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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.


Growth curves for calculated Biomass over thermal time in parental lines. Growth curves were calculated on single plants and these plots are examples of single plants. Circles indicate the calculated data. The solid line indicates the logistic (three-parameter) models. (This figure is available in colour at JXB online.)
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Figure 2: Growth curves for calculated Biomass over thermal time in parental lines. Growth curves were calculated on single plants and these plots are examples of single plants. Circles indicate the calculated data. The solid line indicates the logistic (three-parameter) models. (This figure is available in colour at JXB online.)

Mentions: Different models for fitting Biomass, Plant weight, or Leaf area over time have been tested with the R function for non-linear regression (nls(), with our own self-start functions) on the two parental lines Gladius and Drysdale: exponential, linear, logistic with three (Chen et al., 2014) or four parameters, thye equation of Richards with four or five parameters, Gompertz with four parameters (Chen et al., 2014), and Weibull with three or four parameters. Some models were not adapted to all datasets. Only the linear, exponential, and logistic three parameters converged for all plants, but the logistic equation fitted best (BIC tests; results not shown) and was therefore applied to all data (Fig. 2).


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)

Growth curves for calculated Biomass over thermal time in parental lines. Growth curves were calculated on single plants and these plots are examples of single plants. Circles indicate the calculated data. The solid line indicates the logistic (three-parameter) models. (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 2: Growth curves for calculated Biomass over thermal time in parental lines. Growth curves were calculated on single plants and these plots are examples of single plants. Circles indicate the calculated data. The solid line indicates the logistic (three-parameter) models. (This figure is available in colour at JXB online.)
Mentions: Different models for fitting Biomass, Plant weight, or Leaf area over time have been tested with the R function for non-linear regression (nls(), with our own self-start functions) on the two parental lines Gladius and Drysdale: exponential, linear, logistic with three (Chen et al., 2014) or four parameters, thye equation of Richards with four or five parameters, Gompertz with four parameters (Chen et al., 2014), and Weibull with three or four parameters. Some models were not adapted to all datasets. Only the linear, exponential, and logistic three parameters converged for all plants, but the logistic equation fitted best (BIC tests; results not shown) and was therefore applied to all data (Fig. 2).

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.