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

Rincent, Renaud; Kuhn, Estelle; Monod, Hervé; Oury, François-Xavier; Rousset, M.; Allard, Vincent; Gouis, Jacques Le

doi:10.1007/s00122-017-2922-4

Cited by 56 publications

(55 citation statements)

References 70 publications

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“…Our results were obtained with individuals from the training population chosen at random. Other studies (Akdemir et al 2015; Isidro et al 2015; Rincent et al 2017) found that optimizing the training population to select the most predictive individuals instead of using a random sample increases the predictive ability. We would therefore expect that our results are the baseline for the gain that could be achieved by using replaced phenotyping when the training population is optimized.…”

Section: Discussionmentioning

confidence: 98%

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality

et al. 2018

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Key MessageMulti-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.AbstractMulti-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3186-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 98%

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality

et al. 2018

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“…Therefore, the complexity of plant responses to stressed conditions resulting from G × E interactions, the difficulties in determining all possible genetic responses to all possible combinations of environments and the inability to phenotype large breeding populations avoiding confounding weather effects (Reynolds and Tuberosa 2008) hamper accurate predictions of GY. One potential approach to address this challenge would be to integrate crop growth models with genomic selection models (Rincent et al 2017). …”

Section: Discussionmentioning

confidence: 99%

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

et al. 2018

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Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center’s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress–resilience within years.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3206-3) contains supplementary material, which is available to authorized users.

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“…Crop growth models allow for a more explicit understanding of the underlying causes of the GxE patterns, under the assumption that the crop growth model is sensitive enough to all the meteorological information that is relevant for GxE. For example, Rincent et al (2017) proposed an optimization criterion to identify those locations that allow for a better prediction of wheat flowering time in other locations. This optimization criterion was applied to wheat flowering time data simulated with the Sirius model (Jamieson et al 1998) and validated on real data in field trials across France.…”

Section: Structure Of the Tpementioning

confidence: 99%

Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics

Bustos-Korts

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Bustos -Korts, D. (2017). Modelling of Genotype by Environment Interaction and Prediction of Complex Traits across Multiple Environments as a Synthesis of Crop Growth Modelling, Genetics and Statistics. PhD thesis, Wageningen University, the Netherlands.The main objective of plant breeders is to create and identify genotypes that are well-adapted to the target population of environments (TPE). The TPE corresponds to the future growing conditions in which the varieties produced by a breeding program will be grown. All possible genotypes that could be considered as selection candidates for a specific TPE are said to belong to the target population of genotypes, TPG.Genotypes commonly show different sensitivities to environmental gradients and then genotype by environment interaction (GxE) is observed. GxE can lead to changes in genotypic ranking, complicating the breeding process. The main aim of this thesis was to investigate statistical models and the combination of statistical and crop growth models to improve phenotype prediction across multiple environments. One aspect that determines the quality of phenotype prediction is the set of genotypes used to train the prediction model, especially when the TPG is structured. We proposed a method that uniformly covers the genetic space of the TPG, leading to a larger prediction accuracy than random sampling. We produced positive results for wheat, maize and rice. A second aspect that influences the accuracy of phenotype predictions is the choice of environments used to train the prediction model, which should capture the heterogeneity in the TPE. When accounting for heterogeneity in environmental quality, it is important to distinguish between repeatable and well predictable elements in the environmental conditions from those that are badly predictable. We proposed statistical methods based on the AMMI model and on mixed models to identify groups of environments that show repeatable GxE, illustrating our ideas with multienvironment wheat data in North-Western Europe. The importance of training set construction strategies and multi-environment genomic prediction models was also demonstrated for barley data. If breeders are interested in identifying the genetic basis of the target traits, it is advantageous to have a higher SNP density. In this thesis, we used exome sequence data of the EU-Whealbi-barley germplasm, which corresponds to a unique set of genotypes with a diverse origin, growth habit and breeding history.vi For this diverse data, we assessed the effects of QTLs and haplotypes across multiple environments for awn length, grain weight, heading date and plant height. Our results show that the EU-Whealbi-barley collection possesses a large diversity of promising alleles regulating the four traits we analysed. The last major topic addressed in this thesis is the use of a combination of statistical-genetic models and crop growth models (APSIM) as a strategy to assess the traits and phenotyping schemes to improve the prediction accuracy of a target trait like yield...

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

Cited by 56 publications

References 70 publications

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics

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