernel approach
(Frontiers Media, 2025) Cuevas, J.; Crossa, J.; Montesinos-López, A.; Martini, J.W.R.; Gerard, G.S.; Ortegón, J.; Dreisigacker, S.; Velu, G.; Pérez-Rodríguez, P.; Saint Pierre, C.; Crespo Herrera, L.A.; Montesinos-Lopez, O.A.; Vitale, P.
Genome-enabled prediction using probabilistic neural network classifiers
(BioMed Central, 2016) Gonzalez Camacho, J.M.; Crossa, J.; Pérez-Rodríguez, P.; Ornella, L.; Gianola, D.
Chapter 9. Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction
(Humana Press Inc., 2022) Crossa, J.; Montesinos-Lopez, O.A.; Pérez-Rodríguez, P.; Costa-Neto, G.; Fritsche-Neto, R.; Ortiz, R.; Martini, J.W.R.; Lillemo, M.; Montesinos-López, A.; Jarquin, D.; Breseghello, F.; Cuevas, J.; Rincent, R.
Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
(Wiley, 2021) Fonseca, J.M.O.; Klein, P.E.; Crossa, J.; Pacheco Gil, R.A,; Pérez-Rodríguez, P.; Ramasamy, P.; Klein, R.; Rooney, W.L.
Target population of environments for wheat breeding in India: definition, prediction and genetic gains
(Frontiers, 2021) Crespo Herrera, L.A.; Crossa, J.; Huerta-Espino, J.; Mondal, S.; Velu, G.; Juliana, P.; Vargas Hernández, M.; Pérez-Rodríguez, P.; Joshi, A.K.; Braun, H.J.; Singh, R.P.
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.
Approximate genome-based kernel models for large data sets including main effects and interactions
(Frontiers, 2020) Cuevas, J.; Montesinos-Lopez, O.A.; Martini, J.W.R.; Pérez-Rodríguez, P.; Lillemo, M.; Crossa, J.
Multivariate bayesian analysis of on-farm trials with multiple-trait and multiple-environment data
(American Society of Agronomy, 2019) Montesinos-Lopez, O.A.; Montesinos-López, A.; Vargas Hernández, M.; Ortiz-Monasterio, I.; Pérez-Rodríguez, P.; Burgueño, J.; Crossa, J.
Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials
(Frontiers, 2019) Crossa, J.; Martini, J.W.R.; Gianola, D.; Pérez-Rodríguez, P.; Jarquin, D.; Juliana, P.; Montesinos-Lopez, O.A.; Cuevas, J.
Deep kernel for genomic and near infrared predictions in multi-environment breeding trials
(Genetics Society of America, 2019) Cuevas, J.; Montesinos-Lopez, O.A.; Juliana, P.; Guzman, C.; Pérez-Rodríguez, P.; González-Bucio, J.; Burgueño, J.; Montesinos-López, A.; Crossa, J.
Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting 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 networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data.