Allometry models of leaves and petals in MAGIC lines
To examine the allometric variation of leaves and petals within Arabidopsis thaliana, an allometric method based on a PCA of organ landmarks and outlines was used to quantify this trait. Applying this method allowed low-dimensional spaces to be constructed that captured the key variations in shape and size. Moreover, the MAGIC lines were used to model the allometric traits. Leaf4, Leaf7 and the petals were separately modeled and used to generate a data set that quantitatively described the standard deviations from the mean position of the point within the collection of leaves and petals (Supplementary Fig. 1). Therefore, PCA was applied to the Leaf4, Leaf7, or petal data set to detect the variation in the positions of points and to identify trends in shape and size variations among MAGIC lines. The resulting principal components (PCs) were ranked according to the proportions of the total variance that each of them described (Fig. 1).
In Leaf4, the PCA revealed that 90.92% of the variance in organ shape and size was attributed to two PCs (Fig. 1A). The Leaf4.PC1 of this model accounted for 76.84% of the total variance and affected the leaf size. Higher PC1 values corresponded to larger leaves, whereas lower values yielded smaller leaves. PC2 accounted for 14.08% of the variance and affected mainly shape. Lower values of PC2 yielded rounded leaves with a short petiole, whereas high values yielded elongated leaves with a long petiole. PC3 accounted for 3.30% of the variance and reflected the way the petiole twisted when the leaves were flattened. Its values were not significantly different between genotypes, and it was therefore excluded from further analysis.
In Leaf7, the PCA revealed that 95.25% of the variance in organ shape and size could be attributed to three PCs (Fig. 1B). The Leaf7.PC1 of this model accounted for 80.27% of the total variance and affected mostly size; however, there was also a minor effect on shape. Higher PC1 values corresponded to larger, more elongated leaves, whereas lower values yielded smaller and more rounded leaves. PC2 accounted for 11.42% of the variance and mostly affected the steepness of the transition from petiole to blade, with low values yielding a very gradual transition, and high values yielding a long petiole with a steep transition. PC3 accounted for 3.56% of the variance and affected mainly the shape. Lower values of PC3 yielded more elongated and narrower leaves, whereas higher values of PC3 yielded more rounded and wider leaves.
In petals, the PCA revealed that 92.02% of the variance in organ shape and size could be attributed to two PCs (Fig. 1C). The PC1 of this model accounted for 85.43% of the total variance and affected petal size. Higher PC1 values corresponded to larger petals, whereas lower values yielded smaller petals. PC2 accounted for 6.59% of the variance and affected mainly the shape. Low values of PC2 yielded elongated petals with a narrower shape, and high values of PC2 yielded rounded petals with a wider shape. PC3 accounted for 3.63% of the variance and was reflected in the petal twisting when the petals were flattened. Its values were not significantly different between genotypes, and it was therefore excluded from further analysis.
The allometric variation captured by PCs reflected both genetic differences and environmental variations within the plant growth chamber in which plants were grown. Extensive phenotypic variation was observed for all traits measured among the MAGIC lines (Table 1). An estimate of the relative genetic contribution was made by comparing the variance of each PC among MAGIC lines (which was largely due to genetic differences) to that within each line, which could have other causes (Supplementary Fig. 2). Estimates from an average of about eight plants from each of collected lines suggested that most of the variance (> 60% for the PCs) had an underlying genetic basis (Table 1).
A correlation analysis between shape and size was also performed, and a number of significant pairwise correlations were observed. The Leaf4.PC1 was significantly positively correlated with Leaf7.PC1, which represented the leaf size. The Leaf4.PC2 was significantly correlated with Leaf7.PC2 and leaf7.PC3, which represented the leaf shape. Moreover, the leaf shape and size showed significant correlations with petals. The Petal.PC1 was particularly significantly correlated with Leaf4.PC1 and Leaf7.PC1, which showed the negative size correlation between leaf and petal. Additionally, both the leaf4.PC2 and Leaf7.PC2 were significantly positively correlated with Petal.PC1 and negatively correlated with Petal.PC2 (Table 2). The correlation between the leaf and petal allometry model indicated the genetic dependency and evolution correlation in controlling leaf and petal allometry. Plus, a pairwise correlation analysis was also performed between the life history traits and the leaf and petal allometry model (Table 2). Leaf4.PC1 was correlated with rosette leaf number and stem height; Leaf4.PC2 was highly correlated with branch number and pod number; Leaf7.PC1 was correlated with days to bolt, days to flower and stem height; Leaf7.PC2 was highly positive correlated with days to bolt, days to flower, rosette leaf number, and branch number; Petal.PC1 was correlated with rosette leaf number and branch number; and Petal.PC2 was correlated with days to bolt and days to flower.
QTLs accounted for leaf and petal allometry
To examine the genetic basis for shape and size variation of leaves and petals along the PCs in the MAGIC lines, we treated each PC as a quantitative trait, whose variation frequency showed as a normal distribution (Supplementary Fig. 3) for QTL mapping. In the MAGIC lines QTL mapping, the PCs for leaf and petal allometry model, and 1260 SNP markers among the 19 founder ecotypes were used. We then calculated a series of QTLs associated with the variance of leaf and petal shape and size (Table 3, Supplementary Figs. 4, 5 and 6). In the leaf model, the QTL analysis for Leaf4.PC1 identified four QTLs located on chromosomes 1 and 3 and one QTL located on chromosome 2 for Leaf4.PC2. For the Leaf7.PC1, five QTLs were observed on chromosome 3, one QTL was located on chromosome 2 for Leaf7.PC2, and four QTLs were located on chromosomes 1 and 2 for Leaf7.PC3. In the petal model, three QTLs were identified on chromosomes 1 and 4 in Petal.PC1, and nine QTLs were identified on chromosomes 1, 2, 3, and 5 in Petal.PC2 (Table 3).
After comparing the position for all the QTLs identified, there was some QTL overlapping in the leaf and petal allometry model. The QTLs for PC2 of the leaf (Leaf4.PC2: LF4.5, Leaf7.PC2: LF7.7) and petal (Petal.PC2: PE.8) on chromosome 2 (~ 11 Mb) overlapped, and the alleles from the Ler-0 accession formed the most rounded leaves and petals with the widest shape (Table 4). This QTL likely stemmed from the mutation of ERECTA, which is known to affect fruit length, and is due to the allele from the Ler-0 accession (Abraham et al., 2013). With the exception of the ER locus for the leaf and petal shape, the QTLs LF7.1, LF7.2, LF7.3, LF7.4, and LF7.5 for leaf7.PC1 on chromosome 3 overlapped with QTL PE.9 for Petal.PC2. Moreover, the QTLs LF7.3, LF7.4, and LF7.5 also overlapped with QTL PE.10 for Petal.PC2, whereas these QTLs all showed an uncorrelated allele effect distribution (Table 4). For the fourth and the seventh leaf, except for the overlapped ER locus (Leaf4.PC2: LF4.5, Leaf7.PC2: LF7.7) for PC2 described above, the QTLs LF4.3 and LF4.4 for leaf4.PC1 overlapped with QTL LF7.6 for leaf7.PC1 on chromosome 3, and showed the same allele effect distribution with a maximum value in the Mt-0 accession and a minimum value in the Can-0 accession (Table 4). The overlapped QTLs might have explained the phenotype correlation and indicated the correlated genetic modules for leaf and petal allometry in evolution.
Candidate genes for leaf and petal allometry
The genes that explain natural variations in leaf and petal allometry have remained largely unknown. To identify possible candidate genes, we searched for genes containing nonsynonymous SNPs unique to accession according to PC distribution among these accession alleles (Table 4). Based on the resequencing and reannotation of the 19 parental accessions (Gan et al., 2011), we identified candidate genes unique to the maximal effects accession allele in the 95% confidence region (Supplementary Table 2). In the Leaf4 allometry model, Auxin receptor TIR1, Brassinolide signaling regulator BSL3, and TIR1, contributing to the flowering time repression, had allelic variation in the coding sequence unique to the accession. In the Leaf7 allometry model, hormonal related genes, such as SUA (a suppressor of abi3-5), ARGOS, serine/threonine-protein kinase PID2, BRI1 suppressor 1 (BSU1)-like 3, and ABI4 genes, had allelic variation in the coding sequence. Moreover, the flower time regulators ELF3 and ELF4, the receptor kinase ERECTA, cell-wall modification related genes and some transcription factors conferred allelic variations unique to the maximal effects accession.
In the petal allometry model, there were 23 genes identified with variations unique to the accession (Supplementary Table 2). Among these genes, the PTL in Petal.PC2 encodes a trihelix transcription factor whose expression is limited to the margins of floral and vegetative organs. It is involved in limiting lateral growth of organs, and recessive mutations have been found to be defective in organ initiation and orientation in the second whorl (Kaplan-Levy et al., 2014). The OFP13 in Petal.PC2 encodes a member of the plant-specific OVATE family of proteins. Members of this family have been shown to bind to KNOX and BELL-like TALE class homeodomain proteins and function as a transcriptional repressor that suppresses cell elongation (Wang et al., 2011). The SEU in Petal.PC1 encodes a transcriptional co-regulator of AGAMOUS that coordinates with LEUNIG to repress AG in the outer floral whorls. Other genes, including the cell cyclin-related protein Cyclin A1;1, the protein kinase, the CYP family protein, the photoperiod-associated ELF6, and the transcription-related genes with nonsynonymous SNPs also contribute to the petal PCs. The identified QTL and candidate genes gave us a valuable reference for insight into leaf and petal allometry.
The genetic basis for leaf and petal covariation in allometric models
Leaves and petals are homologous organs sharing mechanisms of developmental control, such that genes that act pleiotropically on both organ types might give rise to coordinating changes in shape or size. In order to examine the genetic basis for shape and size covariation between leaves and petals, the allometry model was also used. The petal and leaf modeled data sets obtained above were combined, which allowed overall trends to be identified. To ensure equal weighting of the data from different organs, the organ size for all plants was multiplied by a constant factor so that the variance in the Leaf4, Leaf7, and petal data sets was equal. The Leaf4, Leaf7, and petal data sets were then combined to create a Leaf4-Petal and Leaf7-Petal data sets containing each plant from the MAGIC line groups. Additionally, a PCA was applied to the Leaf4-Petal and Leaf7-Petal data sets to detect correlated variation in the positions of points and to identify trends in shape and size variation between the two organs.
In the Leaf4-Petal model, the PC1 accounted for 53.58% of the total variance representing the negative size covariation between the Leaf4 and petal. The higher the PC1 value, the larger the petal size, and the smaller the fourth leaf size were. The PC2 accounted for 30.26% of the total variance representing the positive size covariation between the fourth leaf and petal. The higher the PC2 value, the larger the petal and leaf size were. The PC3 accounted for 5.92% of the total variance representing the positive shape (mainly in width) covariation between the fourth leaf and the petal. The higher the PC3 value, the more rounded the leaves and petal, and the shorter the petiole was. The PC4 accounted for 3.23% of the total variance representing the negative shape (mainly in width) covariation between the fourth leaf and the petal. The higher the value, the narrower the leaves, the longer the petiole, and the more rounded the petals were. The other PCs represented only one organ shape or size variance, so they were not considered for further analysis (Fig. 2).
After QTL mapping in the MAGIC lines for the Leaf4-Petal model, three significant QTLs for PC1, one significant QTL for PC2, two significant QTLs for PC3, and six significant QTLs for PC4 were identified (Table 5, Supplementary Fig. 7). In each QTL, the candidate genes containing nonsynonymous SNPs unique to the maximal effects accession in the 95% confidence region were identified (Supplementary Tables 3 and 4). In PC1, there were five genes with the unique maximal effects accession allele, including the cell-proliferation-related genes, such as ARGOS, LOM2, and EXPB5. In PC3, which represented the shape (mainly in width) covariation, four genes were identified: ARGOS, FRS3, BSL3, and extensin proline-rich1. In PC4, representing the negative shape (mainly in width) covariation, there were also four genes containing the unique accession allele. Among these genes, the CYCD2;1 gene acting on the G1 phase of the cell cycle to control cell division rate in both the shoot and root meristems had an allele unique to the Hi-0 accession, and the PRX53 gene influencing cell elongation had an allele unique to the Po-0 accession.
Similar to the Leaf4-Petal model, in the Leaf7-Petal model, the PC1 accounted for 68.58% of the total variance, representing the negative size covariation between the seventh leaf and petal, whereas the PC2 accounted for 22.51% of the total variance, representing the positive size covariation between the seventh leaf and the petal and also the seventh leaf shape variance. The PC3 accounted for 2.84% of the total variance, representing the positive shape (mainly in width) covariation, and the PC4 accounted for 1.99% of the total variance, representing the negative shape (mainly in width) covariation. The other PCs represented only one organ shape or size variance, so they were not considered for further analysis (Fig. 3).
After QTL mapping in the MAGIC lines for the Leaf7-Petal model, two significant QTLs for PC3 and six significant QTLs for PC4 were identified, whereas no significant QTL was identified in PC1 and PC2 (Table 5, Supplementary Fig. 8). Moreover, the candidate genes were also identified as above (Supplementary Tables 3 and 4). The QTL LF7PE.2 in PC3, which represented the positive shape (mainly in width) covariation between the seventh leaf and petal, had the most rounded leaf and petal in Ler-0 and the narrowest leaf and petal in No-0 accession. In the 95% confidence region, there were 34 genes conferring alleles unique to the Ler-0 or No-0 accession. Among these genes, AT2G22840, belonging to the GRF gene family, functioned as a transcription activator that played a role in the regulation of cell expansion in leaves and cotyledon tissues. The mutation resulted in smaller leaves, which indicated the role of the gene in leaf development (Kim et al., 2003). SLOW GROWTH1 (SLO1), which encodes an E-motif-containing pentatricopeptide repeat protein localized to the mitochondrion and its absence, has been found to yield small plants with slow growth and delayed development (Sung et al., 2010). Similarly, ORGAN BOUNDARY1 (OBO1) is one of a 10-member plant-specific gene family encoding a single small domain (133 amino acids); overexpression of OBO1 has been found to cause an abnormal number and size of petals (Cho et al., 2011). Moreover, OFP16, which encodes a member of the plant-specific OVATE family of proteins and functions as a transcriptional repressor, showed small rosette size, later flowering, reduced fertilization, and blunt-end siliques when it was overexpressed (Wang et al., 2011). Furthermore, the cyclin-dependent kinase inhibitor KRP4 (Schiessl et al., 2014) and the serine/threonine-protein kinase PINOID (PID) were involved in the regulation of auxin signaling (Saini et al., 2017). Growth-regulating factor 3 (GRF3), which regulates cell expansion in leaves and cotyledons tissues (Kim et al., 2003), as well as other genes associated with cell differentiation, cell expansion, cell wall modification, and flower time control genes, were also identified. The QTL LF7PE.6 in PC4, which represented the negative shape (mainly in width) covariation, had the narrowest leaves with the longest petiole and the most rounded petals in the Po-0 accession. There were three genes with an allele unique to the Po-0 accession in the CI region, including DME, a transcriptional activator involved in gene imprinting; Peroxidase 2, which influences cell elongation (Jin et al., 2011) and CYP712A2, a member of CYP712A.