Improving nitrogen use efficiency in wheat by genome wide and candidate genes targeted association studies

Book of abstracts, ISBN: 978-3-900932-48-0Abstract p. 73Improving nitrogen use efficiency in wheat by genome wide and candidate genes targeted association studies. 13. IWG

Phenotypic characterization of French elite germplasm in response to drought stress

International audienc

Improving Septoria tritici blotch resistance (STB) in Wheat by Genome Wide Association Studies

National audienc

Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions

International audienceFarmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios. One solution to this challenge is to develop varieties adapted to specific environmental stress scenarios. For this, plant breeders can use genomic predictions coupled with environmental characterization to identify promising combinations of genes in relation to stress covariates. One way to do it is to take into account the genetic similarity between varieties and the similarity between environments within a mixed model framework. Molecular markers and environmental covariates (EC) can be used to estimate relevant covariance matrices. In the present study, based on a multi-environment trial of 220 European elite winter bread wheat (Triticum aestivum L.) varieties phenotyped in 42 environments, we compared reference regression models potentially including ECs, and proposed alternative models to increase prediction accuracy. We showed that selecting a subset of ECs, and estimating covariance matrices using an AMMI decomposition to benefit from the information brought by the phenotypic records of the training set are promising approaches to better predict genotype-by-environment interactions (G × E). We found that using a different kinship for the main genetic effect and the G × E effect increased prediction accuracy. Our study also demonstrates that integrative stress indexes simulated by crop growth models are more efficient to capture G × E than climatic covariates

[HeatWheat] Analyse de la diversité génétique de la réponse au stress thermique via phénotypage fin et génétique d’association

National audienc

Genome-wide association of winter bread wheat (Triticum aestivum L.) response to drought in a multi-environment European network

International audienc

Using environmental clustering to identify specific drought tolerance qtls in bread wheat (<em>t. aestivum</em> l.)

International audienceDrought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world. The acquisition of new high-yielding and stress-tolerant varieties is therefore necessary and requires improved understanding of the physiological and genetic bases of drought resistance. A panel of 210 elite European varieties was evaluated in 35 field trials. Grain yield and its components were scored in each trial. A crop model was then run with detailed climatic data and soil water status to assess the dynamics of water stress in each environment. Varieties were registered from 1992 to 2011, allowing us to test timewise genetic progress. Finally, a genome-wide association study (GWAS) was carried out using genotyping data from a 280 K SNP chip. The crop model simulation allowed us to group the environments into four water stress scenarios: an optimal condition with no water stress, a post-anthesis water stress, a moderate-anthesis water stress and a high pre-anthesis water stress. Compared to the optimal water condition, grain yield losses in the stressed conditions were 3.3%, 12.4% and 31.2%, respectively. This environmental clustering improved understanding of the effect of drought on grain yields and explained 20% of the G x E interaction. The greatest genetic progress was obtained in the optimal condition, mostly represented in France. The GWAS identified several QTLs, some of which were specific of the different water stress patterns. Our results make breeding for improved drought resistance to specific environmental scenarios easier and will facilitate genetic progress in future environments, i.e., water stress environments

Improving water use efficiency in wheat (Triticum aestivum L.) by multi-trait multi-environment genome-wide association studies

National audienc

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

International audiencePlant 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