Mathew B, Léon J, Sillanpää MJ (2018) Impact of residual covariance structures on genomic prediction ability in multi-environment trials. PLoS ONE 13(7): e0201181. https://doi.org/10.1371/journal.pone.0201181
Impact of residual covariance structures on genomic prediction ability in multi-environment trials
|Author:||Mathew, Boby1; Léon, Jens1; Sillanpää, Mikko J.2|
1Institute of Crop Science and Resource Conservation, University of Bonn, 53115 Bonn, Germany
2Department of Mathematical Sciences and Biocenter Oulu, FIN-90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018081733763
Public Library of Science,
|Publish Date:|| 2018-08-17
In plant breeding, one of the main purpose of multi-environment trial (MET) is to assess the intensity of genotype-by-environment (G×E) interactions in order to select high-performing lines of each environment. Most models to analyze such MET data consider only the additive genetic effects and the part of the non-additive genetic effects are confounded with the residual terms and this may lead to the non-negligible residual covariances between the same trait measured at multiple environments. In breeding programs it is also common to have the phenotype information from some environments available and values are missing in some other environments. In this study we focused on two problems: (1) to study the impact of different residual covariance structures on genomic prediction ability using different models to analyze MET data; (2) to compare the ability of different MET analysis models to predict the missing values in a single environment. Our results suggests that, it is important to consider the heterogeneous residual covariance structure for the MET analysis and multivariate mixed model seems to be especially suitable to predict the missing values in a single environment. We also present the prediction abilities based on Bayesian and frequentist approaches with different models using field data sets (maize and rice) having different levels of G×E interactions.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1184 Genetics, developmental biology, physiology
112 Statistics and probability
414 Agricultural biotechnology
Rice data set is available from http://www.ricediversity.org/data/ and the maize dataset is available at http://repository.cimmyt.org/xmlui/handle/10883/1380.
© 2018 Mathew et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.