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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #358000

Research Project: Database Tools for Managing and Analyzing Big Data Sets to Enhance Small Grains Breeding

Location: Plant, Soil and Nutrition Research

Title: Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression

Author
item Ly, Delphine - Clermont Universite, Universite D'Auvergne, Unite De Nutrition Humaine
item Huet, Sylvie - Institut National De La Recherche Agronomique (INRA)
item Gauffreteau, Arnaud - National Institute Of Agronomy, Food Science And Environment (AGROSUP)
item Rincent, Renaud - Clermont Universite, Universite D'Auvergne, Unite De Nutrition Humaine
item Touzy, Gaetan - Université De Lorraine
item Mini, Agathe - Université De Lorraine
item Jannink, Jean-luc
item Cormier, Fabien - Centro De Cooperation Internationale En Recherche Agronomique Pour Le Development (CIRAD)
item Paux, Etienne - Clermont Universite, Universite D'Auvergne, Unite De Nutrition Humaine
item Lafarge, Stephane - Unilever - France
item Le Gouis, Jacques - Unilever - France
item Charmet, Gilles - Clermont Universite, Universite D'Auvergne, Unite De Nutrition Humaine

Submitted to: Field Crops Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/19/2017
Publication Date: 8/25/2017
Citation: Ly, D., Huet, S., Gauffreteau, A., Rincent, R., Touzy, G., Mini, A., Jannink, J., Cormier, F., Paux, E., Lafarge, S., Le Gouis, J., Charmet, G. 2017. Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression. Field Crops Research. 216:32-41. https://doi.org/10.1016/j.fcr.2017.08.020
DOI: https://doi.org/10.1016/j.fcr.2017.08.020

Interpretive Summary: Plant breeding has always sought to develop crops able to withstand environmental stresses, but this is all the more urgent now as climate change is affecting the agricultural regions of the world. It is currently difficult to screen genetic material to determine how well a crop will tolerate various stresses. Genomic selection, the breeding practice of making selections on the basis of predictions from genomic markers, requires field evaluation in relevant stress environments to make predictions for those environments. Multienvironment trials (MET) which include a particular stress condition could be used to train a genomic selection models. Our study focuses on understanding how and predicting whether a plant is adapted to a particular environmental stress. We propose a new genomic prediction model, extending existing models, to predict the response of a genotype to a gradient of stress. Twenty-eight wheat trials in France (3 years, 12 locations, nitrogen or water stress treatments) were split into two METs where different stresses limited grain number and yield. In MET1, drought at flowering was responsible for 46.7% of differential response of genotypes for yield while in MET2, heat stress during booting was identified as the main factor responsible for differential response, explaining 33.6%. In MET1, using drought at flowering information, our new model was more accurate than standard models. Accuracy gains varied from 2.4% to 12.9. In MET2 accuracy gains were modest, varying from -5.7% to 2.4%. When a major stress that differentially affects genotypes is identified, the new model makes it possible to predict the level of adaptation of genotyped individuals to varying stress intensities, and thus to select them. Our study demonstrates how genome-wide selection can facilitate breeding for adaptation

Technical Abstract: Plant breeding has always sought to develop crops able to withstand environmental stresses, but this is all the more urgent now as climate change is affecting the agricultural regions of the world. It is currently difficult to screen genetic material to determine how well a crop will tolerate various stresses. Multi-environment trials (MET) which include a particular stress condition could be used to train a genomic selection model thanks to molecular marker information that is now readily available. Our study focuses on understanding how and predicting whether a plant is adapted to a particular environmental stress. We propose a way to use genomic random regression, an extension of factorial regression, to model the reaction norms of a genotype to an environmental stress: the factorial regression genomic best linear unbiased predictor (FR-gBLUP). Twenty-eight wheat trials in France (3 years, 12 locations, nitrogen or water stress treatments) were split into two METs where different stresses limited grain number and yield. In MET1, drought at flowering was responsible for 46.7% of the genotype-by-environment (G x E) interactions for yield while in MET2, heat stress during booting was identified as the main factor responsible for G x E interactions, but that explained less of the interaction variance (33.6%). Since drought at flowering explained a fairly large variance in G x E in MET1, the FR-gBLUP model was more accurate than the additive gBLUP across all types of cross validation. Accuracy gains varied from 2.4% to 12.9% for the genomic regression to drought. In MET2 accuracy gains were modest, varying from -5.7% to 2.4%. When a major stress influencing G x E is identified, the FR-gBLUP strategy makes it possible to predict the level of adaptation of genotyped individuals to varying stress intensities, and thus to select them in silico. Our study demonstrates how genome-wide selection can facilitate breeding for adaptation.