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Autor: Jose Crossa
Jose Crossa (2020)
Synthetic hexaploid wheat was developed and used in breeding to introduce new genetic diversity into bread wheat, through interspecific hybridization of T. tauschii (diploid) and durum wheat T. turgidum (tetraploid) to produce synthetic derivatives. Therefore, one may infer that the genetic variances of native wild populations vs. improved wheat may be different due differential origin and evolutionary history. We investigate this idea by partitioning the additive variance of grain yield with respect to breed origin using data from a synthetic derivative. Such information is needed to predict breeding values of synthetic derivatives and their parental populations. A mixed model with a heterogeneous covariance structure for breeding values was employed to estimate variance components using a program written by us. Data originated in a multi-year multi-location field trial of synthetic derivatives from the International Maize and Wheat Improvement Center (CIMMYT). Bayesian estimates of additive variances of grain yield from each population were similar for T. turgidum (0.0225) and T. tauschii (0.0208), but they were strikingly different from the one of T. aestivum (0.0131). Segregation variances were higher than zero, indicating differences in gene frequencies between pure breeds. Broad-sense heritability of the 25% synthetic derivative breed group was estimated to be equal to 0,66. Overall, our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.
Dataset
Jose Crossa (2018)
This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.
Dataset
Jose Crossa Rodomiro Ortiz (2022)
Genomic prediction (GP) can be used in the breeding of polysomic polyploid plant species. Different models can be used to generate the genomic prediction including single trait and multi-trait models. The data provided in this dataset were used to investigate the accuracy of four different genomic prediction models use for several traits in potato. The results of the analysis are reported in the accompanying journal article.
Dataset
Grain yield and stability of white early hybrids in the highland valleys of Mexico
Prasanna Boddupalli Jose Crossa (2017)
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Dataset
Deep kernel of genomic and near infrared predictions in multi-environment breeding trials
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
Grain yield and stability of white early hybrids in the highland valleys of Mexico
Prasanna Boddupalli Jose Crossa (2017)
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Dataset
Germano Costa Neto Jose Crossa (2024)
Artículo
Forest Tree Breeding Genomic Relationship Matrix Genomic Selection Best Linear Unbiased Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOREST TREES BREEDING MARKER-ASSISTED SELECTION MYRTACEAE EUCALYPTUS GLOBULUS
13th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data
Susanne Dreisigacker Jose Crossa Jesse Poland (2015)
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Dataset
CIMMYT Maize Genetic Resource Lines
Terence Molnar Juan Burgueño Jose Crossa (2020)
CIMMYT makes available to the public a set of maize inbred lines called CIMMYT Maize Genetic Resource Lines (CMGRL). The CMGRLs are derived from crosses between elite CIMMYT lines and landrace accessions, populations or synthetics from the CIMMYT Germplasm Bank. CMGRLs are intended to be used by breeders as sources of novel alleles for traits of economic importance. These lines should also be of interest to maize researchers who are not breeders but are studying the underlying genetic mechanisms of abiotic and biotic traits. The inaugural group of CMGRLs includes five subtropical-adapted lines with tolerance to drought during flowering and grain-fill and four tropical adapted lines for Tar Spot Complex resistance.
Dataset
J. Jesús Cerón Rojas Jose Crossa (2019)
Multistage selection is a cost-saving strategy for improving several traits because it is not necessary to measure all traits at each stage. A combined linear genomic selection index is a linear combination of phenotypic and genomic estimated breeding values useful to predict the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The main combined multistage linear genomic selection indices are the optimum and decorrelated indices. Using real and simulated data, we compared the efficiency of both indices to predict the net genetic merit in plants in a two-stage breeding context. The criteria used to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real data set, the total decorrelated and optimum index selection responses explained 90% and 97.5%, respectively, of the estimated single-stage combined selection response. In addition, at stage two, the correlation of the optimum and decorrelated indices with the net genetic merit were 0.84 and 0.63, respectively.
Dataset