A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data. Issue 10 (1st October 2019)

• Record Type: Journal Article
• Authors: Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Cuevas, Jaime; Montesinos-López, José C; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Philomin, Juliana; Singh, Ravi
• Is Part Of: G3. Volume 9:Issue 10(2019)
• Page Start: 3381
• Page End: 3393
• Publication Date: 2019-10-01
• Subjects: Bayesian multi-output regressor stacking -- multi-trait -- multi-environment -- GBLUP -- genomic selection -- breeding programs -- regressor stacking -- Genomic Prediction -- GenPred -- Shared Data Resources; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.119.400336
• Languages: English
• ISSNs: 2160-1836

Abstract

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.