Plant material

The panel used in this study was composed of 168 traditional and improved japonica accessions (Table S1). The accessions in the panel were mainly tropical accessions, with a few temperate accessions included for reference purposes. Two additional accessions, IR64, an improved indica variety, and Azucena, a traditional japonica variety, which are known to have contrasting root systems [34], were used as controls. Seeds of the accessions were obtained either from the Centre de Ressources Biologiques Tropicales de Montpellier or from the International Rice Research Institute (IRRI) gene bank (accession numbers in Table S1). For each accession, the seeds were produced by single seed descent over two generations in a Cirad Montpellier greenhouse to ensure that the samples were homogeneous. Seeds of the panel are available for distribution upon request to the first author of this paper as "Orytage japonica panel".

Genotyping

Genomic DNA was extracted from the leaf tissues of a single plant from each accession using the MATAB method described in Risterucci et al. [35] and then diluted to 100 ng/µl. Genotyping was conducted at Diversity Arrays Technology Pty Ltd. (DArT P/L), Australia, using a method of GBS combining Diversity Arrays Technology (DArT) and a next-generation sequencing technique called DArTseq™. The method involves genome complexity reduction using PstI/TaqI restriction enzymes followed by Illumina short-read sequencing. PstI-specific adapters tagged with 96 different barcodes to encode a plate of DNA samples were ligated to the restriction fragments. The resulting products were amplified and checked for quality. The 96 samples were pooled and run in a single lane on an Illumina Hiseq2000 instrument. The PstI adapters included a sequencing primer so that the tags generated were always read from the PstI sites. The resulting sequences were filtered and split into their respective target datasets, and the barcode sequences were trimmed. The sequences were trimmed at 69 bp (5 bp of the restriction site plus 64 bases with a minimum Q score of 10). A proprietary analytical pipeline developed by DArT P/L was used to produce DArT score tables and SNP tables. The remaining 69 bp sequences were aligned to the Os-Nipponbare-Reference-IRGSP-1.0 pseudomolecule assembly [36] using Bowtie v0.12 [37] with a maximum of three mismatches to recover the position of the restriction site for the DArT markers and the position of the polymorphism(s) within the 69 bp sequences for the SNPs. For the DArT markers, the position given is that of the second base of the 6 base PstI restriction site (5'-C|TGCAG-3') because the mutated base is unknown and can be any of the six. The same sequences were then aligned to the pseudomolecules using BLAST (e-value <1.0 e-20) to assess whether additional sequences could be positioned. The sequences that had only one hit on the pseudomolecules or had more than one hit but with a difference of at least 1.0 e-5 between the first and the second hits were retained for further analyses. When the marker position fell within a Michigan State University-annotated gene (http://rice.plantbiology.msu.edu/), the feature was determined (intron, exon, 3' or 5' UTR), and the name and function of the gene were retrieved. Call rates were measured for all markers, and markers with call rates below 80% were discarded. The allele frequency of the remaining markers was then calculated, and markers for which the minor allele had a frequency below 2.5% were also discarded.

Imputation of missing data

The power to detect significant association is linked to population size. To prevent the loss of detection power, missing data were estimated using Beagle v3.3, which enables the inference of haplotypes and imputation of sporadic missing data in large-scale phase-known or phase-unknown genotype datasets [38]. Beagle uses a localized haplotype cluster model. It is a special class of directed acyclic graph which empirically models haplotype frequencies on a local scale and therefore adapts to local structure in the data. The model determines a hidden Markov model that can be used to find the most likely haplotype pair for each individual, given the genotype data for that individual and the graphical haplotype frequency model. The method works iteratively using an expectation –maximization type approach. The imputed missing data, probabilities of missing genotypes and inferred haplotypes are calculated from the model that is fitted at the final iteration. The SNP x accession matrix after imputation is available for download at http://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=RICE as "Orytage dataset".

Linkage disequilibrium

To evaluate the resolution to be expected in association mapping, the linkage disequilibrium within the panel was evaluated by computing the r² values between pairs of SNP markers in a sliding window of 50 markers using Tassel [39] and tabulating the average r² as a function of the physical distances between markers. A power-law (y=ax^k) was fitted to the data to determine the physical position (x) corresponding to a given r² value (y). To prevent bias associated with the poor performances of LD indices for markers with very low allelic frequencies [40], only markers with a minor allele frequency greater than 10% were used in these computations.

Phenotyping

The plants were grown in a hydroponic system set in a growth chamber developed by Cirad and called Rhizoscope that has the capacity to handle 192 plants at a time [41]. The experimental unit was a sandwich of two 50 cm × 20 cm × 2 cm Plexiglas plates (internal dimensions) filled with glass beads of 1.5 mm diameter, called a rhizobox (Figure 1). A trap at the bottom of the sandwich enabled the easy removal of the beads at the end of the experiment. This device greatly simplifies the cleaning step while imposing some degree of mechanical resistance to root penetration that is closer to normal soil conditions than is a pure hydroponics system. The rhizobox can be completely opened as well. Similar to nailboard systems, each rhizobox contains a grid of nails, which holds the root system in place after bead removal when the sandwich is opened. The 192 rhizoboxes were set in four large tanks with a capacity of 48 rhizoboxes each (Figure 2). An aerated nutrient solution (volume of 3,000 l) was circulated continuously through the rhizoboxes (composition in Table S2). After pre-germinating several seeds per accession at 28°C for three days, one well developed seedling per rhizobox was set on the top of the beads. The solution pH was adjusted to and maintained at 5.4±0.2 by automatic pH controllers. A cooling system maintained the temperature of the solution at 27±1°C. The conditions in the growth chamber were 28°C during the day and 25°C at night with a 12:12 photoperiod. The radiation was 400 to 450 µmol photons per m² per s (PAR). The relative humidity was set to 55%.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 1. Rhizoboxes used in the Rhizoscope phenotyping platform.

a. With beads. b. After bead removal.

https://doi.org/10.1371/journal.pone.0078037.g001

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 2. General view of the Rhizoscope phenotyping system.

https://doi.org/10.1371/journal.pone.0078037.g002

After 30 days of growth (corresponding to a thermal time of 790°C days ), the rhizoboxes were taken out of the tanks, and the beads were removed. The whole root system, which remained in position on the nail plate, was photographed. The angles of the most external left and right crown roots to the vertical were measured with Image J [42] before the roots reached the rhizobox sides and could change direction. The sum of these two angles was used as the angle of the root cone (ANGLE) in subsequent analyses. The number of tillers per plant (NBT) was counted, and the length of the longest leaf (LLGTH) was measured as a proxy for plant height. The deepest point reached by the roots was measured in position (DEPTH) and again after the plants were removed from the rhizobox (LENGTH). The number of crown roots reaching 30 cm depth (NBR_30) was counted. Then, the root system was cut into three segments (0-20 cm, 20-30 cm and below 30 cm). Each root segment was carefully washed to remove the remaining beads, if any, dried in an oven at 72°C for three days and weighed (RB0020, RB2030 and RBB30). The total root biomass (RB) and the deep root biomass (DRB) were computed as the sum of the root mass in all three segments and in the two deepest segments, respectively. Deep root proportion (DRP) was computed as the ratio of DRB*100/RB. In rice, root emission is synchronized with tiller emission according to the phyllochron model [43]. Allometric ratios such as the root-to-shoot mass ratio (R/S) are used to describe the coordination between the growth and development of the roots and shoots [4]. Shoot tissues were similarly dried and weighed (SB), and the plant biomass (PB) and R/S were computed. The root and shoot traits measured are summarized in Table 1.

Experimental design

The experimental unit was one rhizobox. The experimental design was an alpha lattice with two replicates of 192 rhizoboxes. The two replicates were grown at a three-month interval due to space constraints. In each replicate, the four tanks were considered the main blocks and virtually divided into three sub-blocks of 16 rhizoboxes each. These replicates, blocks and sub-blocks were used as controlled factors for the design optimization and randomization. The two controls, IR64 and Azucena, were included in each sub-block to ensure an additional control for spatial variability.

Statistical analysis of phenotypic data

An analysis of variance was conducted considering all genotype and block effects as fixed. These effects were tested using SAS v9.2 (SAS Institute, Cary NC, USA), and the means were adjusted for block and sub-block factors. The adjusted means of all accessions are available for download at http://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=RICE as "Orytage dataset". Broad-sense heritabilities based on genotypic means (h²) were computed from the genotype F value of the variance analysis as (F-1)/F [44]. An ANOVA was conducted on the adjusted means to assess the phenotypic differences among sub-populations. Phenotypic correlations were computed from the adjusted means using SAS. Principal component analyses (PCA) were run using some or all of the measured traits with XLStat [45]. The coordinates of the accessions on the main axis together with the adjusted means were used in association mapping.

Analysis of population structure

The structure of the panel was analyzed using a model-based approach complemented by a discriminant analysis of principal components (DAPC [46]), using a subset of 200 SNP markers that were well distributed in the genome and had no missing data before imputation. The DAPC was used to help determine the most likely number of sub-populations in the panel, which can be difficult with the model-based approach. For the model-based approach, the analyses were conducted with the software Structure [47] with the following parameters: K, the number of sub-populations in the panel varying from 1 to 15; 10 runs per K value; for each run, 200,000 burn-ins and 200,000 iterations; haploid data with the possibility of admixture; and correlated allelic frequencies. The analyses were run on Bioportal (http://www.bioportal.uio.no/). The R Adegenet package [48] was used for DAPC. To illustrate the panel organization, an unweighted neighbor-joining (NJ) tree was constructed based on a dissimilarity matrix computed using a shared allele index with DARwin software [49]; subpopulation attributions derived from the model-based approach were projected on the tree. An accession was discretely assigned to a subpopulation when more than 80% of its genome composition came from that subpopulation. The percentages of admixtures from Structure results (Q matrix) were used as covariates in the models to correct the association tests for false positives.

Kinship coefficient

The control of spurious associations is improved when finer levels of relatedness are taken into account by fitting a marker-based kinship matrix in the models [28]. Such control is particularly important for panels involving breeding lines. A simple genetic similarity matrix was shown to work as well as a matrix based on identity by descent for this purpose [50,51]. The coefficients of kinship between pairs of accessions were determined using a set of 2600 SNPs without any missing data. A pairwise dissimilarity matrix was computed based on simple matching index using DARwin [49] and then converted to a similarity matrix (K matrix).

Association mapping

Using the adjusted means for observations on each accession, we compared three models for their capacity to fit the data: a General Linear Model (GLM) using the percentages of admixture (Q matrix) as fixed effects, a Mixed Linear Model (MLM) using the kinship matrix (K) as a random effect (MLM1) and an MLM using both Q and K (MLM2). The best model was chosen on a trait-by-trait basis by comparing the likelihoods of each model using the Bayesian Information Criterion (BIC [52]). The BIC was computed as -2 ln(L) + kln(n) where ln is the natural logarithm, L is the maximized value of the likelihood function for the estimate model, k is the number of estimated parameters and n is the sample size. The model with the smallest BIC was selected. Analyses for model comparisons were conducted using either R [53] for GLM, or Tassel for MLM1 and MLM2. Once the model was chosen, the analyses were conducted using the Linux version of FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) that uses an exact method [54]. In an exact method, the additive genetic and residual variance components (i.e., the random effects of the mixed model) are re-estimated for each SNP in a model including the marker effect rather than estimated under the null hypothesis. This approach increases the detection power. For each SNP, FaST-LMM computed a p-value, a q-value corresponding to the False Discovery Rate (FDR [55]), the log likelihoods of the null and alternative models and the fixed-effect weight of the SNP with its standard error. The threshold to declare a significant association was set at a probability level of 1.0 e-04.

QTL map

A database of QTLs for root traits had been established previously [14]. The physical position and confidence intervals of 137 QTLs for LENGTH, DRB, RB and R_S extracted from this database were used to build a QTL map using the Spidermap Excel macro ( http://jframi.free.fr/wordpress/). The type of mapping population (japonica x japonica, indica x indica, or other type) in which the QTLs were detected was also recovered from the database. The positions of markers that were significantly associated with root phenotypes in this study, as well as the positions of genes known to be involved in root development or nutrient uptake in rice in the literature ("EURoot genes" set extracted from TropgeneDB: http://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=RICE ), were added to the map to assess co-localization.

Marker distribution

The GBS method used yielded 16,664 markers (9,727 DArTs and 6,717 SNPs). Approximately 46% of the markers were in genic regions (5' UTR, exon, intron or 3'UTR), which confirmed that PstI, a methylation-sensitive restriction enzyme, cut preferentially in gene-rich regions. The average heterozygosity, calculated from the SNP markers, was low (1.3%), as expected for DNA extracted from single plants that had been self-fertilized for two generations. The rate of missing data before imputation was 3.8%. Even though the markers with a minor allele frequency below 2.5% had been discarded, the minor allele frequency distribution was still skewed toward low frequencies with an average at 15.5% and 46.2% of the markers with a minor allele frequency below 10%.

The number of markers corresponded to an average density of one marker per 22.5 kb. The markers were relatively evenly distributed, with 69% of the intervals between markers of less than 20 kb and 96% of less than 100 kb. Only 19 segments of more than 500 kb without markers were found, including two intervals of approximately 1.0 Mb on chromosomes 4 and 5. In addition, long segments with low polymorphism (low marker density with a high proportion of markers with low minor allele frequency) were detected on chromosomes 4 (approximate position 22.0 to 28.0 Mb), 5 (7.5 to 12.5 Mb), 9 (15.0 to 20.0 Mb), 11 (8.0 to 15.0 Mb) and 12 (0.0 to 2.0 Mb) (Figure S1).

The decay of LD along physical distance is shown in Table S3. For between-marker distances of 0 to 20 kb, the r² value attained 0.66. The r² value decreased to half this initial level at approximately 150 kb between markers and was 0.2 and 0.1 at 475 kb and 2.4 Mb between markers, respectively. A similar trend was observed for all chromosomes, with similar starting values for the 0-20 kb interval, although the LD decay was more rapid on some chromosomes (e.g., chromosome 5) than on others (e.g., chromosomes 3, 4 and 12). These values are consistent with those expected in such a genetic background. They showed that, on average, the LD in the panel was high and did not decrease rapidly with physical distance. The average marker density (one marker per 22.5 kb) was therefore sufficient for whole-genome association mapping. However, the expected resolution, although better than that achievable with a biparental mapping population of the same size, was still far from the gene level which would require a density higher than one marker per 5 kb.

Panel structure and kinship

Among the 170 accessions phenotyped, two were classified as indica based on their marker patterns. These two accessions and the indica control IR64 were excluded from the association analyses that were conducted on the 167 japonica accessions. The Structure software results suggested that the japonica panel was composed of six subpopulations and a large number of admixed accessions. The subpopulation assignments of the accessions are given in Table S1. The DAPC-based method yielded the same number of subpopulations and the same subpopulation attributions but distributed the admixed accessions into the various subpopulations (data not shown). The structure was partly correlated with geography and partly determined by the breeding program origin. Subpopulation 1, the largest subpopulation (46 acc.), was composed of traditional and improved upland rice varieties from Africa and Latin America, reflecting the intensity of exchanges between the breeding programs in these areas. Subpopulation 2 (30 acc.) was composed mainly of traditional upland varieties of equatorial Asia (Indonesia, the Philippines or Malaysia). Subpopulation 3 (20 acc.) was composed of traditional upland rice varieties from Southeast Asia. Subpopulation 4 (10 acc.) contained several varieties from temperate origins or that had adapted to cold climates. Subpopulation 5 (8 acc.) consisted of Indonesian varieties, some of which belonged to the bulu ecotype, which is adapted to the lowland ecosystem. Subpopulation 6 (6 acc.) was composed of improved accessions derived from the variety Colombia 1. Forty-eight varieties appeared to be admixed. This relatively high admixture percentage (1/3) confirms that gene exchange has occurred among these subpopulations. The projection of the subpopulations on an NJ tree is shown in Figure S2. The kinship matrix recorded values ranging from 0.61 to 0.98, showing a broad range of familial relatedness between pair of accessions.

Phenotyping root architecture

The analysis of variance (Table 2) enabled us to assess the extent of the experimental noise in our phenotyping system. In most cases, the replicate and block effects were highly significant, whereas the sub-block effect was not. These results indicated some degree of heterogeneity in temperature and humidity in the growth chamber. The genotype effect, involving all accessions except the controls, was highly significant for all root and shoot traits.

Moderate to large variation was observed for most root parameters, with CVs varying from 13% for DEPTH to 103% for RBB30 (Table 3). The distribution of the root parameters were globally normal, with the exceptions of NBR_30, RB2030 and RBB30, which had skewed distributions due to the presence of accessions with shallow roots that did not exceed 30 cm in length (Figure 3). The broad-sense heritabilities based on genotypic means, which measure the reproducibility of the experiment, were also reasonably high, varying from 0.66 for NBR_30 to 0.89 for DRP. The six subpopulations of the panel differed in terms of means for all traits except for RB0020 (Table 4). Subpopulations 1 (upland rice varieties from Africa and Latin America), 2 (traditional upland varieties of equatorial Asia) and 3 (upland rice varieties from Southeast Asia) were showing the deepest roots (high NBR_30, DRB, DRP, DEPTH and LENGTH) with subpopulation 3 characterized by a larger biomass (NBT, SB, RB and PB) than subpopulations 1 and 2. Subpopulations 4 (temperate accessions) and 5 (bulu types) were showing a large shoot biomass, similar root mass in the shallow horizon than the other subpopulations but much more limited root mass in depth and low R_S. Sub-population 6 (accessions derived from Colombia 1) was composed of small size accessions with limited shoot biomass (low LLGTH, NBT, SB and PB), limited root development and intermediate R_S. The admixed group was intermediate for most traits.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 3. Distribution of selected traits.

SB =shoot biomass; NBT = Tiller number; DRB = deep root biomass; NBR_30 = number of roots below 30 cm; R_S = root/shoot mass ratio; LENGTH = maximum root length .

https://doi.org/10.1371/journal.pone.0078037.g003

A large degree of positive phenotypic correlations was observed between the traits measured (Table S4). Some of these correlations were expected from the physiological relatedness of the traits. For instance, this was the case for all traits linked with root depth (LENGTH, DEPTH, NBR_30, RN2030 and RBB30) which had correlation coefficients above 0.75 among each other (P<0.0001). However, the root cone angle, which is often considered as a proxy for root depth, showed a weak correlation with root depth in our system and in a direction opposite to what was expected (r² = 0.29 with DEPTH and 0.33 with LENGTH). In the multivariate analysis (PCA) conducted on all the measured traits, the first two principal components summarized 74.5% of the variability. The correlation circle (Figure 4) showed that the first axis was mainly a vigor axis, separating plants with small and large biomasses, whereas the second axis divided shoot or shallow root traits (NBT, LLGTH, SB, RB0020) and deep root traits (LENGTH, DEPTH, NBR_30, DRB or R/S). The accessions on the first plane tended to be grouped by subpopulation (data not shown). A second PCA was conducted using only deep root traits (LENGTH, DEPTH, NBR_30, RBB30, DRB and DRP). The first axis summarized 87.9% of the variation, and the second explained only 5.0% (data not shown). The scores of the accessions on the first axis of the second PCA (PCA1) that separated shallow rooted and deep-rooted accessions were included among the phenotypic traits used in association mapping.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 4. Circle of correlations from the principal component analysis of all traits.

LLGTH = leaf length; NBT = tiller number, SB = shoot biomass; DEPTH = maximum depth reached by the roots in position; LENGTH = maximum root length; ANGLE = root cone angle; NBR_30 = number of roots below 30 cm; RB0020 = root mass in the 0-20 cm layer; RB2030 = root mass in the 20-30 cm layer; RBB30 = root mass below 30 cm; DRB = deep root biomass; DRP = deep root proportion; RB = root biomass; PB = plant biomass; R_S = root to shoot mass ratio .

https://doi.org/10.1371/journal.pone.0078037.g004

Association mapping

Most phenotypic traits were affected by panel structure in similar ways. The comparison of the BICs of the three models (GLM, MLM1 and MLM2) showed that MLM2, which included both the population structure and kinship matrix, was the best model for almost all traits (Table 5). MLM1, which included only the kinship matrix, was the best model for NBT, PB and SB. GLM, which included only population structure, was always inferior to the two other models. The smaller number of false positives in MLM compared to GLM is illustrated by the cumulative distribution of p-values compared to the uniform distribution, as shown on the quantile-quantile plots for DRB, LENGTH and NBR_30 (Figure 5). The synthetic results of the association mapping run with the best model for each trait are presented in Table 6. The Manhattan plots for four selected root traits (RBB30, DRB, NBR_30 and LENGTH) are presented in Figure 6. Nineteen markers were significantly associated with a trait at P<1e-05, which corresponded to a q-value below 0.05; 78 markers were significantly associated with a trait at P<1e-04, which corresponded to a q-value below 0.05 in 30 cases (38%) and to a q-value between 0.05 and 0.10 in 28 cases (36%), with the remaining 20 markers having q-values above 0.10. In a few cases, several markers belonging to the same chromosome segment in full LD were found to have the exact same level of significance (e.g., SNPs in the interval from 34,890,451-34,939,105 bp on chromosome 1 for a range of traits). These segments were less than 50 kb in length, except for one interval on chromosome 8 (460 kb). The number of significant markers at P<1e-04 varied among traits, from 0 to 17. DRB and RBB30, the two traits showing the largest range of phenotypic variation, and NBT were associated with the highest number of significant markers, whereas no significant associations were detected for RB or DRP. Some markers were significantly associated with several traits, which meant that only 51 different sites or segments were found to be significant at P<1e-04 for one of the traits. Among those 51 loci, 53% had a minor allele frequency of less than 10%, which corresponded to the representation of markers with low minor allele frequency in the marker set. Two groups of traits had a high level of co-localization of the significant loci. The first group was composed of traits describing root depth (DRP, RBB30 and NBR_30), with 15 loci significant for two traits (on chromosomes 1, 2, 3, 4, 7, 8, 10, 11 and 12) and five loci significant for all three traits (on chromosomes 1, 2, 7 and 10) among the 24 loci with significance for any of the three traits. RB2030, LENGTH, DEPTH and ACP1 were also related to this first group. The second group of traits was composed of SB, PB and RB0020; among the six loci significant for any of the traits (on chromosomes 4, 5, 7 and 11), four were significant for all three traits. NBT was associated with this group, as was RB, but this association was less clear because the levels of significance were lower for this trait. R_S co-localized erratically. One trait, LLGTH, was almost independent, and another trait, ANGLE, was fully independent of the other traits.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 5. Quantile-quantile plots for four models for three selected traits.

A. Model without correction. B. GLM (correction for population structure). C. MLM1 (correction for kinship). D. MLM2 (correction for both population structure and kinship).

DRB = deep root biomass (red); LENGTH = maximum root length (blue); NBR_30 = number of roots below 30 cm (green).

https://doi.org/10.1371/journal.pone.0078037.g005

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 6. Manhattan plots for four selected root traits.

The negative log₁₀-transformed p-values of each test are plotted against the marker position in the genome. Full line: P=1e-05; dotted line: P=5e-04.

RBB30 = root mass below 30 cm; DRB = deep root biomass; NBR_30 = number of roots below 30 cm; LENGTH = maximum root length; .

https://doi.org/10.1371/journal.pone.0078037.g006

Localization of significant loci

Among the 51 loci significantly associated with one or more traits, 19 were in predicted genes, and ten of these encoded expressed or hypothetical proteins without known functions. The 32 other loci were in intergenic regions. Among the 37 loci associated with a root trait (excluding loci associated only with LLGTH, NBT, SB or PB), 20 co-localized with root QTLs on chromosomes 1, 2, 3, 4, 5, 7, 8, 9, 10 and 11 (Figure 7). The 17 remaining loci did not co-localize with any QTLs considered in this study. When focusing on the 12 QTLs detected only in japonica x japonica mapping populations, i.e. in the same genetic background than the association panel, 4 loci on chromosomes 1, 7 and 9 co-localized with those QTLs. There was almost no co-localization with rice genes with demonstrated role in root development. No marker co-localized with sd1, the major semi-dwarfism-inducing gene located on chromosome 1, which is known to influence plant biomass. In fact, the semi-dwarfism allele, which is very common in improved irrigated varieties, is not commonly used in upland rice breeding. Close physical proximity (20 kb) was observed between a marker associated with both DRB and RB2030 and Dro1, a cloned root angle QTL located on chromosome 9, but the significance of the marker was slightly below the threshold of 1e-04 (Table S5). Given the level of LD in the panel (r20.6 at a distance between markers below 20 kb), we also surveyed the genes that were in an interval of +/-25 kb on both sides of the significant markers. We found 521 genes, of which 261 had a predicted function (Table S6). Among these 261 genes, kinases (27) were over-represented relative to their overall presence in the rice genome (10.3% versus 3.5%). Several other genes appeared as potentially relevant candidates: multicopper oxidases (three in a cluster on chromosome 1); gibberellin dioxygenases (five on chromosomes 1, 2 and 11); glutathione-S transferases (two on chromosomes 1 and 11); and elongation factors (five).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 7. Relative positions of significant markers, genes and QTLs for corresponding root traits.

Significant markers are in black and genes in blue on the chromosome bodies .QTLs in orange and pink were detected in japonica x japonica and indica x indica mapping populations respectively. QTLs in grey were detected in other population types (indica x japonica, japonica x indica or japonica x aus). MRL = Maximum root length DRB= deep root biomass; RB = root biomass; R_S =root to shoot ratio.

Data were extracted from the rice module from TropgeneDB for the root genes ("EURoot genes" set) and the QTLs (http://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=RICE). The QTL numbers correspond to their ID in this database.

https://doi.org/10.1371/journal.pone.0078037.g007