Journal Article

Optimization of multi-environment trials for genomic selection based on crop models

Le Gouis, Jacques, Allard, Vincent, Kuhn, Estelle, Monod, Herve, FSOV-Precocite; BreedWheat ANR-10-BTBR-0003; OptiGBM, Génétique Diversité et Ecophysiologie des Céréales (GDEC) ; Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE) ; Institut National de la Recherche Agronomique (INRA), Oury, Francois-Xavier, Rincent, Renaud, Rousset, Monique

2017

Overview

Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

• Publisher: Springer Verlag,HAL CCSD,Springer Berlin Heidelberg,Springer,Springer Nature B.V
• Subject: Agricultural sciences / Agriculture / Bayes Theorem / Biochemistry / Biomedical and Life Sciences / Biotechnology / breeding value / crop models / Crop yields / Crops / Crops, Agricultural - genetics / ecophysiology / Environment / Genetic aspects / Genomes / Genotype / Genotype & phenotype / Genotypes / Growth models / Life Sciences / marker-assisted selection / Methodology / Models, Genetic / Models, Statistical / Observations / Original / Original Article / Other / Phenology / Phenotype / Phenotyping / Plant Biochemistry / Plant breeding / Plant Breeding - methods / Plant Breeding/Biotechnology / Plant Genetics and Genomics / prediction / Quantitative trait loci / Selection, Genetic / Statistical analysis / Statistics / Triticum - genetics / Triticum aestivum / wheat
• ISBN: 0004055033000