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A reaction norm model for genomic selection using high-dimensional genomic and environmental data

Author: Jarquín, D.
Author: Crossa, J.
Author: Lacaze, X.
Author: Cheyron, P. Du
Author: Daucourt, J.
Author: Lorgeou, J.
Author: Piraux, F.
Author: Guerreiro, L.
Author: Pérez, P.
Author: Calus, M.
Author: Burgueño, J.
Author: Campos, los
Year: 2013
ISSN: 0040-5752
ISSN: 1432-2242
Abstract: In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.
Format: PDF
Language: English
Publisher: Springer
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Switzerland
Pages: 595–607
Issue: 3
Volume: 127
DOI: 10.1007/s00122-013-2243-1
Keywords: Prediction Accuracy
Keywords: Covariance Function
Keywords: Covariance Structure
Keywords: Prediction Problem
Keywords: Multiplicative Operator
Journal: Theoretical and Applied Genetics

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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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