University of Oulu

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

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Author: Mathew, Boby1; Léon, Jens1; Sillanpää, Mikko J.2
Organizations: 1Institute of Crop Science and Resource Conservation, University of Bonn, 53115 Bonn, Germany
2Department of Mathematical Sciences and Biocenter Oulu, FIN-90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2018081733763
Language: English
Published: Public Library of Science, 2018
Publish Date: 2018-08-17
Description:

Abstract

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.

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Series: PLoS one
ISSN: 1932-6203
ISSN-E: 1932-6203
ISSN-L: 1932-6203
Volume: 13
Issue: 7
Article number: e0201181
DOI: 10.1371/journal.pone.0201181
OADOI: https://oadoi.org/10.1371/journal.pone.0201181
Type of Publication: A1 Journal article – refereed
Field of Science: 1184 Genetics, developmental biology, physiology
112 Statistics and probability
414 Agricultural biotechnology
Subjects:
Dataset Reference: 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.
  http://www.ricediversity.org/data/
http://repository.cimmyt.org/xmlui/handle/10883/1380
Copyright information: © 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.
  https://creativecommons.org/licenses/by/4.0/