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Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat.

Parent B, Shahinnia F, Maphosa L, Berger B, Rabie H, Chalmers K, Kovalchuk A, Langridge P, Fleury D - J. Exp. Bot. (2015)

Bottom Line: From the 20 quantitative trait loci (QTLs) found for several traits in the platform, some showed strong effects, accounting for between 26 and 43% of the variation on chromosomes 1A and 1B, indicating that the G×E interaction could be reduced in a controlled environment and by using dynamic variables.Co-located QTLs were found for average growth rate, leaf expansion rate, transpiration rate, and water-use efficiency from the platform with yield, spike number, grain weight, grain number, and harvest index in the field.These results demonstrated that imaging platforms are a suitable alternative to field-based screening and may be used to phenotype recombinant lines for positional cloning.

View Article: PubMed Central - PubMed

Affiliation: Australian Centre for Plant Functional Genomics (ACPFG), University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.

No MeSH data available.


Plots of observed/calculated variables for Plant weight (A), Biomass (B), and Leaf area (C). Circles are data for well-watered plants and squares are for drought-stressed plants. The line is the 1:1 line. The scale is logarithmic for better data visualization but models were selected on raw data. (This figure is available in colour at JXB online.)
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Figure 1: Plots of observed/calculated variables for Plant weight (A), Biomass (B), and Leaf area (C). Circles are data for well-watered plants and squares are for drought-stressed plants. The line is the 1:1 line. The scale is logarithmic for better data visualization but models were selected on raw data. (This figure is available in colour at JXB online.)

Mentions: This procedure produced similar results for Biomass and Leaf area but different results for Plant weight (Table 1). The models were used to infer biological variables with the same parameters for well-watered plants and drought-stressed plants, but the error was higher for Biomass than for Plant weight and Leaf area (Fig. 1). It was decided to keep the same model for plants under well-watered and water-deficit conditions to better compare the treatments and to allow the calculation of variable responses to soil water potential.


Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat.

Parent B, Shahinnia F, Maphosa L, Berger B, Rabie H, Chalmers K, Kovalchuk A, Langridge P, Fleury D - J. Exp. Bot. (2015)

Plots of observed/calculated variables for Plant weight (A), Biomass (B), and Leaf area (C). Circles are data for well-watered plants and squares are for drought-stressed plants. The line is the 1:1 line. The scale is logarithmic for better data visualization but models were selected on raw data. (This figure is available in colour at JXB online.)
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4585424&req=5

Figure 1: Plots of observed/calculated variables for Plant weight (A), Biomass (B), and Leaf area (C). Circles are data for well-watered plants and squares are for drought-stressed plants. The line is the 1:1 line. The scale is logarithmic for better data visualization but models were selected on raw data. (This figure is available in colour at JXB online.)
Mentions: This procedure produced similar results for Biomass and Leaf area but different results for Plant weight (Table 1). The models were used to infer biological variables with the same parameters for well-watered plants and drought-stressed plants, but the error was higher for Biomass than for Plant weight and Leaf area (Fig. 1). It was decided to keep the same model for plants under well-watered and water-deficit conditions to better compare the treatments and to allow the calculation of variable responses to soil water potential.

Bottom Line: From the 20 quantitative trait loci (QTLs) found for several traits in the platform, some showed strong effects, accounting for between 26 and 43% of the variation on chromosomes 1A and 1B, indicating that the G×E interaction could be reduced in a controlled environment and by using dynamic variables.Co-located QTLs were found for average growth rate, leaf expansion rate, transpiration rate, and water-use efficiency from the platform with yield, spike number, grain weight, grain number, and harvest index in the field.These results demonstrated that imaging platforms are a suitable alternative to field-based screening and may be used to phenotype recombinant lines for positional cloning.

View Article: PubMed Central - PubMed

Affiliation: Australian Centre for Plant Functional Genomics (ACPFG), University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.

No MeSH data available.