2018
DOI: 10.1534/g3.118.200435
|View full text |Cite
|
Sign up to set email alerts
|

BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models

Abstract: One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is getting larger, and this increases the computational challenges. In this context, … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
73
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 57 publications
(73 citation statements)
references
References 36 publications
0
73
0
Order By: Relevance
“…Finally, a third limitation of our study is that no environmental component is considered. An area of active research in genomic prediction is the incorporation of genotype-by-environment interactions into predictive models (Burgueño et al, 2012;Cuevas et al, 2017;Granato et al, 2018). Thus, a potential benefit of using transcript information for genomic prediction could be that genotype-by-environment interactions would be picked up by transcript-level signals.…”
Section: Resultsmentioning
confidence: 99%
“…Finally, a third limitation of our study is that no environmental component is considered. An area of active research in genomic prediction is the incorporation of genotype-by-environment interactions into predictive models (Burgueño et al, 2012;Cuevas et al, 2017;Granato et al, 2018). Thus, a potential benefit of using transcript information for genomic prediction could be that genotype-by-environment interactions would be picked up by transcript-level signals.…”
Section: Resultsmentioning
confidence: 99%
“…However, we anticipate that if transcript data collection occurred temporally and/or spatially closer to the phenotype data the predictive power of transcript levels would increase, and likely perform better than genetic marker-based models. Finally, an area of active research in GP is the incorporation of Genotype by Environment (GxE) interactions into predictive models 3941 . One potential benefit of using transcript information for GP could be that GxE interactions would be picked up by transcript level signals.…”
Section: Resultsmentioning
confidence: 99%
“…Therefore, to contribute to this requirement, we developed a Bayesian multi-trait and multi-environment (BMTME) R software that allows the implementation of multi-trait and multi-environment data for performing parameter estimates and evaluating the prediction performance of multiple traits that are studied in many environments. This BMTME package is different from existing ones [sommer (Covarrubias-Pazaran 2016), BGGE (Granato et al , 2018), ASREML (Gilmour et al , 1995) and MCMCglmm (Hadfield et al , 2010)] because it takes into account the genetic correlation between traits and between environments. The main difference of BMTME with sommer and ASREML is that our package was built under a Bayesian framework, while sommer and ASREML were based on a classical approach using restricted maximum likelihood.…”
mentioning
confidence: 99%
“…The main objective of this research was to illustrate the application of the new BMTME with two real toy datasets; with these we show how to use the functions available in the BMTME package for implementing multi-environment (BME function), multi-trait and multi-environment data (BMTME function), as well as the Bayesian multi-output regressor stacking functions BMORS () and BMORS_ENV (). These two functions are very different to what the existing software [sommer (Covarrubias-Pazaran 2016), BGGE (Granato et al , 2018), ASREML (Gilmour et al , 1995) and MCMCglmm (Hadfield et al , 2010)] implements, since the theory behind this function is that of stacking methods. Stacking methods consist of training multiple learning algorithms for the same dataset and then combining the predictions to obtain the final predictions.…”
mentioning
confidence: 99%