Plant materials and accessions

We performed whole genome sequencing (WGS) of 21 elite cultivars from the CIAT rice breeding program and 33 elite cultivars from the United States rice breeding program (see S1 Table for details). These materials comprise a diverse representation of elite lines and commercial varieties from North and South America and Asia that exhibit desirable attributes for high grain yield, cooking quality, disease resistance, plant height, and maturity. Breeders consider these lines as highly relevant for rice improvement in the Americas. CIAT varieties include two advanced lines from IRGA (Instituto Rio Grandese do Arroz do Brasil), two from INIA-Uruguay (Instituto Nacional de Investigación Agropecuaria de Uruguay), five from Fedearroz (Federación Nacional de Arroceros de Colombia), one from INIA-Chile (Instituto de Investigaciones Agropecuarias, Chile) and one from Asoportuguesa (Asociación de Productores Rurales del Estado Portuguesa, Venezuela). Varieties from United States include one advanced line from IAC (Instituto Agronômico de Campinas, Brazil) and two from the Guanxi University of China. To relate our sequencing data with previous knowledge on rice variability, we reanalyzed WGS data publicly available at the NCBI SRA database for 50 accessions available at the International Rice Research Institute (IRRI), which were previously analyzed by [7]. These include 40 accessions distributed among the major populations of O. sativa and 10 varieties from the close wild relatives O. rufipogon and O. nivara.

DNA sequencing

Each variety from the CIAT collection was planted in the greenhouse facility at CIAT. Genomic DNA was prepared from a single plant as follows: 1 g of leaf tissue of a 45-DAP seedling was collected and ground with liquid nitrogen. DNA was isolated according to the urea-phenol extraction protocol modified from [21]. DNA quality was tested before whole-genome sequencing so that the concentration exceeded 500ng/μL and the A260/280 ratio was 1.8. DNA was sequenced on the Illumina HiSeq 2000 by the Yale Center for Genome Analysis (http://medicine.yale.edu/keck/ycga/index.aspx). DNA from the U.S. accessions was isolated and prepared for sequencing as described by [22]. All sequencing data generated for this work are available at public repositories (see Data Availability statement for details). Additional sources for bulk data access include Gramene (ftp://ftp.gramene.org/pub/gramene/release45/data/vcf/oryza_sativa/Duitama/), the data store module of iPlant [23], and the European Variation Archive (EVA) [24]. We are working with major online rice genomics data resources such as Gramene [25] and the Rice Annotation Project [26] to provide the annotations and variation data for rice researchers from their rice genome browser.

Mapping and variant calling

We downloaded the reference genome IRGSP-1.0 [27] from the Rice Genome Annotation Project web page (http://rice.plantbiology.msu.edu/), including their corresponding GFF3 file with gene functional annotations. We used the NGSEP pipeline [28] to align reads to the reference and discover SNPs, indels, repeats and Copy Number Variants (CNVs). NGSEP uses bowtie2-2.1.0 [29] for read alignment, which we ran with default parameters, except for the maximum number of alignments per read, which we set to 3, and the minimum and maximum fragment length for valid paired-end alignments, which we estimated separately for each variety aligning their first 250000 fragments and then plotting the distribution of estimated insert lengths (Script available at the NGSEP web site http://sourceforge.net/projects/ngsep/files/Library/scripts/). We used the recommended parameters of NGSEP for analysis of WGS data: 1) Minimum genotype quality 40; 2) Maximum value allowed for a base quality score 30; and 3) Maximum number of alignments allowed to start at the same reference site 2. We set the prior heterozygosity rate (h option) to 0.0001 to give a larger prior probability to homozygous genotypes. We also used NGSEP for functional annotation of variants, filtering, and conversion from VCF to other formats for further downstream analysis. Flapjack software [30] was used for visualization of SNP genotypes across the samples. To identify characteristic CNVs for a population we used the following procedure: given two populations P₁ and P₂ and a CNV that is identified in x₁% of P₁ and x₂% of P₂ with an average number of copies n₁ and n₂ for P₁ and P₂ respectively, we call such CNV characteristic for P₁ relative to P₂ if x₁ − x₂ > 50% or both x₁ > 50% and n₁ − n₂ > 2.

We ran mrCaNaVaR [31] to compare their predicted CNVs with those predicted by NGSEP. To make the results comparable, we took as input for mrCaNaVaR the alignments provided by bowtie2 and we set a long window span of 500bp and a short window span of 100 bp. We did not mask the repetitive regions in the reference genome to allow mrCaNaVaR to predict CNVs in such regions.

Diversity and population structure

We used the neighbor joining algorithm implemented in SplitsTree4 [32] for construction of distance-based unrooted dendograms. To obtain confidence values we performed 1,000 replicates of the bootstraping analysis available in SplitsTree4. Dendograms with bootstraping confidence values and branch lengths are available as supplementary material (S1–S4 Files). We also used SplitsTree4 for visualization of the dendograms. For population analysis we used the individual-based Bayesian clustering method implemented in STRUCTURE v.2.3.4 [33]. We assumed the admixture model with correlation of allele frequencies and we varied the number of populations (K) from 1 to 8. The length of the burn-in period was set on 10,000 and the number of Markov Chain Montecarlo (MCMC) Reps after burn-in on 20,000. To estimate diversity across the genome, we developed a custom java script that calculates the number of nucleotide changes between each pair of accessions either within a window or within a gene, and we calculated the average number of pairwise distances for each subpopulation and between user-defined pairs of subpopulations as suggested by [7]. Genome-wide plots of diversity were developed using CIRCOS v.0.66 [34]. To calculate the LD-Decay of indica, japonica and overall, we selected high quality SNPs with minor allele frequency (MAF) above 0.1 and then we ran PLINK [35] using a maximum window of 2Mbp for pairwise calculation of r² values.

Admixture analysis

We built a custom java script to identify SNPs segregating for at least one of the main seven Oryza populations (O. rufipogon, O. nivara, aus, aromatic, indica, tropical japonica, temperate japonica). The script takes as input a VCF file, calculates the allele frequency of the reference allele for each population and retains SNPs in which the absolute difference in allele frequencies for two populations is at least 0.6. The script also produces an output VCF file with one additional sample column for each subpopulation. This column contains a genotype representing the most frequent allele within each subpopulation or a heterozygous genotype if the MAF within the population is greater than 0.4. We built a second script that takes as input this VCF file and calculates for each variety and each non-overlapping window of 50 SNPs its closest population assignment using a simple algorithm described in [36]. In brief, for a given pair of genotype calls over 50 SNPs, for each SNP the script adds 1 if at least one of the two genotypes is heterozygous, 2 if the two genotypes coincide, and −2 if the two genotypes are homozygous and different. Hence, a maximum score of 100 will be obtained for a pair of genotype calls over a window of 50 SNPs if and only if they are equal and do not contain any missing or heterozygous call. For each variety and each window the script performs a unique population assignment calculating the score between the genotype calls of the variety against the genotype calls of each of the seven populations. A population will be assigned for a variety within a window if there are at least 40 SNPs genotyped and the score is at least 50. This assignment will be considered unique if the differece between the best and the second score is at least 10. If a unique assignment cannot be made, the script outputs the names and the scores of the two populations ranked first and second. Finally, the script also compares the population genotypes against themselves to identify windows difficult to discriminate due to conservation between subpopulations.

Screening and SNP genotyping for amylose content

A total of 47 elite indica rice accessions were genotyped using the Fluidigm technology (EP1TM system) based on SNPtype assays and allele-specific PCR. Screening the same varieties for amylose content (AC) was carried out in five plants per accession. AC was determined using a near-infrared spectroscopy (NIRSystems 6500®) [37]. To assess significance of the differences between AC for the six haplotypes identified within GBSSI, analysis of variance was performed using SAS version 9.2 (SAS Institute Inc., Cary, NC, USA) using a significance level of 0.05, adjusted with the Bonferroni correction [38].

Whole genome sequencing of elite rice cultivars

Collaboration between independent sequencing efforts combined with availability of data from previous works in public databases allowed us to perform an integrated analysis of whole genome sequencing (WGS) reads for 94 O. sativa varieties and 10 wild relatives (see Methods and S1 Table for details of the sequenced accessions). The whole dataset includes 3.8 billion reads and 699 Gbp of raw data. The initial average coverage per sample ranged between 2.87x and 64.83x. Except for the 13 varieties initially sequenced by the RiceCAP project [22], all other cultivars were sequenced at over 8x average coverage. We could align over 90% of the reads to the Nipponbare reference genome for most of the O. sativa accessions and over 80% of the reads for the O. rufipogon and O. nivara accessions (S2 Table). We ran the NGSEP pipeline [28] to identify Single Nucleotide Polymorfisms (SNPs), indels, repeats, and Copy Number Variation (CNVs) on the 104 sequenced samples. We identified over 23 million polymorphic sites in the whole dataset and we genotyped each of the 104 accessions on these sites. Over 80% of them fall within repeat elements (see next section for details). Table 1 shows the number of SNPs obtained using different filtering strategies that we applied to perform the different types of analysis carried out in this study. From the 4.4 million SNPs found outside repetitive regions, we could genotype over 95% in at least 50 accessions (S1 Fig). We verified that with an average coverage above 10X, we could genotype over 80% of these SNPs in most of accessions, although this percentage was reduced substantially as coverage reduces (S1 Fig). If only the O. Sativa accessions were considered, the number of polymorphic sites in non-repetitive regions was reduced to three million with further reductions observed only if indica or japonica accessions were evaluated (Table 1). Approximately 13% of the selected SNPs were located in coding (non intronic) regions no matter which subpopulation was considered. For each filtering strategy and each subpopulation, we calculated average dN/dS ratios over transcripts with at least one synonymous mutation and we found that, as filters become more stringent, dN/dS values consistently reduce from 0.57 to 0.26. This outcome can be explained not only by the increase in genotyping specificity obtained after applying the different filters, but also by the fact that after removing variants within repeat elements or recurrent copy number variation events, the single copy genes affected by the remaining variants tend to be more conserved to prevent complete loss of function. Only up to 1.5% of the SNPs outside repeat regions produced a stop codon and this percentage was decreased as more stringent filters were applied. As expected, more than 60% of the SNPs in the entire dataset exhibited minor allele frequencies (MAFs) below 0.05 because they were only polymorphic within the 10 O. rufipogon and O. nivara accessions (S2 Fig). Rare alleles were also predominant within O. Sativa, but the percentage decreased to 45% (compared to the percentage obtained including wild relatives). Within the indica subpopulation, SNPs with MAF between 0.05 and 0.15 were more common than SNPs with MAF below 0.05. Finally, about 50% of the SNPs within japonica showed MAF below 0.05, mainly due to the small representation of temperate japonica compared with tropical japonica and the lower overall diversity within japonica.

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Table 1. SNPs among 104 rice cultivars.

https://doi.org/10.1371/journal.pone.0124617.t001

For further validation of our genotype calls, we built neighbor-joining dendograms using the genetic distances estimated from the high quality SNPs (filter 3 in Table 1) identified in the whole dataset and within each subpopulation (Fig 1a, S3 Fig and S1–S4 Files). The dendograms were consistent with those shown in previous studies [4, 7]. Nonetheless, we obtained a clearer separation between indica and O. nivara accessions when compared with [7] presumably due to greater number of indica accessions included in our analysis. Population structure analysis of the high quality SNPs within O. sativa accessions consistently separated the indica, aus, aromatic, temperate japonica and tropical japonica populations as values of the number of allowed populations increased from 2 to 5 (S4 Fig). Pairwise Fst values predicted by structure [33] ranged from 0.1 for tropical vs. temperate japonica to 0.37 for indica vs temperate japonica. These pairwise Fsts were smaller than previously reported [4] probably because the elite lines in our study contributed large haplotypes of outgroup introgressions that reduced the overall segregation between indica and japonica. We calculated for each population and for each filtering strategy the number of private SNPs (polymorphic in only one population) (S5 Fig) and we found that the groups indica and O. rufipogon showed the largest number of private SNPs and that aromatic and temperate japonica showed the smallest numbers of private SNPs. We finally calculated the linkage disequilibrium (LD) decay for O. sativa, indica and japonica and we found that, consistent with previous studies [3, 14], the LD-decay was faster for indica compared to japonica and to O. sativa (S6 Fig).

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Fig 1. Genome-wide diversity patterns for sequenced cultivars of indica and japonica.

a) Neighbor joining dendogram for the full dataset of accessions analyzed in this study. b) Moving from within to outside, the circles have the following information: 1). Density of repeat elements (0% to 100%). 2) Diversity within japonica (0–10). 3) Diversity within indica (0–10). 4) Pairwise Fst between indica and japonica (0–1). For each population, diversity is estimated in 100kbp windows as the average number of pairwise differences per kilobasepair (See methods for details). Green colors indicate values close to the maximum on each category (or larger for the case of diversity values). Red colors indicate values close to zero. Yellow colors indicate intermediate values. Genomic locations of genes related to selective sweeps are shown in red lines.

https://doi.org/10.1371/journal.pone.0124617.g001

To compare diversity within and between indica and japonica across the genome, we calculated the average number of pairwise SNP differences without filters over 100 kbp (kilobasepair) windows. We selected accessions with coverage greater than 10x clearly clustering within the indica or the japonica groups (not including aus and aromatic), and we estimated diversity within indica, within japonica, between indica and japonica, and for the whole group. Diversity values (pairwise differences per kbp) were on average 2.58 within indica, 1.96 within japonica, 5.9 between indica and japonica, and 3.93 overall. These values were about two times larger than those reported by [4] for low coverage sequencing (below 2x), but were relatively consistent with those reported by [7] for the subset of 50 accessions with over 10x coverage also included in this study. Having low coverage per sample reduces the percentage of genotyped sites which consequently reduces the number of differences identified for each pair of samples. The overall Fst between indica and japonica, estimated from these averages as one minus the proportion of the diversity within groups relative to the diversity between groups [7], was 0.64. Although the number of indica accessions included in this analysis (23) was smaller than the number of japonica samples (38), the indica population showed greater overall diversity than japonica, which is consistent with previous studies [4, 11, 14]. However, this pattern of diversity is not consistent across the whole genome. Fig 1b shows the array of diversity for japonica and indica, and the Fst between indica and japonica. Large regions of almost complete differentiation (Fst close to 1) between indica and japonica are observed in every chromosome. However, in most of the genes known to be related to domestication traits the differentiation between indica and japonica and the diversity of both populations is reduced. The longest region of conservation for both populations is located between 10 and 15Mbp of chromosome 5. Indica-specific conserved regions can also be observed in chromosome 7, 8, and the start of chromosome 9. We calculated diversity values for the indica population within the selective sweeps reported by [4] for indica (S7 Fig) and we found that the average diversity in these regions reduced to 1.44 for indica becoming almost equal to the estimate for japonica (1.46). Moreover, we found that 48 of the 60 selective sweeps contain windows in which the diversity within indica was below 1.

Repetitive elements and novel structural variation

Merging results from independent analysis of each sequenced variety, we developed a catalog of 164,372 repetitive regions covering 176.9 Mbp (roughly 45%) of the rice genome. As expected, centromeres and telomeres showed a high density of repeat elements (Fig 1b). Repeat density was also high in known large duplication events such as the starts (first two Mbp) of chromosomes 11 and 12 [39]. We compared the repeats identified using NGSEP with a catalog of annotated repeat elements generated by Rod Wing at The University of Arizona (personal communication). We found that about 80% of the DNA identified as repetitive by NGSEP was annotated as a repeat element and that 78% of the annotated elements were covered by NGSEP repeat regions. A majority (98%) of the annotated repeats that NGSEP could not identify have lengths below 500bp. This is expected because reads can be aligned uniquely to short sparsed repeats taking advantage of the paired-end information. The longest region not identified by NGSEP is a 6.2Kbp region on chromosome 6 covering the retrotransposon LOC_Os06g50200. A blast search of this region back to the reference genome shows that the second best hit only has an alignment length of about 1,924 bp and an identity of 71.73%, which means that although this retrotransposon is a member of a repeat family, it has accumulated enough mutations to be considered a unique sequence for alignment purposes. Similar analysis of other three missed repeats with lengths above 5,000 bp yielded the same outcome. Nevertheless, following a conservative approach for the downstream analysis, we merged the annotated elements with the repeats identified by NGSEP to produce a unified annotation of repeats in the reference genome (hereafter referred to as repeats). We used this unified dataset for characterization of other types of variants. We further compared these repeat regions with the sequences masked as repeats in the version of the reference genome available in the RAP-DB web page (http://rapdb.dna.affrc.go.jp/) and we found that 154.3Mbp (96.65%) of the 159.6Mbp masked by RAP-DB are covered by the regions described above.

We performed on each sample the read-depth analysis provided by NGSEP to identify regions with copy number variation (CNVs). For this analysis we discarded 29 accessions for which the read-depth distribution suggested that coverage was not evenly distributed along the genome (S2 Table). We compared the CNVs identified for 21 indica, 12 temperate japonica, and 18 tropical japonica varieties, which were chosen following the clusters observed in the distance trees. To facilitate comparisons among samples and events with variable lengths, we retrieved and compared the copy number estimation for each sample on non-overlapping bins of 100bp across the genome. For each group we identified between 2.3 and 2.8 million bins with duplication events and between 475 and 725 thousand bins with deletion events. This represents over 10 times more variation than that observed using high-density array comparative genomic hybridization [40] or using the read-depth analysis carried on by [7] for 50 accessions. Figs 2a and 2b shows the distribution of bins with duplication and deletion events as a function of the percentage of samples in which the variation was discovered. Between 55% and 65% of the bins with duplications and between 70% and 95% of the bins with deletions were reported by less than half of the samples within each subpopulation. We also found that most of the bins with duplications (over 97% for common duplications) overlap with repeats. In contrast, only 65% of the bins with predicted deletion events overlap with repeats. After removing bins within repeats and bins with events reported in less than half of the samples within each population, the number of bins with CNVs was reduced to 105,606 for indica, 58,896 for tropical japonica and 30,158 for temperate japonica. This is expected because most of the common duplications within the temperate japonica accessions in our study should already be identified as repeats in the Nipponbare reference sequence which is also temperate japonica. Likewise, common deletions within temperate japonica should mostly correspond with DNA present in Nipponbare and absent in other temperate japonica cultivars. Consequently, filtering out repetitive regions, recurrent duplications are more common than recurrent deletions within temperate japonica, whereas recurrent deletions are more common than recurrent duplications within tropical japonica and within indica. Fig 2c shows the distribution of bins with common CNVs in non-repetitive regions for different average numbers of copies. For every population, homozygous deletions were twice more common than heterozygous (copy number 1) deletions. Moreover, homozygous duplications (copy number 4) were twice more common than heterozygous duplications (copy number 3). Although we do not have a gold-standard set of CNVs to perform a systematic comparison with other methods, we performed an initial comparison of the CNVs identified using NGSEP with the CNVs identified using mrCaNaVaR [31]. On average MrCaNaVaR called deletions on about 70 Mbp for each variety, which is close to 4 times more genomic sites for indica and close to 6 times more sites for japonica compared to NGSEP (S8 Fig). MrCaNaVaR also called 1.5 more regions as duplications for both indica and japonica varieties compared to NGSEP. For most of the samples over 80% of the deletions and over 70% of the duplications identified by NGSEP were also identified by mrCaNaVaR, which provides additional confidence on the events called by NGSEP.

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Fig 2. Comparison of CNV calls in rice cultivars.

Number of 100bp bins with a) duplications, and b) deletions discriminated by the percentage of each population in which the event is reported (red: Indica, blue: japonica overall, yellow: tropical japonica, and light blue: temperate japonica). The lines indicate the percentage of bins for each category falling within repetitive regions in Nipponbare. c) Number of bins not spanning Nipponbare repeats with predicted CNVs common for each subpopulation (indica, japonica, tropical japonica, and temperate japonica) discriminated by the predicted copy number, being two the normal copy number for a diploid region. d) Example of a discriminative duplication between indica and japonica. Reads taken from the two copies of this region present in indica samples align to the same genomic location producing clusters of heterozygous SNPs. Colors in the left panel differentiate the following groups: O. rufipogon (RUF), aromatic (ARO), temperate japonica (TEJ), tropical japonica (TRJ), indica (IND), aus (AUS), O. nivara (NIV), and admixed (ADM). Homozygous genotype calls carrying the reference allele are colored blue. Homozygous genotype calls carrying an allele different from the reference are colored red. Heterozygous genotype calls are colored half blue and half red.

https://doi.org/10.1371/journal.pone.0124617.g002

Finally, we performed pairwise comparisons among the indica, tropical japonica, and temperate japonica populations to identify CNVs characteristic of a particular population (See methods for details). We found over two times more bins with characteristic CNVs for indica relative to temperate japonica or to tropical japonica than the opposite (S3 and S4 Tables). We also detected three times more bins with characteristic CNVs for indica relative to temperate japonica than characteristic CNVs for tropical japonica relative to temperate japonica. Consistent with the percentages observed within each population, ∼ 64% of the bins within characteristic deletions and ∼ 96% of the bins within characteristic duplications overlap repeat regions. Fig 2d shows the SNPs identified in a characteristic indica duplication relative to japonica. Most of the SNPs in this region appeared as heterozygous in the indica varieties because the reads that were sequenced from different copies of indica cultivars align to the single copy present in Nipponbare and differences between copies were identified as clusters of heterozygous SNPs.

Diversity and haplotype patterns in agronomically important genes

Elite cultivars have been developed over the last decades by breeders looking for introgression of specific alleles to improve desirable traits such as high yield, grain quality or resistance to abiotic and biotic stresses. Starting from the 670 thousand non-singleton SNPs in non-repetitive regions and regions with up to two CNVs (Filter 2 in Table 1), we selected 329,819 SNPs that segregated in at least one of the seven identified populations (aus, aromatic, indica, O. nivara, O. rufipogon, temperate japonica and tropical japonica). After identifying the most frequent allele of each SNP within each population, we selected non-overlapping windows of 50 SNPs and calculated for each accession its most likely population origin for each window based on its observed haplotype pattern (See Methods section for details). For most of the varieties sequenced at 10x or more, this analysis identified unique population assignments for at least 100 Mbp. As expected, characteristic haplotypes of temperate and tropical japonica were the most difficult to differentiate. The complete table of population assignments for the 104 varieties is included as a supplementary material (S5 Table).

To assess potential functional effects of the observed patterns of admixture, we selected the SNPs within or close to genes that have been identified as related to agronomically important traits and summarized in the OGRO database [19]. Fig 3a shows the average diversity values for the genes included in the 25 minor trait categories proposed by [19]. As expected, average diversity values within coding regions were lower than averages for the whole genome both within indica and japonica. Peaks of high diversity within indica were observed for genes related to lethality, insect resistance, blast resistance, and other disease resistance. Within japonica large diversity was observed for resistance to diseases such as sheath blight caused by Rhizoctonia solani, blast caused by Magnaporthe grisea and other diseases. These patterns were generally consistent with traits prioritized by CIAT and LSU in their breeding programs.

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Fig 3. Diversity within agronomically important genes.

a) Average diversity (average number of pairwise differences per kilobasepair) for genes grouped following the minor categories defined in the OGRO database. Red and blue dashed lines indicate average diversity values across the whole genome (including non-genic regions) within indica and within japonica respectively. The green dashed line, showing the average pairwise Fst between indica and japonica, is scaled in the secondary Y axis. b) SNPs identified within the gene LOC_Os02g40860 in the 104 varieties analyzed in this study. Characteristic alleles of the temperate japonica haplotype are painted blue. Vertical dashed rectangles show the locations of the two missense SNPs with high minor allele frequency within indica. The horizontal rectangle shows the haplotype of the variety Camponi. Colors in the left panel differentiate the following groups: O. rufipogon (RUF), aromatic (ARO), temperate japonica (TEJ), tropical japonica (TRJ), indica (IND), aus (AUS), O. nivara (NIV), and admixed (ADM).

https://doi.org/10.1371/journal.pone.0124617.g003

The peak observed for lethality (Fig 3a) is partly explained by the fact that only two genes are included in this category (LOC_Os02g40860 and LOC_Os04g38950). The gene LOC_Os02g40860 is a member of the casein kinase I family and it has been related to hybrid weakness and growth retardation [41]. Allelic variation in our dataset shows three more or less differentiated haplotype patters for temperate japonica, tropical japonica and indica, as well as a large number of low frequency SNPs (Fig 3b). Part of the increased variability within tropical japonica and within indica is produced by introgression of haplotypes characteristic from temperate japonica identified in the tropical japonica cultivars Carolina-Gold, Carolina-Gold-Select, LM-1, Arias, Binulawan, Canella-de-Ferro, Curinga, Esperanza and Parao, as well as in the indica cultivar Camponi. Although eight missense SNPs were identified for LOC_Os02g40860, six of them had their minor allele present in only one or two varieties. Two missense SNPs in exons 2 and 13 showed relatively high MAF within indica and hence could be potentially useful to track indica-specific alleles of this gene. Conversely, the introgression observed in Camponi should facilitate interspecific crosses of this cultivar with temperate japonica lines (compared with other indicas) because the effect of autoimmunity observed by [41] should not be observed in this case. The gene LOC_Os04g38950 is a glutamine amidotransferase which has been shown to produce dwarfing, narrow leaves, short roots and abnormal flowers if silenced [42]. Our data shows almost complete conservation of this gene within japonica and an indica-specific haplotype pattern composed by 34 SNPs (S9 Fig). Three of these SNPs located in exons 1 and 2 produce changes in the aminoacid sequence. Most of the variability observed in this region within indica is explained by the introgression of the japonica haplotype in the varieties Ai-Chiao-Hong, Guan-Yin-Tsan, Leungpratew, IR8, Camponi, CT21375, Fedearroz50 and Oryzica 3.

Novel SNP markers for amylose content

For breeding purposes, one of the main goals of performing sequencing of elite cultivars is the identification of markers that could be used for marker assisted selection. Bearing this in mind, we investigated the variation observed within the gene GBSSI, located at 1.76Mbp of chromosome 6, which is known to be related to amylose content [43]. We identified a total of 112 SNPs close to this gene, 82 of them only variable in the admixed variety HaishaCaman (Fig 4). From the remaining 30 SNPs, the minor allele of 17 was carried by at least four varieties. Three of these SNPs (termed Waxy-1, Waxy-2, and Waxy-3) were previously reported as markers for amylose content [44, 45]. Waxy-1 is located in the first splicing site of one of the transcripts identified for GBSSI which probably blocks transcription of this isoform. In this case the defective allele is the minor allele in our population and it is mostly present in temperate japonica accessions. Waxy-2 and Waxy-3 produce single amino acid changes in exons 6 and 10 respectively. For both markers, the advantageous allele is more frequent in indica than in japonica, although the advantageous allele of Waxy-2 is also frequent in temperate japonica. Besides these markers, we selected four additional SNPs with the minor allele present mostly in indica cultivars and we termed them Waxy-4, Waxy-5, Waxy-6 and Waxy-7. Waxy-4 and Waxy-5 are located about 800 bp before the transcription start site. Waxy-6 is located within the first intron, and Waxy-7 is a synonymous SNP in exon 9.

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Fig 4. Observed haplotype patterns within the gene GBSSI for O. sativa accessions.

Alleles of the temperate japonica variety Haginomae Mochi (dashed rectangle), which has the defective alleles for Waxy-1 to Waxy-7, are colored blue. Arrows indicate locations within the gene for the discriminative SNPs Waxy-1 to Waxy-7. Colors in the left panel differentiate the following groups: aromatic (ARO), temperate japonica (TEJ), tropical japonica (TRJ), indica (IND), aus (AUS) and admixed (ADM). The table below shows the six haplotype configurations in an independent group of 48 indica elite varieties with variable amylose contents. The last two columns are the number of samples showing the haplotype and the average amylose content for each haplotype. Characters a, b, c and d in the last column differentiate haplotypes with significant differences in amylose content.

https://doi.org/10.1371/journal.pone.0124617.g004

We genotyped these seven markers in a population of 47 indica accessions with variable amylose content using the Fluidigm chip technology. Consistent with previous studies [43], we found that the haplotype built with the advantageous alleles of Waxy-1, Waxy-2 and Waxy-3, and the indica allele of Waxy-4, Waxy-5, Waxy-6 and Waxy-7 showed the highest amylose contents. Moreover, the indica allele of Waxy-4, Waxy-5, and Waxy-7 significantly differentiates varieties with high and low amylose content. We tested each marker independently for relationship with the trait and we found that, except for Waxy-2, all markers were significantly related to amylose content. Waxy-4 and Waxy-5 are in complete linkage disequilibrium (LD) and they are also in almost complete LD with Waxy-6 and Waxy-7, although a few recombinants between these markers were observed in the sequenced varieties. These results suggest that either Waxy-4, Waxy-5, or another variant upstream of GBSSI and in high LD with Waxy-4 and Waxy-5 spans a cis-regulatory region of GBSSI and hence plays a role in the transcription efficiency of this gene.