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Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping.

Rebolledo MC, Dingkuhn M, Courtois B, Gibon Y, Clément-Vidal A, Cruz DF, Duitama J, Lorieux M, Luquet D - J. Exp. Bot. (2015)

Bottom Line: Associations for NSC and model parameters revealed new regions related to early vigour that had greater significance than morphological traits, providing additional information on the genetic control of early vigour.Twelve associations were related to loci for cloned genes, with nine related to organogenesis, plant height, cell size or cell number.The potential use of these associations as markers for breeding is discussed.

View Article: PubMed Central - PubMed

Affiliation: CIAT, Agrobiodiversity, AA 6713, Cali, Colombia m.c.rebolledo@cgiar.org.

No MeSH data available.


Related in: MedlinePlus

Multiple Factorial Analysis using all the phenotypic variables described in Table 1. Morpho (morphological traits): SDW (shoot dry weight), LLL (Last Ligulated Leaf length), RGR (Relative Growth Rate), DR (Developmental Rate), NBT (Number of Tillers), NBL (Number of Leaves). Metabo (Non-structural carbohydrates concentration in source leaves related traits): STA (starch), SUC (sucrose), FRU (fructose), GLU (glucose), NSC (total non-structural carbohydrates). Model (Ecomeristem model parameters): PLASTO (Phyllochron), DEV_PLASTO_MGR (% deviation from the linear regression between PLASTO and MGR), SLAP (Slope Parameter of the negative logarithmic equation computing SLA for successive leaf ranks), MGR (Meristem Growth Rate), ICT (IC Threshold enabling tiller outgrowth), EPSIB (Light conversion efficiency).
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Figure 2: Multiple Factorial Analysis using all the phenotypic variables described in Table 1. Morpho (morphological traits): SDW (shoot dry weight), LLL (Last Ligulated Leaf length), RGR (Relative Growth Rate), DR (Developmental Rate), NBT (Number of Tillers), NBL (Number of Leaves). Metabo (Non-structural carbohydrates concentration in source leaves related traits): STA (starch), SUC (sucrose), FRU (fructose), GLU (glucose), NSC (total non-structural carbohydrates). Model (Ecomeristem model parameters): PLASTO (Phyllochron), DEV_PLASTO_MGR (% deviation from the linear regression between PLASTO and MGR), SLAP (Slope Parameter of the negative logarithmic equation computing SLA for successive leaf ranks), MGR (Meristem Growth Rate), ICT (IC Threshold enabling tiller outgrowth), EPSIB (Light conversion efficiency).

Mentions: Figure 1 shows the distribution of a wide range of values found in the diversity panel for key traits. The observed variables DR, LLL, and NBT (Fig. 1A, B, C, respectively) were normally distributed. Among the NSC concentrations in source leaves (Fig. 1D, E, F), SUC showed a comparatively narrow band of relative variation (factor 2), whereas FRU concentration varied from 2 to 20mg g–1 (factor 5) and starch from 0 to 50mg g–1 on a dry weight basis. The distributions of genotypic model parameters (Fig. 1G, H, I) were normal with a generally smaller relative range of variation: MGR from 7 to 14cm, PLASTO from 50 to 85 °Cd, and ICT from 0.8 to 1.8. The distribution of other traits can be found in Supplementary Fig. S1 at JXB online. To identify the relation between traits, a Multiple Factorial Analysis (MFA, Fig. 2) was performed using the variables listed in Table 1. Supplementary Table S2 at JXB online shows the corresponding matrix of correlation. Two dimensions explained 43% of the phenotypic diversity (Fig. 2). Dimension 1 (explaining 26%) combined effects of RGR and DR (positive) and DEV_PLASTO_MGR and PLASTO (negative). RGR, the relative growth rate which was also associated with SDW, clustered with variables related to organ number (DR, NBL, and NBT). The correlations between RGR and SDW versus variables related to organ number were significant (P <0.01) (see Supplementary Table S2 at JXB online). Regarding the NSC traits, SUC was the only variable having an effect in Dimension 1 (positive). This was associated with a significant positive correlation (P <0.01) between SUC and both DR and SDW (see Supplementary Table S2 at JXB online).


Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping.

Rebolledo MC, Dingkuhn M, Courtois B, Gibon Y, Clément-Vidal A, Cruz DF, Duitama J, Lorieux M, Luquet D - J. Exp. Bot. (2015)

Multiple Factorial Analysis using all the phenotypic variables described in Table 1. Morpho (morphological traits): SDW (shoot dry weight), LLL (Last Ligulated Leaf length), RGR (Relative Growth Rate), DR (Developmental Rate), NBT (Number of Tillers), NBL (Number of Leaves). Metabo (Non-structural carbohydrates concentration in source leaves related traits): STA (starch), SUC (sucrose), FRU (fructose), GLU (glucose), NSC (total non-structural carbohydrates). Model (Ecomeristem model parameters): PLASTO (Phyllochron), DEV_PLASTO_MGR (% deviation from the linear regression between PLASTO and MGR), SLAP (Slope Parameter of the negative logarithmic equation computing SLA for successive leaf ranks), MGR (Meristem Growth Rate), ICT (IC Threshold enabling tiller outgrowth), EPSIB (Light conversion efficiency).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
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getmorefigures.php?uid=PMC4585419&req=5

Figure 2: Multiple Factorial Analysis using all the phenotypic variables described in Table 1. Morpho (morphological traits): SDW (shoot dry weight), LLL (Last Ligulated Leaf length), RGR (Relative Growth Rate), DR (Developmental Rate), NBT (Number of Tillers), NBL (Number of Leaves). Metabo (Non-structural carbohydrates concentration in source leaves related traits): STA (starch), SUC (sucrose), FRU (fructose), GLU (glucose), NSC (total non-structural carbohydrates). Model (Ecomeristem model parameters): PLASTO (Phyllochron), DEV_PLASTO_MGR (% deviation from the linear regression between PLASTO and MGR), SLAP (Slope Parameter of the negative logarithmic equation computing SLA for successive leaf ranks), MGR (Meristem Growth Rate), ICT (IC Threshold enabling tiller outgrowth), EPSIB (Light conversion efficiency).
Mentions: Figure 1 shows the distribution of a wide range of values found in the diversity panel for key traits. The observed variables DR, LLL, and NBT (Fig. 1A, B, C, respectively) were normally distributed. Among the NSC concentrations in source leaves (Fig. 1D, E, F), SUC showed a comparatively narrow band of relative variation (factor 2), whereas FRU concentration varied from 2 to 20mg g–1 (factor 5) and starch from 0 to 50mg g–1 on a dry weight basis. The distributions of genotypic model parameters (Fig. 1G, H, I) were normal with a generally smaller relative range of variation: MGR from 7 to 14cm, PLASTO from 50 to 85 °Cd, and ICT from 0.8 to 1.8. The distribution of other traits can be found in Supplementary Fig. S1 at JXB online. To identify the relation between traits, a Multiple Factorial Analysis (MFA, Fig. 2) was performed using the variables listed in Table 1. Supplementary Table S2 at JXB online shows the corresponding matrix of correlation. Two dimensions explained 43% of the phenotypic diversity (Fig. 2). Dimension 1 (explaining 26%) combined effects of RGR and DR (positive) and DEV_PLASTO_MGR and PLASTO (negative). RGR, the relative growth rate which was also associated with SDW, clustered with variables related to organ number (DR, NBL, and NBT). The correlations between RGR and SDW versus variables related to organ number were significant (P <0.01) (see Supplementary Table S2 at JXB online). Regarding the NSC traits, SUC was the only variable having an effect in Dimension 1 (positive). This was associated with a significant positive correlation (P <0.01) between SUC and both DR and SDW (see Supplementary Table S2 at JXB online).

Bottom Line: Associations for NSC and model parameters revealed new regions related to early vigour that had greater significance than morphological traits, providing additional information on the genetic control of early vigour.Twelve associations were related to loci for cloned genes, with nine related to organogenesis, plant height, cell size or cell number.The potential use of these associations as markers for breeding is discussed.

View Article: PubMed Central - PubMed

Affiliation: CIAT, Agrobiodiversity, AA 6713, Cali, Colombia m.c.rebolledo@cgiar.org.

No MeSH data available.


Related in: MedlinePlus