Journal
G3-GENES GENOMES GENETICS
Volume 9, Issue 10, Pages 3381-3393Publisher
GENETICS SOCIETY AMERICA
DOI: 10.1534/g3.119.400336
Keywords
Bayesian multi-output regressor stacking; multi-trait; multi-environment; GBLUP; genomic selection; breeding programs; regressor stacking; Genomic Prediction; GenPred; Shared Data Resources
Categories
Funding
- CIMMYT CRP (maize and wheat)
- Bill & Melinda Gates Foundation
- USAID project (Cornell University)
- USAID project (Kansas State University)
- Foundation for Research Levy on Agricultural Products (FFL)
- Agricultural Agreement Research Fund (JA) in Norway through NFR grant [267806]
Ask authors/readers for more resources
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 x 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available