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Title A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
 
Names Montesinos-Lopez, O.A.
Montesinos-Lopez, A.
Crossa, J.
Cuevas, J.
Montesinos-Lopez, J.C.
Salas Gutiérrez, Z.
Lillemo, M.
Juliana, P.
Singh, R.P.
Date Issued 2019 (iso8601)
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 multi-environment (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.
Genre Article
Access Condition Open Access
Identifier 2160-1836