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Autor: Jose Crossa
A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model
Osval Antonio Montesinos-Lopez Jose Crossa (2018)
In this paper, we propose a two-stage analysis for multiple-trait data; in the first stage, we perform a singular value decomposition (SVD) on the resulting matrix of traits responses, and in the second stage, multiple trait analysis on transformed responses is performed. We use simulated as well as wheat and maize data sets
Dataset
BHOJA BASNET Jose Crossa Susanne Dreisigacker (2020)
Conducted genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for Anther Extrusion (AE) in the CIMMYT hybrid wheat breeding program
Dataset
18th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data
Susanne Dreisigacker Jose Crossa Jesse Poland (2015)
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Dataset
1st Semi-Arid Wheat Yield Trial Genotyping-by-sequencing data
Susanne Dreisigacker Jose Crossa Jesse Poland (2016)
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Dataset
Sparse designs for genomic selection using multi-environment data
Yoseph Beyene Juan Burgueño Jose Crossa (2020)
This research study the genomic-enabled prediction accuracy of the composition of the following sparse testing allocation design: (1) all non-overlapping (0 overlapping) lines in environments, (2) all overlapping (0 non-overlapping) lines tested in all the environments, and (3) combinations of the two previous cases where certain numbers of non-overlapping (NO)/overlapping (O) lines were distributed in the environments. We also studied cases where the size of the testing population was decreased. The study used two large maize data sets (T1 and T2). Four different genomic-enabled prediction models were studied, two models (M1 and M2) that do not include the genomic × environment interaction (GE), whereas models M3 and M4 incorporate two forms of modeling GE. The results show that genome-based models including GE (M3 and M4) captured more genetic variability with the GE component than the other models for both data sets. Also, models M3 and M4 provide higher prediction accuracy than models M1 and M2 for the different allocation designs comprising different combinations of NO/O lines in environments. Results indicate that substantial savings of testing resources can be achieved by optimizing the allocation design using genome-based models including genomic × environment interaction.
Dataset
Osval Antonio Montesinos-Lopez Jose Crossa (2020)
The data contained in these datasets can be used to implement Bayesian generalized kernel regression methods for genome-enabled prediction in the statistical software R, The accompanying paper describes the building process of 7 kernel methods (linear, polynomial, sigmoid, Gaussian and Arc-cosine 1, Arc-cosine L).
Dataset
16th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data
Susanne Dreisigacker Jose Crossa Jesse Poland (2015)
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Dataset
15th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data
Susanne Dreisigacker Jose Crossa Jesse Poland (2015)
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Dataset
Evaluation of maize pre-breeding materials under the Seeds of Discovery initiative in 2018
Jose Crossa Cesar Petroli Sarah Hearne (2021)
These data describe the evaluation of landrace-derived pre-breeding materials for biotic and abiotic stress resistance as well as for blue maize production in 2018. Populations of interest for drought stress during flowering time, heat stress during flowering time, Tar Spot tolerance, and blue maize production were evaluated for yield potential and response to the stresses with support from the MasAgro Biodiversidad project and the CGIAR Research Program on Maize.
Dataset
Deep learning genomic-enabled prediction of plant traits
Osval Antonio Montesinos-Lopez Jose Crossa (2018)
Machine learning (ML) is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) from data, without being explicitly programmed to do this. ML is closely related to (and often overlaps with) computational statistics, which also focuses on making predictions through the use of computers. In general, ML explores algorithms that can learn from current data and make predictions on new data, through building a model from sample inputs. The field of statistics and ML had a root in common and will continue to come closer together in the future. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. DL models with densely connected network architecture were compared with one of the most often used genome-enabled prediction models genomic best linear unbiased prediction (GBLUP). We used nine published real genomic data sets to compare the models and obtain a “meta picture” of the performance of DL models with a densely connected network architecture.
Dataset