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

Rincent, R., Malosetti, M., Ababaei, B., Touzy, G., Mini, A., Bogard, M., Martre, P., Le Gouis, J., and van Eeuwijk, F. (2019) Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions. Theoretical and Applied Genetics, 132. pp. 3399-3411.

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Abstract

Farmers 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.

Item ID: 69769
Item Type: Article (Research - C1)
ISSN: 1432-2242
Copyright Information: © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Date Deposited: 03 Nov 2021 01:22
FoR Codes: 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310207 Statistical and quantitative genetics @ 50%
31 BIOLOGICAL SCIENCES > 3108 Plant biology > 310806 Plant physiology @ 50%
SEO Codes: 26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2699 Other plant production and plant primary products > 269901 Climate adaptive plants @ 50%
26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2601 Environmentally sustainable plant production > 260199 Environmentally sustainable plant production not elsewhere classified @ 50%
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