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Genome-wide association mapping of growth dynamics detects time-specific and general quantitative trait loci.

Bac-Molenaar JA, Vreugdenhil D, Granier C, Keurentjes JJ - J. Exp. Bot. (2015)

Bottom Line: Genome-wide association (GWA) mapping of the temporal growth data resulted in the detection of time-specific quantitative trait loci (QTLs), whereas mapping of model parameters resulted in another set of QTLs related to the whole growth curve.The positive correlation between projected leaf area (PLA) at different time points during the course of the experiment suggested the existence of general growth factors with a function in multiple developmental stages or with prolonged downstream effects.In addition, the detection of QTLs without obvious candidate genes implies the annotation of novel functions for underlying genes.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Plant Physiology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands.

No MeSH data available.


Pearson correlations between fresh weight of rosettes (FW) at the end of the experiment (day 28), projected leaf area (PLA) over time (day 8 till 28), and parameters ‘r’ and ‘A0’ of the growth model Expo2. r2-values are given in the left lower part of the figure, whereas corresponding P-values are given in the right upper part of the figure. Blue and red indicate positive and negative correlations, respectively. The stronger the intensity of the colour, the stronger the correlation.
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Figure 2: Pearson correlations between fresh weight of rosettes (FW) at the end of the experiment (day 28), projected leaf area (PLA) over time (day 8 till 28), and parameters ‘r’ and ‘A0’ of the growth model Expo2. r2-values are given in the left lower part of the figure, whereas corresponding P-values are given in the right upper part of the figure. Blue and red indicate positive and negative correlations, respectively. The stronger the intensity of the colour, the stronger the correlation.

Mentions: For PLA and FW, large natural variation was observed, 28–70% of which could be explained by genetic differences (Table 1). Broad-sense heritability (H2) of PLA increased over time (Table 1), most probably because determination of the PLA of small plants was less accurate than that of larger plants. These data demonstrate that top-view imaging of Arabidopsis is a powerful method to compare plant size and growth rate in large panels of plants which differ not only in size but also in developmental traits such as flowering time (Li et al., 2010), number of leaves, and leaf emergence rate (Granier et al., 2006; Tisné et al., 2010). FW at the end of the experiment correlated positively with PLA at the end of the experiment (r2=0.95), as shown earlier (Leister et al., 1999). This high correlation is also reflected in almost equal H2 of FW and PLA at day 28 (H2=0.69 and H2=0.70, respectively). FW also correlated with PLA in weeks 2 and 3 (Fig. 2). In the last week of the experiment leaves started to overlap, and variation for this trait was observed between accessions. Despite this increase in overlap over time, the correlation between FW on day 28 and PLA on the sequential measuring dates increased over time, reaching the highest correlation on day 27 (r2=0.96). This correlation suggests the existence of general growth factors whose effects are visible at the phenotype level during a large part of the plant’s life cycle. Seedling size at day 8, when the cotyledons are unfolded but the first true leaves are not yet visible, is for a large part determined by seed size, germination rate, and the capacity of the seedling to establish. The correlation of PLA during the experiment also suggests that the effects of genes involved in the regulation of these processes are visible at the phenotype level when seedlings develop into plants with many leaves. The water status of the plant was evaluated by the determination of the WC of the largest leaf on the 24th day. A proper water status is important for the plant to maintain growth. WC was high for all plants (between 0.85 and 0.95), indicating that in the conditions used here the water status was not limiting for growth. This corresponds to small variation in WC observed in a collection of 20 accessions (El-Lithy et al., 2004). Significant but very weak correlations were observed between WC and A0, r, FW, and PLA on days 8, 27, and 28, whereas the correlation between WC and plant size on other days was not significant. Because of the low variation observed, WC did not play a prominent role in determining growth differences in this experiment. Because PLA of the rosette was on average doubled during the last 4 d of the experiment, it was decided not to correct for the absence of the largest leaf. In the growth curve of some accessions between day 24 and 25, a dip is observed; however, for many accessions, this dip was hardly visible, suggesting a huge compensation investment in the growth of the remaining leaves. Without correction for the absence of the leaf, the growth modelling resulted in very reliable curve fits for Expo2 and Gom, indicating that the growth rate was hardly influenced by the removal of the largest leaf.


Genome-wide association mapping of growth dynamics detects time-specific and general quantitative trait loci.

Bac-Molenaar JA, Vreugdenhil D, Granier C, Keurentjes JJ - J. Exp. Bot. (2015)

Pearson correlations between fresh weight of rosettes (FW) at the end of the experiment (day 28), projected leaf area (PLA) over time (day 8 till 28), and parameters ‘r’ and ‘A0’ of the growth model Expo2. r2-values are given in the left lower part of the figure, whereas corresponding P-values are given in the right upper part of the figure. Blue and red indicate positive and negative correlations, respectively. The stronger the intensity of the colour, the stronger the correlation.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4585414&req=5

Figure 2: Pearson correlations between fresh weight of rosettes (FW) at the end of the experiment (day 28), projected leaf area (PLA) over time (day 8 till 28), and parameters ‘r’ and ‘A0’ of the growth model Expo2. r2-values are given in the left lower part of the figure, whereas corresponding P-values are given in the right upper part of the figure. Blue and red indicate positive and negative correlations, respectively. The stronger the intensity of the colour, the stronger the correlation.
Mentions: For PLA and FW, large natural variation was observed, 28–70% of which could be explained by genetic differences (Table 1). Broad-sense heritability (H2) of PLA increased over time (Table 1), most probably because determination of the PLA of small plants was less accurate than that of larger plants. These data demonstrate that top-view imaging of Arabidopsis is a powerful method to compare plant size and growth rate in large panels of plants which differ not only in size but also in developmental traits such as flowering time (Li et al., 2010), number of leaves, and leaf emergence rate (Granier et al., 2006; Tisné et al., 2010). FW at the end of the experiment correlated positively with PLA at the end of the experiment (r2=0.95), as shown earlier (Leister et al., 1999). This high correlation is also reflected in almost equal H2 of FW and PLA at day 28 (H2=0.69 and H2=0.70, respectively). FW also correlated with PLA in weeks 2 and 3 (Fig. 2). In the last week of the experiment leaves started to overlap, and variation for this trait was observed between accessions. Despite this increase in overlap over time, the correlation between FW on day 28 and PLA on the sequential measuring dates increased over time, reaching the highest correlation on day 27 (r2=0.96). This correlation suggests the existence of general growth factors whose effects are visible at the phenotype level during a large part of the plant’s life cycle. Seedling size at day 8, when the cotyledons are unfolded but the first true leaves are not yet visible, is for a large part determined by seed size, germination rate, and the capacity of the seedling to establish. The correlation of PLA during the experiment also suggests that the effects of genes involved in the regulation of these processes are visible at the phenotype level when seedlings develop into plants with many leaves. The water status of the plant was evaluated by the determination of the WC of the largest leaf on the 24th day. A proper water status is important for the plant to maintain growth. WC was high for all plants (between 0.85 and 0.95), indicating that in the conditions used here the water status was not limiting for growth. This corresponds to small variation in WC observed in a collection of 20 accessions (El-Lithy et al., 2004). Significant but very weak correlations were observed between WC and A0, r, FW, and PLA on days 8, 27, and 28, whereas the correlation between WC and plant size on other days was not significant. Because of the low variation observed, WC did not play a prominent role in determining growth differences in this experiment. Because PLA of the rosette was on average doubled during the last 4 d of the experiment, it was decided not to correct for the absence of the largest leaf. In the growth curve of some accessions between day 24 and 25, a dip is observed; however, for many accessions, this dip was hardly visible, suggesting a huge compensation investment in the growth of the remaining leaves. Without correction for the absence of the leaf, the growth modelling resulted in very reliable curve fits for Expo2 and Gom, indicating that the growth rate was hardly influenced by the removal of the largest leaf.

Bottom Line: Genome-wide association (GWA) mapping of the temporal growth data resulted in the detection of time-specific quantitative trait loci (QTLs), whereas mapping of model parameters resulted in another set of QTLs related to the whole growth curve.The positive correlation between projected leaf area (PLA) at different time points during the course of the experiment suggested the existence of general growth factors with a function in multiple developmental stages or with prolonged downstream effects.In addition, the detection of QTLs without obvious candidate genes implies the annotation of novel functions for underlying genes.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Plant Physiology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands.

No MeSH data available.