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Poster De Conférence Année : 2017

Exploring QTLxE in a network of fields: identification of QTLs associated with scenarios of heat and drought for predicting yields in future climates

Résumé

Genetic analysis of tolerance to climate change is hampered by climatic variability that generates GxE interactions depending on the timing and intensity of water or heat stresses. We have performed a genome-wide analysis of yield in 29 fields over Europe, with irrigated and rainfed treatments in each location. Six scenarios of heat and drought were first identified by clustering observations over 60 European sites x 35 years. A detailed environmental characterisation was carried out in all experiments, which were ascribed to environmental scenarios. A panel of 244 maize hybrids genotyped with 832K polymorphic SNPs was analysed in these experiments, together with a characterization of phenology in a phenotyping platform. We have first identified by GWAS genomic regions (QTLs) associated with grain yield. Nearly all QTLs had scenario-dependent allelic effects, with positive, negative or null effects depending on heat/drought scenarios. Second, by combining information coming from the field and the platform, we have estimated the responses of yield components to environmental conditions via regression analysis over the whole dataset. We have identified in this way massive effects of temperature and water deficit at flowering time, and of intercepted light during the vegetative stage. The slopes of these responses largely differed between hybrids and were dissected into QTLs. A model combining environmental conditions with QTL of responses accounted for 72% of the variance of yield, including in eight new experiments. This work, therefore, allows prediction of genotypes to cope with climate changes. (Millet et al 2016, Plant Physiology 72:1).
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hal-01605102 , version 1 (02-06-2020)

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  • HAL Id : hal-01605102 , version 1
  • PRODINRA : 389032

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Emilie Millet, W. Kruijer, Stephane Nicolas, Aude Coupel-Ledru, Alain Charcosset, et al.. Exploring QTLxE in a network of fields: identification of QTLs associated with scenarios of heat and drought for predicting yields in future climates. Interdrought V, Feb 2017, Hyderabad, India. 2017. ⟨hal-01605102⟩
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