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A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data.
- Source :
-
G3: Genes | Genomes | Genetics . Oct2019, Vol. 9 Issue 10, p3381-3393. 13p. - Publication Year :
- 2019
-
Abstract
- In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is a generalization of the multi-trait regressor stacking method. The proposed BMORS model consists of two stages: in the first stage, a univariate genomic best linear unbiased prediction (GBLUP including genotype · environment interaction GE) model is implemented for each of the L traits under study; then the predictions of all traits are included as covariates in the second stage, by implementing a Ridge regression model. The main objectives of this research were to study alternative models to the existing multi-trait multienvironment (BMTME) model with respect to (1) genomic-enabled prediction accuracy, and (2) potential advantages in terms of computing resources and implementation. We compared the predictions of the BMORS model to those of the univariate GBLUP model using 7 maize and wheat datasets. We found that the proposed BMORS produced similar predictions to the univariate GBLUP model and to the BMTME model in terms of prediction accuracy; however, the best predictions were obtained under the BMTME model. In terms of computing resources, we found that the BMORS is at least 9 times faster than the BMTME method. Based on our empirical findings, the proposed BMORS model is an alternative for predicting multi-trait and multi-environment data, which are very common in genomic-enabled prediction in plant and animal breeding programs. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21601836
- Volume :
- 9
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- G3: Genes | Genomes | Genetics
- Publication Type :
- Academic Journal
- Accession number :
- 139204545
- Full Text :
- https://doi.org/10.1534/g3.119.400336