Genotypic variability of maize growth and developmental traits under semi-controlled conditions
2015
Alvarez Prado, Santiago | Grau, Antonin | Cabrera Bosquet, Llorenç | Welcker, Claude | Tardieu, Francois
DROPS develop strategies for yield improvement under dry environments and for enhanced plant water-use efficiency. An association panel of 255 maize hybrids was genotyped with 50K and 600K chips of Single Nucleotide Polymorphism (SNPs) and phenotyped in a network of fields, in platforms and via omic data. In particular, three experiments with two water treatments were carried out in the phenotyping platform PhenoArch to assist those in the field in view of possible confusions of effects in the field. Hybrids were characterized via the progression of phenological stages (phyllochron), those of leaf area, plant height and plant volume dynamics, and finally metabolites concentrations and aquaporin activity at key developmental stages. Phyllochron ranged from 0.19 to 0.26 leaves per day at 20°C, plant height ranged from 705 to 1756 cm, leaf area from 0.13 to 0.54 m2 and plant fresh weight from 117 to 577 g. All traits showed high heritability values along the vegetative phase (H²= 0.60-0.91). Variations in plant volume were closely and positively related to changes in plant evapo-transpiration in all situations. Genotypic variability for water use efficiency ranged from 34.6 to 73.4 g FW/l and from 40.3 to 64.9 g FW/l for well-watered and water deficit scenarios, respectively. Plant growth traits were linked to alleles of genes through Genome Wide Association Studies (GWAS). Significant SNP were detected for all traits in all situations showing several SNP co-location for phenotypically correlated traits. The results will be useful to (i) assist field experiments, in particular for the timing of plant development, (ii) compare QTLs in the field and in the platform and (iii) modelling for selecting specific genotypes for drought scenarios : several variables measured in the platform can serve as parameters of a crop model.
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