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      Genomic Prediction Accounting for Genotype by Environment Interaction Offers an Effective Framework for Breeding Simultaneously for Adaptation to an Abiotic Stress and Performance Under Normal Cropping Conditions in Rice.

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          Abstract

          Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6-4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed.

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          Inference from Iterative Simulation Using Multiple Sequences

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            The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

            Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”
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              Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.

              Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1% on average and the variability in prediction accuracy decreased by 10.8%. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.
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                Author and article information

                Journal
                G3 (Bethesda)
                G3 (Bethesda, Md.)
                Genetics Society of America
                2160-1836
                2160-1836
                July 02 2018
                : 8
                : 7
                Affiliations
                [1 ] CIRAD-Centre de Coopération International en Recherche Agronomique pour le Développement, UMR AGAP-Unité Mixte de Recherche Amélioration Génétique et Adaptation des Plantes, F-34398 Montpellier, France.
                [2 ] UMR AGAP-Unité Mixte de Recherche Amélioration Génétique et Adaptation des Plantes, Université Montpellier, CIRAD-Centre de Coopération International en Recherche Agronomique pour le Développement, INRA-Institut National de Recherche Agronomique Montpellier SupAgro, Montpellier, France.
                [3 ] CREA-Council for Agricultural Research and Economics, Research Center for Cereal and Industrial Crops, Vercelli, 13100, Italy.
                [4 ] CIRAD-Centre de Coopération International en Recherche Agronomique pour le Développement, UMR AGAP-Unité Mixte de Recherche Amélioration Génétique et Adaptation des Plantes, F-34398 Montpellier, France nourollah.ahmadi@cirad.fr.
                Article
                g3.118.200098
                10.1534/g3.118.200098
                6027893
                29743189
                64652b9a-c0ac-4781-83af-5d3fcfd3b70e
                Copyright © 2018 Ben Hassen et al.
                History

                GenPred,Genomic Selection,G×E interaction,Shared Data Resources,alternate wetting and drying (AWD),progeny prediction,rice

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