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Bayesian regularized quantile regression: a robust alternative for genome-based prediction of skewed data

Creator: Perez-Rodriguez, P.
Creator: Montesinos-Lopez, O.A.
Creator: Montesinos-Lopez, A.
Creator: Crossa, J.
Year: 2020
Language: English
Publisher: Elsevier
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Type: Article
Place of Publication: Netherlands
Pages: 713-722
Issue: 5
Volume: 8
DOI: 10.1016/j.cj.2020.04.009
Keywords: Laplace Distribution
Keywords: Robust Regression
Keywords: Bayesian Quantile Regression
Keywords: Genomic-Enabled Prediction
Description: Genomic prediction (GP) has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle. A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself. In this study, we propose to use Bayesian regularized quantile regression (BRQR) in the context of GP; the model has been successfully used in other research areas. We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression (BRR; equivalent to genomic best linear unbiased predictor, GBLUP). In addition, BLUP can be used with pedigree information obtained from the coefficient of coancestry (ABLUP). We have found that the prediction ability of BRQR is comparable to that of BRR and, in some cases, better; it also has the potential to efficiently deal with outliers. A program written in the R statistical package is available as Supplementary material.
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ISSN: 2214-5141
Journal: The Crop Journal

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

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