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Pérez-Rodríguez, P.

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Pérez-Rodríguez
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Pérez-Rodríguez, P.

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Now showing 1 - 10 of 19
  • Genome-enabled prediction using probabilistic neural network classifiers
    (BioMed Central, 2016) Gonzalez Camacho, J.M.; Crossa, J.; Pérez-Rodríguez, P.; Ornella, L.; Gianola, D.
    Publication
  • 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.
    Publication
  • 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.
    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
  • 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.
    Publication
  • 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.
    Publication
  • 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.
    Publication
  • 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.
    Publication
  • Joint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments
    (Genetics Society of America, 2019) Howard, R.; Gianola, D.; Montesinos-Lopez, O.A.; Juliana, P.; Singh, R.P.; Poland, J.; Shrestha, S.; Pérez-Rodríguez, P.; Crossa, J.; Jarquin, D.
    Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.
    Publication
  • Modeling genotype × environment interaction using a factor analytic model of on-farm wheat trials in the Yaqui Valley of Mexico
    (American Society of Agronomy, 2019) Vargas Hernández, M.; Ortiz-Monasterio, I.; Pérez-Rodríguez, P.; Montesinos-Lopez, O.A.; Montesinos-López, A.; Burgueño, J.; Crossa, J.
    On‐farm trials of bread and durum wheat in the Yaqui Valley region of southern Sonora, Mexico, were established for three cropping seasons (2012, 2013, and 2015) using the management practices implemented by farmers. The trials comprised bread and durum wheats that were sown together under two regimes: full irrigation and reduced irrigation. The experiments were replicated and unbalanced, as several bread wheat and durum wheat lines were not repeated during the 3 yr. We studied the interaction between bread and durum wheats and environments (farmer‐irrigation‐year combinations). To model the crossover interaction (COI) and the non‐COI components of the genotype × environment interaction (G×E) between the wheat lines and the environments, we used a linear mixed model with the Factor Analytic (FA) model, a parsimonious model that is similar to the multiple regression of lines on environments based on latent variables. In this case, we modeled the combined effects of the wheat lines and their interactions with the farmer‐irrigation‐year combinations. Results show the separation of the dynamic (unpredictable) component of the interaction (year) from the more static component of the interaction due to farmer and irrigation level. The FA model offers a useful alternative for modeling interactions in agronomy‐breeding experiments to dissect and account for complex interactions that are common in agriculture experiments. Furthermore, stable wheat lines across all environments were also detected, as well the environments that caused most of the interaction.
    Publication