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Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

Creator: Montesinos-López, A.
Creator: Gutierrez-Pulido, H.
Creator: Montesinos-Lopez, O.A.
Creator: Crossa, J.
Year: 2020
Language: English
Publisher: Genetics Society of America
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Type: Article
Place of Publication: Bethesda, MD (USA)
Pages: 4083-4102
Issue: 11
Volume: 10
DOI: 10.1534/g3.120.401733
Keywords: Maximum a Posteriori Estimation
Keywords: EM Algorithm
Keywords: Genomic Prediction
Keywords: Support Vector Machine
Keywords: Multinomial Ridge Regression
Keywords: Genomic Selection
Keywords: GenPred
Keywords: Shared Data Resources
Description: Due to the ever-increasing data collected in genomic breeding programs, there is a need for genomic prediction models that can deal better with big data. For this reason, here we propose a Maximum a posteriori Threshold Genomic Prediction (MAPT) model for ordinal traits that is more efficient than the conventional Bayesian Threshold Genomic Prediction model for ordinal traits. The MAPT performs the predictions of the Threshold Genomic Prediction model by using the maximum a posteriori estimation of the parameters, that is, the values of the parameters that maximize the joint posterior density. We compared the prediction performance of the proposed MAPT to the conventional Bayesian Threshold Genomic Prediction model, the multinomial Ridge regression and support vector machine on 8 real data sets. We found that the proposed MAPT was competitive with regard to the multinomial and support vector machine models in terms of prediction performance, and slightly better than the conventional Bayesian Threshold Genomic Prediction model. With regard to the implementation time, we found that in general the MAPT and the support vector machine were the best, while the slowest was the multinomial Ridge regression model. However, it is important to point out that the successful implementation of the proposed MAPT model depends on the informative priors used to avoid underestimation of variance components.
Agrovoc: MODELS
Related Datasets:
Related Datasets:
ISSN: 2160-1836
Journal: G3: Genes, Genomes, Genetics

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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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