Genome wide association mapping of time-dependent growth responses to moderate drought stress in <em>Arabidopsis</em>
2016
Bac-Molenaar, Johanna A. | Granier, Christine, C. | Keurentjes, Joost J. B. | Vreugdenhil, Dick | Wageningen University and Research [Wageningen] (WUR) | Écophysiologie des Plantes sous Stress environnementaux (LEPSE) ; Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Laboratory of Plant Physiology ; Wageningen University and Research [Wageningen] (WUR) | 'Learning from Nature' programme, supported by the Dutch Technology Foundation (STW grant number 10996) | European Project: 284443,EC:FP7:INFRA,FP7-INFRASTRUCTURES-2011-1,EPPN(2012)
International audience
Afficher plus [+] Moins [-]anglais. Large areas of arable land are often confronted with irregular rainfall resulting in limited water availability for part(s) of the growing seasons, which demands research for drought tolerance of plants. Natural variation was observed for biomass accumulation upon controlled moderate drought stress in 324 natural accessions of Arabidopsis. Improved performance under drought stress was correlated with early flowering and lack of vernalization requirement, indicating overlap in the regulatory networks of flowering time and drought response or correlated responses of these traits to natural selection. In addition, plant size was negatively correlated with relative water content (RWC) independent of the absolute water content (WC) indicating a prominent role for soluble compounds. Growth in control and drought conditions was determined over time, and modelled by an exponential function. GWA mapping of temporal plant size data and of model parameters resulted in the detection of six, time-dependent, QTLs strongly associated with drought. Most QTLs would not have been identified if plant size was determined at a single time-point. Analysis of earlier reported gene expression changes upon drought enabled us to identify for each QTL the most likely candidates.
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