Journal Article

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

Lacaze, Xavier, de los Campos, Gustavo, Calus, Mario, Lorgeou, Josiane, Guerreiro, Laurent, Du Cheyron, Philippe, Burgueño, Juan, Crossa, José, Pérez, Paulino, Piraux, François, Jarquín, Diego, Daucourt, Joëlle

2014

Overview

KEY MESSAGE : New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. 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.

• Publisher: Springer-Verlag,Springer Berlin Heidelberg,Springer,Springer Nature B.V
• Subject: Accuracy / Agricultural production / Agriculture / Biochemistry / Biomedical and Life Sciences / Biotechnology / Breeding / breeding values / covariance / crops / dense molecular markers / enabled prediction / Environmental aspects / environmental factors / factorial regression / France / Gene-Environment Interaction / genes / Genetic aspects / Genetic markers / genetic values / genetics / Genome, Plant / Genomics / Genotype / grain yield / Information systems / Life Sciences / marker-assisted selection / methodology / mixed models / Models, Genetic / multi-trait / new methods / Original Paper / phenology / Phenotype / Physiological aspects / Plant Biochemistry / Plant Breeding/Biotechnology / Plant Genetics and Genomics / Plant selection / prediction / qtl / Quantitative Trait Loci / quantitative traits / Selection, Genetic / single nucleotide polymorphism / trials / Triticum / Triticum - genetics / Triticum aestivum / variance / wheat